With the growing amount of available geographical data, it is useful to be able to visualize one’s data on top of a map. Visualizing numeric and/or categorical information on top of a map is called a map chart.
Two student teams in my Fall data mining class explored and displayed their data on map charts: one team compared economic, political, and well-being measures across different countries in the world. By linking a world map to their data, they could use color (hue and shading) to compare countries and geographical areas on those measures. Here’s an example of two maps that they used. The top map uses shading to denote the average “well-being” score of a country (according to a 2004 Gallup poll), and the bottom map uses shading to denote the country’s GDP. In both maps darker means higher.
Another team used a map to compare nursing homes in the US, in terms of quality of care scores. Their map below show the average quality of nursing home in each US State (darker means higher quality).These two sets of maps were created using TIBCO Spotfire. Following many requests, here is an explanation of how to create a map chart in Spotfire. Once you have your ordinary data file open, there are 3 steps to add the map component:
- Obtain the threesome of “shapefiles” needed to plot the map of interest: .shp file, .dbf file, and .shx file (see Wikipedia for an explanation of each)
- Open the shapefile in Spotfire (Open>New Visualization> Map Chart, then upload the shp file in Map Chart Properties> Data tab> Map data table)
- Link the map table to your data table using the Map Chart Properties> Data tab > Related data table for coloring (you will need a unique identifier linking your data table with the map table)
The tricky part is obtaining shapefiles. One good source with free files is Blue Marble Geographics (thanks to Dan Curtis for this tip!). For US state and county data, shapefiles can be obtained from the US Census Bureau website (thanks to Ben Meadema for this one!) I’m still in search for more sources (for Europe and Asia, for instance).
I thank Smith MBA students Dan Curtis, Erica Eisenhart, John Geraghty and Ben Meadema for their contributions to this post.