Quick hands-on: Tableau adds shapefile, KML & GeoJSON imports for custom maps

Map created by Tableau 10.2
Screenshot of map created with Tableau 10.2 (U.S. Demography Agency data)

Tableau has significantly expanded its custom mapping capabilities in version 10.ii, allowing for importing your own geospatial files in GeoJSON, shapefile, KML and MapInfo formats. I finally had a adventure to take Tableau 10.2's GIS functions for a spin, and information technology'south a welcome addition.

If you're a Tableau user and upgraded to 10.2, you probably noticed that "Spatial file" is at present an bachelor data connections. Importing a geospatial file works the same every bit whatsoever other information source -- y'all pick the file, make a few decisions, and import.

I started with a shapefile of information from the U.S. Census Bureau that had both geometry and data -- somewhat easier than trying to merge geospatial info with a dissever data file. I chose median earnings in major occupation categories by Congressional District (and so did some research to make up one's mind that column B24021027 was median earnings for Computer and mathematical occupations).

As soon as I dragged and dropped the geometry field onto my master piece of work surface area, a map of Congressional Districts appeared. It couldn't have been easier. Color-coding that map past median earnings was a scrap less intuitive, though. I took care to filter out a "Full for all of the US" row, but no matter how much I dragged, dropped and rearranged, my map stayed a single colour.

I finally caved and looked for map-specific instructions on Tableau'south website. The solution: Even though the imported file has numerous defined areas, by default those areas are aggregated into a single unit of measure, which is why all your data gets applied to the entire map. You, the user, need to "disaggregate" the polygons by going to the Assay menu and unchecking "Amass Measures." I'm not sure why Tableau engineers thought that defaulting to "treat all polygons in a shapefile as one unit of measurement" was a good idea, simply information technology was simple enough to correct once I discovered upwards the cause.

Later on that, dealing with geospatial data was pretty much like any other Tableau data source. Joining a geography file with a separate data file is like other joins: Y'all have to make sure the common-column keys utilize the same format in both files. I discover this tends to be particularly challenging with GIS files, since there are and then many ways to identify a place. But you can plow to already-existing Tableau tools such every bit creating new, calculated fields so that one file'southward joining column matches another. (Yet, I tend to be more than comfy doing that kind of data wrangling in a command-line surround such equally R).

I didn't endeavor this feature out, just Tableau maps tin accept more than than 1 layer, such every bit showing transit routes inside cities or counties, strengthening the analysis component for those files.

Bottom line: The ability to import any shapefile into Tableau is a plus for situations like mapping urban center voting precincts or a company'due south custom sales districts. Mapping within general BI software probably won't be a substitute for full-fledged GIS software like ArcGIS or QGIS anytime soon if you need very sophisticated spatial calculations and analysis, or mapping capabilities for other interactive applications. Even so, I can meet how this can come in handy (such as last fall, when I volunteered to generate election maps for a friend'southward blog.)

It looks like mapping will be ane of the next areas where data visualization vendors will be competing, peculiarly after Microsoft partnered with GIS powerhouse Esri to bring some ArcGIS capabilities to Power BI. (As far every bit I know, you however can't yet import custom shapefiles into Power BI, although many shapefiles are available through Esri public collections). I suspect by the time the adjacent major American ballot rolls around, geospatial capabilities in full general data-analysis software like Tableau and Power BI volition exist even more robust. And that's good news for anyone who needs to analyze geospatial data.

Interactive version of my Tableau map of data from the 2015 American community survey:

Want to read more than well-nigh GIS? See Mapping in R but got a whole lot easier.

Sharon Machlis is Executive Editor, Data & Analytics at IDG, where she works on information analysis and in-firm editor tools in improver to writing and editing. Her book Practical R for Mass Advice and Journalism was published in December 2018.

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