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.

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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 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.