Kelly Anderson[ES1]

March 25, 2010

GEOG 370

Lab #4: Spatial Analysis

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 they might represent?

The points and lines do not represent the data with the same level of abstraction or in other terms, points and lines don’t have the same level of complexity. Points are able to represent data such as cities or specific locations from a distance (post offices in a city, water fountains on a campus, etc…) whereas lines represent a path or flow, such as a road or a river. It takes multiple (or at least two) points to make a line, therefore increasing the level of abstraction from point data to linear data. The level of abstraction would increase from line data with regards to polygon data. [ES2]

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

The identify tool can be used to access stored information about various layers of data, making it simple to learn something about a specific location on a map. It shows the attributes of the data at that location and can be accessed by feature and by layer in the Identify window. For example, in this exercise, when you use the Identify tool to click on the state of California, you get its area, name, population, etc…

I find that the option to change the layers being identified very useful. [ES3]This way, you can be sure that when you click on a river in the state of California, you get all the attributes of that river, instead of the state.

  1. Why do youthink the Field Definition requires that you differentiate between text and numeric data types?[ES4]

Field Definition requires you to differentiate between the text and numeric types because there are seven different field types: four for numbers (short integer, long integer, double, and float), and three for text, large image or geometry data (text, date, and BLOB). Also, one should take into account that there are different functions that can be applied to text and numeric data types separately. For example, you can make a new column in the attribute table of a numeric value divided by two. However, you cannot and don’t need to do this with a textual description.

  1. What has changed in the table after joining?

After joining the data, new columns were added to the States attribute table, including another column for State name (null except for the ones I added) and then a column describing the weather for the states that I added.

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

The weather data has null values for the states that I didn’t add to the table. In addition, it is added onto the end of the table. Thus, by scrolling to the right side of the table, I can see the data that has most recently been added.

  1. 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 you joined the data from the states layers to the weather data, you would only get some of the information from the states table (only the information for the states that you defined the weather for). [ES5]

  1. Print screen of selected record.

  1. Print screen of new attribute table. (I scrolled over to the part of the attribute table where one can see the new weather column on the right hand side.)

Part 2:

  1. What does the reclassification in Step 1 accomplish?

The reclassification in step one allows us to define how many classes we want for our road score map (in this case 5), and to determine those classes based on the distance these locations are from the major roads. We can change the values so instead of getting a distance (0-60), we just get a score of how appropriate that distance is for what we are analyzing (10).

  1. 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 your lab report.[ES6][ES7][ES8]
  2. 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 your lab report.

[ES9][ES10][ES11]

  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, we have combined the road scores and hydro scores to create a total suitability score based solely on these two criteria (we have not added potential zones yet). The highest scores represent the locations that are most desirable (i.e. the places closest to major roads and farthest away from streams) and the lower scores represent the areas that are least desirable (those that are far from big roads and close to streams). It is important to note that this is how the scale was designed (the scale numbers on the road map get larger as you get closer to bigger roads, but the scale numbers on the hydro map get larger as you get further from streams).[ES12]

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

Step 4 allows us to see all of the zones that are approved for “office/institutional” uses and “mixed-use and office/institutional” uses. All other areas are automatically given a suitability score of 0 (purple). This adds into effect the final criteria for our client developer whom we are selecting the site for. Then, from looking at those layers, you can deduce how far the viable sites each are from major roads, waterways, etc…

  1. 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 your lab report.

[ES13][ES14][ES15]

  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 choice for her office project.

A spatial analysis of the Chapel Hill area was conducted in order to locate the most desirable locations for the construction of a large office building. We took into account the positive benefits of close proximity to major roads for transportation purposes, as well as the negative affects that might result from locations near streams, as these are more subject to flood risks and high insurance costs. In addition, the building can only be constructed with the Office/Institutional (O/I) or Mixed-Use and Office/Institutional (MU-OI) zoning districts. In order to take into account all of these factors, we were able to locate all the major roads in the area and rank the land sections around them based on their proximity to the roads. Oppositely, we were able to do the same thing for the Chapel Hill waterways, but we ranked locations based on how far away from the water they were. By combining these two data sets, we were able to find the locations that were ranked the highest; i.e. those that were the closest to major roads and farthest from waterways. Lastly, we added a third data layer to the mix and gave a ranking of 0 to all of the zoning parcels that weren’t located in our proposed zoning districts.

From these analyses, we were able to produce the above map. There are multiple prime locations for the proposed office building, but I would like to recommend the area near the intersection of Estes Drive and Highway 86 (ranked 16-18), the area on the northern part of Franklin Street (ranked 16-18), or the section of land on the northern part of the map near Highway 86 (ranked 16-18). All three of these land parcels are extremely close to major roads in the area, but are a considerable distance from Chapel Hill’s waterways. I, personally, believe that the first land parcel that should be looked at is the one near the intersection of Estes Drive and Highway 86, as this is a large land parcel with plenty of land around it ranked a 12 or a 14 as well. One might be able to find more development options in this large area.

[ES1]42/50

[ES2]It’s important to note that choosing among point, line, or polygon depends on what attributes you are trying to present of a feature. (-0.5)

[ES3]Me too.

[ES4]Missing second part of question: “Why do you need to specify field width?” Field width must be specified in order to set a proper space for the data’s attributes. (-2)

[ES5]The root of the problem is that although the states layer is spatial, the weather layer is not, so the join would not work. (-1)

[ES6]Source listed is the working folder for this lab, which is not where you acquired the data. The source would be more appropriately be cited as J:\isis\html\courses\2010spring\geog\370\006\data\lab4 or M:\data\lab4. (-0.5)

[ES7]Name missing from layout. (-0.5)

[ES8]Missing roads in legend. (-0.5)

[ES9]Name missing from layout. (-0.5)

[ES10]Scale text running together (-0.5)

[ES11]Source listed is the working folder for this lab, which is not where you acquired the data. The source would be more appropriately be cited as J:\isis\html\courses\2010spring\geog\370\006\data\lab4 or M:\data\lab4. (-0.5)

[ES12]Well said.

[ES13]Missing roads in legend. (-0.5)

[ES14]Name missing from layout. (-0.5)

[ES15]Source listed is the working folder for this lab, which is not where you acquired the data. The source would be more appropriately be cited as J:\isis\html\courses\2010spring\geog\370\006\data\lab4 or M:\data\lab4. (-0.5)