On October 14, the New York Times released their Campaign Finance API, a simple interface to the Federal Election Commission’s candidate fund-raising data. Data are available for the entire country, for all primary candidates, from the beginning of the 2008 primary campaign up until the end of August. Though summary data are available, the API also allows requests for individual donors by zip code, and includes name, address, amount, and date for all donations.

With a simple script, we were able to string together requests for multiple zip codes and parse the resultant XML into a CSV file that could be loaded and geocoded with the SpatialKey Data Importer (all while staying under the Times’ generous 5000 requests/day limit). Doing so yielded complete datasets for a number of major cities and battleground states; here we present campaign donations data from the Democratic strongholds of Chicago and Denver.

Chicago donations to Obama and McCain, August 2008

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This dataset includes all available donations from Chicago received by either major presidential candidate in August, the most recent month for which individual-level data are available. The over 4000 donation records provide a unique view into political spending in Obama’s home base during a month in which the candidates were nearly even in the polls.

With our new and in-development Data Exploration template, you can filter the donation records by any attribute, allowing you to exclude individual donors or candidates, or select a range of dates or donation amounts to display. Filters are immediately applied to the visualization, and many trends in the data are revealed only after such filtering has been applied. For example, displaying only donations to John McCain reveals a much different geographic trend than the unfiltered dataset, as nearly all donations from Obama’s neighborhood of Hyde Park are erased, leaving hotspots only in the downtown, Gold Coast, and far northwest neighborhoods.

Denver donations to Obama and McCain, Summer 2008

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Denver provides another compelling case study of campaign fundraising, given its position as the capital of a Western battleground state, and the host of the Democratic National Convention in late August. Both candidates also visited the city multiple times throughout the summer months. The temporal trends and geographic clustering in this data can be explored via our Data Exploration template. The scented filtering widgets allow you to visualize the distribution of attributes while filtering them down to reveal previously hidden geographic trends on the map. For example, the screenshot above shows only donations over $500 to both candidates. This cuts out nearly half of Obama’s receipts, though only about one-third of McCain’s, and produces a markedly different geographic distribution than the unfiltered receipts.

Finally, though the above has concentrated on trends and aggregated visualizations, the Data Exploration template also allows you to drill down into the data to pull out individual records. After viewing the overall trend for a city or neighborhood, click anywhere on the map from which donations originated, and you can page through a list of individual donations in the vicinity, as shown in the screenshot above.

This technology preview is not a formal release, and is designed to show what’s possible with Flex mapping and browser-based visualization, and to generate feedback on what we’ve done so far. For more information on this preview, please see spatialkey.com. If you’re anxious to see your own data, stay tuned for SpatialKey Personal, a soon-to-be-released version of our system that will allow you to map thousands of points of your own geotemporal data. If you’re interested in mapping larger datasets, say millions of points, or would like to speak to us about a custom visualization solution, please contact us.

This technology preview is all about examples, specifically dataset + visualization template pairings. These are meant to show off our advanced visualization and mapping tools, and to present potential use cases for this technology. Each template (there are currently four in this preview) allows you to look at a dataset in a particular way, while each remains general enough to accommodate most geotemporal datasets. Below are screenshots and links to the examples in the current preview; we’ll be posting more over the coming weeks. For more information on these examples, please see the Gallery page. To stay informed about SpatialKey, join our beta, subscribe to this blog, and/or check back often!

the radial spread of Wal-Mart

where prostitutes are arrested in San Antonio

construction slowdown in D.C.

Sacramento residential burglaries

the spread of a geo-social network: BrightKite

Wal-Mart stores, supercenters, and distribution centers

increasingly violent terrorist attacks on infrastructure

Sacramento sex crime arrests

a month of crime in Sacramento

housing slump in Sacramento