Mapping & GIS for Absolute Beginners
Here at Mango I speak to of dozens clients every week who want to produce web maps that visualises their data but are new to the world of mapping. They know what they want the finished web map to look like, but they’re not sure where to start, and not familiar with the jargon required to put Google to work.
If this sounds familiar to you, then you’re in the right place.
The aim of this article is to give you a 10,000 feet overview of modern mapping, and I hope by the end you will be equipped with a basic understanding of how interactive maps work and you will have a basic understanding of the common jargon that you will need in order to self-study by getting the search engines to reveal their secrets.
Geographic Information Systems (GIS)
Almost all digital maps are produced using a GIS. The term GIS is a catch all term for any system that can store, manipulate and display digital map data, they range from powerful desktop systems such as QGIS and Esri’s ArcGIS through to web based systems such as Mango.
GIS Data Formats
The two main types of data used in a GIS system are vector and raster. Vector data is the most common form of map data and is made up of points, lines and polygons that are drawn to represent physical features or locations. Rasters are images such satellite imagery or aerial photography and are much less common.
All maps begin with data, stored in a range of formats such as Shapefile, KML, GeoJSON and many others. All vector map data formats share one thing in common: they contain spatial data and attribute data. The spatial data contains the geometry that explains where a feature is located on the earth and the attribute data tells us additional information about that feature for example it’s name, size or even the number of people it contains that have eaten a chicken curry in the last month.
The de facto standard for storing and exchanging map data in the GIS world is the Shapefile. Despite it’s name, a Shapefile is usually made up of four files (although it can be more). It’s not critical that you understand what each of those files does, but if you wish to find out more you can checkout our guide to GIS data.
If you are new to mapping, it’s likely you don’t have a Shapefile. It’s much more likely that you have a spreadsheet where each record contains an item that could be placed on a map such as an address, zip code or county name. A spreadsheet like this is effectively only attribute data — it doesn’t contain any usable spatial data — but if the spreadsheet contains a column with addresses or zip codes it can easily be converted to a Shapefile. Here some tutorials to help you do that:
GIS is Data Driven
GIS maps are data driven, what this means is that to change the presentation of a map we change the data using styling rules called “classes”. For example if we wanted to map a dataset that showed how people voted by state in an election we would make a class that would make states on the map red if they voted Republican, or blue if they voted Democrat. Let’s say the way a state went on election night changed on the final count and we needed to change the colour, we wouldn’t edit the feature on the map, but rather we update the data the map is using, and the map will update itself.
Legends & Symbology
The classes discussed in the previous section are commonly referred to as symbology and are displayed on the map as a legend, the legend lets the user of the map know what they are looking at. The three most common types of symbology used in a legend are single symbol, graduated colors (aka choropleth map) and category.
Single symbol will give each feature in a dataset the same styling. This might be used for rivers, roads, city locations or other feature sets where the difference between each features is not of interest.
Graduated colors are used when a column in the data contains numbers and we want to change the strength of the color based on the size of the value. An example of a graduated color legend could be a map that shows the poverty rate by county where light red indicates low poverty and dark red contains high poverty.
Categories allow us to give each feature a different color based on an attribute. An example might be a map showing the location of fast food restaurants in your town, whereby McDonalds is Yellow, KFC is red and Subway is green.
Often the purpose of a map is to answer a question through a visualisation.
With maps it’s far easier to identify patterns and clusters than it would be using a spreadsheet or a database. Web mapping tools like Mango allow you to perform basic analyses such as finding features that match certain criteria.
For more heavy analysis that involves comparing and joining multiple datasets, you will often have to prepare your data using a desktop GIS system such as QGIS before uploading your data to Mango. This type of analysis is beyond the scope of this post but please read this tutorial for an example of how using a spreadsheet of U.S colleges and a Shapefile of U.S counties you could produce a heat map showing counties by the number of colleges they contain.
That’s it for this post, you should now be familiar with the core concepts and vocabulary of GIS.
If you’d like to dive deeper into GIS, check out our comprehensive guide: The Beginner’s Guide to GIS
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Originally published at mangomap.com