Another way to celebrate GIS day.

Elena Palao
Nov 18 · 5 min read

November 18th is GIS day and there are at least 101 ways of participating. We (Vizzuality’s Science team) wanted to participate by creating way #102: giving non-GIS people a quick intro to GIS! This was prompted by our teammate Martin’s question in the science channel.

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Our teammates are welcome to drop in — and out — of the science slack channel any time they like.

In this blogpost we will do a quick recap on what GIS is, the very basic components, and a quick workflow demo. Let us know in the comments if there are any further questions you would like to have answered, we might not have the answers, but we will try to find them.

What does GIS mean?

GIS provides us with the ability to generate powerful maps and analysis that can tell a story in a language that everyone can understand. And for those who wonder how often does a map change the world? John Snow’s map (no, not the one from Game of Thrones) of cholera outbreaks from nineteenth century London changed how we saw a disease spreading through the population (sounds quite like a relevant theme these days).

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Visualisation of John Snow’s map of cholera. A fun experiment made by Janett Baresel, Cheryl Lau and Raluca Nicola. Check it out on GitHub.

This fact demonstrates that GIS is not something new but new technologies have sped things up and provided the ability to generate powerful visualization in near-real time — as we recently saw in the US elections.

Therefore, by using GIS, many different types of information can be compared and contrasted such as deforestation alerts, fire alerts, land use information or vulnerability indexes to produce powerful insights. Just like we usually do in our projects. One important use of time-based GIS technology involves creating time-based maps that show processes occurring over different areas and during long periods of time.

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GFW fires alerts (VIIRS) in the Central African Republic between October 14 2020 and November 14 2020. Source: Global Forest Watch, accessed 17 November 2020.

At the heart of GIS: Vectors and rasters.

GIS cartographers also use satellite and remote sensing data to explore patterns and relationships.

Projections — the cartographer’s Dilemma.

Find this on Twitter.

Use case: what is going on in Madrid apart from COVID?

We thought about doing a simple analysis for Madrid, our hometown, by collecting simple data that we usually use in our projects. In this case we are using the widely used GADM dataset combined with WWF ecoregions. These are two sets of polygons, one representing the borders of administrative units and the other one representing the limits of described ecoregions.

The first thing we usually do right after downloading the data is a data exploration in order to understand the proper nature of the data. You can do this by exploring the attribute table, for example, and by understanding the metadata, etc.

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Attribute table for the GADM level 4 dataset showing the selection of all the attributes which NAME_1 is equal to Comunidad de Madrid (yep, that’s it, Spain inside Spain).

From this data exploration we end up selecting all the administrative areas within the Comunidad de Madrid as we can use these shapes to perform a quick analysis by combining them with other datasets (e.g. the ecoregions dataset). We have decided to perform all the analysis by GADM level and by community level, and to do so we need to generate a boundary layer which can be obtained by dissolving the GADM level 4 dataset.

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GADM level 4 with NAME_1 equal to Comunidad de Madrid.
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Boundary of the Comunidad de Madrid after dissolving all the GADM level 4 shapes by the attribute NAME_1. The internal borders have disappeared.

Once we have the boundary datasets we can combine them with the ecoregions datasets. The ecoregion layer can be clipped using the boundary and therefore we can compute the area of each ecoregion within the Comunidad.

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Clip of the ecoregions layer with the Comunidad de Madrid boundary which shows the three different types of ecoregions within the Comunidad. The image also shows the attribute table with the calculation of the area of each type of ecoregion within the boundary.

This is a quick analysis which provides a quick conclusion: In Madrid there are 1879 km2 of Iberian conifer forests, 6138 km2 of Iberian sclerophyllous and semi deciduous forests and 10 km2 of Northwest Iberian montane forests.

This simple workflow can be repeated with different datasets to obtain more complete contextual understanding. Using other GIS tools Geodescriber extracts the information from different sources. Somehow if you have been using Geodescriber, you’ve been doing some GIS yourself!

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The Half-Earth Project uses geodescriber, which when you zoom in it gives back information on the highlighted cells. The cells are polygons and they intersect information from GADM, forest cover and ecoregions.

Let us know if you do some data exploration with other datasets. We hope you have enjoyed this walkthrough, let us know if you have any questions so you can keep exploring!

Elena is a Scientist with experience in geospatial data analysis, statistics, and environmental engineering. She loves being outdoors and is always keen to try a new sport!

Vizzuality Blog

Posts on data design, user research, open data, and…

Elena Palao

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Vizzuality Blog

Posts on data design, user research, open data, and software development. We create tools and applications with a lasting benefit to society and the environment.

Elena Palao

Written by

Vizzuality Blog

Posts on data design, user research, open data, and software development. We create tools and applications with a lasting benefit to society and the environment.

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