How To Use Mapsdata
The Mapsdata online app allows you to make sense of data by visualizing it on a map. It is simple and can be used by anyone. Click on the links below to learn about the three steps to follow to create great data visualizations:
- How to get data – Want to create great data visualizations but don’t know where to start? Finding good data is the first step, and it’s easy thanks to our exhaustive lists of free data sources.
- How to structure your data – Now that you have it (or if you already did) here is how to organize your data so the Mapsdata online app can understand it and map it. A simple, well-structured spreadsheet will do the trick.
- How to visualize your data – When your spreadsheet is ready, simply upload it to the Mapsdata online app. Now all that’s left is to pick the right kind of visualization for your data. Learn about heat maps, bubble maps, cluster maps and marker maps, as well as how to customize them.
Now that you’ve created and customized your data visualization, here is how to embed it on your own site and share it with the world.
For inspiration we have examples of A-to-Z data visualizations. Check out our showcase pages on Mapping Earthquakes… and Nuclear Tests? as well as visualizing The Busiest Stations on the London Underground.
How to Get Data
Check the containers to the right for links to high quality and free data sets listed according to the source.
Some of our favourite open data portals are:
Most sources provide files in common formats such as .xls or .csv. Download these files to your computer before moving on to the next step: structuring your data.
- data.gov.au — The Australian government’s open-data portal
- gov.opendata.at — The Austrian government’s open-data portal
- data.belgium.be — The Belgian government’s open-data portal
- dados.gov.br — The Brazilian government’s open-data portal
- data.gc.ca — The Canadian government’s open-data portal
- datos.gob.cl — The Chilean government’s open-data portal
- data.overheid.nl — The Dutch government’s open-data portal
- opendata.ee — The Estonian government’s open-data portal
- data.gouv.fr — The French government’s open-data portal
- govdata.de — The German government’s open-data portal
- data.gov.in — The Indian government’s open-data portal
- satupemerintah.net — The Indonesian government’s open-data
- data.gov.it — The Italian government’s open-data portal
- opendata.go.ke — The Kenyan government’s open-data portal
- date.gov.md — The Moldavian government’s open-data portal
- data.gov.ma — The Moroccan government’s open-data portal
- data.govt.nz — The New Zealand government’s open-data portal
- data.norge.no — The Norwegian government’s open-data portal
- dados.gov.pt — The Portuguese government’s open-data portal
- opengovdata.ru — The Russian government’s open-data portal
- datos.gob.es — The Spanish government’s open-data portal
- datos.gub.uy — The Uruguayan government’s open-data portal
- statistics.gov.uk — The UK’s National Statistics Office website
- indicators.ic.nhs.uk — The NHS indicators website
- guardian.co.uk/data — The Guardian Newspaper’s Data Store
- dvn.iq.harvard.edu/dvn/ — Harvard University’s Institute for Quantitative Social Science
- projects.iq.harvard.edu/eda/data — Harvard University’s Election Data Archive
- data.uni-muenster.de — Scientific data from the University of Münster
- tables.googlelabs.com — Google’s Fusion Tables
To suggest a new source to add to these lists, please send us an email.
How to Structure Data
Mapsdata allows you to create great data visualizations straight from your spreadsheets. In order to be able to visualize your data, however, your spreadsheets needs to be structured in a way that is understandable to the Mapsdata online app. There are a few simple things to do to achieve this.
- The first row of your spreadsheet should be a heading row. It should only contain headings such as Latitude and Longitude, or Country, as well as whatever additional information you want to display, e.g. Value, Date or Comments columns.
Importantly, the data in each column needs to be uniform. So keep only latitude information in a column titled Latitude, etc.
- The key information for a map-based data visualization is, obviously, location. Make sure your location information is structured correctly, depending on which type you use.
Types of geocoding information recognized:
If your data does not contain any, check our help page on how to add geocoding information to your data set.
• Decimal Degrees should be presented as 25.197127 and 55.274311
• Degrees, Minutes, Seconds should be presented as 25°11’49.66″ and 55°16’27.52″
• Degrees, Decimal Minutes should be presented as 25° 11.828′ and 55° 16.459′
• Note that there is no need to use N, S, E or W. If a point occurs South of the equator or in West of Greenwich, i.e. S or W, then please use the minus “-” symbol. If the latitude and longitude for your data use N, S, E and W instead, click here to find out how to convert it.
A sample data set using latitude and longitude is available for download by clicking here.
This sample lists the 20 busiest airports in the world.
The title of the column should be “MGRS” and the full MGRS information, e.g. 42SUA5246314470, should feature in the cell.
The title of the column should be “UTM” and the full coordinates, e.g. 30U 70198mE 5711981mN, should feature in each cell.
A sample data set using UTM coordinates is available for download by clicking here.
This sample lists the position of the 50 highest peaks in California, based on data from a mountaineering website (source).
Simply title the column appropriately, with “Country”, “ISO2 Code” or “ISO3 Code” and use either the names, 2-letter codes, or 3-letter codes. For example: Argentina, AR or ARG.
A sample listing two and three-letter ISO codes as well as the different names recognized by the system is available for download by clicking here.
If your data does not include US cities, use two columns titled “City” and “Country” and provide both the city name in the form listed in our database and the country name or code, as described in the container above. Add a third “US State” column if the data includes US cities, filling it as explained in the container below, and leaving it blank for the non-US cities in the data.
The title of the column should be “US State”, and either full state names or 2-letter abbreviations should be used, e.g. Alabama, or AL.
We support the use of United States Postal Service ZIP codes to geocode data.
The title of the column should be “ZIP” and the full 5-digit ZIP code should be used, without the state abbreviation, e.g. 20500.
The title of the column should be “Postcode” and the full postcodes should be used, e.g. EC2A 4BX.
We support the British National Grid (BNG) system to geocode data. Formerly known as the National Grid Reference (NGR), it is often used in UK government publications.
Two columns should be used, titled “Easting” and “Northing”.
Having problems with your data?
If you’re having problems loading your data onto the online app, check that you have followed the rules above for how to structure your spreadsheet.
- Incorrect column headings are a common issue. For geocoding information, check that your columns are labeled as explained above depending on the geocoding format you use.
- We also often find spreadsheets containing other pieces of information in the first row that aren’t relevant to the visualization (e.g. the name of the author or the organization). Try to limit your spreadsheet to information that is relevant to the visualization such as the location, the primary value and other attributes you want to be displayed when interrogating a data point.
- Check for invisible blank spaces in your spreadsheet, in particular in the column(s) containing your geocoding information.
- Adding geocoding information with VLOOKUP - If your data does not contain geocoding information recognizable by Mapsdata, this can help you.
- Fixing the Column Labels Error - If you’re getting the error “Check that your columns are labeled properly in…” this will help you understand why, and how to fix it.
- Replacing S and W with minuses - If the latitude and longitude for your data use N, S, E and W instead of simply positive figures for N and E and negative ones for S and W, here is how to fix it.
- Separating Latitude and Longitude - If the coordinates (whether lat-lon or BNG) in your data are in one column, this explains how to split it in two and make it recognizable by Mapsdata.
- Using CSV and TSV – If your data comes in CSV or TSV files and you’re not sure what to do with it, this will teach you everything you need to know.
How to Visualize Data
Now that your data is structured, load your spreadhseet onto the Mapsdata online app for visualization: click Import (top left) and drag-and-drop your file or browse your computer to find it.
The only thing left to do is to choose which type of visualization is best suited to your data and what you want to do with it.
Mapsdata currently offers four different types of visualizations: heat maps, cluster maps, marker maps and bubble maps. Each of them is customizable to help you create the perfect data visualization.
Explore the tabs below to find out in more detail why, when and how to use each type of visualization.
Heat maps are useful to show density of data points or intensity of phenomena.
Displaying density through a color scale, heat maps are visually compelling and let viewers understand a lot from the data in a single glance.
The opacity, intensity and radius used to create the map are customizable to ensure your visualization looks just right.
This is a great visualization tool to make sense of larger data sets.
Cluster maps group data points that are close together and display the total for each grouping on the map.
They can often be used alongside heat maps, and although they may be slightly less visually compelling, they have the advantage of displaying exact numbers. Clicking on a cluster will display the list of entries with the full information for each.
Radius, color and opacity are customizable to create the perfect visualization.
Cluster maps are perfect for larger data sets where it is necessary to be able to interrogate data points.
Marker maps are the classic data visualization: each event or entry is individually plotted on the map.
Clicking on a marker enables viewers to interrogate each data point to display further information, like date and time, or whatever else is present in the data set.
The type of marker used, as well as its size and color are customizable directly from the “View” menu.
Use markers for data sets where it is important to display the exact location of each individual data point. For more zoomed-in views, markers can also be a great complement to heat maps.
Bubble maps are another classic, they allow you to display a given value for each geocoded data point. Again, each can be interrogated: just click on a bubble for further information.
The color and opacity are customizable for better viewing, and the “Select Column” menu allows you to visualize different data from a single file.
Bubble maps are perfect for visualizing a given variable in addition to the position of each point. They allow you to compare values for different locations in a single glance, just like in this example on the left, which shows footfall at London Undergound stations.
Now you’re ready to create your data visualization:
If you still have questions, we’re always happy to help. Send us an email on firstname.lastname@example.org and we will get back to you as soon as possible.