Planet Georezo » Planet OSGeo

Un Planet est un site Web dynamique qui agrÃ¨ge le plus souvent sur une seule page, le contenu de notes, d'articles ou de billets publiÃ©s sur des blogs ou sites Web afin d'accentuer leur visibilitÃ© et de faire ressortir des contenus pertinents aux multiples formats (texte, audio, vidÃ©o, Podcast). C'est un agrÃ©gateur de flux RSS. Il s'apparente Ã un portail web.
Vous pouvez lire le billet sur le blog La Minute pour plus d'informations sur les RSS !

Feeds

3683 items (0 unread) in 115 feeds

Dans la presse

Du côté des éditeurs

Toile géomatique francophone

Géomatique anglophone

Blogs GeoRezo.net

Autres planets

Planet OSGeo

Abonnez vous à cette page (RSS) !

Planet OSGeo

Skip to page: 1 2 3 ... 9

07 August 2020
14:02
gvSIG Team: gvSIG Online: Custom geocoder
sur Planet OSGeo

In the latest version of gvSIG Online, it’s possible to create custom geocoders from our own SHP layers. It allows to include place names that do not appear in the most common geocoders such as Nominatim or Google by default, such as local elements, or place names in other languages.

In the case of having two or more official languages in a region or country, and when the aforementioned geocoders do not include the place names of any of them, we could, for example, load our local street map with the place names in that language.

The steps to create a geocoder are as simple as uploading the SHP file to the platform, exporting it to the database, and creating the geocoder from it.

Thus, when searching for a place name in any of the projects, it would already show us the results of that layer.

In this video you can see how it works:
14:00
gvSIG Team: gvSIG Online: Geocodificadores personalizados
sur Planet OSGeo

En la última versión de gvSIG Online se ha incorporado la posibilidad de crear geocodificadores personalizados a partir de capas SHP propias. Esto permite incluir topónimos que por defecto no aparecen en los geocodificadores más comunes como Nominatim o Google, como pueden ser elementos locales, o topónimos en otros idiomas.

En el caso de disponer de dos o más lenguas oficiales en una región o país, y de que los geocodificadores mencionados no incluyesen los topónimos de alguna de ellas, podríamos cargar por ejemplo nuestro callejero local con los topónimos en dicha lengua.

Los pasos para crear un geocodificador son tan sencillos como subir el fichero SHP a la plataforma, exportarlo a la base de datos, y crear el geocodificador a partir de ella.

De esa forma, al realizar la búsqueda de un topónimo en cualquiera de los proyectos, ya nos mostraría los resultados de esa capa.

En este vídeo podéis ver mejor su funcionamiento:
04 August 2020
13:04
gvSIG Team: gvSIG Online: Perfil de elevaciones
sur Planet OSGeo

Una funcionalidad muy utilizada en algunos sectores como el de medio ambiente o la topografía es la de calcular perfiles de elevaciones, una herramienta que se ha incluido en la última versión de gvSIG Online, gracias al proyecto de la IDE de Uruguay.

Con esta herramienta, dibujando un perfil longitudinal sobre el geoportal, al finalizar dicha polilínea (que puede estar formada por varios segmentos) se calcula el perfil de elevaciones a partir del Modelo Digital de Terreno incluido, y se muestra la gráfica, que puede guardarse como imagen para insertar por ejemplo en una presentación o informe.

En este vídeo podéis ver mejor su funcionamiento:
13:02
gvSIG Team: gvSIG Online: Elevation profile
sur Planet OSGeo

A widely used functionality in some sectors such as environment or topography is that which allows to calculate elevation profiles, a tool that has been included in the latest version of gvSIG Online, thanks to the Uruguay SDI project.

With this tool, when drawing a longitudinal profile on the geoportal, and finishing that polyline (which can be made up of several segments) the elevation profile is calculated from the included Digital Elevation Model, and the graph is shown, which can be saved as image to be inserted in a presentation or report.

At this video you can see how it works:
11:00
CARTO Blog: Real Estate Demand by Nationality on the Spanish Coast
sur Planet OSGeo

This post was originally published on idealista’s website and has been translated and adapted.
03 August 2020
15:28
OTB Team: DiapOTB remote module available
sur Planet OSGeo

Developped from CNES historical Diapason Chain, adaptated python modules and OTB filters and applications are now available for the OTB community under the remote module called DiapOTB. OTB filters and applications can be used independently or chained via python module scripts. Four interforemetric module scripts are proposed as examples : diapOTB.py and diapOTB_S1IW.py for interferometric […]
15:14
Fernando Quadro: Como configurar o módulo Key Authentication do GeoServer
sur Planet OSGeo
O módulo de Autenticação por Chave do GeoServer permite um protocolo de autenticação muito simples, projetado para clientes OGC que não podem lidar com nenhum tipo de protocolo de segurança, nem mesmo a autenticação básica [HTTP.]

Para esses clientes, o módulo permite uma forma mínima de autenticação, acrescentando uma chave exclusiva na URL que é usado como o único token de autenticação. Obviamente, essa abordagem está aberta à detecção de tokens de segurança e sempre deve estar associada ao uso de conexões [HTTPS.]

Um exemplo de solicitação autenticada é semelhante a:
```
http://localhost:8080/geoserver/topp/wms?service=WMS&version=1.3.0&request=GetCapabilities&authkey=ef18d7e7-963b-470f-9230-c7f9de166888
```
Onde authkey=ef18d7e7-963b-470f-9230-c7f9de166888 está associado a um usuário específico. Quando o parâmetro authkey é fornecido, os backlinks também contêm a chave de autenticação, permitindo que qualquer cliente compatível acesse recursos protegidos.

Na sequencia, os passos para você instalar e configurar o módulo de segurança no GeoServer:

1. Pré-requisitos para a instalação
- Baixar o plugin no site do GeoServer (no meu caso estou usando o GeoServer 2.17.1)
- Realizar as configurações de segurança
2. Instalação
- Parar a execução do GeoServer
- Descompactar o arquivo do plugin e colocar os arquivos .jar na pasta [GEOSERVER_HOME]/webapps/geoserver/WEB-INF/lib
- Reiniciar a execução do GeoServer
3. Configuração

Agora que o plugin já está instalado, você deve logar na interface web do GeoServer e ir no menu Security e clicar em Authentication. O GeoServer irá lhe apresentar uma tela com algumas opções, procure por Authentication Filters e clique em Add New.

Na tela acima clique na opção AuthKey, e a tela apresentará as opções de configuração. Preencha da seguinte forma:
- No campo Name insira “authkey”
- No campo Name of URL parameter deixe o valor padrão “authkey”
- No campo Authentication key to user mapper selecione a opção “Property file”
- No campo User/Group Service selecione a opção “default”
- Feito isso, clique no botão Synchronize user/group service para que ele possa gerar o arquivo authkeys.properties com as chaves por usuário/grupo
- Clique em Save
O arquivo authkeys.properties fica localizado em: $GEOSERVER_DATA_DIR/security/usergroup/default/authkeys.propeties

Um exemplo desse arquivo seria:

Pra finalizar, você precisa substituir as opções onde tem Basic Authentication por Authentication Key dentro das opções do FilterChain, e deixar como na figura abaixo:

Verifique quais as opções que você quer a autenticação por chave. Eu por exemplo não alterei a opção do Rest.

Agora basta você pegar as chaves geradas no arquivo authkeys.properties, e passar por parâmetro nas suas requisições ao GeoServer, conforme o exemplo abaixo:
```
http://localhost:8080/geoserver/topp/wms?service=WMS&version=1.3.0&request=GetCapabilities&authkey=ef18d7e7-963b-470f-9230-c7f9de166888
```
Se você quiser, pode ver o processo detalhado acima no YouTube:

Fonte: GeoServer
11:00
CARTO Blog: CARTO & Google Cloud Announce Partnership
sur Planet OSGeo

Since Google Maps was launched over 15 years ago, Google has become a leader in location. Today over 1 billion people use their services every month, and with vast amounts ...
29 July 2020
2:01
GRASS GIS: New GRASS GIS website
sur Planet OSGeo

The new GRASS GIS website is out! In occasion of its 37th birthday the GRASS GIS project is proud to present its new website! The site has been redesigned with modern tools to be responsive and also easier to maintain. Content is more discoverable now and easy to browse too. What’s cool The Learn page offers a curated list of tutorials in different languages and links to videos. The new Try online section provides links to Binder and rollApp online applications that allow testing GRASS GIS without installing it.
2:00
Ecodiv.earth: Tree species diversity distribution
sur Planet OSGeo
Intro
In this post I’ll use the r.series.diversity addon (Breugel 2016) for GRASS GIS (GRASS Development Team 2020) to compute the tree species diversity distribution patterns in the contiguous USA, based on tree distribution data from Wilson et al. (2013). With the addon, you can compute a number of diversity indices, including the Renyi entropy index and a number of specialized cases of the Renyi enthropy, viz.the species richness, the Shannon index, the Shannon based effective number of species (ENS), the Simpson index (inverse and gini variants), and Pielou’s eveness (Legendre and Legendre 1998). Install the addon first (see here how).
The data
Wilson et al. (2013) published raster maps with the estimated live tree basal area (square feet/acre) for 324 tree species in the United States. The raster maps, at a resolution of 250 meter and representing the time period 2000-2009, are based on MODIS imagery, raster data describing relevant environmental parameters, and field plot data of tree species basal area. The methods are described in (2012) and Wilson et al. (2013). We’ll use the basal area as proxy for the species abundances.
Data import
You can download the data from this website. There are three zip files, each containing approximately 80 raster layers (img format), one per species. The map projection is Albers Conical Equal Area (datum NAD83). First step is to download the data and unzip the rasters from the three zip files in one folder.
Working with data in GRASS GIS differs from what you may be used to in e.g. QGIS. So for readers less familiar with GRASS GIS, I would recommend to first read the introduction to GRASS GIS here. Here you are also introduced to another important concept, the region.
The next step is to create a new location and mapset. Below the steps are oulined in more details. Skip these steps if you are already familiar with how to create a new location and mapset.

Steps to create a new mapset
1. Open GRASS and select the option to create a new location
Figure 1: Create new location
1. Enter a name and optionally a description. In the example, I named the new location NAD83CA, which is the location in which I will keep all source data in Albers Conical Equal Area (datum NAD83) projection.
Figure 2: Define database and location name
1. Select the method to create a new location. In this case, we want to read the projection and datum terms from the georeferenced file.
Figure 3: Choose method to determine crs
1. In the next window, you can select one of the raster images you downloaded and unzipped.
Figure 4: Choose layer to be used to set teh crs
1. In the next window you are presented with a summary of the new location you are about to create. If it all looks good, click finish.
2. Now you are asked if you want to import the data layer that you used to create the location. If you select yes, the data layer will be imported in the PERMANENT mapset. The default region extent and resolution will be set based on the imported data layer. Selected ‘no’. You’ll import the layer together with the others in one go in the next steps.
3. If you selected ‘Create user mapset’ in step 2, you are now asked for a name for the new mapset.
Figure 5: Set name for new mapset

After creating the new location and mapset, you can continue with importing the data layers in your newly created mapset. The files have an .img extension. We can check with the function gdalinfo what kind of raster file this is.

Check the raster file type

Run the following on the command line.
```
# Go to the directory with your raster files
cd Desktop/treesUSA

# Get information about the layer
gdalinfo s10.img
```
This will give you a lot of information, but what we are after is the file type. You can find this near the top of the output; it is a HFA/Erdas Imagine Image (.img).

Now that you know the type of the raster layers, you can import them using the r.import function.

Importing all raster layers in a folder

You can use the function r.import to import all the layers in a folder in one go. Go to Menu > File > Import raster data > Simplified raster import with reprojection. Don’t worry about the reprojection bit. The coordinate reference system (crs) of the mapset is equal to that of the raster layers (obviously, we used one of the raster layer to define the crs of the mapset), so no reprojection is carried out.

Figure 6: Import the data

See the numbers in the image above: (1) Select as source type Directory and (2) select the directory with the raster layers. (3) Select Erdas Imagine Images (.img) as format and make sure the extension is set to img. After selecting the directory and raster format, a list will appear with all raster layers (4). Select all layers, and deselect Add imported layers into layer tree (5). Optionally, go to Import settings (6) to change some settings (e.g., if you have some RAM to spare, increase the maximum memory to be used).

Regions and masks
Before we compute the diversity indici, we need to set the computational region and define a mask. The region defines the extent and resolution that will be used for all raster computations. It can be set with the g.region function.

Set the region

The region should match the raster layers we just imported. To do so, open one of them and right-click on the layer name. In the context menu, select ‘Set computation region from selected map’

Figure 7: Set computation region

Alternatively, you can use the g.region function on the command line or console.
```
g.region raster=s10
```
The tree distribution data is for the continental part of the USA, excluding Alaska. So we want to mask out all other areas. To be able to do so, first download the USA boundaries, e.g., from the GADM website and import the data. The vector layer can subsequently be used to define the mask.

Set the mask

To download a geopackage with the vector data, use this link. After download, extract the geopackage. Note that the the data is in latlon (wgs84). Easiest is to use the v.import function, which reprojects the data on the fly. Make sure to select the layer with the national boundary gadm36_USA_0.

Figure 8: Import vector layer with USA boundaries

You can also run this from the command line or console.
```
v.import input=gadm36_USA_gpkg/gadm36_USA.gpkg layer=gadm36_USA_0
```
Next, we can create a mask with the r.mask function. This function essentially converts the vector layer to a raster layer, with the special name MASK. You can display this layer like any other layer.
```
# Set the mask
r.mask vector=gadm36_USA_0
```
Remember that all raster operations use the extent and resolution set by the region. This is true for the MASK layer as well. This means that only the areas within the bounds of the region are included. E.g. Alaska falls outside the region.

Compute diversity
Now we are finally ready to compute the tree species diversity, using the r.series.diversity addon for GRASS GIS. This can be done in two steps on the command line.

Get a list of input layers

You can create a list with the names of the raster layers using the g.list function. The names of our raster layers all start with a s. The following command will create a list with all names, and assign these to the variable MAPS.
```
# List all raster layers with species
MAPS=`g.list type=raster pattern=s* separator=comma mapset=trees`
```
The variable MAPS can now be used as input in the r.series.diversity function.

Use this as input for the r.series.diversity function.

Compute diversity indici

The flags indicate which diversity indici to calculate. In the code below, these are (r) Renyi enthropy index, (s) the richness index, (h), the shannon index, (p) the reversed Simpson index, (g) the Gini-Simpson index,(e) the Pielou’s evenness index, and (n) the Shannon effective number of species.
```
# Compute the diversity layers
r.series.diversity -s -h -p -g -e input=$MAPS output=TreeDiversity
```
Note that with this dataset, the computation will take a really long time. Long enough for you to forget about it for the rest of the day, hopefully to have it finished next morning. So yes, the code definitely needs some optimization (any suggestion as to how to accomplish this are most welcome).
The results
The output are maps with patterns of tree diversity across contiguous United States. In Figure 9 the maps with the species richness and the Shannon-wiener diversity index are shown. The maps show that in the mideastern states like West Virginia, Kentucky and Tennessee, tree diversity is relatively high, while tree diversity in the states on the west coast is clearly lower.

Figure 9: Tree diversity patterns across the USA

The observed patterns correspond fairly well with the map of the tree diversity by Heath et al. (2015). This is not surprising. Both the map by Heath et al. (2015) and the species distribution maps by Wilson et al. (2013) are based on the Forest Inventory and Analysis (FIA) field plot data from the U.S. Forest Service.

The tree diversity map by Jenkins et al. (2015) shows a different pattern, with a clear diversity hotspot in the southeastern United States. The use of different sources of information (and underlying methodologies and assumptions) might be one reason for the differences. Jenkins et al. used the range maps of tree species by Little (1971), digitized by the US Geological Survey (1999).

In addition, the diversity map by Jenkins et al. is based on the range maps of 641 species, out of the more than 700 different native and naturalized tree species in the United States (Little 1979). The Wilson et al. (2013) dataset contains a much smaller subset of 324 species. If these are e.g., the most common species, this could result in an underestimation of the species richness in regions with more rare or endemic species. And indeed, these maps show that the southeastern United States has a high number of endemic tree species. Another point to reiterate is that the basal area was used as proxy indicator for tree abundance.
Download
I have uploaded the data to Open Science Framework (OSF) data sharing site from the Center for Open Science. If you use the data, please cite as follows: Van Breugel, P. (2020, August 3). Tree diversity distribution patterns in contiguous USA. [https:]] .

References
Breugel, Paulo van. 2016. “R.series.diversity - a GRASS GIS Addon to Compute Diversity Indici over Input Layers.” [https:]] .

GRASS Development Team. 2020. Geographic Resources Analysis Support System (Grass Gis) Software. USA: Open Source Geospatial Foundation. [https:]] .

Heath, Linda S., Sarah M. Anderson, Marla R. Emery, Jeffrey A. Hicke, Jeremy Littell, Alan Lucier, Jeffrey G. Masek, et al. 2015. “Indicators of Climate Impacts for Forests: Recommendations for the US National Climate Assessment Indicators System.” NRS-GTR-155. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [https:]] .

Jenkins, Clinton N., Kyle S. Van Houtan, Stuart L. Pimm, and Joseph O. Sexton. 2015. “US Protected Lands Mismatch Biodiversity Priorities.” Proceedings of the National Academy of Sciences 112 (16): 5081–6. [https:]] .

Legendre, P., and L. Legendre. 1998. Numerical Ecology. Second English edition. Developments in Environmental Modelling 20. Amsterdam: Elsevier.

Little, Elbert L. Jr. 1971. Atlas of United States Trees: Volume 1: Conifers and Important Hardwoods. First Edition edition. United States Government Printing Office,, Washington:

Little, Elbert Luther. 1979. Checklist of United States Trees (Native and Naturalized). D.C. : Forest Service, U.S. Department of Agriculture.

US Geological Survey. 1999. “Digital Representation of Tree Species Range Maps from "Atlas of United States Trees" by Elbert L. Little, Jr.” Geosciences; Environmental Change Science Center, USGS. [esp.cr.usgs.gov] .

Wilson, Barry Tyler, Andrew J Lister, R I Riemann, Douglas M Griffith, and M Douglas. 2013. “Live Tree Species Basal Area of the Contiguous United States (2000-2009).” Newtown Square, PA: USDA Forest Service, Rocky Mountain Research Station. [dx.doi.org] .

Wilson, B. Tyler, Andrew J. Lister, and Rachel I. Riemann. 2012. “A Nearest-Neighbor Imputation Approach to Mapping Tree Species over Large Areas Using Forest Inventory Plots and Moderate Resolution Raster Data.” Forest Ecology and Management 271 (May): 182–98. [https:]] .
28 July 2020
16:33
Jackie Ng: Back into the groove of MapGuide things
sur Planet OSGeo

Getting back into the groove of MapGuide development to finally make some progress on that long arduous journey to MapGuide Open Source 4.0

First item on the books is a small improvement to WMS GetMap. Namely, I don't think you will need to whip out a dedicated WMS client to preview your WMS layers anymore after this, we can just generate an OpenLayers viewer for you straight from the mapagent so you can see for yourself!
27 July 2020
13:00
Fernando Quadro: Automatizar processos no GeoServer
sur Planet OSGeo
Prezados leitores,

O post de hoje falará um pouco sobre automatização de processos no GeoServer. Para isso eu irei utilizar um servidor linux (CentOS 8), a API Rest do GeoServer, e scripts em bash.

Essa demanda apareceu pois no momento estou prestando consultoria para uma startup ajudando eles a escalarem os mapas. Além de alterar o paradigma que eles estavam utilizando (arquivos KML lidos pela API JavaScript do Google Maps), e implantar o GeoServer (e por consequência os padrões OGC), eu também organizei/configurei todos os dados dentro do GeoServer.

Nessa etapa, me deparei com uma camada com precisão de 5m. Só pra dar uma dimensão de tamanho, essa camada apenas para o estado do Rio de Janeiro tem 5 GB. Não teve como fazê-la por Estado, ela teve que ser dividida por munícipio, e ao final ser criado um layer group pra então poder realizar o agrupamento por Estado novamente. Vou apresentar pra vocês como resolvi o problema.

1. Configurar Store e Layers

Como eu estou trabalhando com shapefiles, eu decidi criar um store do tipo “Directory of spatial files (shapefiles)”. Antes de executar o comando para criar o Store no GeoServer eu coloquei os shapes dentro da pasta de dados do GeoServer.
```
curl -v -u admin:geoserver -XPUT -H "Content-type: text/plain" -d "file:///var/lib/geoserver-data/data/riodejaneiro/" "http://localhost:8080/geoserver/rest/workspaces/topp/datastores/riodejaneiro/external.shp?configure=all"
```
Ao executar o comando acima, o GeoServer irá criar o seu “Directory of spatial files”, e como eu utilizei ao final da requisição o parâmetro “configure=all” ele irá criar as camadas automaticamente a partir de cada shapefile contido na minha pasta riodejaneiro.

2. Alterar os estilos

Ao criar as camadas, o GeoServer aplicou o estilo padrão para elas. Porém, eu criei um estilo que será aplicado a todas as camadas (o mesmo estilo). Dessa forma, eu preciso passar por cada camada e alterar o estilo. Fazer isso “na mão” seria muito trabalhoso, dessa forma o comando abaixo (que precisa ser executado dentro da pasta /var/lib/geoserver-data/data/riodejaneiro/) vai fazer o trabalho “sujo” pra mim.
```
for filename in *.shp; do
  curl -v -u admin:geoserver -XPUT -H "Content-type: text/xml" -d "<layer><defaultStyle><name>estilo_errejota</name></defaultStyle></layer>" [https:] {filename%.shp}" 
done
```
O comando acima, faz um iteração, e pra cada camada ele aplica o estilo “estilo_errejota”.

3. Criar o Layer Group

Como mencionei no início do post, eu precisei dividir a camada por município, e agora, que elas já estão todas criadas e com os estilos aplicados corretamente, eu preciso agrupá-las para apresentar no WebGIS. Pra poder criar um Layer Group via Rest, eu precivo enviar um arquivo XML com o nome das camadas e o estilo de cada camada, conforme o modelo abaixo:
```
<layerGroup>
  <name>nyc</name>
  <layers>
    <layer>roads</layer>
    <layer>parks</layer>
    <layer>buildings</layer>
  </layers>
  <styles>
    <style>roads_style</style>
    <style>polygon</style>
    <style>polygon</style>
  </styles>
</layerGroup>
```
Pra gerar esse XML e enviar pro GeoServer via Rest, eu criei o seguinte script em bash:
```
# script para adicionar layer group no GeoServer
# Author: Fernando Quadro

restapi=http://localhost:8080/geoserver/rest
login=admin:geoserver
workspace=topp
layer_group=errejota
dir=/var/lib/geoserver-data/data/riodejaneiro/layer_group.xml

echo "<layerGroup>" > $dir 
echo "<name>$layer_group</name>" >> $dir
echo "<layers>" >> $dir

for filename in *.shp; do
	echo "<layer>$workspace:${filename%.shp} </layer>" >> $dir	
done
    
echo "</layers>" >> $dir
echo "<styles>" >> $dir

for filename in *.shp; do
	echo "<style>estilo_errejota</style>" >> $dir	
done

echo "</styles>" >> $dir
echo "</layerGroup>" >> $dir

curl -v -u admin:geoserver -XPOST -d @layer_group.xml -H "Content-type: text/xml" $restapi/layergroups
```
Após a execução do script acima, que também deve estar dentro da pasta onde estão os shapefiles, você terá então seu layer group criado automaticamente. Lembre-se de ao final do processo retirar o script e o arquivo XML da pasta onde estão os shapes.

Se você quiser, pode ver o processo detalhado acima no YouTube:
12:00
Narcélio de Sá: TecnoloGEO 16 – Conhecimentos que todo geoprocesseiro deve saber II
sur Planet OSGeo
Neste podcast: No TecnoloGEO 16 traz até você a continuação do bate-papo iniciado no episódio 12 onde fizemos uma discussão sobre conhecimento essenciais que todo Geoprocesseiro deve saber. Nesse episódio Narcélio de Sá apresenta mais alguns conhecimentos que todo geoprocesseiro que se preze tem que saber para lidar com as rotinas do dia-a-dia no mundo das geotecnologias.
Aperte o ? para começar o TecnoloGEO #16
O tecnoloGEO é encabeçado por Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), o projeto tem a pretensão de ser semanal e contar sempre com convidados, que sejam relevantes ao assunto em pauta da vez, pra bater aquele bate papo descontraído e informativo sobre as Geotecnologias.
Referências e Indicações do TecnoloGEO 16: Livros:
- Musashi – Box 3 volumes – Eiji Yoshikawa
Filmes/Séries
- Bacurau (2019) – Juliano Dornelles , Kleber Mendonça Filho
Música
- Fausto Nilo – Verso e Voz (Ao Vivo) 2012
Deixe seu comentário e participe!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:

Luiz Sadeck
Murilo Cardoso
Narcélio de Sá
The post TecnoloGEO 16 – Conhecimentos que todo geoprocesseiro deve saber II appeared first on Narcélio de Sá.
26 July 2020
17:58
Free and Open Source GIS Ramblings: Extracting trajectories from massive movement datasets
sur Planet OSGeo

To explore travel patterns like origin-destination relationships, we need to identify individual trips with their start/end locations and trajectories between them. Extracting these trajectories from large datasets can be challenging, particularly if the records of individual moving objects don’t fit into memory anymore and if the spatial and temporal extent varies widely (as is the case with ship data, where individual vessel journeys can take weeks while crossing multiple oceans).

This is part 2 of “Exploring massive movement datasets”.

Roughly speaking, trip trajectories can be generated by first connecting consecutive records into continuous tracks and then splitting them at stops. This general approach applies to many different movement datasets. However, the processing details (e.g. stop detection parameters) and preprocessing steps (e.g. removing outliers) vary depending on input dataset characteristics.

For example, in our paper [1], we extracted vessel journeys from AIS data which meant that we also had to account for observation gaps when ships leave the observable (usually coastal) areas. In the accompanying 10-minute talk, I went through a 4-step trajectory exploration workflow for assessing our dataset’s potential for travel time prediction:

Click to watch the recorded talk

Like the M³ prototype computation presented in part 1, our trajectory aggregation approach is implemented in Spark. The challenges are both the massive amounts of trajectory data and the fact that operations only produce correct results if applied to a complete and chronologically sorted set of location records.This is challenging because Spark core libraries (version 2.4.5 at the time) are mostly geared towards dealing with unsorted data. This means that, when using high-level Spark core functionality incorrectly, an aggregator needs to collect and sort the entire track in the main memory of a single processing node. Consequently, when dealing with large datasets, out-of-memory errors are frequently encountered.

To solve this challenge, our implementation is based on the Secondary Sort pattern and on Spark’s aggregator concept. Secondary Sort takes care to first group records by a key (e.g. the moving object id), and only in the second step, when iterating over the records of a group, the records are sorted (e.g. chronologically). The resulting iterator can be used by an aggregator that implements the logic required to build trajectories based on gaps and stops detected in the dataset.

If you want to dive deeper, here’s the full paper:

[1] Graser, A., Dragaschnig, M., Widhalm, P., Koller, H., & Brändle, N. (2020). Exploratory Trajectory Analysis for Massive Historical AIS Datasets. In: 21st IEEE International Conference on Mobile Data Management (MDM) 2020. doi:10.1109/MDM48529.2020.00059

This post is part of a series. Read more about movement data in GIS.
23 July 2020
2:00
Ecodiv.earth: Adding functions to the QGIS toolbox
sur Planet OSGeo
The graphical modeler makes it easy to create models by chaining together different operations/algorithms available in the toolbox. But what if a function is not available in the toolbox? In that case you can write your own script and add this as a new function to the toolbox.
Download the data
For example, I want to create a model to download zipped ascii raster layers, unzip them, convert them to geotif files (if not already) and store them on my computer. The first step, downloading the data, can be automated using the Download function in the toolbox (Figure 1).

Figure 1: The QGIS model to download raster layers.
Extract the rasters
For the next step, unzipping the files, no function is available in the toolbox. So apart from doing this part manually, the option is to write a small script to extract raster layers from a zip file. The tricky part is to write a function that can be used in the QGIS toolbox. This means amongst others that the function’s input parameters need to be defined with the initAlgorithm function and the addParameter function. This is illustrated with the code snippet below, which defines the input and output parameters for the unzip functions.

Code
```
def initAlgorithm(self, config=None):
    self.addParameter(QgsProcessingParameterFile(
        'INPUT',
        'INPUT', 
        optional=True, 
        behavior=QgsProcessingParameterFile.File, 
        fileFilter='All files (*.*)',
        defaultValue=None))
    self.addParameter(QgsProcessingParameterFile(
        'OUTPUT',
        'OutputFolder', 
        optional=True, 
        behavior=QgsProcessingParameterFile.Folder, 
        fileFilter='All files (*.*)',
        defaultValue=None))
```
With the input parameters defined, the code snippet below gets a list of the files names in the zipfile, selects the raster layers (based on their file extension) and extracts them.

Code
```
# Output folder
out_dir = parameters['OUTPUT']
# Input zip file
yzf = parameters['INPUT']
# Temporary download folder
temp_dir = tempfile.mkdtemp()

with zipfile.ZipFile(yzf) as zf:     
    # Get list with file names in zipfile
    files = zf.namelist() 
    # Get list with all raster layers in zip file (ascii and tif rasters)
    lrf = ('.asc', '.tif', '.bil', '.dwg', '.hdr')
    filesasc = [i for i in files if i.endswith(lrf)] 
    # Extract the raster layers to the user defined folder
    for asciifile in filesasc:
        file_path = os.path.join(temp_dir, asciifile)
        f = open(file_path, 'wb')
        f.write(zf.read(asciifile))
        f.close()
```
You can check out the complete script here. Besides the code snippets above, it contains a header section, it loads the required libraries, and it contains some extra (html formatted) information that will be shown as help when running the script from the toolbox. After loading the script in the QGIS toolbox, it can be used like any other function.

Figure 2: The unzip function, with as input the name and location of the zip file, and the output folder.
Convert the rasters
The translate function can be used to convert raster layers in another format to geotifs. My initial idea was to import the above script in the toolbox, and build a model in which the output of this particular function, the downloaded and unzipped raster file, would be used as input in the translate function. But the downloaded zip files can contain more than one raster file, complicating things. I therefore decided to include the raster conversion part in the script. The code snippet below shows the part where the raster file is converted using the gdal::translate function.

Code
```
outputfiles = []
for rasterfile in myfiles:
    # If other than geotif, convert raster to tif
    if pathlib.Path(rasterfile).suffix != '.tif':
        # Replace suffix with tif and temp folder path for path output folder
        tmpfilename = pathlib.Path(rasterfile).with_suffix('.tif')
        transfile = os.path.join(out_dir, ntpath.basename(tmpfilename))
        # Translate (convert format)
        alg_params = {
            'COPY_SUBDATASETS': False,
            'DATA_TYPE': 0,
            'EXTRA': '',
            'INPUT': rasterfile,
            'NODATA': None,
            'OPTIONS': '',
            'TARGET_CRS': None,
            'OUTPUT': transfile
        }
        outputs['TranslateConvertFormat'] = processing.run(
            'gdal:translate', 
            alg_params, context=context, 
            feedback=feedback,
            is_child_algorithm=True)
```
You can find the complete script here. What the script does is to download a zip file, extract the raster(s), and, if not already in geotif format, convert the raster layer(s) to a geotif raster layer(s). The latter is, obviously, skipped, if the downloaded file is already in geotif format. And lastly, the intended output format can be changed by the user. You can load the script in your QGIS toolbox and run it (Figure 3).

Figure 3: The unzip and convert function.
Combining the different steps
To create one combined workflow from download to conversion, I created a simple model in which a zip file is downloaded using the Download file function, and the raster layers it contains are extracted and converted to a geotif format using the newly created unzipconvert function (Figure 4). You can get the model here. Load it in the QGIS toolbox and try it out. Or download the Python script here and load that in your toolbox instead.

Figure 4: The download and unzipconvert functions combined in one workflow.
Further reading
A great source of information is the QGIS manual. Make sure to check it out to learn more about scripting and about how to get started with the graphical modeler.

There is also this template to get you on the way. It is based on some enhancements to the QGIS scripting framework, making it easier to write a script. Wish I had seen that earlier. But alas, something to try out next time.
21 July 2020
18:13
GeoTools Team: GeoTools 23.2 Released
sur Planet OSGeo

The GeoTools team is pleased to share the availability of GeoTools 23.2 : geotools-23.2-bin.zip geotools-23.2-doc.zip geotools-23.2-userguide.zip geotools-23.2-project.zip This release is published to the osgeo maven repository, and is made in conjunction with GeoServer 2.17.2 . This is a stable release and is a recommended upgrade for all users of the GeoTools
15:58
GeoServer Team: GeoServer 2.17.2 Released
sur Planet OSGeo

Enclosure: [download]
We are pleased to announce the release of GeoServer 2.17.2 with downloads (war|zip), documentation and extensions.

This release is made in conjunction with GeoTools 23.2 and GeoWebCache 1.17.2. This is a stable release recommended for production systems.

Thanks to everyone who contributed, and Jody Garnett (GeoCat) for making this release. Additional thanks to everyone who participated in the July bug-fix sprint organized by GeoSolutions.
Improvements and Fixes
This release includes a number of improvements, including:
- GetFeatureInfo templates can now be used for GeoJSON output
- The WPS download community module can now generate elevation models accounting for vertical grid shift when changing datum.
- Oracle extension now ready to use with included JDBC driver, the driver changed to a “Oracle Free Use Terms and Conditions” license allowing us to distribute.
- PostreSQL driver, Oracle driver, Eclipse EMF library, spring-framework, and spring-security version updates.
- Image Mosaic REST API can now be used to update the native bbox when harvesting or deleting granules
Fixes included in this release:
- GetFeatureInfo fixed to work on raster layer dynamically produced by a rendering transformation.
- Lots of WMTS fixes resolving SRS list duplicates and handling of otherSrs.
- SLD Service handling of percentages with custom classes
- Layer preview fix, was miscounting number of pages when security restrictions used to limit number of layers available
- Binary download on windows fixed to autodect Java when spaces used in path
- WFS Cascade fixed to remove duplicate SRS elements when connecting to WFS 2.0.0 layer
- MongoDB extension fixed, was missing a jar
- Database use of select distinct in the WPS Unique process fixed
- Image mosaic fixes for WPS download process bbox generation, and WMS GetMap respect of CLIP vendor parameter,
- GeoPackage supports raster tables with special characters
For more information check the 2.17.2 release notes.
Community Updates
For developers building from source, our community modules are a great place to collaborate on functionality and improvements.
- The JSON-LD module is under active development with templating now supporting filtering, environmental variable substitution. Thanks to the sponsorship of the French geological survey – BRGM and the French environmental information systems research center – INSIDE.
- The GSR community module working on packaging, addressed a startup problem.
About GeoServer 2.17
Features, presentations and reference material on the 2.17 series:
- New security tab on each layer, layer group and workspace page
- Option to use date created and date modified to sort UI lists
- New resource browser extension
- New Mapbox style extension
- FOSDEM GeoServer Orientation presentation (slides|video)
- Full OSM data directory for GeoServer available on GitHub
- Code of Conduct
- Release Notes (2.17.2|2.17.1|2.17.0|2.17-RC)
15:01
Andrea Antonello: SMASH 1.3.0 and GSS 3.0.0 are out, in sync
sur Planet OSGeo
Since the last SMASH release we have been mostly concentrating on enhancing the server side to have a better synchronization experience.

That said (and discussed below), some enhancements have come to SMASH:

Kalman filter for GPS logs
GPS logs are now created using both the original GPS coordinates and filtered coordinates (in this case a Kalman filter). It is possible to select what to view by double tapping on the log icon:

One can view the filtered data while hiding the original GPS data:

or view the original and hide the filtered:

or, as I prefer it, have the original in transparency and see the filtered:

which helps me to understand how bad my GPS performs in tunnels :-)

geopackage enhancements
Editing of the attributes table is now possible if the data table has a primary key. If a table is editable, the editing icon is visible:
Now let's wait for geometry editing ;-)

Visibility settings

We already have seen how gps logs visibility settings can be changed. In general we now have on the actions buttons that support it, the following paradigm:
- single tap: add new
- double tap: settings
- long tap: view list
As such, double tapping on the notes button, opens a view mode dialog, which allows to configure if the use wants to see the notes with or without label, or hide them at all:

Other icons that support double tap:
- the GPS icon: toggles automatic centering on GPS.
- the layers icon: opens the plugin dialog to enable or disable plugins:
This is all too difficult to remember? Well, you are lucky! We just added coach markers!
Coach markers

Coach markers help the user on screen as a user manual would do. The first time SMASH is launched they are run, but they can be accessed from the upper right question mark icon:
et voila', easy to know what the actions do.
The Geopaparazzi Survey Server
So what is new with the GSS? Well, for one thing, it has been reviewed and has a more straightforward user interface:

But how does sync work in 3 screenshots?
On SMASH you take a form based note:

Assuming you set the right server in the settings, you then export to GSS. You will get a summary of what will be uploaded:

If you choose to upload, each item will start in direction server:

And on the server it will be visible the same way it was on smash, with the same icon, the same label:

The manuals!!
Would you believe it? We updated the manuals!!!

So, to see all the features for the GSS just run here: https://www.geopaparazzi.org/gss/

to see everything about SMASH, here: [https:]]

Well, that's all folks, enjoy! We have a timeline for smash 1.4.0, 1.5.0 and 1.6.0 with some features, so expect some updates during the coming year.

INSTALL
SMASH is already on Google Play and will be on the Apple store in the next hours.
The GSS is available as docker image. The above link has installation instructions to install it using postgis.
11:00
CARTO Blog: Using Location Intelligence to return to Valencian beaches
sur Planet OSGeo

The COVID-19 pandemic has had a devastating impact on the tourism industry, particularly in Spain which last year was the second most visited country in the world with spen...
20 July 2020
19:16
Stefano Costa: La peste a Marzano negli anni 1656-1657
sur Planet OSGeo

Quasi tutti ricordano la peste che fa da sfondo ai Promessi Sposi di Alessandro Manzoni. L’epidemia colpì Milano nel 1630 ma risparmiò Genova e la Liguria. Nel 1656 però un’altra ondata di peste bubbonica arriva qui. Colpisce duramente gli abitanti di grandi città come Roma, Napoli e Genova. Non rimangono immuni nemmeno i centri abitati più piccoli come Torriglia e soprattutto, per questa terza pillola storica, anche a Marzano arriva la peste.

Proprio come avviene oggi con l’epidemia Covid, vengono prese misure drastiche. Marzano è tra le “ville” sospese dalle quali è vietato spostarsi, insieme a Tercesi e Olcesi, perché è un luogo di contagio. Un lockdown in piena regola.

I controlli sono effettuati da guardie armate delle milizie, che per ogni villa sono sotto un caporale. Niente moduli di autocertificazione, ma proprio come oggi le forze dell’ordine sono impegnate per un rigido controllo degli spostamenti. Conosciamo almeno un caso di violazione del divieto nel mese di ottobre 1656, quando Geronimo e Antonio Costa di Marzano sono denunciati dal caporale Benedetto Mangini per essersi allontanati più volte dai confini imposti.

In questi anni gli abitanti di Marzano sono circa un centinaio divisi in 18 famiglie (fuochi). Lo sappiamo dal giuramento prestato il 29 novembre 1654 al principe Gio Andrea Doria, a cui prendono parte tutti i capifamiglia. Non abbiamo idea del numero di vittime dell’epidemia, ma a Genova le stime sono nell’ordine delle decine di migliaia di morti e secondo alcuni cadde il 70% della popolazione. Se andassimo a consultare i registri dei defunti nell’archivio parrocchiale avremmo un’idea più precisa. Certo, in un luogo di poche case come Marzano è più facile mantenere (consciamente o meno) quel distanziamento che aiutava a limitare la diffusione del morbo. Non c’erano strumenti particolari per aumentare i controlli, mentre oggi i droni e le celle telefoniche ci guardano di continuo (a proposito: chi manovra il drone che ogni tanto vola sopra le case?).

Per contrastare l’epidemia si ricorre ovviamente anche alla fede. La cappella della Panteca, intitolata tra gli altri a San Luigi Gonzaga (e forse anche a San Rocco) sembra proprio un esempio di ex voto collettivo e penso che la sua costruzione risalga a questo periodo. D’altronde, la stessa chiesa di San Bartolomeo a Marzano era stata consacrata pochi anni prima.

Queste notizie sono tratte dai documenti conservati nell’archivio comunale, grazie al lavoro minuzioso di Mauro Casale pubblicato nei due libri La magnifica comunità di Torriglia e Castrum Turrilie. Quindi mi sono basato in prevalenza sulla lettura di questi volumi. Nel 2017 c’è stata una bella mostra dedicata alla peste di Genova del 1656-1657 al Museo dei Beni Culturali Cappuccini. Il libro recente più completo sull’argomento è quello di Romano Da Calice del 2004, La grande peste : Genova 1656-1657.

Pillole di storia marzanina
La chiesa di San Bartolomeo · Marzano 200 anni fa · La peste a Marzano nel 1656-1657
17:54
GeoSolutions: MapStore – Sleek Open Source WebGIS – Webinar
sur Planet OSGeo
Dear Reader,

Web GIS applications have hugely evolved in the last decades. The first web map server was made available early in the 90's, and since then we've since an explosion of companies and application that help use understand what can we find in a place, where is a place that meets my conditions, where is the best route, and so on. The speed of computers and internet has empowered developers to make faster and better applications that run in personal computers and mobile devices. Most of these applications provide a map, a list of categories, a search interface and maybe a menu that allows the user to run some analysis.

At the same time, the power for people around the world to communicate has increased. We now share blogs, videos and images, almost becoming part of our daily live. What if we want to share a story but also combining geospatial data? This will allow us to provide a geospatial context to the information that we want to convey. At the same time, it will allow us to enrich Web GIS clients by providing multimedia content that is hard to convey with only maps.

MapStore with its most recent release allows to develop GeoStories in a very easy way. It allows to combine the power of interactive maps and charts with text, images and videos. I invite you to see a demo GeoStory at the MapStore demo website.
[caption id="attachment_5473" align="aligncenter" width="800"] Screenshot from our demo GeoStory[/caption]
Other tools exist that do the same, but MapStore is unique because of these combined functionalities:
- It allows to create GeoStories combining dynamic maps and charts with text, images and videos
- It is Open Source (Simplified BSD license)
- The data that creates the maps and dashboard comes via OGC Open Standards (WMS, WFS)
- It is based on fast web popular technology: OpenLayers, Leaflet, Cesium and ReactJS
- Has a powerful editor
- It allows to configure users and groups
- Provides the capabilities to share maps and stories to be embedded in other web pages or as single web applications
A recent GeoSolutions blog covers the new functionality of MapStore based on the 2020_02_00 release. We are hosting a webinar on Aug 3rd that will introduce MapStore, the latest bells and whistles from this release and will provide you guidance on how to get started. I cordially invite you to a free webinar on Monday Aug 3rd at 11:00 Eastern Time (5 PM CEST) by registering at the link below! Hope to see you virtually on Aug 3rd, meanwhile stay safe and keep strong! Cordially, Luis
14:03
Jackie Ng: Announcing: vscode-map-preview 0.5.5
sur Planet OSGeo
This release adds partial support for the Mapbox SimpleStyle spec for GeoJSON features

Many thanks to @vohtaski for the PR to add this support.

The other new feature is a new opt-in configuration property to de-clutter vector feature labels. To illustrate, here's a point KML preview with de-cluttering disabled (current behavior)

And here's the same KML preview with de-cluttering enabled

The other points/markers will be visible as you zoom in where there will be more "breathing space" for OpenLayers to draw the extra labels.

This release also updates:
- OpenLayers to 6.3.1
- ol-layerswitcher to 3.5.0
13:22
gvSIG Team: gvSIG Online: Quick Filter
sur Planet OSGeo

A very interesting tool included at gvSIG Online in its latest version, which makes elements searching easier for non-technical users, is the quick filter.

Until the previous version, if a user wanted to search a specific municipality, a river, etc., or the elements that meet a numerical condition (for example, municipalities with more than a specific number of inhabitants), they should open the attribute table, perform a search (which in the first case mentioned could be done directly by typing the place name in the table’s quick filter, but in the second case the advanced filter available within the attribute table should be used, which might not be easy for non-technical users), and with the results obtained, select them and zoom the selection.

With the new quick filter, available directly from the Table of Contents, through the new added tab, the user will be able to select the group of layers, the layer, the field of the table for the filter, and the value to filter. And in the case of numeric fields, user can select the “greater than” and less than” operators, and manually enter a number, allowing them to search elements that meet a certain condition in a simple way.

In this video you can see how it works:
13:22
gvSIG Team: gvSIG Online: Filtro rápido
sur Planet OSGeo

Una novedad muy interesante que incluye gvSIG Online en su última versión es el filtro rápido, que facilita a los usuarios no técnicos la búsqueda de elementos en un proyecto.

Hasta la versión anterior, si un usuario quería buscar un municipio concreto, un río, etc., o los elementos que cumpliesen una condición numérica (por ejemplo los municipios con más de un número concreto de habitantes), debía abrir la tabla de atributos, realizar una búsqueda (que en el primer caso mencionado se podía hacer directamente escribiendo el topónimo en el filtro rápido de la tabla, pero en el segundo caso se debía utilizar el filtro avanzado disponible dentro de la tabla de atributos, lo que podía no ser sencillo para usuarios no técnicos), y con los resultados obtenidos seleccionarlos y hacer un zoom a la selección.

Con el nuevo filtro rápido, directamente desde la Tabla de contenidos, a través de la nueva pestaña añadida, el usuario podrá seleccionar el grupo de capas, la capa, el campo de la tabla por el cual filtrar, y el valor a filtrar. Y en el caso de campos numéricos, se podrá seleccionar los operadores “mayor que” y menor que”, y escribir manualmente un número, con lo que permite buscar elementos que cumplan una determinada condición de una forma sencilla.

En este vídeo se puede ver mejor su funcionamiento:
11:00
Narcélio de Sá: TecnoloGEO 15 – Disclaimer
sur Planet OSGeo
Neste podcast: O TecnoloGEO 15 reúne toda a equipe, Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), para explicar o hiato de duas semanas sem episódio lançados por conta de problemas de agenda e logística de edição. Nesse programa apresentamos também alguns temas de programas futuros do tecnologeo.
Aperte o ? para começar o TecnoloGEO #15
O tecnoloGEO é encabeçado por Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), o projeto tem a pretensão de ser semanal e contar sempre com convidados, que sejam relevantes ao assunto em pauta da vez, pra bater aquele bate papo descontraído e informativo sobre as Geotecnologias.
Referências e Indicações do TecnoloGEO 15: Live Geocast:
Nessa semana, teremos uma live um pouco diferente. Iremos receber o Igor para falar um pouco sobre o pacote que ele criou, o “florestal”.

O pacote, voltado para análises de inventário florestal calcula parâmetros de amostragem, parâmetros fitossociológicos, além do volume lenhoso a partir da equação inserida pelo usuário ou por uma das equações utilizadas no Inventário Florestal Nacional, listadas por estado e por fitofisionomia. O pacote segue os métodos descritos em Pellico e Brena (1997): [https:]] .

O Igor Cobelo Ferreira é engenheiro florestal pela UnB e mestrando em Recursos Naturais do Cerrado pela UEG.

Link para o canal: [www.youtube.com]

?Apresentação:
Igor Cobelo Ferreira Maurício Vancine
Citados no episódio
- GeocastBrasil
Deixe seu comentário e participe!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:

Luiz Sadeck
Murilo Cardoso
Narcélio de Sá
The post TecnoloGEO 15 – Disclaimer appeared first on Narcélio de Sá.
19 July 2020
23:11
GRASS GIS: New GRASS GIS source code analysis with GitHub Actions
sur Planet OSGeo

Now using GitHub Actions to build (compile), test, and perform static code analysis
18:31
Free and Open Source GIS Ramblings: M³ Massive Movement Model: aggregating movement data using prototypes
sur Planet OSGeo

Visualizations of raw movement data records, that is, simple point maps or point density (“heat”) maps provide very limited data exploration capabilities. Therefore, we need clever aggregation approaches that can actually reveal movement patterns. Many existing aggregation approaches, however, do not scale to large datasets. We therefore developed the M³ Massive Movement Model [1] which supports distributed computing environments and can be incrementally updated with new data.

This is part 1 of “Exploring massive movement datasets”.

Using state-of-the-art big gespatial tools, such as GeoMesa, it is quite straightforward to ingest, index and query large amounts of timestamped location records. Thanks to GeoMesa’s GeoServer integration, it is also possible to publish GeoMesa tables as WMS and WFS which can be visualized in QGIS and explored (for more about GeoMesa, see Scalable spatial vector data processing ).So far so good! But with this basic setup, we only get point maps and point density maps which don’t tell us much about important movement characteristics like speed and direction (particularly if the reporting interval between consecutive location records is irregular). Therefore, we developed an aggregation method which models local record density, as well as movement speed and direction which we call M³.

For distributed computation, we need to split large datasets into chunks. To build models of local movement characteristics, it makes sense to create spatial or spatiotemporal chunks that can be processed independently. We therefore split the data along a regular grid but instead of computing one average value per grid cell, we create a flexible number of prototypes that describe the movement in the cell. Each prototype models a location, speed, and direction distribution (mean and sigma).

In our paper, we used M³ to explore ship movement data. We turned roughly 4 billion AIS records into prototypes:

M³ for ship movement data during January to December 2017 (3.9 billion records turned into 3.4 million prototypes; computing time: 41 minutes)

The above plot really only gives a first impression of the spatial distribution of ship movement records. The real value of M³ becomes clearer when we zoom in and start exploring regional patterns. Then we can discover vessel routes, speeds, and movement directions:

The prototype details on the right side, in particular, show the strength of the prototype idea: even though the grid cells we use are rather large, the prototypes clearly form along vessel routes. We can see exactly where these routes are and what speeds ship travel there, without having to increase the grid resolution to impractical values. Slow prototypes with high direction sigma (red+black markers) are clear indicators of ports. The marker size shows the number of records per prototype and thus helps distinguish heavily traveled routes from minor ones.

M³ is implemented in Spark. We read raw location records from GeoMesa and write prototypes to GeoMesa. All maps have been created in QGIS using prototype data published as GeoServer WFS.

If you want to dive deeper, here’s the full paper:

[1] Graser. A., Widhalm, P., & Dragaschnig, M. (2020). The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science. doi:10.1080/13658816.2020.1776293.

This post is part of a series. Read more about movement data in GIS.
18 July 2020
2:00
PostGIS Development: PostGIS 3.1.0alpha2
sur Planet OSGeo

The PostGIS Team is pleased to release the second alpha of upcoming PostGIS 3.1.0 release.

Best served with

PostgreSQL 13beta2, GEOS 3.7 or higher is recommended.

ST_MaximumInscribedCircle requires compilation with GEOS 3.9.0 in development to be enabled.

pgRouting 3.1.0 which will also be released soon.
Continue Reading by clicking title hyperlink ..
17 July 2020
18:58
GeoSolutions: MapStore Release 2020.02.00
sur Planet OSGeo
Dear Reader,

We are pleased to announce the release 2020.02.00 of MapStore, our flagship Open Source WebGIS product. The full list of changes for this release can be found in the GitHub Project. This blog highlights the most interesting ones.
Important changes for this release
This release comes with many new features and functionalities. The two important ones are:
- GeoStory: you can now create and share fascinating, interactive stories combining text, interactive maps, and other rich-multimedia content. Special thanks to the MapStore team for this.
- Application Contexts: as administrator you can now build and configure your own map viewer, share it and create maps on it. Special thanks to the MapStore team for this too!
Other new functionalities include:
- Click Filtering: When a user clicks on a vector feature on the map, the attribute table is filtered accordingly.
- Measurements: A user can now measure distance, area and bearings by just drawing on the map.
- Import/Export: A user can now import and export Web Map Context (WMC) files, that allows to share a map context between clients. The file includes information like list of layers, projections, coordinates, zoom and extension.
- Catalog Tool: MapStore connects to a catalog to find services and get data or images to be added to the viewer. Tile Map Services and Web Feature Services are now supported.
- Identify Tool: The identify popup provides more information about the feature on the map. A user can also now switch to edit mode by opening the attribute table.
- Layer Settings: A user can now edit the legend size in MapStore, adjusting to the user needs.
Storytelling with GeoStory
Yes, finally MapStore is able to create also Stories or, better than that, GeoStories!

GeoStory is a tool that allows to create inspiring and immersive stories by combining text, interactive maps, and multimedia content (e.g. images, video or other third party content). Through this tool you can easily tell your stories on the web, publish them, and share them. Stories can be shared with configured user groups in MapStore or publicly to everyone around the world.
[caption id="attachment_5394" align="aligncenter" width="961"] GeoStory - Sample of a story in view mode.[/caption]
Are you still wondering what GeoStory is? Well, it provides capabilities to tell a geospatial story using existing resource types in MapStore (i.e. Maps and Dashboards). You will notice that a new button is available in the MapStore homepage that allows you to create a GeoStory .
[caption id="attachment_5375" align="aligncenter" width="272"] Creating a new GeoStory[/caption]
If you click on it, MapStore brings you to the GeoStory editor: an advanced web editor that provides a well-stocked set of tools to configure your story. Through this amazing tool, you can build your story by adding different kind of sections, one after the other, organized in a continuous scrolling layout (Scrollytelling). You can add and configure the following sections:
- Title section / cover
- Paragraph section
- Immersive section
- Media section
- Web Page section
A Media Editor gives you the possibility to easily manage your media so that you can include Images, Videos as well as Maps.
[caption id="attachment_5395" align="aligncenter" width="870"] GeoStory - Media Editor.[/caption]
You can add one of the existing maps in a story or create a new one directly inside the GeoStory editor. In addition you are given the possibility to quickly edit your Map section with an easy to use Map Inline Editor.
[caption id="attachment_5396" align="aligncenter" width="848"] GeoStory - Advanced map editor inside a story.[/caption]
GeoStory gives you control over the sections created through an action toolbar, one per section, as shown in the image below. The action toolbar allows you to perform quick actions on your section's content.
[caption id="attachment_5397" align="aligncenter" width="933"] GeoStory - Section toolbars available[/caption]
Moreover, the GeoStory editor gives you control over other story parameters and settings through a side panel where you can freely organize your sections by simply using drag&drop. Here you can:
- Scroll to a specific section or rename the section itself, drag and drop a section to change its position inside the story
[caption id="attachment_5384" align="aligncenter" width="596"] GeoStory - Side Panel, zoom to a section or rename it[/caption]
- Open the story settings to configure things like the story theme, the navigation toolbar or other settings as shown below
[caption id="attachment_5398" align="aligncenter" width="346"] GeoStory - Settings of a Story[/caption]
Last but not the least, you can manage your stories, in a similar way that you are already use to do, with Maps and Dashboards. You can manage permissions for specific groups of users, you can share your story, and more. A sample of a StoryMap is available in our MapStore live demo here (you can login with user demo and password 123demoUser).

Special thanks to the Neftex Petroleum Consultants Limited, who funded this new feature.
Creating custom viewers with the Application Context
The Application Context Manager is an administrative tool designed to build and configure different MapStore viewer configurations for the same MapStore installation. Through a multisteps wizard the administrator is able to configure a custom MapStore viewer by choosing:
- The name of the context (the viewer will have its own specific URL)
- The default map configuration and map contents (like layers, backgrounds, catalog sources, the map's CRS etc.)
- The set of plugins available for the viewer. You can build you own custom plugin and install it in your instance directly using the wizard. We made a sample plugin that is ready for you to test and reuse.
In previous versions of MapStore, there was only a single application configuration with the possibility to define and load additional ones with a specific URL parameter. This is still possible allowing users to define different viewer configurations for the same MapStore instance, but this new development gives the users the possibility to manage different viewer contexts through the MapStore admin user interface.

The Application Context has been conceived to easily create multiple MapStore viewers with a single installation. Each installation has its own set of plugins, look and feel, default map contents and Viewer's URL.

A user can also create his own maps starting from a context. A new administrative section has been added to open the application context manager from the MapStore home page.
[caption id="attachment_5412" align="aligncenter" width="359"] Application Context - Opening the manager from home[/caption]
The manager allows you to manage your contexts in the same way as other MapStore resources. You can share the context, manage access permissions and properties in the same way you do for Maps, Dashboards and Stories.
[caption id="attachment_5413" align="aligncenter" width="951"] Application Context - Contexts manager[/caption]
Let us now see each wizard's step in more details.

Step #1 allows MapStore administrators to setup the name of the context (it will appear in the URL of the context) and the browser's window title.
[caption id="attachment_5415" align="aligncenter" width="494"] Application Context - Step #1 of the wizard[/caption]
In step #2 administrators can setup the default map contents of the context. The image below shows the available tools for setting up the default Viewer's map.
[caption id="attachment_5416" align="aligncenter" width="960"] Application Context - Step #2 of the wizard[/caption]
In step #3 we can setup which plugins need to be included and enabled in the context viewer. You can:
- Select plugins to enable
- Go to the online plugins documentation to check available configuration properties for a specific plugin
- Configure each plugin through a text area where you can customize its configuration properties (a validator helps you on this)
- Mark a plugin as mandatory for the context or ready to be added/removed on the fly by the user! Look at online documentation to learn more about the user's Extension Library
- Install your custom plugin by uploading the extension package in zip format!
[caption id="attachment_5420" align="aligncenter" width="960"] Application Context - Step #3 of the wizard[/caption]
If enabled by administrators, a user can save his context maps and browse all maps available in the MapStore catalog through the Map Catalog tool. You do not need to leave the Viewer anymore to switch to other maps!
[caption id="attachment_5424" align="aligncenter" width="478"] Application Context - Map Catalog[/caption]
If you want to know more about this feature, please check the online documentation which covers other functionalities not described of this blog post. For example, Map Templates are ready-made map contents that can be added to a Map.

Special thanks to Renne Métropole, who funded this new feature for the geOrchestra community. We will write a separate blog post about the adoption of MapStore as the geOrchestra viewer.
Ongoing and future work
For the next releases we are working on the following (in sparse order) functionalities:
- Improvements of the Style Editor to improve the user experience when editing styles in MapStore.
[caption id="attachment_5429" align="aligncenter" width="1024"] Style Editor - Improved UI[/caption]
- Improvements on the MapStore SDK to simplify the life of developers creating downstream projects like the GeoNode integration.
- Enhancements for the Annotations and Identify tools to improve the user experience with a new user interface and additional capabilities.
If you want to play with the current release, you can access the live demo here and login with username demo and password 123demoUser.

If you are interested in learning more about how we can help you develop a WebGIS using MapStore or help you achieve your needs related to GeoServer, GeoNode and GeoNetwork through our Enterprise Support Services and Subscription Services please contact us!
The GeoSolutions team,
11:00
CARTO Blog: Building COVID-19 Dashboards with Google BigQuery
sur Planet OSGeo

Together, CARTO and Google BigQuery provide a next generation spatial data infrastructure for organizations looking to analyze their location data.
10:13
gvSIG Team: “La importancia de la Geografía, la geomática y la soberanía tecnológica. La solución gvSIG”, Webinar organizado por el Colegio de Ingenieros Geógrafos de Bolivia
sur Planet OSGeo

Para los interesados, este viernes se impartirá la charla “La importancia de la Geografía, la geomática y la soberanía tecnológica. La solución gvSIG”, organizada por el Colegio de Ingenieros Geógrafos de Bolivia.

Podéis inscribiros rellenando el siguiente formulario: https://forms.gle/tVLcawb7z5DEWk2z8

Toda la información aquí:
16 July 2020
14:37
Fernando Quadro: Combo Curso Python com GIS
sur Planet OSGeo

A Geocursos hoje está lançando o seu Curso Online de Python com GIS (Módulo 02) que é curso que pretende ensinar tópicos avançados de programação Python no mundo GIS. O curso tem uma carga horária de 20 horas, e ementa abaixo:

1. Rasters – Estatísticas Zonais
2. Interpolando dados
3. Automatizando Caderno de Mapas
4. Plugins no QGIS

Você pode ver a ementa detalhada na página do curso no seguinte link: [www.geocursos.com.br]

Se você ainda não fez o Curso de Python (Módulo 01), as inscrições estão abertas no nosso site ( [www.geocursos.com.br] ).

PROMOÇÃO! Preparamos uma promoção para aqueles que tiverem interesse em adquirir os dois módulo, que é o Combo Python com GIS, para isso basta acessar o link:

[www.geocursos.com.br]
14:30
Narcélio de Sá: Linha de chamada para rótulos no QGIS – Atualizado
sur Planet OSGeo

Enclosure: [download]
Tempos atras eu publiquei um post sobre como adicionar linha de chamada, Label callouts, no QGIS. As linhas de chamada, ou frases de destaque na tradução pt-br do QGIS, são linhas entre rótulos e as feições em um mapa.

Elas são úteis quando restrições de espaço forçam um rótulo a se afastar do da feição. Nesse caso a linha de chamada ajuda a reconectar o rótulo a feição ao qual ele se refere.

Em 2016 eu encontrei uma super dica do Alexandre Neto, do blog SIG sem grilhetas, de como inserir rótulos com linha de chamada no QGIS. O Alexandre usava uma view no Postgis para automatizar a criação das linha de chamada para os rótulos que são movidos de sua posição original. Veja o resultado abaixo:

Quebrando um pouco a cabeça consegui chegar num resultado idêntico ao do Alexandre. Mas no meu caso sem a necessidade de um banco de dados espacial, usando apenas a tabela de atributos e a função de geração de geometria. Veja a postagem original com as expressões, que ainda podem ser úteis para customizar soluções no QGIS.

Atualmente o QGIS conta com uma ferramenta própria para a geração das linhas de chamada, ou Frases de destaque. Veja esse vídeo tutorial e aprenda utilizar essa funcionalidade do QGIS.
Download da camada de Estados brasileiros Passo a passo para adicionar uma linha de chamada a um rótulo
Ative a guia Frases de destaque e ative a aba frases de destaque. Então você pode:
1. Selecione o estilo do conector, um dos seguintes:
  - Linhas simples : uma linha reta, o caminho mais curto
  - Estilo Manhattan : uma linha a 90 °
2. Selecione o estilo de linha com todos os recursos de um símbolo de linha, incluindo efeitos de camada e configurações definidas por dados
3. Definir o comprimento mínimo das linhas de frase de destaque
4. Defina a opção Deslocamento para feição : controla a distância do recurso (ou seu ponto de ancoragem, se um polígono), onde as linhas de frase de destaque terminam. Por exemplo, isso evita desenhar linhas diretamente nas bordas dos recursos.
5. Defina a opção Deslocamento para área rótulo : controla a distância do canto mais próximo da etiqueta em que as linhas da frase de destaque terminam. Isso evita desenhar linhas diretamente contra o texto.
6. Desenhar linhas para todas as partes da feição relacionada ao rótulo
7. Defina um ponto de ancoragem na feição (polígono) para usar como ponto final da linha do conector: as opções disponíveis são:
  - Polo de inacessibilidade
  - Ponto no exterior
  - Ponto na superfície
  - Centroide
Para mais tutorias de QGIS como esse acesse: [https:]]

O que vocês acharam deste tutorial Linha de chamada para rótulos no QGIS - Atualizado ?

Aguardo seus comentários, dicas e sugestões.
Não deixe de curtir nossa página no Facebook , inscreva-se no nosso canal no Youtube e não esqueça de seguir nosso Twitter

The post Linha de chamada para rótulos no QGIS – Atualizado appeared first on Narcélio de Sá.
15 July 2020
2:00
Ecodiv.earth: GISQuick
sur Planet OSGeo
For the QGIS users, there are different ways to publish maps online. In this post I want to highlight the option to publish your geospatial data using the Gisquick open source platform. The motto on their website is "Let’s share GIS much quicker’. And they make it very easy indeed.

As they explain on their website, Gisquick consists of QGIS plugin, Django based server application, QGIS-server, and web/mobile clients. For those who want to set up their own server, the different Gisquick server components can be easily deployed in Docker containers.

For those not so much into setting up servers or deploying docker containers, you can share data through their public instance. To do so, you’ll first need to create an account. The account is free, which is great. Note, this is a demo site. So for anything beyond testing the toolset, I would first contact the developers.
The GISQuick plugin
The process of data publishing with Gisquick is quite easy. Obviously, you first have to install the plugin. To do so, open the plugin window (Plugin > Manage and install plugins), search for the Gisquick plugin and install it. After installation, you’ll find a new Web > Gisquick menu and a new button on the toolbar.

Figure 1: The new Gisquick menu item in the Web menu (1) and on the toolbar (2)

The next step is to create and set up your projects with QGIS. For those already using QGIS, no further explanation will be required I assume. If you are new to QGIS, check out the QGIS documentation, for example, to learn more about QGIS projects. Don’t forget to fill in the layer properties for the QGIS server. This is the layer information that will be shown in the Gisquick map.

Figure 2: Layer metadata to be shown by the server
Publishing your maps
After setting up your QGIS project, you can use the GISQuick plugin to export the project as a zip file, ready to upload to the server on the project management page (Figure 3). After upload, you can open the mapviewer (click on the map icon), modify existing projects, or upload new projects (Figure 3).

Figure 3: The Gisquick project management page.

To modify an existing project, click on the project name. This will open a settings page, where you can make your changes. For example, on the page shown in Figure 4, you can define new, or modify existing Topic pages (alternative project views with different combinations of layers).

Figure 4: Editing the project settings, like adding, deleting or modifying topics

There is also the option to directly publish from QGIS (See Q publish in Figure 3). To be able to do so, you’ll need a complementary Gisquick plugin, which you can download from the same page. With this plugin you connect directly to the server from QGIS. Once connected, you can see and upload updated files directly.

Figure 5: Updating modified files using the direct QGIS connection. Files with their name in orange are changed. With the ‘upload files’ button these will be updated on the server.
An example
Last school year, our 2^nd year students of Geo Media and Design carried out fieldwork to map soil properties of a 16 hectare site in the municipality of Meierijstad in the south of the Netherlands. The site is being developed as forest garden, and these soil measurements will serve as a baseline for the monitoring of changes in the soil properties. If you can read Dutch, see this website for more information about this initiative.

The students spend a day in the field taking soil samples. From these samples, they measured the pH, organic matter content, and the percentage of loam in the soil. Next, they tested different spatial interpolation methods available in the processing toolbox of QGIS to characterize patterns in these soil properties across the site.

Of course, there is not much use in doing all this work without sharing the results. So I though to give the QGIS Gisquick plugin a try. I already had put together the data in a QGIS project, so all I had to do was to export the data and upload it to the server. You can see the result here.

Figure 6: Screenshot of the webapp with maps of soil properties of Hardekamp location of the Schijndel Forest Garden project.

Most work is in setting up the QGIS project, but that is something I would have done anyway. Publishing the results to the server was really a breeze, and it not only makes available the maps, but also gives users access to the data in the attribute tables of the vector layers. It would have been nice if the user would be able to query the raster values as well. And when printing out a map (only available if you are logged in), the baselayer is not included. But hé, there should always be left something to wish for :-).
12 July 2020
12:08
Free and Open Source GIS Ramblings: Spatial on air #2: spatiotemporal everything!
sur Planet OSGeo
We’ve done it again!

This time, Daniel O’Donohue and I talked about spatiotemporal data in GIS, including – of course – Time Manager, the new QGIS temporal support, and MovingPandas.

Since we need both data and tools to do spatiotemporal analysis, we also talked about file formats and data models. If you want to know more about data models for spatiotemporal (especially movement) data, have a look at the latest discussion paper I wrote together with Esteban Zimányi (MobilityDB) and Krishna Chaitanya Bommakanti (mobilitydb-sqlalchemy):
- Graser, A., Zimányi, E., & Bommakanti, K.C. (2020). From Simple Features to Moving Features and Beyond? arXiv:2006.16900.
Data model of the Moving Features standard illustrated with two moving points A and B. Stars mark changes in attribute values. (Source: Graser et al. (2020))

For more details and all options for listening to this podcast, visit mapscaping.com.
11 July 2020
22:35
Stefano Costa: Marzano 200 anni fa
sur Planet OSGeo

Come era Marzano 200 anni fa? Posso dirvi che su una carta geografica era più o meno così:
Marzano in una carta degli anni 1816-1827 (Istituto Geografico Militare)
Questa è una piccola porzione della carta denominata “RIVIERA DI LEVANTE ALLA QUARTA DELLA SCALA DI SAVOIA OSSIA DI 1 A 9.450. FOGLIO 83 (Torriglia)“. Mentre per la “pillola” storica precedente mi sono basato su una vecchia fotocopia conservata in mezzo a un libro, per questa seconda ho attinto al portafogli e comprato dall’Istituto Geografico Militare ben due riproduzioni in alta risoluzione.

Ho deciso di spendere per avere questa riproduzione perché credo che sia la carta più antica in cui Marzano è rappresentato in modo dettagliato. Non sono riuscito a trovare una carta così dettagliata più antica di questa, in altre parole. Le carte settecentesche lo indicano semplicemente come centro abitato in modo simbolico, come è abbastanza normale per l’epoca quando si tratta di aree rurali.
Una porzione della carta “La Riviera di Levante, Stato di Genova ed altri confinanti” di Matteo Vinzoni (1748, Archivio di Stato di Genova)
La differenza è notevole, e nella carta di inizio ‘800 possiamo riconoscere singole case o gruppi di case. La seconda versione, cioè la minuta di campagna, contiene qualche dettaglio in più anche se la scala è maggiore. Soprattutto sono indicate strade che poi nella versione acquerellata non sono state disegnate.
Marzano nella minuta di campagna della carta “RICOGNIZIONI ESEGUITE ALLA SCALA DI 1/20.000 CHE COMPRENDANO LE DUE RIVIERE DEGLI EX STATI SARDI E PARTE DELLE ATTUALI PROVINCIE DI ALESSANDRIA E DI PAVIA – FOGLIO 58“
In queste carte la forma del paese è ben diversa da quella attuale. Manca ancora il caratteristico “curvone” della strada carrabile che verrà costruita solo verso la fine del XIX secolo. Le strade principali sono quelle che vanno verso Fallarosa e i Sciutti e quella che arriva dalla Crocetta. Stranamente, sembra che quest’ultima strada passi a Est rispetto a tutte le case.

Si riconoscono già alcuni gruppi di abitazioni che esistono ancora oggi. La chiesa è isolata su tutti i lati. Non ci sono case più a Est dell’attuale civico 49 di Via IV Novembre. Non ci sono case lungo la “Via Vecchia Marzano”. La piazzetta “della Costa” alle spalle di Piazza Felice Cavallotti è solo il retro del gruppo di case. Si riconoscono i tre blocchi “a schiera” che ancora oggi si trovano sopra Via Fallarosa. Non sembra esistere il “voltino” ma sicuramente ci sono le case più vecchie anche a valle di Via Fallarosa, fino all’altezza dei trogoli. Si riconoscono i Pontelli e le Chistane.

Non è facilissimo individuare le singole case, anche perché qualcuna nel frattempo è andata in rovina ? limitandomi alle Chistane ne conosco almeno tre (inclusa la storica casa di “Giaculeu” abbattuta 40 anni fa) e immagino ce ne siano anche altre. Non è ovviamente possibile stabilire quanto sia preciso il disegno della carta, possono esserci errori, omissioni o sviste, ma riesco a contare 24 edifici più la chiesa e se confronto questo numero con quelli del numero di famiglie di questo periodo o della seconda metà del ‘700, è più o meno lo stesso.

La base cartografica verrà poi utilizzata a metà XIX secolo per comporre la Gran Carta degli Stati Sardi (ricca di strafalcioni nei nomi) pubblicata nel 1852: sembra che la strada proveniente dalla Crocetta sia più importante delle altre.
Marzano e dintorni nella “Gran Carta degli Stati Sardi, pubblicata dal Corpo Reale di Stato Maggiore” (1852, Archivio di Stato di Torino, foglio 68 Torriglia)
Quanto è cambiato il paese in 200 anni! Sicuramente nelle vecchie foto si riconoscono più facilmente le antiche case che esistevano allora.

Pillole di storia marzanina
La chiesa di San Bartolomeo · Marzano 200 anni fa · La peste a Marzano nel 1656-1657
22:35
Stefano Costa: Marzano 200 anni fa
sur Planet OSGeo

Come era Marzano 200 anni fa? Posso dirvi che su una carta geografica era più o meno così:
Marzano in una carta degli anni 1816-1827 (Istituto Geografico Militare)
Questa è una piccola porzione della carta denominata “RIVIERA DI LEVANTE ALLA QUARTA DELLA SCALA DI SAVOIA OSSIA DI 1 A 9.450. FOGLIO 83“. Mentre per la “pillola” storica precedente mi sono basato su una vecchia fotocopia conservata in mezzo a un libro, per questa seconda ho attinto al portafogli e comprato dall’Istituto Geografico Militare ben due riproduzioni in alta risoluzione.

Ho deciso di spendere per avere questa riproduzione perché credo che sia la carta più antica in cui Marzano è rappresentato in modo dettagliato. Non sono riuscito a trovare una carta così dettagliata più antica di questa, in altre parole. Le carte settecentesche lo indicano semplicemente come centro abitato in modo simbolico, come è abbastanza normale per l’epoca quando si tratta di aree rurali.
Una porzione della carta “La Riviera di Levante, Stato di Genova ed altri confinanti” di Matteo Vinzoni (1748, Archivio di Stato di Genova)
La differenza è notevole, e nella carta di inizio ‘800 possiamo riconoscere singole case o gruppi di case. La seconda versione, cioè la minuta di campagna, contiene qualche dettaglio in più anche se la scala è maggiore. Soprattutto sono indicate strade che poi nella versione acquerellata non sono state disegnate.
Marzano nella minuta di campagna della carta “RICOGNIZIONI ESEGUITE ALLA SCALA DI 1/20.000 CHE COMPRENDANO LE DUE RIVIERE DEGLI EX STATI SARDI E PARTE DELLE ATTUALI PROVINCIE DI ALESSANDRIA E DI PAVIA – FOGLIO 58“
In queste carte la forma del paese è ben diversa da quella attuale. Manca ancora il caratteristico “curvone” della strada carrabile che verrà costruita solo verso la fine del XIX secolo. Le strade principali sono quelle che vanno verso Fallarosa e i Sciutti e quella che arriva dalla Crocetta. Stranamente, sembra che quest’ultima strada passi a Est rispetto a tutte le case.

Si riconoscono già alcuni gruppi di abitazioni che esistono ancora oggi. La chiesa è isolata su tutti i lati. Non ci sono case più a Est dell’attuale civico 49 di Via IV Novembre. Non ci sono case lungo la “Via Vecchia Marzano”. La piazzetta “della Costa” alle spalle di Piazza Felice Cavallotti è solo il retro del gruppo di case. Si riconoscono i tre blocchi “a schiera” che ancora oggi si trovano sopra Via Fallarosa. Non sembra esistere il “voltino” ma sicuramente ci sono le case più vecchie anche a valle di Via Fallarosa, fino all’altezza dei trogoli. Si riconoscono i Pontelli e le Chistane.

Non è facilissimo individuare le singole case, anche perché qualcuna nel frattempo è andata in rovina ? limitandomi alle Chistane ne conosco almeno tre (inclusa la storica casa di “Giaculeu” abbattuta 40 anni fa) e immagino ce ne siano anche altre. Non è ovviamente possibile stabilire quanto sia preciso il disegno della carta, possono esserci errori, omissioni o sviste, ma riesco a contare 24 edifici più la chiesa e se confronto questo numero con quelli del numero di famiglie di questo periodo o della seconda metà del ‘700, è più o meno lo stesso.

La base cartografica verrà poi utilizzata a metà XIX secolo per comporre la Gran Carta degli Stati Sardi (ricca di strafalcioni nei nomi) pubblicata nel 1852: sembra che la strada proveniente dalla Crocetta sia più importante delle altre.
Marzano e dintorni nella “Gran Carta degli Stati Sardi, pubblicata dal Corpo Reale di Stato Maggiore” (1852, Archivio di Stato di Torino, foglio 68 Torriglia)
Quanto è cambiato il paese in 200 anni! Sicuramente nelle vecchie foto si riconoscono più facilmente le antiche case che esistevano allora.

Pillole di storia marzanina
La chiesa di San Bartolomeo · Marzano 200 anni fa
10 July 2020
12:03
QGIS Blog: QGIS Grant Programme 2020 Results
sur Planet OSGeo
We are extremely pleased to announce the winning proposals for our 2020 QGIS.ORG grant programme. Funding for the programme was sourced by you, our project donors and sponsors! Note: For more context surrounding our grant programme, please see: QGIS Grants #5: Call for Grant Proposals 2020.

The QGIS.ORG Grant Programme aims to support work from our community that would typically not be funded by client/contractor agreements. This means that we did not accept proposals for the development of new features. Instead proposals focus on infrastructure improvements and polishing of existing features.

Two proposals focusing on documentation improvements were funded directly from the documentation budget. The remaining 10 proposals continued on to the voting.

Voting to select the successful projects was carried out by our QGIS Voting Members. Each voting member was allowed to select up to 6 proposals. The full list of votes are available here (on the first sheet). The following sheets contain the calculations used to determine the winner (for full transparency). The table below summarizes the voting tallies for the proposals:

Thanks to the generous support by our sponsors and donors, we are happy that all proposals will receive funding, even if QEP#124 had to be reduced in scope (core part only, no GUI: €2,600 from QGIS grants & €1,400 sponsored by OPENGIS).

A couple of extra notes about the voting process:
- Voting was carried out based on the technical merits of the proposals and the competency of the applicants to execute on these proposals.
- No restrictions were in place in terms of how many proposals could be submitted per person / organization, or how many proposals could be awarded to each proposing person / organization.
- Voting was ‘blind’ (voters could not see the existing votes that had been placed).
We received 34 votes from 21 community representatives and 13 user group representatives.

On behalf of the QGIS.ORG project, I would like to thank everyone who submitted proposals for this call!
09 July 2020
18:14
Free and Open Source GIS Ramblings: Movement data in GIS #31: exploring massive movement datasets
sur Planet OSGeo
Exploring large movement datasets is hard because visualizations of movement data quickly get cluttered and hard to interpret. Therefore, we need to aggregate the data. Density maps are commonly used since they are readily available and quick to compute but they provide only very limited insight. In contrast, meaningful aggregations that can help discover patterns are computationally expensive and therefore slow to generate.

This post serves as a starting point for a series of new approaches to exploring massive movement data. This series will summarize parts of my PhD research and – for those of you who are interested in more details – there will be links to the relevant papers.

Starting with the raw location records, we use different forms of aggregation to learn more about what information a movement dataset contains:
1. Summarizing movement using prototypes by aggregating raw location records using our flexible M³ Massive Movement Model [1]
2. Generating trajectories by connecting consecutive records into continuous tracks and splitting them into meaningful trajectories [2]
3. Extracting flows by summarizing trajectory-based transitions between prototypes [3]
Besides clever aggregation approaches, massive movement datasets also require appropriate computing resources. To ensure that we can efficiently explore large datasets, we have implemented the above mentioned aggregation steps in Spark. This enables us to run the computations on general purpose computing clusters that can be scaled according to the dataset size.

In the next post, we’ll look at how to summarize movement using M³ prototypes. So stay tuned!

But if you don’t want to wait, these are the original papers:

[1] Graser. A., Widhalm, P., & Dragaschnig, M. (2020). The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science. doi:10.1080/13658816.2020.1776293.
[2] Graser, A., Dragaschnig, M., Widhalm, P., Koller, H., & Brändle, N. (2020). Exploratory Trajectory Analysis for Massive Historical AIS Datasets. In: 21st IEEE International Conference on Mobile Data Management (MDM) 2020. doi:10.1109/MDM48529.2020.00059
[3] Graser, A., Widhalm, P., & Dragaschnig, M. (2020). Extracting Patterns from Large Movement Datasets. GI_Forum – Journal of Geographic Information Science, 1-2020, 153-163. doi:10.1553/giscience2020_01_s153.

This post is part of a series. Read more about movement data in GIS.
08 July 2020
11:00
CARTO Blog: Combining Social Media & Credit Card Data in Retail Analytics
sur Planet OSGeo

As the impact of the COVID-19 pandemic continues to be felt across all industries, particularly retail, there are both ongoing record declines as well as some encouraging s...
07 July 2020
11:00
CARTO Blog: Simpler Data Science Workflows with CARTO + Databricks
sur Planet OSGeo

In our continuous efforts to make sure CARTO works well with other technologies, we’re always assessing how we can improve our customers’ workflows so that they can easily ...
10:00
gvSIG Team: Where is open source GIS software going? gvSIG article at TWINGEO Magazine
sur Planet OSGeo

TWINGEO magazine asked us for an article that answered the question “Where is open source GIS software going?”

At a technical level, the software applied to geographic information management will follow similar dynamics, regardless of its license. It must be remembered that the “open” concept applies to the conditions of use of a software, the rights that are granted to the user, regardless of their capabilities or technological focus. Therefore, from our perspective, “Where is open source GIS software going to” question must have different answers depending on the approach we have to it. We will focus on two main points of view: technology and industry.

Read the full article here:
9:45
gvSIG Team: gvSIG en II Jornadas de SIG y Teledetección libres de Costa Rica
sur Planet OSGeo
La Escuela de Geografía de la Universidad de Costa Rica organiza las II Jornadas de SIG y Teledetección libres de Costa Rica, que se realizarán en modalidad online/virtual. Un conjunto de interesantes talleres y conferencias magistrales que tendrán lugar entre los días 6 y 10 de julio de 2020 en los que gvSIG tiene una participación destacada.
- Miércoles 8 de julio, conferencia: “La importancia de la dimensión geográfica, la relevancia de la soberanía tecnológica. Suite gvSIG: software libre para gestión territorial” (Álvaro Anguix)
- Jueves 9 de julio, taller “Un análisis vale más que 1.000 imágenes: SIG básico con gvSIG 2.5” (Sergio Acosta y Lara).
- Jueves 9 de julio, taller “Teledetección térmica con gvSIG para mapeo de islas de calor” (Rubén Martínez)
Desde la organización nos indican que el límite de inscritos se ha completado, pero posteriormente se dará acceso a la grabación de cada una de las sesiones.
06 July 2020
17:44
Jackie Ng: Announcing: FDO Toolbox 1.5
sur Planet OSGeo
Another release already? Yes indeed!

There were 2 reasons for this new release.

Firstly, the previous 1.4 release had a major oversight where the windows installer did not bundle a required dll needed for the new FdoCmd tool to work, so it was totally broken out of the box in both the 1.4 and 1.4.1 releases. This release now properly bundles the missing dll, making this tool finally operational for the first time for you all.

Secondly, this release include a new major feature that was originally slated to be part of the 1.4 release, but got shelved due to stability issues around the FDO .net wrapper that was resolved with the 1.4.1 release. And for this announcement post, I'll be talking about this feature in great detail.

That feature, is the long overdue integration of MgCoordinateSystem from the MapGuide API for coordinate system support. Here's how we use the MgCoordinateSystem integration in FDO Toolbox.

Transforming Features in Bulk Copy

Let''s start with the obvious place, bulk copying now supports optional transformation of geometry features. This is expressed in several different ways.

In the main bulk copy editor, when specifying a spatial context override with a different coordinate system WKT like so.

There is now a new flag that you can toggle to indicate that this override should be interpreted as an instruction to transform geometries for the source coordinate system WKT to the coordinate system indicated by the override WKT.

This method of enabling transformation may look a bit un-intuitive if you're accustomed to using tools like ogr2ogr where you state upfront what the source CS is and what target CS you want to transform to. The reason for this is due to the concept of spatial contexts in FDO. In FDO, coordinate systems are not set explicitly, they are inferred through its spatial context. Thus the UI to enable transformation stems from the constraint imposed by FDO's spatial contexts. We can't set a source or target CS, we have to override what is inferred by FDO.

When using the SDF/SQLite dump context menu command, the UI has an option to allow transforming the dumped features to the specified coordinate system.

When you click the Pick CS button, a dialog appears that you should be familiar with. It's the same coordinate system picker dialog from MapGuide Maestro and fulfills the same purpose here in FDO Toolbox as it does in Maestro.

Streamlining Various UI

Any UI that deals with spatial contexts now take advantage of the newly integrated coordinate system picker to help auto-fill most, if not, all of the fields in question.

For example, creating a spatial context now can use the coordinate system picker to auto-fill in most of the fields in the UI

Similarly, when creating a new RDBMS data store, you can use the coordinate system picker to pre-fill the sections about the coordinate system and extents

And from the previous bulk copy example, you can easily override a source spatial context by picking an existing coordinate system.

FdoCmd enhancements

Not only will you finally get a functional FdoCmd tool in this release, it has also been enhanced with various integrations with the new coordinate system functionality.
- Any command that outputs geometry data will have new options to allow transforming it to a target coordinate system
- Creating spatial contexts can use an existing coordinate system to fill in most of the required spatial context information.
- The bulk copy commands has support for transformation and can use an existing coordinate system as the basis for specifying the WKT of any override spatial context
- When listing spatial contexts, we'll now use the new coordinate system facilities to resolve/display the corresponding mentor/EPSG code for any coordinate system displayed
Also FdoCmd receives a whole series of new commands for interacting with the coordinate system facilities:
- enumerate-cs-categories for listing all categories from the coordinate system catalog
- enumerate-cs for listing all coordinate systems for a given category
- find-cs-by-code for fetching and displaying details of a coordinate system from an input mentor CS code
- find-cs-by-epsg for fetching and displaying details of a coordinate system from an input EPSG code
- wkt-to-cs-code for obtaining a mentor CS code from an input coordinate system WKT
- wkt-to-epsg for obtaining an EPSG code from an input coordinate system WKT
- cs-code-to-wkt for the inverse of wkt-to-cs-code
- epsg-to-wkt for the inverse of wkt-to-epsg
- is-valid-wkt for validating coordinate system WKT strings
A small caveat
If you've used CS-MAP (or its MgCoordinateSytem wrapper in MapGuide), you'll know about its legendary support for nearly every coordinate system in existence on Planet Earth.
But that support comes at a price: The size of our installers/installation suffers as a result.
To support so many coordinate systems requires country-specific grid data files that are:
- Huge
- Difficult to compress due to their binary nature
So to avoid making this installer a 300MB file, and making it a 30MB file instead, this release of FDO Toolbox only bundles the core CS-MAP dictionary data files and omits all of the country-specific grid files. Such files are available for download as a separate zip file on the 1.5 release page.
If you need these grid files for your coordinate system transformation (most likely, you are transforming from one CS to another and it is failing because it is looking for a certain grid file), just download the zip file (from the releases page) and extract the contents into the Dictionaries directory of your FDO Toolbox installation.
Other changes
- FDO Toolbox now ships with libpq.dll and libmysql.dll (dlls courtesy of the VS2015 build of GDAL/OGR from gisinternals) allowing for the MySQL and PostgreSQL providers to work out of the box without having to source these dlls yourself. King Oracle provider still requires you to source the Oracle 11g Instant Client binaries yourself.
- When bulk copying, we no longer try to create spatial contexts for names that already exist on the target connection
- More cases are handled when trying to convert an incompatible feature class when applying a schema to a target connection
In Closing
Barring bug fix releases to address any critical issues that show up after this release, I believe that FDO Toolbox 1.5 will be the last major release of FDO Toolbox I will be putting out for a while and the project will most likely return back to hibernation. I had restarted this journey a few months ago to address some long standing pain points that had built up and with the completion of this coordinate system integration in this release, I feel this journey is now complete ...
... Until another series of annoyances and pain points builds up to critical mass in 5/10 years time perhaps :)
Download
17:25
Stefano Costa: La chiesa di San Bartolomeo a Marzano (Torriglia)
sur Planet OSGeo

Marzano è la frazione del comune di Torriglia (GE) dove sono cresciuto. Un paesino piccolo, luogo di origine dei miei antenati Costa. In questo periodo di isolamento ho avuto la fortuna di passare qui molti mesi e ho raccolto vecchi e nuovi appunti sulla sua storia, per quanto minore e frammentaria.

La chiesa di San Bartolomeo è l’unica chiesa di Marzano. Non è molto grande e viene variamente definita come cappella, oratorio o chiesa sussidiaria. La sua posizione centrale sulla piazza del paese (che è intitolata a Felice Cavallotti ma è chiamata da tutti “piazza della chiesa”) la rende facilmente riconoscibile. Non credo sia l’edificio più antico esistente in paese, ma è sicuramente quello su cui le notizie storiche sono più numerose, per quanto scarne. E con la storia della chiesa inevitabilmente si racconta anche la storia della comunità.

I maggiori dettagli storici si ricavano da un articolo del profilico don Giovanni Carraro, pubblicato sul notiziario parrocchiale nel 1938. Una descrizione architettonica più dettagliata che riprende in parte alcune notizie storiche si ricava dalla scheda di catalogo CEI. Riporto il contenuto dell’articolo quasi integralmente con alcuni commenti e soprattutto in ordine cronologico.
L’articoletto di don Carraro
La “grande cappella” dedicata a San Bartolomeo Apostolo fu edificata nella prima metà del XVII secolo. Tuttavia, nelle carte dell’archivio parrocchiale viene spesso indicata come cappella dedicata a San Terenziano, vescovo tudertinus (e non terdonensis). In effetti questo dualismo permane ancora oggi: il giorno di San Bartolomeo (24 agosto) viene celebrata la messa, ma è nel giorno dedicato a San Terenziano (San Ransiàn in dialetto, il 3 settembre) che si svolge la festa popolare più laica, quella con i frisceu per capirci. La grande tela che si trova all’interno della chiesa ritrae comunque entrambi i santi insieme a San Rocco, Santa Lucia e Santa Apollonia. La presenza di San Terenziano a Marzano è interessante ed è solo una tra le molte località dell’Appennino dove San Terenziano persiste, talvolta da un millennio. Significativo che le date siano così ravvicinate e a pochi giorni di distanza dal 29 agosto in cui ricorre la Madonna della Provvidenza / Madonna della Guardia.
La facciata della chiesa oggi
L’atto di fondazione della cappella fu rogato nel 1648 assegnando una dote di 15 lire annue su terreni vincolati: 10 lire per 8 messe l’anno e 5 lire per riparazioni.

Nel 1677 fu costituito un censo di 44 soldi su terra l. d. Canivella e un altro censo di 24 soldi.

Nel 1694 fu rinnovato lo strumento di dotazione della chiesa, autorizzando i massari a riscuotere la somma dalle 27 famiglie, tra cui 5 Guano, 6 Fascia, 2 Pregola, 12 Costa. Vediamo che almeno tre di questi cognomi rimangono tuttora tra quelli caratteristici di Marzano, pur con una differenza di grafia.

Nella visita pastorale del 28 agosto 1707 si parla di un oratorio campestre intitolato a San Terenziano martire e si descrive lo stato lacunoso delle suppellettili. Nello stesso anno, G. Casazza lasciava una terra detta Fasolai del valore di mezza Genovina.

Nel 1775 fu concesso di benedire il nuovo altare (che molto probabilmente è quello attuale), purché avesse le misure prescritte.

Nel 1850 fu dato in locazione per 6 anni un terreno con due castagni e un cerro, detta fascia della chiesa, e un altro terreno detto dell’orto, per 50 lire.

Il pavimento in graniglia alla veneziana porta la data del 1862 e il nome di Giacomo Costa.

Nel 1887 viene consentita la costruzione di una nuova casa addossata al coro (esistente ancora oggi), che quindi rende impossibili successivi ampliamenti dell’edificio.

Nel 1896 viene restaurata dentro e fuori, e provveduta di volta a botte. Nel 1897 viene rinfrescata la facciata e nuovamente nel 1927.

I lavori di restauro più recenti sono di circa 20 anni fa.

Pillole di storia marzanina
La chiesa di San Bartolomeo · Marzano 200 anni fa · La peste a Marzano nel 1656-1657
12:15
GeoServer Team: GeoServer 2.16.4 released
sur Planet OSGeo
We are pleased to announce the release of GeoServer 2.16.4 with downloads (war|zip), HTML documentation and extensions.

This is a stable release recommended for production systems.
Improvements and Fixes
This release includes a number of improvements, including:
- The GetFeatureInfo GeoJSON output can now be controlled with freemarker templates.
- Image mosaic REST API now allows updating the native BBOX of the mosaic during update and delete operations. It’s also now possible to store the mosaic index in SQL Server.
- Documentation on how to integrate the app-schema plugin with MongoDB has been improved.
- The JSON-LD community module has a new option to filter output contents.
- The WPS download community module can reproject the eventual pixel value as well, in case it is an elevation, using a grid shift file.
For more information and the small assorted bug fixes, check the 2.16.4 release notes.
About GeoServer 2.16
Features, presentations and reference material on the 2.16 series:
- State of GeoServer 2.16 (video|slides)
- GeoServer Feature Frenzy 2019 (video|slides)
- New SLDService extension using data classification for style generation
- New authentication key extension available
- Server status page now includes system status details
- GDAL 2.x binaries are now used for GDAL image formats along with OGR WFS output and WPS output formats
- Release Notes (2.16.2 | 2.16.1|2.16.0|2.16-RC1)
11:46
GeoTools Team: GeoTools 22.4 released
sur Planet OSGeo

The GeoTools team is pleased to announce the release of GeoTools 22.4: geotools-22.4-bin.zip geotools-22.4-doc.zip geotools-22.4-userguide.zip geotools-22.4-project.zip maven repository GeoTools 22.4 is a stable release and is recommended for new development and production systems. This release is made in conjunction with GeoServer 2.16.4. What's new Improvements: The ImageMosaic plugin
11:00
CARTO Blog: Google BigQuery Visualization: Mapping Big Spatial Data
sur Planet OSGeo

Supported by Google Jigsaw, the GDELT Project monitors the world’s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identi...
9:22
gvSIG Team: MAPPING 200. Un número muy especial disponible para descarga
sur Planet OSGeo

Cualquier persona relacionada con la geomática conoce la revista MAPPING. Un referente en el mundillo que lleva años acompañándonos (al que esto escribe desde que estaba en la Universidad y de eso hace ya un tiempo).

Para celebrar su número 200, Miguel Ángel y Marta, los responsables a los que debemos la larga vida de MAPPING decidieron lanzar un número muy especial con la siguiente premisa que enviaron a los diferentes autores participantes en su redacción: “la idea común para todos los participantes, es que ya que nuestra revista va a cumplir 30 años desde su creación, nos gustaría que nos hicieran una redacción, desde su punto de vista y campo profesional, de donde estábamos hace 30 años, donde estamos ahora y donde estaremos posiblemente , los próximos 30 años.“.

Así que además de otras muchas historias y puntos de vista que es conveniente leer, en MAPPING 200 podréis encontrar un artículo que desde gvSIG hemos escrito sobre el tema. Nuestra ya no tan breve historia, enmarcada en los acontecimientos de los últimos años, y una visión de qué podemos esperar del futuro…¿o al futuro no se le espera?

Aquí podéis descargar el PDF de la revista completa: Número 200

Y aquí del artículo de gvSIG: gvSIG. Una mirada al ayer y un pronóstico del futuro desde el presente
03 July 2020
16:34
Jo Cook: Blogging in the time of Covid
sur Planet OSGeo

(with apologies to Gabriel García Márquez)

It’s weird posting to a mostly tech blog at the moment. The world is on fire, but life goes on, yet I’ve felt utterly paralysed and unable to write anything here. It feels strange and somehow wrong to write about tech stuff, which lets face it, is not that important in the grand scheme of things, when so much other stuff is going on. Yet here we are, blogging.

So what did I want to blog about? Something that I read recently that I have taken to heart, and that has proved strangely apposite during #lockdown. I’m interested in hacking myself so that good/productive behaviour becomes easier and requires less “processing power” to achieve. Things like “Getting Things Done” by David Allen, and the Bullet Journal Technique are important parts of my life, though before you ask my “Bullet Journal” technique is a bit stripped down and doesn’t require the purchase of expensive custom journals…

Consequently I love finding out new ways of fooling my brain into good behavioural patterns, but I have struggled to find the key that works for both “work stuff” and my other great love, climbing. However I recently read “Atomic Habits” by James Clear, and he set out an idea that has stuck with me, and shown benefits in both work and climbing throughout the last few months. It’s really very obvious, but the idea is that training or hard work should be viewed as deposits into a long-term savings account that you’ve set up to save for something really important. Every bit of hardf work or physical training that you undertake will probably not show any benefits in the short-term, but it all adds up. Every time you (or really I) slack off, it’s the equivalent of making a withdrawal. You lose more than just the cash-value, you lose the long-term interest too.

I’ve applied this mainly to climbing training during #lockdown, following a rather gruelling program from lattice training, which involves a minimum of an hour’s training for five out of seven days a week (after work). For two weeks in every four, this goes up to 1.5 hours a day. Whenever I’m bored or tired, which happens a lot, I remember the idea of the savings account, and manage to keep going. So what is the savings account for in this case? Mainly just being the best climber I can be (given the limitations of being a mid-40s female with a full-time job and a bad back). And yes, now #lockdown has eased somewhat, and climbing locally is OK, I can see obvious improvements in my performance, which make it all worthwhile.

How does this relate to open source gis though, I possibly hear you ask? Well, it’s more a work ethos than anything specific, particularly when I’m wading through stuff that’s new to me, or difficult. Cutting corners, or doing a half-arsed job, are all withdrawals from the savings account. Taking the time to really understand something? That’s a deposit.

The flipside of this is that sometimes you just can’t make that deposit. Maybe you’re literally too tired to train, or to think straight. Maybe you’ve over-committed by agreeing to give five different presentations over three conferences, one of which you’re also helping to organise, in the space of two weeks (ahem). It’s not the end of the world though. Missing one deposit is not going to stop you from meeting your target. The key thing is to give yourself the space you need, and then pick it right back up again. Remember also that it’s counter-productive to save for too many things at once- it’s better to limit yourself!

I won’t labour the point any longer, suffice to say that “this one simple hack” has helped me have a surprisingly productive #lockdown, and that having these “long-term savings goals” has kept me focused and sane. I am well aware though, that I’ve been in a position of immense privilege, being able to continue life relatively normally over this period. Not everyone has been as lucky.
02 July 2020
11:00
CARTO Blog: MBI's global spatial data available in CARTO's Data Observatory
sur Planet OSGeo

In today’s rapidly changing economy, being able to access and analyze global high-quality spatial data is fundamental to organizations using Location Intelligence. With tho...
01 July 2020
20:56
GeoSolutions: GeoServer Bug Stump Code Sprint July 2-3 2020
sur Planet OSGeo

Dear Reader,
GeoSolutions, along with the GeoServer community, is participating in a two days long “GeoServer Bug Fix Code Sprint”, on July the 2nd and the 3rd. The sprint also happens to overlap with the Bolsena Online Code Sprint 2020, so we’ll end up sharing some of the infrastructure and enjoying the good company of other sprinters! This sprint revives and seeks to improve the old monthly bug fix code sprint, that we used to do before. From those sprints, we learned that bugs that can be assigned to less experienced developers, because they seem like a one day fix, are hard to find. That was often coupled with lower participation of experienced developers, who need to review the pull requests for the fixes, and sit on the side (now virtually) of other developers to provide guidance.
So this time the sprint is two days long. We hope to get more tickets looked at, while allowing participant developers to tackle harder to fix problems.

Please join us on the GeoServer gitter channel, and don’t forget to go say hi to the other Bolsena sprinters on the OSGeo/Sprint channel!

Cordially,
Luis
11:00
CARTO Blog: Google Analytics Spatial Data Visualized with BigQuery
sur Planet OSGeo

This post was written by Liveli, our master reseller in Australia and New Zealand and featured by Google. –
30 June 2020
11:00
CARTO Blog: Announcing #SDSC20 Online: Spatial Data Science On Demand
sur Planet OSGeo

We are thrilled to announce that this year, the Spatial Data Science Conference will be available to our largest audience ever as a free virtual conference taking place bet...
10:00
gvSIG Team: 16th International gvSIG Conference (online event): Communication proposals submission
sur Planet OSGeo

The 16th International gvSIG Conference will be held from November 4th to 6th, and they will be an online event this year due to the current health emergency situation. The conference is organized by the gvSIG Association and the School of Engineering in Geodesy, Cartography and Surveying (Universitat Politècnica de València, Spain) under the slogan “New realities, new solutions“.

Being a virtual event, any person or organization will be able to present their project about gvSIG from anywhere in the world, just needing internet connection. Communication proposals submission is now open, and they can be sent to the email address: conference-contact@gvsig.com, following the information indicated at Communications section of the event website.

Registrations will be free of cost and they will be able to be done once the program is published.

All the information related to the conference, including workshops information, will be published at gvSIG Blog.

We expect your participation!
9:56
gvSIG Team: 16as Jornadas Internacionales gvSIG (evento online): Presentación de comunicaciones
sur Planet OSGeo

Del 4 al 6 de noviembre de 2020 se celebrarán las 16as Jornadas Internacionales gvSIG, un evento que este año será online debido a la actual situación de emergencia sanitaria mundial. Las jornadas están organizadas por la Asociación gvSIG y la Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica (Universitat Politècnica de València, España), y el lema de este año es “Nuevas realidades, nuevas soluciones”.

Al ser unas jornadas virtuales cualquier persona u organización podrá presentar su proyecto sobre gvSIG desde cualquier parte del mundo, necesitando únicamente una conexión a internet. A partir de ahora se abre el periodo de envío de propuestas de comunicación, que pueden enviarse a la dirección de correo electrónico conference-contact@gvsig.com, siguiendo la información indicada en el apartado ‘Comunicaciones’ de la web de las jornadas.

La inscripción a las jornadas será gratuita, y podrá realizarse una vez publicado el programa de las mismas.

Toda la información sobre las jornadas, incluyendo los talleres que se impartirán, se irá publicando en el blog de gvSIG.

¡Esperamos vuestra participación!
29 June 2020
11:00
CARTO Blog: Comparative Market Analysis Real Estate: Identifying Twin Areas
sur Planet OSGeo

Site Selection, Relocation, and Consolidation are often referenced as the defacto use cases of Location Intelligence, particularly in verticals such as Retail & Real Es...
8:20
gvSIG Team: gvSIG Online is used at Cullera municipality, in Valencia, to improve security on its beaches in the face of COVID-19
sur Planet OSGeo

Due to the current world health emergency situation, many municipalities are looking for solutions to reinforce security measures on their beaches and prevent COVID-19 infections. One of the main measures is to reduce the capacity of the beaches, so that the minimum safety distance can be observed. In this sense, one of the actions carried out by Cullera City Council, in Valencia (Spain), to inform citizens about the situation of the beaches, has been to generate a geoportal on its gvSIG Online platform, where the percentage of people in relation to the total capacity allowed in each sector or beach is shown at any time. The portal includes a legend that indicates whether or not it is advisable to go to that part of the beach or if it has an optimal situation.

Thanks to the new custom legends functionality in gvSIG Online, when updating number of attendees at the beach in the layer at any time, the percentage of capacity of each sector or beach is calculated based on its maximum permitted capacity, previously calculated, and based on its surface. It is shown in the set colour.

Cullera municipality has recently implemented gvSIG Online as a Spatial Data Infrastructure, which has allowed to have a web platform where different geoportals/viewers for any need can be generated in just a few minutes.

In recent weeks, the municipality technicians had generated several projects for the internal management of the cartography, with the intention of making some geoportals publicly available shortly, with useful information for citizens, such as for tourism, mobility and transportation, queries in the Urban Planning Department, etc., thus expanding its Electronic Office. But the urgent need to manage access to the beaches to reinforce security in the new normal phase after the end of the state of alarm, and with arrival of good weather, has led to the City Council to make public this first project.

The viewer is accessible from any web platform, so that citizens can consult the capacity of the beaches in a simple way, both from their smartphones and tablets and from their personal computers. In addition, a QR code has been provided with direct access to the geoportal.

On the other hand, the development of INFO Cullera is being carried out in parallel, an application based on gvSIG MApps for Android and iOS, in which citizens can see the geoportal directly on it, and which will be available in the coming days.
7:59
gvSIG Team: El Ayuntamiento de Cullera utiliza gvSIG Online para mejorar la seguridad en sus playas frente al COVID-19
sur Planet OSGeo

Con motivo de la situación actual de emergencia sanitaria mundial muchos ayuntamientos están buscando soluciones para reforzar las medidas de seguridad en sus playas y prevenir contagios de COVID-19. Entre las medidas principales está la de reducir el aforo de las playas, de forma que pueda respetarse la distancia mínima de seguridad. En este sentido, una de las actuaciones llevadas a cabo por el Ayuntamiento de Cullera, en Valencia (España), para informar a los ciudadanos sobre la situación de las playas, ha sido la de generar un geoportal en su plataforma de gvSIG Online, donde muestra en cada momento el porcentaje de personas respecto al total de aforo permitido en cada sector o playa, mediante una leyenda que indica si es recomendable o no acudir a esa parte de la playa o si tiene una situación óptima.

Gracias a la nueva funcionalidad de leyendas personalizadas de gvSIG Online, al actualizar el dato de asistentes en la capa en cada momento, se realiza el cálculo del porcentaje de aforo de cada sector o playa en función de su aforo máximo permitido, calculado previamente en función de su superficie, y se muestra en el color designado.

El Ayuntamiento de Cullera ha implementado recientemente gvSIG Online como Infraestructura de Datos Espaciales, lo que le ha permitido disponer de una plataforma web en la que puede generar diferentes geoportales/visores para cualquier necesidad en apenas unos minutos.

En las últimas semanas los técnicos del Ayuntamiento habían generado varios proyectos para la gestión interna de toda la cartografía, teniendo previsto hacer públicos en breve algunos geoportales con información útil para el ciudadano, como por ejemplo para turismo, movilidad y transportes, consultas en el Departamento de Urbanismo, etc., ampliando así su Sede Electrónica, pero la necesidad urgente de gestionar el acceso a las playas para reforzar la seguridad en la nueva normalidad tras el final del estado de alarma, y con la llegada del buen tiempo, ha llevado al Ayuntamiento a hacer público este primer proyecto.

El visor es accesible desde cualquier plataforma web, por lo que los ciudadanos pueden consultar el aforo de las playas de forma sencilla, tanto desde sus smartphone y tablets como de sus ordenadores personales. Además se ha facilitado un código QR con el acceso directo al geoportal.

Por otro lado, se está abordando en paralelo el desarrollo de INFO Cullera, una aplicación basada en gvSIG MApps para Android e iOS, en la que el ciudadano podrá ver el geoportal directamente sobre ella, y que estará disponible en los próximos días.
5:00
Narcélio de Sá: TecnoloGEO 14 – Arquivo Confidencial.
sur Planet OSGeo
Neste podcast: O TecnoloGEO 14 reúne toda a equipe, Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), para bater um papo estilo Arquivo Confidencial. A ideia é conhecer um pouco mais de cada um dos nossos hosts, falar um pouco mais sobre suas histórias, criações, inspirações, backgrounds, etc. Quer saber mais sobre o nosso time?
Aperte o ? para começar o TecnoloGEO #14
O tecnoloGEO é encabeçado por Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), o projeto tem a pretensão de ser semanal e contar sempre com convidados, que sejam relevantes ao assunto em pauta da vez, pra bater aquele bate papo descontraído e informativo sobre as Geotecnologias.
Referências e Indicações do TecnoloGEO 14: Live Geocast:
5 coisas que precisamos saber sobre PostgreSQL

Link para o canal: [www.youtube.com]

?Apresentação:
Kyle Felipe e Fábio Telles Rodriguez
Livros:
Filmes/Séries
Citados no episódio
- GeocastBrasil
Deixe seu comentário e participe!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:

Luiz Sadeck
Murilo Cardoso
Narcélio de Sá
The post TecnoloGEO 14 – Arquivo Confidencial. appeared first on Narcélio de Sá.
26 June 2020
14:53
EOX' blog: Saharan dust event from space
sur Planet OSGeo

EO Data visualization on the browser Here at EOX we have already talked about how we manage and visualize data in the browser, here some references to previous blog posts if you want to refresh your memory [https:] To help us […]
10:05
gvSIG Team: Asociación gvSIG, miembro institucional de la Red Destino Turístico Inteligente
sur Planet OSGeo

El pasado 11 de junio, en la Comisión Plenaria de la Red e Destinos Turísticos Inteligentes, se aprobó la incorporación de la Asociación gvSIG como miembro institucional de la Red DTI (Destino Turístico Inteligente). La Comisión Plenaria de la Red de Destinos Turísticos Inteligentes que aprobó la incorporación de la Asociación gvSIG, estuvo presidida por S.M. el Rey, acompañado por la ministra de Industria, Comercio y Turismo, Reyes Maroto; la secretaria de Estado de Turismo, Isabel Oliver y el presidente de SEGITTUR, Enrique Martínez. Los participantes de la Comisión permiten vislumbrar la relevancia de esta Red.

La Asociación gvSIG pone a disposición de la Red DTI su catálogo de tecnologías libres para gestión de información con dimensión geográfica, aportando soluciones y conocimiento que ayuden a modernizar el sector turístico. De hecho, en la actualidad, desde la Asociación gvSIG ya se están realizando diversos proyectos (de los que en breve daremos más información) en esta línea tanto para administraciones locales como autonómicas.
¿Qué es la Red DTI?
La Secretaría de Estado de Turismo de España lidera el proyecto DTI, pionero a nivel internacional, impulsado y gestionado por SEGITTUR, cuyo objetivo es implantar un nuevo modelo de mejora de la competitividad y desarrollo turístico basado en la gobernanza y la corresponsabilidad turística. Esta iniciativa, surgida del Plan Nacional e Integral de Turismo 2012-2015, genera los mecanismos adecuados para facilitar la rápida incorporación de innovaciones en los destinos.

Destino Turístico Inteligente es un destino turístico innovador, consolidado sobre una infraestructura tecnológica de vanguardia, que garantiza el desarrollo sostenible del territorio turístico, accesible para todos, que facilita la interacción e integración del visitante con el entorno e incrementa la calidad de su experiencia en el destino y mejora la calidad de vida del residente.

La transformación en un Destino Turístico Inteligente supone la revalorización del destino a través de la innovación y la tecnología.

Y si una tecnología es fundamental en este ámbito son los Sistemas de Información Geográfica; la Suite gvSIG – un completo catálogo de soluciones SIG en software libre – tiene múltiples aplicaciones, tanto a nivel interno y de gestión, como externo y de divulgación/promoción. Es momento de apostar por la innovación y nuevos modelos productivos que permitan encarar el futuro con garantías.
2:00
Ecodiv.earth: Import NASA DEM in GRASS GIS
sur Planet OSGeo
NASADEM
After nearly 20 years, a major update of the Shuttle Radar Topography Mission (SRTM) data was released in February this year. The product is called NASADEM, and consists of a combination of reprocessed SRTM data and auxiliary data from a variety of sources, including ASTER GDEM and Advanced Land Observing Satellite (ALOS) (Crippen et al. 2016). The dataset consists of three different layers: hgt, num and swb. The hgt layer is the void-filled DEM. The category values of the num layer indicate the number of DEM tiles used and the source of the data. And the swb layer is the updates SRTM water body data, with two classes; 0 for land and 255 for water. See for more information the NASADEM website or this comparison guide.
Importing in GRASS
GRASS GIS is one of the better tools to work with large high resolution raster data. And it has always been relatively easy to import raster data like the SRTM DEM into GRASS GIS (Neteler 2005). This applies to the NASADEM data as well. But with the r.in.nasadem add-on, developed by Markus Metz, it is just downright easy. The add-on imports all NASADEM tiles covering the current region, or region extents given with the region option into GRASS, and automatically patches the tiles together.
For the readers less familiar with GRASS GIS, the computational region is a cornerstone concept in GRASS GIS. The region defines the extent and resolution at which raster operations are carried out. Read the Wiki page for more information.
To import the NASADEM data for your area, you first need to install the r.in.nasadem addon. You can use the g.extension for this. Furthermore, to get access to the data, you’ll need to register at the Earthdata website. After registration, go to your user profile on the earthdata site and make sure the LP DAAC Data Pool and Earthdata Search applications are approved.

Now we can start with importing the data. The data comes in WGS 84 (EPSG 4326). Therefore, you need to import the data in a location with that reference coordinate system. If you don’t know what a location means in this context, read here about how a grass gis database is structured.

In your mapset, make sure to set the extent to match the region for which you want to download the data. Furthermore, set the resolution to 1 arc-second, which is the resolution of the NASADEM data. In this example, I’ll set the region to match a region in the central Oder valley in Poland.

You can run this directly from the command line, you can use the GUI, or using Python code. For the latter, you’ll need to import the PyGRASS module.

Bash
```
g.region n=52:38:40N s=51:08:02N w=15:36:05E e=18:30:02E res=0:00:01
```
GUI Go the Settings menu and select computational region > set region. Or you type g.region on the command line. Either way, you’ll get the g.region screen . Under the Bounds tab, fill in the upper, lower, left and right edges. In addition, select the Align region to resolution box to ensure the resolution will be exactly 1 arc-seconds.

Under the Resolution tab > 2D grid resolution fill in the target resolution (1 arc seconds).

And of course, don’t forget to push the Run button.

Python
```
from grass.pygrass.modules import Module
Module("g.region", n="52:38:40N", s="51:08:02N", w="15:36:05E", e="18:30:02E",
       res="0:00:01")
```
After installing the r.in.nasadem addon, you can find it under the Modules tab, in the Addons section. Alternatively, run r.in.nasadem on the command line or in the Console. Below, I’ll import the data for the region I defined above. To state the obvious, replace ‘your_username’ and ‘your_password’ with your own username and password of your account on EarthData.

Bash
```
r.in.nasadem output=NASAdem username=your_username password=your_password
```
Python
```
Module("r.in.nasadem", output="NASAdem", username="your_username",
       password="your_password")
```
GUI

I already filled in the name of the output raster, NASAdem. Under the tab Optional you can probably leave the default NASADEM version as it is. Likewise, keep the NASADEM layer to hgt, unless you want to import one of the other two available layers. The user name and password are for the Earth data website mentioned above. You can leave the base URL as it is, unless you already have downloaded the data, in which case you have to point the addon to the right folder.

As mentioned above, the r.in.nasadem imports all tiles required to cover this region, and patched them together. The resulting map likely covers a larger area than your study region. If you only want to keep the data for your study area, you can use r.mapcalc, like in the example below.

Bash
```
r.mapcalc "NASAdem = NASAdem" --overwrite
```
Python
```
Module("r.mapcalc", expression="NASAdem = NASAdem", overwrite=True)
```
So what did I do just there? Remember that the region defines the extent and resolution at which raster operations are carried out. So the output of any raster calculation is limited to the region extent. Furthermore, note that I use the -overwrite flag as GRASS GIS by default does not overwrite existing layers. There are other ways to do this, but this method is easy and results in raster layers with exactly the required extent and resolution. Did I already mention how much I like this concept of the computational region?

OK, and a mandatory image below created using the wxGUI 3D View Mode in GRASS. If you hover over the image, the colors representing the elevation are replaced by colors representing the different Corina Land Cover (CLC) categories (Copernicus 2019).

One thing about the SRTM is that in areas with a moderate to thick vegetation canopy, the elevation as provided by the SRTM is close to that of the canopy top rather than ground level (JPL n.d.; Weydahl et al. 2007; Orlandi et al. 2019). You can see this fairly clearly if you compare the elevation map with the CLC map (the green colors on the CLC map represent various forest types).
References
Copernicus. 2019. “Corine Land Cover (CLC) 2000, Version 20.” Dataset (raster, vector). European Environment Agency (EEA). [https:]] .

Crippen, R., S. Buckley, P. Agram, E. Belz, E. Gurrola, S. Hensley, M. Kobrick, et al. 2016. “Nasadem Global Elevation Model: Methods and Progress.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June): 125–28. [https:]] .

JPL, NASA. n.d. “Shuttle Radar Topography Mission. Frequently Asked Quetions.” Shuttle Radar Topography Mission. Accessed June 27, 2020. [https:]] .

Neteler, M. 2005. “SRTM and VMAP0 Data in OGR and GRASS.” GRASS Newsletter 3: 2–6.

Orlandi, Alex Gois, Osmar Abílio de Carvalho Júnior, Renato Fontes Guimarães, Edilson de Souza Bias, Douglas Corbari Corrêa, Roberto Arnaldo Trancoso Gomes, Alex Gois Orlandi, et al. 2019. “Vertical Accuracy Assessment of the Processed Srtm Data for the Brazilian Territory.” Boletim de Ciências Geodésicas 25 (4). [https:]] .

Weydahl, D. J., J. Sagstuen, Ø B. Dick, and H. Rønning. 2007. “SRTM DEM Accuracy Assessment over Vegetated Areas in Norway.” International Journal of Remote Sensing 28 (16): 3513–27. [https:]] .
25 June 2020
21:37
gvSIG Team: Implementación de la suite gvSIG para la gestión y toma de datos, de infraestructuras de prevención de incendios forestales y bienes amenazados
sur Planet OSGeo
Hoy os traemos un excelente trabajo desarrollo por Antonio Iván Rodríguez Guerrero en el marco de los trabajos de prevención y extinción de incendios forestales.

El documento podéis descargarlo aquí.
¿En qué ha consistido el trabajo desarrollado?
En el ámbito de las emergencias por incendios forestales el conocimiento de territorio es fundamental para establecer estrategias y operaciones seguras. Conocer el estado de las pistas forestales, determinar qué punto de agua está más cerca o conocer cuántos habitantes residen en un bien que está amenazado, agiliza la toma de decisiones y aporta certidumbre a las acciones que se puedan desarrollar para hacer frente a la emergencia. Para esto disponer de cartografía adecuada es muy importante.

El trabajo desarrollado por Antonio Iván Rodríguez Guerrero parte de la necesidad de disponer de cartografía adecuada para utilizarla en las intervenciones y tecnología para poder trabajar de forma eficiente con ella. Utilizando la Suite gvSIG, un catálogo de soluciones SIG en software libre, desarrolla una metodología sencilla para inventariar pistas forestales, puntos de agua, accesos y bienes amenazados.

El entorno de trabajo se ha desarrollado en el municipio de Yeste, Albacete, concretamente en una zona de trabajo, Hueco de Tus, que presenta unas características especiales de dispersión de núcleos habitados, gran densidad de establecimientos turísticos y unas condiciones de acceso limitadas a una sola entrada.

Este inventario muestra unos resultados que se implementan en un proyecto denominado PREVIFOR YESTE dentro de un geoportal generado con gvSIG Online, a modo de una Infraestructura de datos espaciales IDE , al que además se le han incluido otra serie de capas cartográficas. El proyecto es un buen ejemplo de como estas tecnologías pueden ayuar a dispositivos contra incendios, administraciones o corporaciones privadas, para el desarrollo de proyectos de inventariado vinculados a la prevención de incendios forestales y al análisis y caracterización de bienes amenazados.

Como complemento final enlazamos un articulo que ha publicado recientemente Iván.

Post relacionados:
20:47
QGIS Blog: QGIS 3.14 Pi is released!
sur Planet OSGeo

We are pleased to announce the release of QGIS 3.14 ‘Pi’!

Installers for all supported operating systems are already out. QGIS 3.14 comes with tons of new features, as you can see in our visual changelog.

We would like to thank the developers, documenters, testers and all the many folks out there who volunteer their time and effort (or fund people to do so). From the QGIS community we hope you enjoy this release! If you wish to donate time, money or otherwise get involved in making QGIS more awesome, please wander along to qgis.org and lend a hand!

QGIS is supported by donors and sustaining members. A current list of donors who have made financial contributions large and small to the project can be seen on our donors list. If you would like to become a sustaining member, please visit our page for sustaining members for details. Your support helps us fund our six monthly developer meetings, maintain project infrastructure and fund bug fixing efforts.

QGIS is Free software and you are under no obligation to pay anything to use it – in fact we want to encourage people far and wide to use it regardless of what your financial or social status is – we believe empowering people with spatial decision making tools will result in a better society for all of humanity.
11:00
CARTO Blog: Integrate CARTO & QGIS Using the New Direct SQL Connection
sur Planet OSGeo

Manipulating geospatial data in a powerful desktop environment that allows the use of advanced editing tools and the derivation of new datasets —by applying complex geoproc...
9:49
EOX' blog: The Joy of using Leaflet/Openlayers with Vue.js
sur Planet OSGeo

Introduction Webapps are becoming more and more complex, and the user interface requirements towards reactivity and snappiness are becoming more and more state of the art. In such an environment, it is helpful or even necessary to find helpers for […]
24 June 2020
11:00
CARTO Blog: What Geospatial Analysis tells us about Meatpacking & COVID-19
sur Planet OSGeo

Given the large number of open source spatial analysis libraries available today it can often be difficult to understand how best to combine the strengths of each tool - es...
8:29
uDig Team: uDig Release Candidate 2.2.0.RC1
sur Planet OSGeo

The uDig community is pleased to announce Release Candidate 2.2.0.RC1
Release Details
Release Notes, Tag, and Downloads for Mac, Linux, and Windows : uDig 2.2.0.RC1 Release Canditate
Features, Bugfixes and Improvements#314 Allows to configure display delay for edit tools
#379 Improved copy behavior for feature attributes
#367 Fixed Display access for Message Bubble
#363 Fixed Layer Memory Leak on Feature Events
Java 11 SupportFrom now on uDig can be run in Java 11 Runtime Environments.

#354 Removed joda-time dependency
#378 Update Eclipse Platform to Release 2019-03
#361 Update GeoTools to 22.1 (with dependencies)

This implies that uDig cannot support 32bit Operation Systems anymore, for details see Eclipse Platform Issue 536766.
OthersMailing Lists for both, Developers and Users changed:

For Users: udig-users@eclipse.org (was udig-users@locationtech.org)
For Developers : udig-dev@eclipse.org (was udig-dev@locationtech.org)
OpenSource is a team effort and we would like to thank everyone who helped to develop and test the release candidate: Emily Gouge, Nikolaos Pringouris, Silvia Franceschi, Andrea Antonello!
23 June 2020
15:16
GeoSolutions: State of GeoNode 3.0 Free Webinar
sur Planet OSGeo

Dear Reader,
One of the core technologies in a Spatial Data Infrastructure (SDI) is a catalog. It’s like a town center where food providers and food consumers meet. Shoppers walk around and visually or by smelling they discover the food they want. They engage in a small chat and if they agree with the price they buy it. In the case of an SDI, a catalog is the virtual place where data providers and data consumers meet. Data consumers discover resources and via rich descriptions (metadata) and previews, they make a decision about using or not a particular data or service. However, not all organizations have implemented a catalog and in the case of a disaster, discovery of data is still a big issue as highlighted in the recently published OGC Development of Disaster Spatial Data Infrastructures for Disaster Resilience Engineering Report.
Discovery of data is only part of the challenge. Once you find the service or dataset, you want to access it and use it. If the resource is not available via open standards, like the ones promoted by OGC and ISO, then it's like buying a piece of bread that you can only cut with one knife. It becomes frustrating and expensive to buy this kind of bread!

What if you are a data provider and in minutes you can register your data or service on the web, add metadata to it, and immediately have it available via open standards? This is what GeoNode does pretty well. It’s a content management system for geospatial, build on open standards and open source. It powers lots of Spatial Data Infrastructures in the world. For example GeoSolutions has helped install GeoNode in various countries (Malaway, Afghanistan, Mozambique, Ghana, Uganda, Nepal, and Madagascar).
The World Bank, in particular, has greatly supported its development and even published a report about the return over investment they encountered when investing in the development of GeoNode. A great blog about it is available at the World Bank web site. So, with all these said, GeoNode 3.0 was released about a month ago. See GeoNode blog for complete details. There is usually a presentation GeoSolutions does at the FOSS4G meeting and at the GeoNode summit about the latest release, but unfortunately the two meetings were cancelled, so we are doing a free webinar on July 7th at 11:00 Eastern Time. Some highlights of what’s new:
- GeoNode components have been updated to new versions: Python upgrade 3.7, Django upgrade 2.2.12, GeoServer upgrade 2.16.2.

- MapStore is now the default client greatly enriching the experience for viewing the data.

- Greatly improved analytics and getting information about who is visiting the website and their actions. For example, you can see unique visitors who trigger a specific type of event.

- Improved full GeoNode backup & restore.

- Extended GeoNode shared permission via GeoLimits to restrict users or groups to a specific geographical area (see below).
[caption id="attachment_5335" align="aligncenter" width="799"] GeoLimits at work for GeoNode 3[/caption]
I just highlighted my favorite enhancements. To learn more about GeoNode 3,0 and enjoy a 20 minutes of Q&A time with our lead GeoNode Lead Developer Alessio Fabiani, Francesco Bartoli, as well as, Giovanni Allegri and Florian Hoedt also part of the GeoNode Project Steering Committee. I cordially invite you to a free webinar on July 7 at 11:00 Eastern Time by registering at the link below!
Hope to see you virtually on July 7th, meanwhile stay safe and keep strong! Cordially, Luis
2:00
Ecodiv.earth: QGIS 3.14 is there
sur Planet OSGeo

There is always this buzz that comes with new releases of QGIS. I guess this is because the development goes so quick, and each and every release brings something new and exciting. In fact, I haven’t even had the change to try out all the new functionalities in version 3.12 (see here).

But OK, what will QGIS 3.14 Pi brings us? Again, a lot. The changelog isn’t officially online yet, but check out the link to the sneakpreview on that page. Highlights are further improvements to vector tile support, new cartographic functionality, and of course, the new temporal support. For the Dutch readers, Anita Graser talked about it on the Grote Geo Show. I also like the new functions in the processing toolbox, like the random raster generating algorithms and the new modeler algorithms for creating conditional branches, to name just two.

But this is just the top of the iceberg. Check out yourself the long list of new functions and changes. Or even better, go over to qgis.org and install the latest and greatest yourself. I for one just did. And to prove it, below a screenshot of the use of the new renderer for drawing contour lines that are calculated on the fly from the source raster band.
22 June 2020
5:19
Narcélio de Sá: TecnoloGEO 13 – Por que o Facebook adquiriu a Mapillary?
sur Planet OSGeo
Neste podcast: O TecnoloGEO 13 vem até você em clima de São João nas ondas da Rádio TecnoloGEO ZYC-42 para comentar a compra da Mapillary pelo Facebook. Compra essa que pode ser considerada como mais um passo na tentativa do Facebook de quebrar o monopólio do Google em relação aos mapas digitais.

Mais sobre a notícia:

O Facebook adquiriu na quinta-feira, 18/06/2020, a empresa sueca de tecnologia de mapeamento Mapillary. A empresa usa imagens capturadas por qualquer usuário para montar mapas com vista da rua, como o Google Street View.

O Facebook confirmou a transação, mas se recusou a divulgar os termos. Mapillary não respondeu imediatamente a um pedido de comentário.Com isso o Facebook tenta ter sucesso onde Apple (com o Apple Maps) e Microsoft (Bing Maps) já falharam: quebrar o monopólio do Google no mercado de mapas digitais interativos.
Aperte o ? para começar o TecnoloGEO #13
O tecnoloGEO é encabeçado por Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), o projeto tem a pretensão de ser semanal e contar sempre com convidados, que sejam relevantes ao assunto em pauta da vez, pra bater aquele bate papo descontraído e informativo sobre as Geotecnologias.
Referências e Indicações do TecnoloGEO 13:
Pesquisa Mercado de Trabalho para Geotecnologias:
Você já participou da pesquisa organizada pela Women in GIS Brasil sobre Mercado de Trabalho para Geotecnologias?? Então participe!!

A pesquisa Mercado de Trabalho para Geotecnologias lançada pela Women in GIS propõe avaliar a realidade brasileira de quem trabalha no setor das Geotecnologias.

A Women in GIS Brasil promove ações para o desenvolvimento de profissionais de Geotecnologias, principalmente em favor das mulheres, e trabalha para superar a discriminação no trabalho, as diferenças salariais e o isolamento profissional.

Segue o link da pesquisa: [https:]]
Saiba mais sobre a Women in GIS Brasil em: [https:]
Lives Geocast:
We R Live 08: Introdução à estatística Espacial – Processos Pontuais II

Dando seguimento à série de lives sobre estatística espacial, vamos apresentar um pouco sobre análises de dependência espacial para processos pontuais.

Link para a live: [https:]]

?Repositório no GitLab: [https:]]

?Apresentação:
Maurício Vancine
Felipe Sodre M. Barros
Citados no episódio:
[https:]]

Map With AI announces a new partnership with Microsoft, expanded coverage, and new tools

Home

Why did Facebook acquire geospatial startup Mapillary?

Mapillary joins Facebook from openstreetmap

[https:]]

[https:]]

Mapillary, the crowdsourced database of street-level imagery, has been acquired by Facebook

UPDATED: Apple buys Polar Rose for a rumoured $29 million

[https:]]

[https:]]

[https:]]

Facebook quietly acquired another UK AI startup and almost no one noticed

[https:]]

Facebook creates COVID-19 symptom tracking map for US

Mapillary raises $8M to take on Google’s Street View with crowdsourced photos

Deixe seu comentário e participe!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:

Luiz Sadeck
Murilo Cardoso
Narcélio de Sá
The post TecnoloGEO 13 – Por que o Facebook adquiriu a Mapillary? appeared first on Narcélio de Sá.
21 June 2020
2:00
Ecodiv.earth: Legend for continuous raster in QGIS composer
sur Planet OSGeo
- Step 1
- Step 2
- Step 3
- Step 4
- Step 5
- Step 6
- Step 7
The better the software, the more demanding you may get as an user. But is it really too much to ask to be able to include a continuous legend for floating point raster maps in the print layout? It certainly isn’t new on the wishlist, as you can see on this Q&A. I have resorted to various solutions, including writing the GRASS GIS add-on r.out.legend to create and export raster legend as an image in GRASS GIS, which I would than add to the map layout.

Another solution I resorted to was creating a legend in Inkscape. But then I realized that I could probably do all of this in QGIS directly. And indeed, no third-party tools required. Let me explain the steps below. Seven steps, it may seem a lot, but it really isn’t much work, you’ll see.
Step 1
Create a legend with a limited number of steps. In the image below, I have opted for 5 equal interval classes. You can use any interval, but using equal intervals makes things easier, as you will see later.
Step 2
Next, open the layout manager, create a new layout and add the raster layer (and any other layer you want) and the legend.
Step 3
The numbers in the text refer to the numbers in the figure below. Select the legend, and open the Item properties window. Under the Symbol options, change the symbol height to 0.5 mm (1) and deselect the ‘draw stroke for raster symbol’ (2). Under the Spacing options, increase the space between symbols (3).
Step 4
Zoom in on the legend, and place horizontal guides just above the upper and below the lower symbol (1). Next, draw a rectangle next the legend (2). Make the rectangle snaps to the upper and lower guides. Now, under the * Item properties* tab, click on the Style (3). This opens the Symbol settings window.
Step 5
Select as Symbol layer type the ‘gradient fill’ (1). Below, you select the radio button next to ‘Color ramp’ (2). Next, you click on the color ramp (3) to edit the color ramp. This will open the *Select Color Ramp’ window (4).
Step 6
We have a legend with five colors at equal distances. So we have stops at 0, 25, 50, 75 and 100%. We already have the first and last stop, so we need to add three stops. You can simply add a stop by double clicking on the color ramp (1). You can precisely position the stops by setting the *relative position (2).

Now select the first stop, and the sample tab (3). Click on the sample color button (4) and sample the color of the upper color of your legend (5). Likewise, select the second, third, fourth and fifth stops and sample the colors from the corresponding legend boxes.
Step 7
Now the finishing touch. Remove the guides, move the rectangle to the right to cover the legend boxes. Or if you like, keep part of the original legend symbols uncovered, so they serve as ticks. Optionally, you can group the legend and rectangle so you can move them easily around.

All in all, not the ideal solution, but not too complicated either.
20 June 2020
21:49
Martin Davis: JTS OverlayNG - Tolerant Topology Transformation
sur Planet OSGeo
This is another in a series of posts about the new OverlayNG algorithm being developed for the JTS Topology Suite. (Previous ones are here and here). Overlay is a core spatial function which allows computing the set-theoretic boolean operations of intersection, union, difference, and symmetric difference over all geometry types. OverlayNG introduces significant improvements in performance, robustness and code design.
JTS has always provided the ability to specify a fixed-precision model for computing geometry constructions (including overlay). This ensures that output coordinates have a defined, limited precision. This can reduce the size of data transfers and storage, and generally leads to cleaner, simpler geometric output. The original overlay implementation had some issues with robustness, which were exacerbated by using fixed-precision. One of the biggest improvements in OverlayNG is that fixed-precision overlay is now guaranteed to be fully robust. This is achieved by using an implementation of the well-known snap-rounding noding paradigm.
Geometric algorithms which operate in a fixed-precision model can encounter situations called topology collapse. This happens when line segments and points become coincident due to vertices or intersection points being rounded. The OverlayNG algorithm detects occurrences of topology collapse and transforms them into valid topology in the overlay result.
Topology collapse during overlay with a fixed precision model
As a bonus, handling topology collapse during the overlay process also allows it to be tolerated when present in the original input geometries. This means that some kinds of "mildly" invalid geometry (according to the OGC model) are acceptable as input. Invalid geometry is transformed to valid geometry during the overlay process.
Specifically, input geometry may contain the following situations, which are invalid in the OGC geometry model:
- A ring which self-touches at discrete points (the so-called "inverted polygon" or "exverted hole")
- A ring which self-touches in one or more line segments
- Rings which touch other ones along one or more line segments
Note that this does not extend to handling polygons that overlap, rather than simply touch. These are "strongly invalid", and will trigger a TopologyException during overlay.
An interesting use for this capability is to process individual geometries. By simply computing the union of a single geometry the geometry is transformed into an OGC-valid geometry. In this way OverlayNG functions as a (partial) "MakeValid" operation. A polygon which self-touches in a line transforms to a valid polygon with a hole
A polygon which self-touches in a point transforms to a valid polygon with a hole
A collection of polygons which touch in lines transforms to a valid polygon with a hole
Moreover, some spatial systems use geometry models which do not conform to the OGC semantics. Some systems (such as ArcGIS) actually specify the use of inverted polygons and exverted holes in their topology model. And in days of yore there were systems which were unable to model holes explicitly, and so used a "connected hole" topology hack (AKA "lollipop holes".) This represented holes as an inversion connected by a zero-width corridor to the polygon shell. Both of these models are accepted by OverlayNG. Thus it provides a convenient way to convert from these non-standard models into OGC-valid topology.
This is one more reason why overlay is the real workhorse of spatial data processing!
2:00
Ecodiv.earth: Creating a map with inset using tmap
sur Planet OSGeo
Plotting maps with tmap
Figure 1: A map of the distances to the nearest Natura2000 areas in a region in the central Oder basin.

The tmap R package greatly simplifies creating good looking maps. Like in ggplot, but also similar to most GIS tools, it allows you to build up a map layer by layer. If tmap is new to you, good starting points are the Geocomputation with R online book and the tmap get started page. And you can of course google for examples.

One of the features I like, is the ability to include inset maps, like in Figure 1. To be able to do this, you need the viewport function of the grid package. This is not a function I use a lot, so every time I want to make a map with an inset map, I have to look up the details. One of those details is how to properly align the legend and the inset map. As a note to self, but perhaps also useful to others, below the code I used to create this figure.
Get the libraries
First step is to load the required libraries. Besides the aforementioned tmap and grid libraries, the following packages are used: the tmaptools, sf, sp and rgrass7 libraries. The rgrass7 library is required because I want to use two layers I keep in a GRASS GIS database. If you want to import for example a geotif or vector layer in a geopackage, you can use the raster package and sf packages instead.
```
# libraries
library(tmap)
library(tmaptools)
library(rgrass7)
library(grid)
library(sf)
library(sp)
```
Main map
The readRAST and readVECT functions are used to read in the raster and vector data from a GRASS GIS database. To import raster data, rgrass7 needs to be instructed to use the older sp package, while for the vector layer, the sf package can be use. This is done by the functions use_sp and use_sf respectively.
```
# import raster data
use_sp()
rd <- readRAST("natura2000dist")

# import natura2000 vector layer
use_sf()
n2 <- read_sf("natura2000.gpkg")
```
Next, create a vector layer of the study area based on the bounding box of the raster layer, using the bb_poly function from the tmaptools package. This is later used to show the location of the study area in the inset map.
```
sg <- bb_poly(rd, projection = 3035)
```
To be able to properly align the different map elements, you need to know the aspect ratio of the input layers. To calculate this, extract the lower and upper x- and y-coordinates using the st_bbox function from the sf package. With those values, calculate the width and height, and subsequently the aspect ratio.
```
xy <- st_bbox(sg)
asp <- (xy$ymax - xy$ymin)/(xy$xmax - xy$xmin)
```
Now you have all the data, you can create the main map. I won’t go into all the details, the functions and parameters are clearly explained in the help pages. Just make sure to define the inner and outer margins (here both set to 0), the position of the legend, and the legend height and width. Important, you’ll notice that I used negative numbers for the legend width and height. The minus sign is used to tell tmap to use the exact width and length as provide. If you give a positive number, tmap will decrease the actual legend width and height automatically based on the legend content and font sizes.
```
mainmap <- tm_shape(rd) +
  tm_raster(style="cont", title="Distance (m)") +
  tm_shape(n2) +
  tm_fill(col="white", alpha=0.5, lwd=0.5) +
  tm_borders(col="lightgrey", lwd=0.01) +
  tm_layout(legend.outside=F, legend.text.size = 0.8,
            legend.title.size=0.9,
            legend.frame=TRUE, legend.position=c(0.985, 0.985),
            legend.just = c("right", "top"), legend.width=-0.25,
            legend.height=-0.4, outer.margins = c(0,0,0,0),
            inner.margins = c(0,0,0,0))
```
Inset map
The raster layer covers a region in the central Oder river basin in Poland. The idea is for you to add an inset map of Poland, including the region covered by the raster layer. You can get the boundary data from the GADM database of Global Administrative Areas. The data can be downloaded in various formats, including the rds data format. The rds file contains a sf vector object. By combining the readRDS and url functions, you can download and import the required files in one go (you’ll need to get the download urls first of course).
```
### Get boundary data
link1 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsf/gadm36_POL_0_sf.rds"
link2 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsf/gadm36_POL_1_sf.rds"
PolCntr <- readRDS(file = url(link1, "rb"))
PolProv <- readRDS(file = url(link2, "rb"))

### Project to EPSG:3035 (the same crs as the raster data)
PolCntr <- st_transform(PolCntr, crs=3035)
PolProv <- st_transform(PolProv, crs=3035)
```
The two layers will be used as input for the inset map. To properly align this inset map with the legend, we need to know the aspect ratio of the vector data. Again, use the st_bbox function to get the minimum and maximum x- and y-values, and use these to calculate the aspect ratio.
```
xy <- st_bbox(PolCntr)
asp2 <- (xy$xmax - xy$xmin)/(xy$ymax - xy$ymin)
```
Now you’re set to create the inset map. Set the outer.margins to 0. If you want to set the inner margins to a certain value (as I have done here), make sure they are the same on all sites. Otherwise, you’ll need to adapt the aspect ratio.
```
insetmap = tm_shape(PolCntr) + tm_fill(col="lightgrey") +
  tm_shape(PolProv) + tm_borders(lwd = 1, col="grey") +
  tm_shape(sg) + tm_borders(lw=2, col="red") +
  tm_layout(inner.margins = c(0.04,0.04,0.04,0.04), outer.margins=c(0,0,0,0))
```
Create the final map
For the final map, use the viewport function to determine where the inset map will be printed, and at what size. For the latter, define the width and height. The ratio of these two need to correspond to the aspect ratio of the inset map. So first set the width to match the width of the legend of the main map. Next, compute the corresponding height by multiplying the width by the aspect ration you calculated earlier.

Now you just need to ensure the inset map aligns neatly with the legend by setting the x and y and the justification of the viewport relative to the x and y location.
```
w <- 0.25
h <- asp2 * w
vp <- viewport(x=0.985, y=0.585, width = w, height=h, just=c("right", "top"))
```
And, as the very last step, save the final map as a png image. To avoid white spaces on the top and bottom, or left and right, make sure the aspect ratio of the output image is the same as that of the map.
```
tmap_save(mainmap,filename="dist2nat2000.png",
          dpi=100, insets_tm=insetmap, insets_vp=vp,
          height=asp*91, width=91, units="mm")
```
All the code
Quite a bit of code for such a simple map. But on the other hand, once you have created the map, it is very easy to update or adapt it if for example new data comes available.
```
# libraries
library(tmap)
library(tmaptools)
library(raster)
library(rgrass7)
library(grid)
library(sf)

tmap_mode("plot")

# Main map
#-------------------------------------------------------------------------------

# import raster data
use_sp()
rd <- readRAST("natura2000dist")
sg <- bb_poly(rd, projection = 3035)

# import natura2000 data
n2 <- read_sf("natura2000.gpkg")

# Aspect ratio raster data
xy <- st_bbox(sg)
asp <- (xy$ymax - xy$ymin)/(xy$xmax - xy$xmin)

# Create map
mainmap <- tm_shape(rd) +
  tm_raster(style="cont", title="Distance (m)") +
  tm_shape(n2) +
  tm_fill(col="white", alpha=0.5, lwd=0.5) +
  tm_borders(col="lightgrey", lwd=0.01) +
  tm_layout(legend.outside=F, legend.text.size = 0.8,
            legend.title.size=0.9,
            legend.frame=TRUE, legend.position=c(0.985, 0.985),
            legend.just = c("right", "top"), legend.width=-0.25,
            legend.height=-0.4, outer.margins = c(0,0,0,0),
            inner.margins = c(0,0,0,0))

# Inset map
#-------------------------------------------------------------------------------

# Get boundary data and project to EPSG:3035
link1 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsf/gadm36_POL_0_sf.rds"
link2 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsf/gadm36_POL_1_sf.rds"
PolCntr <- readRDS(file = url(link1, "rb"))
PolProv <- readRDS(file = url(link2, "rb"))
PolCntr <- st_transform(PolCntr, crs=3035)
PolProv <- st_transform(PolProv, crs=3035)

# Get bounding box and aspect ratio of Poland map
xy <- st_bbox(PolCntr)
asp2 <- (xy$xmax - xy$xmin)/(xy$ymax - xy$ymin)

# Create inset map
insetmap = tm_shape(PolCntr) + tm_fill(col="lightgrey") +
  tm_shape(PolProv) + tm_borders(lwd = 1, col="grey") +
  tm_shape(sg) + tm_borders(lw=2, col="red") +
  tm_layout(inner.margins = c(0.04,0.04,0.04,0.04), outer.margins=c(0,0,0,0))

# Create viewport
w <- 0.25
h <- asp2 * w
vp <- viewport(x=0.985, y=0.585, width = w, height=h, just=c("right", "top"))

# Final map
#-------------------------------------------------------------------------------
tmap_save(mainmap,filename="dist2nat2000.png",
          dpi=100, insets_tm=insetmap, insets_vp=vp,
          height=asp*91, width=91, units="mm")
```
19 June 2020
13:00
MapTiler: Vector tiles in QGIS 3.14
sur Planet OSGeo
The new QGIS 3.14 version adds support for the native loading of vector tiles. The easiest way to load them is via the recently released plugin.
New plugin with vector tiles maps
One of the most important new features in QGIS 3.14 is the built-in support for vector tiles. The MapTiler plugin allows anybody to easily load map data of the entire planet, with details down to the street level.

The plugin automatically loads maps from MapTiler Cloud in vector or raster tiles, but can also open maps from any other URL.
Print vector maps in high-resolution
For the very first time, high-quality maps of the entire world can be printed in QGIS so easily. You don’t need to care about DPI settings, because vector tiles behave as any other vector-based technology and scale infinitely with the same resolution.

If you don’t have a large format printer at hand, export your map into PDF and zoom in deeply to inspect the level of sharpness.
Styling the vector data
A ready-to-use list of beautiful map styles is available to QGIS users. Those who prefer customized maps can make their own map design in a few clicks using the Customize tool. Users can set their own colors, fonts, or choose the language of map labels.

For power users, there is the Edit tool, with advanced functionality to allow the addition of one’s own data and the manipulation of layers.

All map styles from the MapTiler Cloud, including user-customized ones, are translated to QGIS styling. This allows you to use the styling editor in QGIS and give your maps a final touch-up.

Anywhere on Earth, in high detail
The MapTiler plugin for QGIS allows users to load maps of the entire world (from the OpenStreetMap project), maps from official government data in the Netherlands (Kadaster maps via Cartiqo), the United Kingdom (Ordnance Survey Open Zoomstack) and Japan (GSI data), high-resolution aerial imagery, hillshading, contour line, and much more.

Geospatial data of any size can be uploaded to MapTiler Cloud and served to QGIS via MapTiler plugin.

Maps appear quickly, thanks to the tiles technology for both raster and vector data. The high speed of delivery is also reached because maps are cached on more than 150 CDN servers on five continents.
The coordinate system of your choice
A huge advantage of vector tiles compared to raster tiles is flexibility. Reprojecting to any coordinate system inside QGIS doesn’t impact the visual appeal of the map tiles. All labels rotate accordingly, warped objects stay sharp, and much more!

You can reproject maps into any coordinate system: the default Mercator, French Lambert, Dutch RD-new, or global WGS84 to name a few.

Export rasters, vector images, or CAD files
All maps can be exported as images in GeoTIFF, PNG, or compressed JPEG, for use on the web or in raster graphics editors like Adobe Photoshop or GIMP.

For vector graphics editors like Adobe Illustrator or Inkscape, exports in SVG and PDF formats are available.

Exporting to CAD software like AutoCAD and others is possible via DWG and DXF formats.

Before making exports, print, or republishing, please contact us.
Download the plugin for free
Learn more about the plugin or get it by searching maptiler in the plugin service in QGIS.

Installing the plugin is straightforward:
1. Open QGIS
2. Click on the menu Plugins ? Manage and Install Plugins…
3. Search for maptiler
4. Click on the Install Plugin
5. Restart QGIS
6. Right-click on the MapTiler plugin ? Account…
7. Follow the link after Get a FREE key
Now, just add the key and start loading maps. Enjoy :-)
Big thanks to the open-source community!
The development is happening on GitHub. Feel free to report any issue or send a pull request, community feedback is welcomed!

We want to acknowledge people who made all this possible, namely Martin Dobias from Lutra Consulting, and Kanahiro Iguchi from MIERUNE. Thank you!
11:00
CARTO Blog: How to use COVID-19 Public Data in Spatial Analysis
sur Planet OSGeo

For the past few months we have been making our platform freely available for those working on COVID-19 analysis, regularly adding public data sets from a wide range of pro...
1:14
Martin Davis: JTS OverlayNG - Noding Strategies
sur Planet OSGeo
In a previous post I unveiled the exciting new improvement in the JTS Topology Suite called OverlayNG. This new implementation provides significant improvements to the core function of spatial overlay. Overlay supports computing the set-theoretic boolean operations of intersection, union, difference, and symmetric difference over all geometric types.

One of the design goals of JTS is to create modular, reusable data structures and processes to implement spatial algorithms. This increases development velocity and testability, and makes algorithms easier to understand. In spatial algorithms it is not always obvious how to identify appropriate abstractions for reuse, so this is an on-going effort of design and refactoring.

After the implementation of spatial overlay in the very first release of JTS, it became clear that overlay can be split into the following phases:
1. Noding, in which an set of possibly-intersecting linestrings is converted to an arrangement in which linestrings touch only at endpoints
2. Topology Analysis, during which the topology graph of the noded arrangement is determined
3. Result Extraction, in which the geometric components of the desired result are extracted from the topology graph
It also became clear that the Noding phase is critical, since it determines the overall performance and robustness of the overlay computation. Moreover, tradeoffs between these two qualities can be made by using different noding strategies. For instance, the "classic" JTS noding approach is fast, but susceptible to robustness issues. Alternatively, noding using the well-known snap-rounding paradigm is slower, but can be made fully robust.

To encapsulate this concept, JTS introduced the Noder API. Since it post-dated the original overlay code, using it in overlay had to await a reworking of that codebase. The OverlayNG project provided this opportunity. OverlayNG allows supplying a specific Noder class to be used during overlay.

One of the main goals of the OverlayNG project was to develop a noder to provide fully robust noding. This would eliminate the notorious TopologyException errors which bedevil the use of overlay. The effort has paid off with the development of not one, but two new noders. The Snapping Noder has very good performance and (with the addition of some heuristics, and so far as is known) provides robust full-precision evaluation. And the Snap-Rounding noder provides guaranteed robustness as well the ability to enforce a fixed-precision model for output.

So now OverlayNG can be run with the following suite of noders, depending on use case. The images show the result of intersection and union on the following geometries:

Fast Full-Precision Noder

The MCIndexNoder noding strategy has been available since the early days of JTS. It has very good performance due to the use of monotone chains and the STRtree spatial index. However, it is a relatively simple algorithm which due to numerical robustness issues does not always produce a valid noding. In overlay it is always used in conjunction with a noding validator, so that noding failure can be detected and an alternative strategy used to perform the operation successfully.

Intersection and Union with full-precision floating noding

Snapping Noder
The SnappingNoder is a refinement of the MCIndexNoder which snaps existing input and computed intersection vertices together, if they are closer than a snap distance tolerance. This dramatically improves the robustness of the noding, with only minor impact on performance.
Noding robustness issues are generally caused by nearly coincident line segments, or by very short line segments. Snapping mitigates both of these situations. The choice of snap tolerance is a heuristic one. Generally, a smaller snap distance has less chance of distorting the topology, but it need to be large enough to resolve intersection computation imprecision. In practice, excellent robustness is provided by using a very small snap distance (e.g. a factor of 10^12 smaller than the geometry magnitude).

Snapping of course risks creating topology collapses, but OverlayNG is designed to handle these correctly. However, there are occasional situations where the snapped arrangement is too invalid to be handled. This can be detected, and with some simple heuristic adjustments (e.g. a more aggressive snap distance) the overlay can be rerun. This strategy has proven to be fully robust in all cases tried so far.

Intersection and Union with Snapping Noding (snap tolerance = 0.5)

Snap-Rounding Noder

The SnapRoundingNoder implements the well-known snap-rounding paradigm. It provides fully robust noding by rounding and snapping linework to a fixed-precision grid. This has the unavoidable effect of rounding every output vertex to the precision grid. This may or may not be desirable depending on the situation. A useful side effect is that it provides an effective means of reducing the precision of geometries in a topologically valid way.

In the early stages of OverlayNG design and development I expected that snap-rounding would be required to ensure fully-robust overlay, in spite of the downside of fixed-precision output. But the development of the SnappingNoder and accompanying heuristics means that this noder need only be used when control over overlay output precision is desired.

Using an appropriate precision model is a highly worthwhile goal in spatial data management, since it reduces the amount of memory needed to represent data, and improves robustness and portability. This is unfortunately often neglected, mostly due to lack of tools available to enforce it. Hopefully this capability will encourage users to maintain a precision model which is better matched to the true precision of their data.

Intersection and Union with Snap-Rounding noding (precision scale = 1)

Segment Extracting Noder

This is a special-purpose noder which is really more of a "non-noder". It simply extracts every line segment in the input. It is used on geometry collections which form valid, fully-noded, non-overlapping polygonal coverages. When used with OverlyNG, this has the effect of dissolving the duplicate line segments and producing the union of the input coverage. By taking advantage of the structure inherent in the coverage model the SegmentExtractingNoder offers very fast performance. It can also operate on fully-noded linear networks.

Union of a Polygonal Coverage with SegmentExtractingNoder

The support for pluggable noding and the development of a suite of fast and/or robust noders constitutes the biggest advance of the OverlayNG code. It finally allows JTS to provide fully robust noding and true support for a fixed-precision model! This has been a dream of mine for more than a decade. It's good to think that the end of the era of TopologyException issues is in sight!
18 June 2020
22:45
Geomatic Blog: FOSS4G UK Online
sur Planet OSGeo

Yesterday’s FOSS4G UK Online was the first fully virtual geospatial event I ever attended. Over the previous years I’ve joined remotely some FOSS4G and SoTM events, like last year excellent Bucharest 2019 FOSS4G. Having everyone joining remotely makes things quite different, since you don’t feel like a second class attendant. The OSGeo UK Local Chapter organized this fantastic conference in just a few weeks, gathering speakers around three different tracks of talks. There was even a fourth track simulating a coffee corner where you could join and have a chat maybe in the frequent breaks in between talks, what a brilliant idea!

I attended along with Jennifer Allen, the Kibana Maps team lead. We were the whole day grabbing notes around the same document, capturing links, resources, screenshots, and what not. This was way easier from the comfort of my workplace at home compared to the quick notes you can take when running from one session to the next. Surprisingly, I felt also I could get more focused during talks compared with being in a real conference venue; maybe because I didn’t had to worry about going from one room to another to find a good spot to see and listen well, or because I could simply disconnect and get a cup of tea anytime.

No doubt I missed the practical workshops, the hallway conversations, and the after event beers; but as for the way to consume the content, my awkward reflection, at least at this moment, is that I think I prefer a remote conference over in person one. Is it worth to make folks to travel all around the world for a quite exclusive exchange of knowledge? Aren’t we already used to collaborate and learn together at a global scale without the need to make such a big expense? What about of those that don’t feel welcomed at this kind of event for many different, and all legit, reasons? What about those that can’t afford them? I still find value on real gatherings, but maybe I find more bearable to do this once per year or even less, and by the way, totally separated from any holidays plan.

Regarding the content itself, as always there were quite a few great talks. National FOSS4G events tend to attract more use cases and consultancy projects than “core” product development. In this conference I liked the opportunity to learn about the last updates from Geoserver, and all the great work behind the Input App for data collection on the field that can be synchronized with QGIS projects using a Software as a Service called Mergin. Thinking strategically was a good talk with some reflections about the geo industry, and how small it is in the ocean of the data analytics world; the Q&A session afterwards was also quite interesting. The Women in Geospatial+ talk showed all the good work this global community is doing to promote diversity in our industry. Finally I wanted to mention the talk that showcased the OSM regions project, since it shares quite a lot of objectives, tools, and workflows with the stuff we are doing for the Elastic Maps Service. I’d love to put some time to go through the project details and eventually try to find collaboration opportunities.

And that’s it, I think I could have tried to have a talk on this event, and I feel a bit bad for not stepping forward back when the request for talks was launched but I’m sure I will have plenty of opportunities to present Elastic Geo features to the FOSS4G community in the future. As a closing call for action, take a look to the Twitter #FOSS4GUKOnline hashtag to find more opinions and resources, and more importantly, check the recordings when they get up in a couple weeks, you won’t regret it.

Happy Summer!!
11:00
CARTO Blog: Supply Chain Network Optimization: SEUR Case Study
sur Planet OSGeo

Supply chains are a critical part of our everyday lives, even more so as we move towards the new normal. The current situation has put a lot of pressure on food and pharmac...
10:11
gvSIG Team: gvSIG Online: Importing vector layers and web services
sur Planet OSGeo

An interesting tool has been included in the latest version of gvSIG Online that allows unregistered users to add an external vector layer or a web service into a project.

In both cases, a group of layers is created at the end of the Table of Contents, below the rest of the groups, and it only remains active during that session, so it disappears when the web page is refreshed. One of the advantages is that if the user prints a geoportal with an imported layer, it is shown in the PDF file.

The vector layer import tool allows to load SHP, KML and JSON files. In the case of SHP files, the 3 mandatory files (SHP, SHX and DBF) must be compressed into a .ZIP file. In the current version, the reference system of these files must be EPSG 4326 (WGS84), although the objective in the future is that they can be imported in another reference system.

Regarding the web services import tool, it currently allows to load WMS and WFS service layers.

Here you have a video about how this tools work:

More information about gvSIG Online implementation: info@gvsig.com
9:56
gvSIG Team: gvSIG Online: Importación de capas vectoriales y servicios web
sur Planet OSGeo

En la última versión de gvSIG Online se ha incluido una interesante herramienta que permite a los usuarios no registrados añadir una capa vectorial externa o un servicio web a un proyecto.

En ambos casos se crea un grupo de capas al final de la Tabla de Contenidos, por debajo del resto de grupos, y solo permanece activo durante dicha sesión, por lo que en cuanto se refresca la página desaparece. Una de las ventajas es que si se realiza una impresión del geoportal, teniendo una capa importada, esta se muestra en el PDF.

La herramienta de importación de capas vectoriales permite cargar ficheros SHP, KML y JSON. En el caso de los ficheros SHP deben estar comprimidos los 3 ficheros obligatorios (SHP, SHX y DBF) en un fichero .ZIP. En la actual versión, el sistema de referencia de estos ficheros debe ser EPSG 4326 (WGS84), aunque el objetivo en un futuro es que se puedan importar en el resto de sistemas.

Respecto a la herramienta de importación de servicios web, actualmente permite cargar capas de servicios WMS y WFS.

En este vídeo podéis ver mejor su funcionamiento:

Más información sobre la implementación de gvSIG Online: info@gvsig.com
17 June 2020
11:00
CARTO Blog: COVID-19 Inequality: A Focus on Brazil Coronavirus Maps
sur Planet OSGeo

As Brazil approaches one million confirmed cases of Coronavirus (at time of writing) and is expected to surpass the US in terms of cases and deaths by the end of July this ...
11:00
Lutra consulting: Overview of QGIS 3.14 Mesh Features
sur Planet OSGeo
Mesh layer is now supported for the 1D, 2D and 3D data frames. It also comes with integration to the temporal controller in QGIS 3.14.

The releases of QGIS 3.14, MDAL 0.6.1 and Crayfish 3.4.4 are planned for this Friday We are delighted to have made improvements for the upcoming release:
- full integration with the new temporal controller
- 1D meshes support (e.g. to support urban drainage system models)
- New rendering and styling options for mesh layers
- improving greatly rendering speed for large datasets
- various other enhancements and bug-fixes
If you are eager to try the latest features, you can always install QGIS nightlies/master.

Special thanks to all contributors and the sponsors of these features
Native QGIS Temporal Controller
The long awaited native temporal support in QGIS is finally materialised. We managed to port all QGIS Mesh Layer code to the new infrastructure and remove the custom time slider in the Mesh Layer properties/styling dialog. The native temporal support will greatly benefit users in the long run. We can now share the code components for time handing with raster, vector and WMS-T and other temporal data types. Also user can load multiple temporal layers and change time-domain for all of them at the same time.

Unfortunately, the new temporal framework changed the API, so if you use QgsMeshLayer in your plugins, consult the QGIS documentation for required changes.
New MDAL logo and docs
We have started writing the proper documentation for all supported formats and also reference documentation for developers. We welcome all contributions to improve the documentations. Just go to [https:]] and click “Edit on GitHub”!

Support for 1d meshes
MDAL and QGIS now supports 1D meshes, currently for UGRID and 3Di formats. Other formats should be easy to add. If you are interested in supporting a format, drop us a line.

You can read the full technical description in the following QGIS Enhancement Proposal.

The 1D meshes can be styled with the various renderers, for example use of pressure data to calculate line width:

Furthermore, Crayfish plugin has been extended to support such mesh data frames. For example, 1D plots in Crayfish with the snapping and routing along the network can be used:
2D mesh rendering improvement
2D meshes streamlines/particles/arrows can be now styled by the colour ramp. You can better display streamlines and mesh vectoral data varaition:
Meshes in 3D map view
The mesh layer is now fully supported in the 3D map view. You can visualise your terrain and overlay vector arrows (e.g. velocity) and other mesh quantities (e.g. water surface) in the 3D map view.

Other features and bugfixes QGIS
- The mesh rendering speed increased by 2-20 times depending on various mesh/zoom/map/data combinations
- Ability to show only subset of dataset groups in the Mesh styling window
- Support for reading multiple meshes in one file (e.g. UGRID)
- Ability to resample vertex values to faces
- Support for saving/loading of QML styles
- Full usage of temporal controller, even in print templates, animation, …
MDAL
- The GRIB and NetCDF datasets are now loaded twice as fast
- Support for UGRID datasets with magnitude and direction
- Support for reading of UGRID classified data and its usage in QGIS color ramps
- Renamed the pointers and functions in the API for consistency
- Created brand new logo and started the online-docs
- Support XMS Tin
Crayfish
- Ported to new temporal API
- Added 1D plots
- Fixed various UI bugs
16 June 2020
10:21
gvSIG Team: gvSIG Online: Measurement tool in 3D Views
sur Planet OSGeo

One of the novelties of the latest version of gvSIG Online is that it is possible to measure distances, areas and angles in 3D Views. Until now these tools were only available in 2D Views.

In the case of distances, it shows the distance accumulated at the itinerary drawn on the View and when double-clicking it shows the total distance. The angle measure shows the angle between each two segments of an itinerary, and also the total distance of the itinerary drawn on the View.

At this video you can see how it works:
10:20
gvSIG Team: gvSIG Online: Medidas en Vistas 3D
sur Planet OSGeo

Una de las novedades de la última versión de gvSIG Online es la de poder medir distancias, áreas y ángulos en Vistas 3D. Hasta ahora estas herramientas solo estaban disponibles en las Vistas 2D.

En el caso de las distancias se va mostrando en cada tramo el acumulado del itinerario dibujado sobre la Vista, y al darle doble-click se muestra el total de la distancia. En la medida de ángulos se muestra el ángulo entre cada dos segmentos dibujados de un itinerario, y también el total de la distancia del itinerario dibujado.

En este vídeo podéis ver su funcionamiento:
15 June 2020
20:56
Narcélio de Sá: TecnoloGEO 12 – Conhecimentos que todo geoprocesseiro deve saber.
sur Planet OSGeo
Neste podcast: O TecnoloGEO 12 traz até você um bate-papo com Murilo Cardoso, Narcélio de Sá e onde iremos discutir conhecimento essenciais que todo Geoprocesseiro deve saber. Então listamos aqui alguns conhecimentos que todo geoprocesseiro que se preze tem que saber para lidar com as rotinas do dia-a-dia no mundo das geotecnologias.
Aperte o ? para começar o TecnoloGEO #12
O tecnoloGEO é encabeçado por Murilo Cardoso (@niilistativo) , Narcélio de Sá (@narceliodesa) e Luiz Sadeck (@sadeckgeo), o projeto tem a pretensão de ser semanal e contar sempre com convidados, que sejam relevantes ao assunto em pauta da vez, pra bater aquele bate papo descontraído e informativo sobre as Geotecnologias.
Referências e Indicações do TecnoloGEO 12: Livros:
Filmes/Séries
Música
Citados no episódio
Deixe seu comentário e participe!

Entre em contato com o TecnoloGEO:
Equipe do TecnoloGEO:

Luiz Sadeck
Murilo Cardoso
Narcélio de Sá
The post TecnoloGEO 12 – Conhecimentos que todo geoprocesseiro deve saber. appeared first on Narcélio de Sá.
11:00
CARTO Blog: Identifying Walk to Work Hotspots in London
sur Planet OSGeo

As we all speculate about our return to the new normal, consumer concerns about using mass transit to return to work are top of mind. Many companies, such as Twitter and Sq...
13 June 2020
13:46
Paulo van Breugel: Ecostudies EOL
sur Planet OSGeo

I have kept a blog on this site for quite some years. It was mostly about using open source GIS tools. Although I enjoyed writing the posts, other things became more important, and I haven’t written a post for quite a while. However, I am planning to pick it up again. But not on this …
Continue reading Ecostudies EOL
13:06
QGIS Blog: QGIS Pi Mapping Contest Results
sur Planet OSGeo

As you may have noticed, the next release version will be 3.14 and therefore, we will call it ‘Pi’.

Usually our versions are named for community meeting locations and the splash screen shows a map related to this location.

For 3.14 we were looking for creative maps that capture the essence of Pi.
Submissions
The submission phase was open for two weeks and we received numerous inspiring submissions:

Public voting
From these submissions, a short list of top 3 candidates was compiled and put up for the public vote:

Candidate #1 Ezequiel Orquera writes about his submission: “As an agronomist, Pi is used every single time that you need to develop a pivot irrigation system (those nice circles we can see on sat. images), making most use of Pi number and the radius. In this image, we can see the circles in contrast with rectangles shapes. The interest thing is that on most of the circles you can see the irrigation system arm that is coming from the center of each circle, making it the radius. Furthermore we all know that Pi x r^2 = circle area. This is useful to estimate for example, crop yields.”

Candidate #2 Francis Josef Gasgonia writes about his submission: “This map would not be complete without the use of Pi. Multicentric ring buffers represent potential danger zones in this map of Mt. Isarog in the Philippines. The calculations necessary to develop the ring buffers depend on Pi. Mt. Isarog is classified as a potentially active stratovolcano. This map best represents the use of Pi in a map because these buffers are crucial in disaster planning, especially now in a Covid-19 pandemic world; wherein ring buffers, and other types of buffers are in use for humanitarian and logistics planning.”

Candidate #3 Michel Stuyts writes about his submission: “Since Pi is very much linked to circles, I looked for the most circular place I could find. The place I made a map of is Vahanga, an atoll in the Pacific Ocean. I decorated the map with angles of a circle in radians as divisions of Pi. Because QGIS splash screens usually show a map of a location where a developer meeting was, the chance it would ever have a map of this part of the world is just as irrational as Pi, because the closest inhabited place is more than a 1000km away. My map is also linked to Corona (the reason there was no dev meeting in the first place), because the atoll is crown shaped and Corona means crown. Besides being linked to Pi and Corona, my map is also very much linked to QGIS, because it’s 100% made with QGIS. The angles in radians where made with “geometry generators” from the central point. The fills where made using “random marker fills” combined with an expression using “randf()” to set the size of the markers with a “Data defined override”. For the shallow water on the inside of the atoll I used a “Shapeburst fill”.“
And the winner is …
In the public vote, Francis Josef Gasgonia’s map received the most votes (46%):

Congratulations Francis and thank you again to everyone who participated in this fun contest to ensure that QGIS 3.14 Pi will have great visuals!
2:00
Ecodiv.earth: New GRASS GIS website
sur Planet OSGeo

GRASS GIS is a fantastic software package, not only one of the oldest GIS packages still around, but also one that is continuously updating and improving thanks to a vibrant community. The GRASS GIS website on the other hand definately needed some love, with, let’s be honest, a somewhat outdated look.

But that is about to change. The GRASS GIS Development team is working hard on a completely revamped website, with a great look and easy to navigate. You can visit, comment and edit the website at [https:]] . Check it out, and perhaps there is something you can contribute to.
2:00
Ecodiv.earth: De Grote Geo Show
sur Planet OSGeo

The Grote Geoshow is a Dutch show on Twitch (live) and YouTube by the OSGeo.nl foundation and friends. It airs every Thursday afternoon and is all about open geo topics. The show last Thursday, the 9th episode, was dedicated to QGIS, with amongst others Anita Graser a.k.a. underdark as guest. As always, a very enjoyable show, with the right mix of fun and interesting topics. If you want to watch earlier episodes, or want to watch the next show life, go to [https:]] .
12 June 2020
11:00
CARTO Blog: Introducing Direct SQL Connection for QGIS, Databricks & more
sur Planet OSGeo

Working with multiple software tools can take up a lot of your time connecting workflows and platforms across your IT infrastructure. Here at CARTO, we’re on a mission to h...
11 June 2020
16:39
Oslandia: (Fr) Financement mutualisé du logiciel libre: le cas QGIS
sur Planet OSGeo

Sorry, this entry is only available in French.
11:00
CARTO Blog: COVID-19 Inequality in the US: How Coronavirus is killing more Black Americans
sur Planet OSGeo

The COVID Racial Data Tracker gathers the most complete race and ethnicity data on COVID-19 in the United States. Race and ethnicity data are reported by state, both for th...
10:00
Paul Ramsey: Developers Diary 2
sur Planet OSGeo
Have you ever watched a team of five-year-olds play soccer? The way the mass of children chases the ball around in a group? I think programmers do that too.

There’s something about working on a problem together that is so much more rewarding than working separately, we cannot help but get drawn into other peoples problems. There’s a lot of gratification to be had in finding a solution to a shared difficulty!

Even better, different people bring different perspectives to a problem, and illuminate different areas of improvement.
Maximum Inscribed Circle
A couple months ago, my colleague Martin Davis committed a pair of new routines into JTS, to calculate the largest circles that can fit inside a polygon or in a collection of geometries.

We want to bring all the algorithmic goodness of JTS to PostGIS, so I took up the first step, and ported “maximum inscribed circle” to GEOS and to PostGIS.

When I ported the GEOS test cases, I turned up some odd performance problems. The calculation seemed to be taking inordinately long for larger inputs. What was going on?

The “maximum inscribed circle” algorithm leans heavily on a routine called IndexedFacetDistance to calculate distances between polygon boundaries and candidate circle-centers while converging on the “maximum inscribed circle”. If that routine is slow, the whole algorithm will be slow.

Dan Baston, who originally ported the “IndexedFacetDistance” class got interested and started looking at some test cases of his own.

He found he could improve his old implementation using better memory management that he’d learned in the meantime. He also found some short-circuits to envelope distance calculation that improved performance quite a bit.

In fact, they improved performance so much that Martin ported them back to JTS, where he found that for some cases he could log a 10x performance in distance calculations.

There’s something alchemical about the whole thing.
- There was a bunch of long-standing code nobody was looking at.
- I ported an unrelated algorithm which exercised that code.
- I wrote a test case and reported some profiling information.
- Other folks with more knowledge were intrigued.
- They fed their knowledge back and forth and developed more tests.
- Improvements were found that made everything faster.
I did nothing except shine a light in a dark hole, and everyone else got very excited and things happened.
Toast Caching Redux
In a similar vein, as I described in my last diary entry, a long-standing performance issue in PostGIS was the repeated reading of large geometries during spatial joins.

Much of the problem was solved by dropping a very small “TOAST cache” into the process by which PostGIS reads geometries in functions frequently used in spatial joins.

I was so happy with the improvement the TOAST cache provided that I just stopped. Fortunately, my fellow PostGIS community member Raúl Marín was more stubborn.

Having seen my commit of the TOAST cache, and having done some work in other caching parts of PostGIS, he took up the challenge and integrated the TOAST cache with the existing index caches.

The integrated system now uses TOAST identifiers to note identical repeated inputs and avoid both unneccessary reads off disk and unncessary cache checks of the index cache.

The result is that, for spatial joins over large objects, PostGIS 3.1 will be as much as 60x faster than the performance in PostGIS 3.0.

I prepared a demo for a bid proposal this week and found that an example query that took 800ms on my laptop took a full minute on the beefy 16-core demo server. What had I done wrong? Ah! My laptop is running the latest PostGIS code (which will become 3.1) while the cloud server was running PostGIS 2.4. Mystery solved!
Port, Port, Port
I may have mentioned that I’m not a very good programmer.

My current task is definitely exercising my imposter syndrome: porting Martin’s new overlay code from JTS to GEOS.

I knew it would take a long time, and I knew it would be a challenge; but knowing and experiencing are quite different things.

The challenges, as I’ve experienced them are:
- Moving from Java’s garbage collected memory model to C++’s managed memory model means that I have to understand the object life-cycle which is implicit in Java and make it explicit in C++, all while avoiding accidentally introducing a lot of memory churn and data copying into the GEOS port. Porting isn’t a simple matter of transcribing and papering over syntactic idiom, it involves first understanding the actual JTS algorithms.
- The age of the GEOS code base, and number of contributors over time, mean that there are a huge number of different patterns to potentially follow in trying to make a “consistent” port to GEOS. Porting isn’t a matter of blank-slate implementation of the JTS code – the ported GEOS code has to slot into the existing GEOS layout. So I have to spend a lot of time learning how previous implementations chose to handle life cycles and call patterns (pass reference, or pointer? yes. Return value? or void return and output parameter? also yes.)
- My lack of C++ idiom means I spend an excessive amount of time looking up core functions and methods associated with them. This is the only place I’ve felt myself measurably get better over the past weeks.
I’m still only just getting started, having ported some core data structures, and little pieces of dependencies that the overlay needs. The reward will be a hugely improved overlay code for GEOS and thus PostGIS, but I anticipate the debugging stage of the port will take quite a while, even when the code is largely complete.

Wish me luck, I’m going to need it!

If you would like to test the new JTS overlay code, it resides on this branch.
If you would like to watch me suffer as I work on the port, the GEOS branch is here.
2:00
Ecodiv.earth: Open source tools
sur Planet OSGeo
Introduction
Most of the tools we use are open source. In part because I believe that the use of open source software and open data contributes towards a more transparent, rapid and inclusive science, especially in ecology, biogeography and conservation sciences. But also because open source tools such as R, GRASS GIS and QGIS are amongst the best tools available today. And thanks to the extensive documentation and the incredible support offered by the developer and user communities, it is possible to relatively quickly build simple tools yourself. I have created a few tools in the past years, which I am glad to share below.
GRASS addons
The GRASS addons I have created or contributed to are listed below. For installation you should preferably use the extension manager (g.extention or via GUI). If that doesn’t work, see the download link (and see here for installation instructions). For the full list of addons available for GRASS GIS, see here.
r.mess
A GRASS GIS addon to computes multivariate environmental similarity surface (MES) and some additional supporting layers. manual page
r.meb
Compution of the multivariate environmental bias, which compares the medium environmental conditions in an area (e.g., a country) to those in a subset of that area N (e.g., the protected areas in that country). It thus provides a measure to estimate how well conditions in the subset of an area represents conditions in the whole area,or the other way around, how biased the distribution of the subset is in environmental space. manual page
r.exdet
Detection and quantification of novel uni- and multi-variate environments following methods developed by Mesgaran et al. (2014). manual page
r.series.diversity
Computes diversity indices based on 2 or more input layers representing species (or other categories being used). Indices currently implemented are the Renyi entropy index and a number of specialized cases of the Renyi enthropy, viz.the species richness, the Shannon index, the Shannon based effective number of species (ENS), the Simpson index (inverse and gini variants), and pielou’s eveness. manual page
r.niche.similarity
The function computes two metrics to quantify niche similarity or overlap between all pairs of input raster layers: (D) the niche equivalency or similarity for two species following Warren et al. (2009) based on Schoeners D (Schoener, 1968). manual page
r.vif
Function to compute the variance inflation factor (VIF) and the square root of the VIF. The variable with the highest VIF will be dropped and the VIF will be recomputed. This will be repeated till an user-defined VIF threshold value is reached. manual page
r.out.legend
Create and export image file with legend of a raster map.manual page
r.random.weight
Generates a raster layer with a weighted random selection of the raster cells (selected cells are assigned a value 1, other a value 0) based on a user defined weight raster layer. manual page
r.recode.attr
Reclass/recode a raster layer based on values in a csv table. manual page
v.what.rastlabel
GRASS GIS addon (available on github) to uploads raster values and labels at positions of vector points to the vector attribute table. manual page
r.category.trim
Export categories and corresponding colors as QGIS colour file or csv file. Non-existing categories and their colour definitions will be removed. manual page
r.maxent.lambdas
Compute raw and/or logistic prediction maps from a lambdas file produced with MaxEnt 3.3.3e. manual page
v.maxent.swd
Export raster values at given point locations as text file in SWD format for input in Maxent. manual page
r.edm.eval
Evaluate how well a modeled probability distribution predicts an observed distribution using all or restricted set of background / absence points. manual page
R/GRASS scripts
Combining the power of GRASS GIS, which excels in handling and analysis of spatial data, and the vast number of functions for (spatial) data visualization and analysis in R (see R Spatial View) and you have a winner at hand. Below an example of a R script that garners the strength of both tools. It uses GRASS GIS in the background for the data crunching and R for the graphical output.
r.eval.strip
R function to create a response plot to evaluate the relationship between environmental variables and the fitted probability of occurrence of individual or ensemble suitability modelling algorithms. on github
GIS styles
Spatial data layers often come with standard legend / colour keys, which provides the visualization and even meaning to the data. As a side note, try to avoid the latter! Unfortunately, they are often shared in proprietary format which cannot be used in other programs and are not human readable. To avoid vendor lock in and to enable everybody to use it is therefore preferable to share color and legend keys in an open format that is also human readable (i.e., text based files). See this github page for some examples.
ZIM-wiki export templates
Zim-wiki is a graphical text editor to notes in the form of wiki pages. A great feature of Zim-wiki is that you can easily export the notebook (or part of it) as a website. You can thereby use export templates to determine how the webpages will look like. The current website is an example of an website written in Zim (with some scripted post-processing). Below you can find export templates that you can use for your own websites.
Ecodiv-responsive
Template to export your Zim notebook as a responsive/adaptive website that can be viewed in desktop and mobile devices alike. We use this template ourself for the Vegetationmap4africa website and for our tutorials.Github page
Ecodiv-mobile
Template to export your Zim notebook as a website with mobile theme. Build with jquerymobile to create a website with a focus on mobile devices, but which will also look good on the desktop. The template offers some pointers to customize the design to fit the users need. Github page
10 June 2020

18:27

Jackie Ng: Announcing: FDO Toolbox 1.4.1

sur Planet OSGeo

13:00
MapTiler: Say hello to the new QGIS plugin
sur Planet OSGeo
Adding beautiful maps that fit your needs is now straightforward with MapTiler plugin for QGIS. You can select one of the predefined map styles, load custom maps and use geodata hosted on a global infrastructure.
Really fast-loading maps
MapTiler Cloud offers a set of beautiful maps to give your data context. There are street and satellite maps of the entire world based on the OpenStreetMap project, maps in custom coordinate systems and local maps from government open data. All maps can be easily loaded to QGIS using the new plugin.

Installing the MapTiler plugin is straightforward:
1. Open the QGIS application
2. Click on the menu Plugins ? Manage and Install Plugins…
3. Search for maptiler
4. Click on the Install Plugin
5. Restart QGIS
Now just add your key, which you will find in the MapTiler Cloud administration on the web and you can start loading maps. Add them as a new layer and maps appear immediately.
Maps with your own colors and fonts
Making your own map design can be done in a few mouse clicks. Select a map you like ? right-click on the map ? select Customize in Cloud ? now change colors and fonts in the Customize tool the way you like ? Save ? Publish.

To add customized map back to QGIS, right-click on the plugin ? select Add new map ? pick From URL tab ? fill in a Name you like and paste the 256x256 JSON URL from MapTiler Cloud and click on the OK button. That’s it, your own map is loaded to QGIS.

MapTiler Cloud also allows hosting geodata of any size, including very large analytical datasets. They can be easily added to QGIS in the same way as custom maps.

All maps and geodata are hosted on a reliable global infrastructure using more than 150 servers on 5 continents.

Agricultural data on top of the satellite map
Open-source code on GitHub
The MapTiler plugin for QGIS is released under an open-source license on GitHub. Feel free to report issues or send pull requests!

We would like to thanks our partner, MIERUNE, which made a significant part of the work on the plugin and is a long-term supporter of the QGIS community.

For the upcoming QGIS 3.14, there is an ongoing work by Lutra Consulting on native support for vector tiles. MapTiler plugin is already able to load them - you can test it using the QGIS nightly releases.
11:00
CARTO Blog: WorldPop in CARTO: global demographic insights at low granularity
sur Planet OSGeo

Understanding population distribution has important implications in a wide range of geospatial analysis including: human exposure to hazards and climate change, impact on e...

Skip to page: 1 2 3 ... 9

← Blog Interopérabilité &...

Communauté	Ressources	L'emploi	Le marché	L'association	Fils RSS
Forum Blogs Wiki Planet Carte des membres Sites amis	Annuaire Agenda Biblio Netvibes Fiches AMO-CNIG Paris PCGIS Téléchargements	Offres d'emploi Banque de CV Carte des CV Formations Démarche Métiers	Geo-Entreprises Geo-Communiqué Appels d'offres	L'association Qui sommes-nous ? Nous aider Bienfaiteurs Partenaires Historique Statuts	Tout le site Job Géomatique Services GeoBlogs ...	Faire un don Mentions légales

Le portail francophone de la géomatique

Feeds

Planet OSGeo

Enclosure: [download]

Enclosure: [download]