Developing a Community Socio-Economic Resilience Profile

Allison Innman, Lindsey Langsdon, Hal Robinson and Greg Easson
The University of Mississippi Geoinformatics Center

Goals

Our task has been to provide a coherent conceptual and operational framework for creating a community socio-economic resilience profile (SERP) through GIS so it can be used to develop tools that may allow local citizens and planners to assess and enhance their community’s resilience.

GIS Methods

Three counties in south Mississippi (Hancock, Harrison, and Jackson) were used for the development of the SERP because they had been affected during Hurricane Katrina. The calculations of the resilience score for these areas could be compared to how they fared after the hurricane. The resilience score was determined by analyzing critical private services, major employers, and government and social services based on their number per half-mile square.

ArcGIS Desktop was utilized for the creation of map outputs to represent the resilience index. The first step in creating a geospatial output for the resilience index involved the creation of a “fishnet.” A fishnet is a polygon composed of a grid of half-mile by half-mile (user-defined) squares which cover the entire study area (Figure 1). The primary reason for using a fishnet grid was to coarsen any data under investigation within the study area. Dividing the study area with a fishnet also allows for the data to be represented uniformly. The fishnet is also beneficial in that it creates a more consistent method of measurement within a particular geographic space. The “fishnet tool,” found in ArcGIS Desktop, was utilized in order to automate the creation of the grid.

The second step was to obtain business data to be investigated within the fishnet grid. The business data that was utilized in this project was obtained from the US Data Corporation for a fee. The data was comprised of 17,237 business entries. These entries included company name, trade name, address, city, state, zip code, SIC code, SIC description, SIC primary, salary codes, employee codes, sales volume, employee size, and year started. Once the data was geocoded there were a total of 6% (1094 entries) that were unmatched and were either manually located or removed. A total of 187 businesses with P.O. Box addresses were removed from the dataset, leaving a total of 17,050 businesses for data analysis.

The particular attributes that were analyzed within the business data included: SIC codes, description of SIC codes, number of employees, and addresses of each business location. Some errors existed within the dataset, such as: wrong or misidentified addresses, missing addresses, misspelled addresses, and incorrect or unlikely number of employees at a particular address. Data was omitted in these cases if the problem could not be resolved.

Once the business data was obtained it had to be geocoded. Geocoding is the process of turning a table (Excel spreadsheet) of addresses into geographically referenced points which can be displayed, combined, and analyzed with other geographically referenced data. The first step in geocoding the business data was to obtain street data from the TeleAtlas 2008 data. In order to be able to geocode, the street data must have specific address components like street number, street name, street type, street direction, and postal code. All features with P.O. boxes or missing addresses were removed. An address locator was created from the street data using ArcCatalog. The data was then geocoded using the address locator. Each unmatched address was then examined and either placed by hand or referenced using online maps.

The next step of the project was to determine which types of businesses were essential to economic resilience. Once these types of businesses were determined, they were classified and broken down into separate shapefiles based on their SIC code. In order to accomplish this task, the geocoded data was incorporated into an ArcMap document. Each subcomponent (or business) within each index component was separated out by using the “search by attribute” tool and the associated SIC codes. Features were selected with the desired SIC codes and exported to new shapefiles so that each subcomponent was represented by a separate shapefile.

Once the shapefile for each subcomponent of the indexes (one through three) were created they could then be analyzed by use of a buffer in conjunction with the half-mile by half-mile fishnet grids. A buffer is a geoprocessing tool which creates a polygon whose boundary is a specified distance from the boundaries of another feature. For example, a three-mile buffer around a point feature creates a circle with a three-mile radius around its center (location of the original point feature). Combined with other geoprocessing techniques, buffers can be used to determine, for example, the number of features within a specified distance of a certain point (Figure 1). For this project, five one-mile concentric buffer rings were used in order to count the number of businesses within the allotted distances (1 - 5 miles) for each half-mile by half-mile grid cell. Over 12,000 cells of the fishnet were analyzed by use of the buffer.

Figure 1: Fishnet polygon and 5-mile buffer ring example.

Figure 1: Fishnet polygon and 5-mile buffer ring example.

Because individual cell-by-cell analysis would be very time consuming, this process was automated by using a Python script. The Python script automated the process of using buffers to determine counts for each of the fishnet cells. For example, if we wanted to know how many grocery stores were within five miles of each fishnet cell, we could specify the shapefile for grocery stores, and a five-mile buffer. This would add a field (column) to the fishnet which notes the number of grocery stores within five miles of each cell. We ran the script for each subcomponent with one-, two-, three-, four-, and five-mile buffers.

After the subcomponent values for each half-mile cell were determined using the Python script, the resilience score was calculated for each subcomponent by putting equations into the field calculator. Figure 2 shows the composite resilience score for the study area. Note the higher score near Gulfport, MS and the lower score near Waveland, MS. When Hurricane Katrina hit the Gulf Coast, Waveland had a more difficult time recovering compared to the Gulfport area. The resilience score for this area may reflect that.

Figure 2: Composite resilience score for Hancock, Harrison, and Jackson Counties, Mississippi.

Figure 2: Composite resilience score for Hancock, Harrison, and Jackson Counties, Mississippi.

Ultimate Use

The SERP research is significant in its potential for the development a national resilience strategy. This resilience strategy would develop a planning model for local planners to assess resilience by community sector.

Collaborators

University of Mississippi Department of Economics
University of Mississippi Department of Political Science
Oak Ridge National Laboratory



Contact Information

For more information, please contact Allison Innman.
E-mail: asinnman@olemiss.edu
Phone: +1 (662) 915-6598
umgc@olemiss.edu