This was the meeting point of over 100 QGIS developers, users, and trainers from all over the globe. It was the first time I met the QGIS community in person, including some of the people whose work I have admired for years. The event took place over 3 days – 2 days of workshops and 1 day of talks. I am putting some of my notes, takeways and links to resources shared on other channels (twitter, telegram, email) for the benefit of folks who were not present
Converting between different coordinate reference systems or projection is a fairly standard feature in a GIS. There are a number of command line tools also available for performing bulk-conversions. cs2cs program, part of the PROJ.4 library is my favorite.
I recently attended the OpenAQ workshop in Delhi . The workshop’s goal was to bring tech, science and media folks working on air quality together and brainstorm how to use open data to tackle air pollution challenges. Below are my notes and links to materials presented during the workshop.
Below are my notes and links to materials presented during the workshop.
This weekend, I got an opportunity to volunteer with a non-profit called Junglescapes. We took a day trip to the Bandipur forest in Karnataka where they have done extensive work in forest restoration. One of their success stories is working with the locals to remove invasive species such as Lantana from the forest. Junglescapes volunteers and locals carry out regular line transact surveys to determine the impact of their interventions. One of the goals for my participation was to see if we can replace the cumbersone paper forms and handheld GPS devices with a mobile-phone based survey using ODK. I am sharing my notes on how we setup the survey and mapping of the result.
GeoPDF is a unique data format that brings the portability of PDF to geospatial data. A GeoPDF document can present raster and vector data and preserve the georeference information. This can be a useful format for non-GIS folks to consume GIS data without needing GIS-software. While GeoPDF is a proprietary format, we have a close alternative in the open Geospatial PDF format. GDAL has added support for creating Geospatial PDF documents from version 1.10 onwards. In this post, I will show how to create a GeoPDF document containing multiple vector layers.
Get the Tools
OsGeo4W is the best way to install GDAL on Windows. The default installation gives your GDAL tools with PDF format support. You can use the GDAL tools via the OsGeo4W Shell included in the install.
KyngChaos providers a convenient GDAL installer for Mac. You also need to install the additional GeoPDF plugin to enable support for PDF format.
Once installed, add the path to GDAL library to your .bash_profile file to be able to use the commands easily from the terminal. Launch a Terminal and type in the following commands.
Installation instructions will vary with the distribution. On Ubuntu, you can install the gdal-bin package.
sudo apt-get install gdal-bin
Verify GDAL Install
If you already have GDAL installed, or just installed it, run the following command in a terminal to verify that your GDAL installation is working and has support for GeoPDF format.
gdalinfo --formats | grep -i pdf
If you see Geospatial PDF printed in the output – you are all set. If you do not get any output or get an error, your install is not correctly configured.
Get the Data
For this example, I chose to use OpenStreetMap Metro Extracts from MapZen. Download the shapefiles (OSM2PGSQL SHP format) for the city of your choice. I am using the extract for Bangalore city in this example. Unzip the downloaded file to a folder on your computer.
The process for creating a GeoPDF file from a bunch of shapefiles is the matter of running a single gdal_translatecommand. But we need to prepare the data and figure out the correct command-line options. So follow along to understand how you can arrive at the final command – or simply scroll to the end to see the final command-line.
latuviitta.org has a comprehensive overview of all the options available for GeoPDF creation via GDAL. The follow steps are adapted and simplified version of that guide.
First step is to create a .vrt file that can hold all the vector layers we want in the PDF. If you just need a single layer in the PDF, you can skip creating the .vrt file and directly reference the layer in place of the VRT. Note the <SrcSQL> tag in the VRT file. This is for filtering out all features where the ‘name’ field is empty. You can leave that out or modify to suit your dataset. Name this file osm.vrt and save it on the same folder with your data.
<SrcSQL dialect="sqlite">SELECT name, highway, geometry from bengaluru_india_osm_line where name is not NULL</SrcSQL>
<SrcSQL dialect="sqlite">SELECT name, geometry from bengaluru_india_osm_point where name is not NULL</SrcSQL>
GeoPDF is a raster format that can overlay vectors on top. So we need a raster layer as the base. If you have some satellite imagery or scanned raster for the area, you can use it as the base layer, or we can create an empty raster for the extent of the vector layer. ogrtindex command creates a bounding box polygon from the given input layers. gdal_rasterize command then fills this polygon with the given value and creates a raster. The -tr option specifies the pixel resolution of the raster in degrees. You can tweak that to get the output size you need. cd to the directory where you have extracted the vector layers and run the following commands.
Once the conversion finishes, you can open the resulting bangalore.pdf file in any PDF viewer. Opening it in Adobe Acrobat viewer, you can see the map data layers. You can browse the features in the layer panel, search for any attribute value and zoom/pan the map.
Another popular use of GeoPDF files is to use it as offline base maps using programs such as Avenza PDF Maps. Loading the bangalore.pdf file on Avenza Maps on your mobile phone, you can use the GPS to view your current location or trace a GPS route on top. Search also works across layers in the PDF.
You can download the sample bangalore.pdf Geospatial PDF format file for exploring the format yourself.
Mapshaper is a free and open-source tool that is best known for fast and easy simplification. Other tools for simplification – like QGIS or ogr2ogr – do not preserve topology while simplifying. This means you may get sliver polygons or missing intersections. Mapshaper performs topologically-aware simplification and gives you much more control on the process.
Other popular open-source tools, PostGIS and GRASS can do topologically-aware simplifications as well. But Mapshaper is much more than a simplification tool. It is in active development and has many more data processing and editing capabilities now. It also has a command-line version of the tool which can be run from a terminal. In this post, we will explore the command-line tool to carry out some complex geoprocessing tasks.
Mapshaper is a Node.js application. Download and install Node.js for your platform. You will need the Node Package Manager (NPM) to install mapshaper, so make sure it is enabled while going through the installer.
Once Node.js is installed, launch the Windows Command Prompt (cmd.exe) and run the following command to install mapshaper.
npm install -g mapshaper
Get the Data
Review the data and problem statement from the Performing Table Joins tutorial. Download the Census Tracts shapefile tl_2013_06_tract.zip and the Population CSV ca_tracts_pop.csv. Unzip the tl_2013_06_tract.zip file and extract it to a folder.
Mapshaper command takes an input, an output and a sequence of commands to execute. Each command is followed by options specific to that command. All the commands and options are well documented at the Mapshaper Wiki.
Let’s start with simplification. We will take the census tracts shapefile and simplify it to reduce the number of vertices and the total size. The command for simplification is -simplify. You can supply a percentage value as an option to specify how aggressiveness of the simplification. Another useful option is keep-shapes which ensures that none of the polygons from the input will get deleted. Run the following command. Make sure you cd to the directory where the data has been downloaded.
Note: The percentage value in the -simplify command can be a little misleading. The value indicates how many vertices to keep and not how many to remove. So a lower value would result in MORE simplification
Mapshaper can also do Table Joins. We can now join the population field D001 from the ca_tracts_pop.csv file. The join will match the fields we specify as keys and add it to the output file. For the join to work correctly, we need to specify the field types in the CSV file. (Similar to how a .csvt file is needed by QGIS). We can ‘chain’ the -join command after the -simplify command to perform both the operation in a single command.
Mapshaper can also dissolve features. In my testing, Mapshaper’s dissolve operation was many times faster than QGIS or GRASS. Let’s add a -dissolve command and merge all census tracts for a county. We can also sum up the values of the D001field to get the total population of the county from the sum of individual census tracts.
The output format needed by many web apps is geojson or topojson. Mapshaper can write the output in these formats as well. Let’s add a format=geojson option to the -o command to write a geojson output.
Finally, let’s visualize our output. Go to geojson.io and upload the resulting output.geojson. You will be able to visualize the output shapes and their properties
By now, you must have figured out that we have a very powerful tool on our hands. In just a single line of command and just a few seconds of computing, we did Simplification, Table Join, Dissolve and Format translation.
GDAL and OGR libraries come with handy command-line tools. These tools are quite powerful and can save you a lot of effort if you know how to use them. Here I will show how to use the ogrinfo and ogr2ogr tools to perform spatial joins. A single command can do complex operations on your spatial data and save you a lot of clicking-around and data-munging in a GIS.
Review the data and problem statement from the Performing Spatial Joins tutorial. Download the Borough Boundaries and Nursing Homes shapefiles.
OGR command line tools accept only 1 input. But we have 2 inputs for the spatial join. An easy way to fix this, is to use a VRT file. A VRT file allows us to specify multiple inputs and pass them to the command-line tool as layers of a single input.
Unzip the input shapefiles in a single folder on your drive. Create a file named input.vrt in the same folder with the following content.
Open the OSGeo4W shell and cd to the directory containing the shapefiles and the vrt file. Run the ogrinfo command to check if the VRT file is correct.
OGR tools can run SQL queries on the input layers. We will use the ST_INTERSECTS function to find all nursing homes that intersect the boundary of a borough and use the SUM function to find the total nursing home capacity of a borough. Run the following command.
ogrinfo -sql "SELECT b.BoroName, sum(n.Capacity) as total_capacity from
boroughs b, nursinghomes n WHERE ST_INTERSECTS(b.geometry, n.geometry) group
by b.BoroName" -dialect SQLITE input.vrt
You can see that in a single command we got the results by doing a spatial join that takes a lot of clicking around in a GIS environment. We can do a reverse spatial join as well. We can join the name of the Borough to each feature of the Nursing Homes layer. Using the ogr2ogr tool we can write out a shapefile from the resulting join. Note that we are adding a geometry column in the SELECT statement which results in a spatial output. Run the following command:
ogr2ogr -sql "SELECT n.Name, n.Capacity, n.geometry, b.BoroName from
boroughs b, nursinghomes n WHERE ST_INTERSECTS(b.geometry, n.geometry)"
-dialect SQLITE output.shp input.vrt
Open the output.shp in a GIS to verify that the new shapefile as attributes joined from the intersecting borough. You can use ogrinfo command to check that as well.