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

Launch this visualization

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

Launch this visualization

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.

…it’s a combination map of satellite images, street map, and my geographically assigned patrol beat map in layers. The company has made it so user friendly that I can literally research and plot over 400,000 calls for service in the last 5 years in about 30 seconds. The system is web based so quickly analyzing What-Ifs can be accomplished anytime, anywhere…

Chief Greiner further highlights the ease-of-use of SpatialKey and its appeal to younger officers:

At the end of the day I want to give each of my officers the ability to do What-Ifs from home. My newest officers are gamers raised in a world of video games. I want this to become their new game of choice in helping solve crimes and arrest suspects more quickly. OPD [Ogden Police Department] officers work the same area for a year and this gives them a tool for their area to use during their un-committed time.

With SpatialKey, our goal is to put powerful visualization and reporting tools into the hands of those who need them most: the decision-makers. We continue to work with the Ogden PD and other departments to test our tools, implement their feedback, and ensure that they are able to make best use of their geotemporal data.

The article — about the use of browser-based visualizations and analytical dashboards for business intelligence — features an interview with Chief Jon Greiner of the Ogden Police Department in Utah. Ogden is the first police department in the country to implement the enterprise version of the SpatialKey Law Enforcement Dashboard:

Today, the officers are using the new BI tools to perform geographic profiling of crimes and analysis of police data “in seconds,” he says. Before, it could take days for the department’s single crime analyst to fulfill a report request. An added bonus is that experienced police officers with extensive street experience are now able to apply their firsthand knowledge to crime analysis.

It’s great to see our crime mapping tools featured in an article aimed at the broader business intelligence and IT fields, especially as we expand the focus of our tools with the SpatialKey Technology Preview and SpatialKey Personal.

The standard cartographic approach to symbolizing point data with attributes attached is the proportional symbol map.  Such maps, which are one of the three symbolization methods currently offered in SpatialKey, use symbols (typically circles) scaled proportional to point values.

Offering multiple symbolization options — each available at all times and map scales — allows users to switch to a more appropriate rendering for their dataset and switch back-and-forth to see their data in new ways.  In addition to the proportional symbols shown above, our templates currently offer two raster-based aggregated renderings: the heat grid and heat map.

In the above screenshots, the colored heat grid and heat map represent the density of Wal-Mart stores. In this case, store locations are aggregated to an arbitrary, scale-dependent grid. Thus, the brightest grid squares (and the hottest areas on the continuous heatmap representation) represent the areas of highest Wal-Mart concentration.

These renderings can also be used to show attributes of point data, in which case the hottest/brightest areas represent the areas with the highest average or total value for a given attribute. And of course mousing over the map reveals tooltips with exact values for these “quadrats” (screenshot below shows Sacramento home sales data).

The heatmap symbolization is newer, less vetted by the cartographic community, and perhaps less straightforward than the proportional symbol and heat grid renderings. But with the proper user interface, and in concert with other available renderings, we believe heatmaps can help facilitate effective visualization of geotemporal data.

We are particularly interested in feedback on the use of the heatmap symbology for mapping attributes of data (like home prices). We are excited to add additional symbolization options to our templates, as well as improvements to existing renderings, as we continue to develop the SpatialKey visualization system.

Droughts, floods, storm events — all are visible in our visualization templates utilizing this free government data.  See, for example, the Great Flood of the Missouri and Mississippi Rivers in 1993 by visualizing streamflow (in cubic feet per second) measured at dozens of stations along these rivers.

Launch this visualization

The symbols on the map are sized proportional to the streamflow readings, allowing us to see the peak of the flooding in July and August.  This dataset shows off many unique aspects of the SpatialKey visualization system.  First, the templates excel at aggregating both spatially and temporally.  In the screenshot above, the St. Louis and Grafton stations are aggregated together, because they are so close and would overlap, producing illegibility and reducing clarity, if they were displayed separately.  The average of the two stations’ readings is symbolized; zoom in to reveal more detail, separating the St. Louis and Grafton stations’ symbols:

In addition to the spatial aggregation, this example also demonstrates temporal averaging.  In the above screenshot, each of the symbols represents and average of one month of daily readings.  Selecting different time ranges on the time chart instantly reveals the new average, and any range can be animated in the Playback template.

All of the SpatialKey visualization templates include extent-filtering, which updates the time chart to only display the temporal trend for points in the current map extent.  Seeing the streamflow trend for an individual station is therefore as easy as zooming into that station:

The dataset shown off in this example is different from the others in our gallery.  While the others demonstrated an ability to aggregate and visualize thousands of independent points across space and time (home sales, Wal-Mart store openings, arrests), the dataset presented here has only dozens of points, but includes thousands of streamflow readings (the dataset is over 12,000 rows long).  This “remote sensor” style of dataset is easily accomodated by our templates.  A third type of point dataset tracks assets (shipping containers, police cruisers) whose locations and attributes (speed, weight, etc.) may change over time.  We have developed solutions in the past for this latter type of dataset, and will show off asset tracking in future visualization templates.

Please comment here or contact us if you have any comments or questions.

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

Sacramento’s housing market offers a case study of the nationwide trend toward lower home prices over the last few years. This dataset, acquired by Universal Mind from the Sacramento Bee, includes nearly 10,000 home sales from 2003 through May, 2008. Only sales of homes over 3000 square feet are included.

Launch this visualization

The visualization linked above starts out with a comparison of average house prices between 2006 and 2007. This use case illustrates the mapping of aggregates (like home sale price), which provides added value over simply visualizing the total number of points. The slump is clearly visible in the map rendering, much lighter in most areas on the 2007 map. The trend can also be seen on either map’s time chart — a combined timeline/histogram. After a period of steady price increases, the slump appears to begin around June 2006.

The trend is obviously not the same over the entire Sacramento area; some areas may have slumped sooner, while others maintained price stability over the entire period. The two maps are live linked, so mousing over either map will reveal specific sale price numbers for both maps. Use this to further explore the spatial and temporal variation in the housing slump in Sacramento.

The D.C. construction market provides a good example of a complex geotemporal (location + time) system. As in any city, construction increases and decreases over both time and space. The particular ebbs and flows of construction in the D.C. area, though, are particular to that metropolis. The dataset for this visualization comes from DCStat, a pioneer in open city data, and includes all completed construction projects reported by the District Department of Transportation from 2004 to 2007.

Launch this visualization

A quick view of this dataset in the SpatialKey Map Comparison template shows the construction hotspots across the city and the time chart (a combined timeline/histogram) reveals the linear and seasonal trends over time. The Map Comparison template uses a visualization technique called small multiples (in this case, only two multiples are used). Rather than showing one view, and requiring users to switch back-and-forth between different areas or time periods, you can view and manipulate two maps at the same time. This template is especially useful for visualizing data where specific geographic or temporal comparisons are meaningful. In the D.C. construction dataset, comparing year-to-year reveals a marked decrease from 2006 to 2007 (see screenshot above). Moreover, the trend is not a simple decrease, as some areas decreased more than others, and some neighborhoods actually experienced an increase in construction activity. The biggest decrease took place in the Mount Pleasant neighborhood, while the Georgetown and Palisades neighborhoods actually experienced a growth in construction.

Such trends may or may not be visible in our other visualization templates, but the Map Comparison Template is particularly well suited at revealing them. Another powerful aspect of this template is the live linking of the two views. By mousing over an area of D.C., the same area is highlighted on the other map (if in view), with both maps revealing the specific number of construction sites for that area. Live linking is also demonstrated on the time charts — mousing over a histogram bar in one chart reveals the tooltip on both charts, displaying different values if the maps are showing different locations or scales.

The growth of Wal-Mart provides a particularly compelling use case for the SpatialKey Animation Template.  Though most know of Wal-Mart’s southern provenance (Sam Walton opened the first Wal-Mart in Rogers, Arkansas, in 1962), the chain’s subsequent spread and resulting dominance over American retail is a more complex phenomenon.  Utilizing a dataset of over 3000 Wal-Mart store openings, from 1962 to 2005, our animation template shows the radial spread, increasing density, and overall coverage of the retail giant.

Launch this dataset in the animation template

The dataset used in our animation template comes from an economics paper, “Diffusion of Wal-Mart and Economies of Density” by Thomas J. Holmes, and is freely available here.  As Holmes notes,

Wal-Mart started in a relatively central spot in the country (near Bentonville, Arkansas) and store openings radiated from the inside out. Wal-Mart never jumped to some far off location to later fill in the area in  between. With the exception of store number one at the very beginning, Wal-Mart always placed new stores close to where they already had store density.

This pattern is clearly visible in our cumulative animation - a playback mode in which points accumulate over time on the map.  Switching the animation style to noncumulative allows you to select a decade at a time (or any time range), which can then be played over the timeline.  Highlighting only the store openings in the 1990s reveals a strategy aimed at the Northeast and California, with relatively few openings in the rest of the country.  Though this differs markedly from the early history of Wal-Mart, it is much more in line with the population centers of the United States, and reveals a company no longer rooted in the South.

To demonstrate some of the additional filtering and aggregating capabilities of our geovisualization toolset, the same Wal-Mart dataset can be visualized in our Drill Down template, which allows filtering by map extent, time, type, or list. Filtering by type allows you to show only the stores that have been converted to, or have always been, Supercenters. As noted by Holmes,

With this [Supercenter] format, Wal-Mart added a full-line grocery store alongside the general merchandise of a traditional Wal-Mart. Again, the diffusion of the Supercenter format began at the center and radiated from the inside out.

Thus, the spatial trend of Supercenter distributions appears about a decade behind the trend when all stores are included. Is this because Supercenters are largely made up of converted regular Wal-Marts? Or because shoppers in the South are more amenable to grocery shopping at Wal-Marts than shoppers in the rest of the country, where the presence of Wal-Mart is still somewhat novel? We’re not sure. But visualization is often as much about spurring questions as answering them. This dataset has been mapped before (see here and here), but doing so in a flexible geovisualization environment reveals interesting geographic and temporal patterns, only a few of which have been explored in this post.