John Dougherty[ES1]
Part 1: Questions for Lab Report
1.Do the points and lines represent the data with the same level of abstraction? Discuss in termsof their representation of the two data layers(cities, roads) that we have added so far, and interms of other types of data that they might represent.
Points and lines represent data with different levels of abstraction. Each is used for a different type of data and represent data in unique ways. While a point may be used to represent data that is not essentially linear (such as a city or building), lines are used to represent items of a linear nature (such as faults, rivers or roads.) By offering different levels of abstraction, points and lines can be used to change data structure to bring out or display desired characteristics of data[ES2].
2. What happens when you use the identify tool? Is the option to change the layer(s) being
identified useful?
The Identify tool allows a user to access attributes of an item while in the data view without having to enter the attribute table. By simply clicking on a particular item, all known data about the item can be accessed. Being able to change the layer being identified makes the identify tool more useful because it can eliminate confusion while clicking and minimize misidentifications.
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?
Perhaps definitions of different types are stored in separate ways. Text and numbers are interpreted in different ways and it’s important that ArcMap knows what type of data it is working with so it knows how to compute the data. Certain functions can be performed on only certain types of data, so the definition makes a difference.[ES3]
Field width provides a measure of how much information the table is required to store about each entry. The field width must be larger enough to accommodate each entry within a set, but making the field width unnecessarily large would require ArcMap to store excessive amounts of unneeded filler.[ES4]
4. What has changed in the table after joining?
An extra column of data has been added to the table and another set of attributes is now joined to the items.
5. How is the original attribute data from the States layer distinguished from the Weather data thatyou joined?
The new Weather data is placed at the far right of the States layer attribute table. The Weather data table is displayed within the table of context, however because it lacks a spatial nature it is not shown in the Data View
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)?
The data would join on the basis of State name, but would only join for the 11 states for which I entered Weather data. Also, the join would not be visually useful as the Weather data lacks a spatial [ES5]aspect.
7. Print screen of selected record
8. Print screen of new attribute table
Part 2: Questions for Lab Report
9. What does the reclassification step in Step 1 accomplish?
The reclassification creates a more understandable basis of comparison between distances from major roads. It creates distinct distance levels and then assigns suitability values to those distance intervals.[ES6]
10. Please include a JPEG of roadscore (end of Step 1). This should be a completed map (i.e.
ready for display), exported into your student folder, and inserted as a picture into lab report.
[ES7][ES8][ES9]
11. Please include a JPEG of hydroscore (end of Step 2). This should be a completed map (i.e.
ready for display), exported into your student folder, and inserted as a picture into lab report.
[ES10]
12. 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 at the end of Step 3 provides an evaluation of suitability based on road and hydrology location. This map does not account for zoning codes. The highest scores represent the most suitable areas for development; the lowest scores represent the least suitable.
13. What does Step 4 accomplish towards producing the final suitability data layer?
Step 4 improves upon the map created in Step 3 by taking into account the zoning codes of the area. Because the office building can be constructed only in areas coded for office/institutional (OI) or Mixed Use & Office/ Institutional (MU-OI), those are the only areas we needed to be concerned. Regardless of road/hydrology location, other areas are not suitable on the basis of coding.
14. Please include a JPEG of final suitability layer (end of Step 4). This should be a completed map. (i.e. ready for display), exported into your student folder, and inserted as a picture into lab
report.
[ES11][ES12][ES13]
15. 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 thinkwould be the best choices for her office project
After performing a GIS suitability survey taking into account proximity to transportation and hydrology and zoning codes, I’ve prepared the attached Final Suitability Survey Map. The map features a color-coded display of the areas best suited for development. Using the legend as a reference of the color scheme, one can decipher the most suitable areas.
This analysis was performed by first creating a distance measurement from each area on the map from major roads. The measures were then reclassified and evaluated to create an objective and empirical basis of comparison between different distances. The areas closest to major roads were weighted more highly and given a greater suitability value as they provide greater transportation flexibility.
A similar analysis was performed in terms of proximity to hydrology. The only major difference between the transportation and hydrology surveys is that the values were weighted inversely. Whereas proximity to roads was assigned a higher value, areas farther from water received a higher value in the hydrology survey.
Finally, an analysis was performed to account for Chapel Hill zoning codes. By eliminating all areas which are neither office/institutional (OI) nor Mixed Use & Office/ Institutional (MU-OI), the final scores include only areas which are suitably coded for development.
As the map demonstrates, the areas best suited for development are located 1. in Eastern Chapel Hill directly south of NC-54, 2. to the Northwest of the Airport Road/Estes Road intersection and 3. near the intersection of Franklin Street and I-40 in Northeastern Chapel Hill and 4. Along the intersection of I-40 and Estes Road.
[ES1]46.5/50
[ES2]Yes, it’s important to note that choosing among point, line, or polygon depends on what attributes you are trying to present of a feature.
[ES3]Yes, it’s important to differentiate between data types, because you may need to do calculations, and these can only be done on numeric data types and not text data for example.
[ES4]Yes, field width must be specified in order to set a proper space for the data’s attributes.
[ES5]Yes! The join would not work because of this fact!
[ES6]Yes, this makes the classes more appropriate for this study.
[ES7]Need data source. (-0.5)
[ES8]Need to clear selected major roads, because they cover up the areas scored with a suitability of 10. (-0.5)
[ES9]Need to include roads on legend since they are included in the final layout. (-0.5)
[ES10]Need data source. (-0.5)
[ES11]Need data source. (-0.5)
[ES12]Need to include roads in legend. (-0.5)
[ES13]Layout slightly harsh visually speaking. Should choose a lighter background color than blue for the areas scored 0, and the roads should be darked, because it’s aesthetically unappealing to search for the light pink roads in the dark, bright blue. (-0.5)