GEOG 483  Lesson 1

Project 1

Problem:  Perform a site selection using spatial and attribute queries for an ice cream shop that meets the following criteria:

Business Criteria:

•     Greater than 500 farms for milk production,

•     A labor pool of at least 25,000 individuals between the ages of 18 and 64 years of age, and

•     An interstate highway within 20 miles of the city.

Quality of Life Criteria:

•     A low crime index (less than or equal to 0.02,

•     At least one recreation area within 10 miles,

•     A population of less than 150 persons per square mile, and

•     A location near a university.

Using the pre-defined data downloaded for this specific lesson, the following cities meet the criteria:

• Driggs

• Nittanytown

• Geyserville

• Whitney

Results:

The following (Figure 1) is a screen capture of the resultant site selection map.  I did find that by right clicking the cities resulting from the various queries, I could label the cities.  Additionally, rather than do a screen print, I can export the resulting map (Figure 2) to a .jpg file for direct insertion into a web page.  The resulting map for the spatial and attribute queries is shown below the following screen print.

Figure 1:  Spatial and Attribute Analysis Screen Print

Figure 2:  Spatial and Attribute Analysis Exported Map Image

Try This

In the "Try This" exercise, we were asked to narrow down the selection criteria to only consider sites that were within 10 miles of a lake or river, and to be within 40 miles of a landmark.  The hydrology layer included features above and beyond lakes and rivers, so I needed to limit the records considered in the hydrology dataset to only those that equaled "lakes" or "rivers".  Using these additional criteria, only the mythical town of Driggs met the requirements for the establishment of the ice cream store.  Figure 3 is the screen print from this additional spatial and attribute analysis and Figure 4 is the exported map showing the achieved result.

Figure 3:  Lesson 1 Try This Screen Print

Figure 4:  Lesson 1 Try This Map Export

Additional Thoughts

As I worked through these exercises, it struck me that developing the attribute table for the various layers might be the relatively easier task.  So far, we have been presented with pre-defined shape files, i.e. data that has already been collected, digitized and even geo-referenced.  In the overall scheme of things, it would seem that the analysis can be completed fairly quickly, but that the actual data collection can be both time consuming and extremely expensive which if not performed correctly can result in a hugely expensive, if not embarrassing, mistake.