Data Visualization: Adding Spatial Components to Data

Buffer Zones

Flood risk classifications
Buffer around a river or below a dam
Evacuation area buffer around an active incident
Chemical spill
Forest fire
Rising water
Hostage situation
Installation of a new gas main
To identify and notify abutters

Straight-line Distance or Radius Search

Distance from an earthquake epicenter
Proximity to an airport

For enforcing noise regulations

Distance from a nuclear power plant
Define audible warning area and planned evacuation zone
Distance to nearest hydrant
To establish insurance rates

Spatial Enabling Methods
There are many possible ways to spatially enable data so it can be visualized geographically. A few examples are provided here.

Combining Existing Geography with Tabular Attributes
Existing geography can be combined with tabular attributes. If there is a table containing statistics, gathered by a physical area, the attributes can be joined to a geographic representation of that area. For example, a tabular database might contain some demographics by ZIP code. Perhaps there are average figures for household income, age, and the number of dependents. A corporation would like to locate a new store near a relatively large number of higher-income households with few dependents. It is not possible to create a map directly from the demographic table to visualize potential store locations.

To solve this, it is possible to start with some existing map data representing ZIP code boundaries. By joining the demographic table to the ZIP boundary polygons, using the ZIP code as a join field, the demographic data can be added to the geography. Then, using mapping software, thematic map layers can be created showing both income levels and numbers of dependents using different colors or shading. Looking at the map, it will be more obvious either where to locate the store, or why it is not easy to place it using the first set of demographics. In the latter case, additional maps may be helpful, built using different statistics. As with traditional decision support systems, sometimes a conclusion is reached from a particular map presentation. Other times, new questions are generated, and the process repeats with requests for additional maps and reports. In this example, for instance, another logical inquiry would be to show where commercially zoned property was located, and which properties are for sale.

When joining tabular data to mapping polygons, be aware of some potential problems. Make sure the join field is defined in the same way on each side, or develop a scheme to cope with the differences. For example, one data set may define a ZIP code as a numeric field with no leading zeros. The other may define it as a character field, with leading zeros, 6 characters wide and a leading or trailing space. Data currency can be an issue as well. In this example using ZIP codes, be aware that the Postal Service makes frequent changes to ZIPS. New codes are often created in high growth areas. Stable regions may have a ZIP code eliminated as a cost-cutting measure. It is possible that some values may not join because they don’t exist in both the geography and demographic data. Expect problems like this when joining on fields that are believed to be similar. Make a plan for handling the exceptions.

Using Existing Geography as Building Blocks
Existing map data can be used to generate new geographic entities. For example, sales territory polygons could be built by aggregating state or county boundary data. The best method for doing this will depend on which mapping software is used, the complexity involved in describing each territory and how large the relevant databases are.

Another example would be to construct a travel route using an existing database of street geography. Individual street and/or highway segments could be selected from the database to form a complete itinerary. Once created, the route could be analyzed to calculate drive time, distance or average speed. A given route could be compared with other possible paths to select the best plan for a particular situation. Again, the preferred method will depend on the software used. Some mapping packages include routing features to assist with finding an optimal path between two points, selected based on parameters such as the shortest distance, shortest travel time or most/least use of highways.

A third example is to generate a list of households to be included in an advertising mailing. Perhaps, due to postal regulations and bulk discounts, the mailing is to be done to all households within a set of carrier routes. It would be necessary to identify which postal carrier routes are to be involved in the mailing. Starting with a base map of carrier route polygons, the polygons can be intersected spatially with the desired area for the total mailing. Then, a carrier route list can be generated for the relevant Post Office(s) involved. A household count per carrier route can be determined, and the mailing pieces can be printed and delivered to the Post Office.