Matthew Whitehead 711490109

Geography 370 Lab

Lab 4

Part 1

1.Do the points and lines represent the data with the same level of abstraction? Discuss in terms of their representation of the two data layers(cities, roads) that we have added so far, and in terms of other types of data that they might represent.

The points and lines represent the same level of abstraction. The two data layers we have added so far have been points (cities) and lines (roads). Without knowing what each layer was showing someone could assume that the lines represented rivers, migration routes, etc. and the point layer could be locations of universities, habitat locations, etc..

2.What happens when you use the identify tool? Is the option to change the layer(s) being identified useful?

The identify term allows you to select an attribute of a certain layer and obtain information on that attribute. For example on the map in “identified” a point and found out that it was Asheville. Changing the layers being identified is useful because it allows you to focus on a certain layer and you don’t need to worry about clicking the wrong attribute. It works really well when the map is cluttered with a lot of layers. You are also able to select multiple layers and find out information on intersecting attributes.

3.Why do you think the Field Definition requires that you differentiate between text and numeric data types? Why do you need to specify the field width?

Field Definition requires that you differentiate between the types of data being used so algebra can be applied to fields. It’s a way of standardizing fields to allow joining and other functions. The field width specifies the number of characters or numbers that can be entered into a specific field.

4.What has changed in the table after joining?

The table added new fields and added the information I typed in the weather field.

5.How is the original attribute data from the States layer distinguished from the Weather data that you joined?

The newly added fields are distinguished from the original fields because the new fields from the weather data start with “weather.” instead of “states.”.

6.What would happen if you tried to join the attributes from the States layer to the Weather data (rather than joining the Weather data to the States data as you just did)?

If I joined the States layer to the Weather attribute table the new table would have all the information that the weather table and state layers have but it would only appear for the 11 states with weather data.

7.Print screen of selected record. 8. Print screen of new attribute table

Part 2.

  1. What does the reclassification step in Step 1 accomplish?

The reclassification in step 1 allows you to create different regions based on the distance from major roads. You are able to set the specific perimeters (scores) to view the data in a specific way to make for easy analyzing.

  1. At the end of Step 3, what does the map tell you in terms of the developer’s office building project? What do the highest scores represent? What do the lowest scores represent?

At the end of Step 3 the combined scores of the distance from major roads and hydrology. This shows the best locations for the office building projects. High scores indicate areas near major roads that are away from streams. Low scores indicate far distance from major roads and close proximity to streams.

  1. What does Step 4 accomplish towards producing the final suitability data layer?

The end of step 4 leaves us with a suitability map that includes the zoning restrictions in the area. This is the final perimeter to help decide where the office project should take place. This step eliminates the areas that are zoned in a way that does not allow development.

  1. Prepare a brief executive summary (~2 paragraphs) to the developer, summarizing your results. Include a short description of the analysis you performed and indicate the locations you think would be the best choices for her office project.

To determine the suitable locations for the office project I considered proximity to major roads, proximity to streams, and the zoning restrictions for the area. I gave values to areas depending on their distance to major roads with high values being close to roads and low values being far from roads. I also gave values to areas depending on their distance from streams with high values being far from streams and low values being near streams. I added these to values up to find the best location for the office project. I excluded the areas that had zoning restrictions that would not allow the development of an office project.

Through this analysis I was able to find a few areas that would be prime for office development. Zoning was the man factor when determining a site for the office project. There is a small district to the west and small district to the south east that have the best suitability for building the office project.