Jaime Neill
March 18th 2007
Part 1 13/14 good job!
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 point data represents a single location in space while the line data is a collection of continuous points strung together. Another point feature would be a specific address or a specific landmark. Line features would be features such as streams.
2.What happens when you use the identify tool? Is the option to change the layer(s) being identified useful?
The identify tool allows you to select a feature and then it will tell you the attributes related to that feature. The option to change layers is useful because if you only want to look at cities than having all of the layers on could bring up both the states and the roads if they are close by the particular city you want to look at.
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?
The type of data determines how it is stored, with numeric data you can do number manipulations and other commands which you cannot do with text. Specifying field width would limit the amount of storage that that attribute would take up since even if all of the spaces aren’t used up the program still has to store blank spaces.
4.What has changed in the table after joining?
The field for weather has been added to the State table.
5.How is the original attribute data from the States layer distinguished from the Weather data that you joined?
It has the file extension before the field category like weather.state and weather.weather rather than just state and weather.
Now have text and numeric data -1/2
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)?
Only the data from the states that were listed in the weather table will be added instead of all 50 states listed in the states data.
-1/2 Because one is spatial data(States) and one is not, the join would not work.
7.Print screen of selected record
8.Print screen of new attribute table
Part 2
- What does the reclassification step in Step 1 accomplish?
It takes the many data sets of classified data and breaks it up using different intervals and a different number of classes. So the 10 classes become 5 classes that have been defined by an ordinal number based on their distance.
- 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?
The map tells you what areas are suitable to build on based on distance from roads and distance from streams. The highest scores represent the areas that are the most suitable for development while the lowest scores represent areas that are not suitable for development.
- What does Step 4 accomplish towards producing the final suitability data layer?
This combines the suitability scores based on distance from major roads and streams with the zones that the office building can be built in, the Office/Institutional and the Mixed Use and Office/Institutional zones. By setting the zones that are not suitable for building to zero and then multiplying the total suitability scores calculated from the road and stream distance, these non-building zones all become zero while the zones that are suitable for building stay as their total suitability score. Adding roads to the map gives the suitability scores a spatial reference.
- 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.
The results showed that there are a few locations that are suitable for development of an office building area. The most suitable areas to build an office building are areas that are zoned for office/institutional and mixed-use office/institutional that are both close to major roads and far from streams. The most suitable areas are along Durham-Chapel Hill Road right before the intersection between I-40, along Pittsboro and Columbia near the Franklin St. intersection, along Estes Dr. west of the Airport Rd. intersection, and southeast of the intersection of Airport Rd. and I-40.
The analysis for finding suitable locations for office building development took into account the distance from roads and streams as well as the zoning codes of the Chapel Hill area. The best areas for office buildings were determined to be both close to the main roads and far from streams. Major roads and streams were mapped and given a score based on their distance from the road or stream. The two data layers were combined so that the final suitability score fell between 0 and 20. The areas that were too close to the streams and too far from the main roads received a score of 0 while the areas that were the closest to the main roads and the furthest from the streams received a score of 20. Then the zoning code data layer was used to determine which of the suitable areas based on distance from the main roads and streams were available to build on. The areas coded as office/institutional and mixed-use office/institutional were given a 1 while the other codes were given a 0. By multiplying the stream/road suitability with the new zoning layer, the suitable zones continued to have the same suitability score as the stream/road layer while unsuitable zones were given a score of 0. Areas with a score of 16, 18, or 20 would be the best areas because these scores show that they must be somewhat suitable for both road distance and stream distance.