Geotemporal visualization: theory + solutions
This blog is written by the team behind the SpatialKey visualization system. For more on the application, please see our Technology Preview or check out the examples in the Gallery page. We'll write here about potential use cases for SpatialKey, as well as issues related to GIS, cartography, crime mapping, and geographic visualization.
28 August 2008
USGS Water Data is a data goldmine that includes over 25,000 stations across the U.S. Each measures variables such as streamflow, water temperature, precipitation, and other hydrological and meteorological properties. Though sub-hourly readings are taken, daily data are available for many stations going all the way back to 1980. And all are available via automated retrieval. With just a bit of hacking, we can run the returned tab-delimited text files through our SpatialKey Data Importer, allowing us to load them into the visualization templates.
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.
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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.
Posted in Uncategorized with 1 comment
6 August 2008
On behalf of the whole SpatialKey team here at Universal Mind, I’m happy to announce a technology preview of our SpatialKey visualization system. SpatialKey includes an advanced mapping and visualization toolset, and is designed specifically for geographic data with a temporal component. Think arrest locations, or data on the spread of a social network, or sales data — any dataset that needs to be searched and analyzed for trends and patterns in order to make decisions.
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
Posted in examples, mapping, visualization tagged examples, mapping, visualization with 1 comment
3 August 2008
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.
Posted in examples, mapping, visualization tagged animation, real estate with no comments
1 August 2008
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.
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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.
Posted in examples, mapping with no comments
1 August 2008
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.
Posted in examples, mapping, solutions tagged animation, examples, wal-mart with 2 comments
