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.
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?
A Geographic Information System (GIS) is basically a computer system for capturing, storing, checking and displaying data related to position on Earth’s surface. GIS can show many different kinds of data on one map enabling people to more easily see, analyse and understand patterns and relationships by locations.
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).
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.
At the heart of GIS: Vectors and rasters.
The two major types of GIS formats are rasters and vectors. Raster formats are pixels (grids of cells) for storing GIS data while vector formats are polygons, points and lines. Somehow, they are all points, but sometimes connected (lines) and sometimes connecting in a loop (a polygon).
GIS cartographers also use satellite and remote sensing data to explore patterns and relationships.
Projections — the cartographer’s Dilemma.
Like all mappers, GIS cartographers must also align all datasets to conform to a single scale and projection. For thousands of years cartographers have been exploring different formulas to describe how points from one surface are transferred to another which is what we call map projections. However, every map projection distorts the world in some way. For example, the Mercator Projection stretches the top and the bottom making Greenland huge!
Use case: what is going on in Madrid apart from COVID?
Finally the most interesting part, a practical use case, which has been done using QGIS. The idea of using this open source software is to show that it is really easy to use and that everyone can have a go playing with it (if you want to, of course).
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.
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.
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.
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!
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!