Emily Snow

3/19/8

(11.6/14)

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 decision to use points versus lines depends on the scale that one has chosen to use. In a very fine scale map, roads should not be represented as lines but as polygons. In a very coarse scale map, roads should be represented as lines, otherwise the map would get overcrowded and would not represent its features as well as possible. A city may be either a point or a polygon depending on scale as well. Fine scale maps need polygons, where as coarse scale maps need points. Rivers, for example, may be represented as either lines or polygons, depending on the scale as well. Lines are good for coarse scale maps, but rivers actually vary in width, which cannot be reflected in a simple line. In a coarse scale map, a river would be represented as a polygon, widening and narrowing. Buildings are a good example of points versus polygons as well. Buildings vary in size and shape, which is not represented with points on a coarse scale map, but they can be represented in a fine scale map with polygons.

I got your points, and I think your description also makes sense. However, the choice of line or polygon does not only depend on the map scale but also the feature characteristics that you want to present. For example, if you just want to present the location of the city, just point type will fine. However, if you want to describe what exist inside the city, you definitely choose polygon for city boundary even though the map scale is coarse.

(-0.2)

Point: one or a series of points; example: cities, building

Line: collection of points; example: highway, streams

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

When you use the identify tool, you are able to select certain features to view their attributes. The option to change the layers being identified makes it easier to select certain features from the particular layer you want to view when there are many features of different layers all bunched up together in one space.

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 text and numeric data types because numeric data types are able to be used for calculations. You need to specify the field width in order to conserve memory. The more characters you use, the more memory you take up. The less characters you use, the less memory you take up. These files are big enough as they are without making them any larger than necessary.(-0.2) We need to specify the field width in order to set a proper space for the data. If the width is set too short, the entirety of a data (e.g. state name, or distance) may not be included.

4. What has changed in the table after joining?

The table has the new values that I put in for weather. There are a lot of null values because I didn’t put the weather information, but for those states that I did supply general weather information, it was now placed with the respective states that I initially identified.

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

The Weather data that I just joined is different because it is labeled differently in the field names (with weather.States and weather.Weather), and the fields themselves are located at the end of the table. They were also very easy to find, because of the null values that I discussed earlier.

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)?

Personally, I don’t think it would work. I only specified 11 states, and there would be plenty of others left that wouldn’t have a place to go. The process of joining would also be backwards, because I joined by state name. If I deleted the fields of the other states in the larger database, it may work with tweaking.

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

(Q1) What does the reclassification step in Step 1 accomplish?

The reclassification step allows the user to change the subdivisions and select representative numbers for the values mapped for road scores.

(Q2) 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?

This map tells me which areas are both close to road and far from streams as well as far from roads and close to streams. It assigns values by proximity to roads and streams. The highest numbers are for being close to roads and far from streams. The lowest numbers are for being far from roads and close to streams. Ideally, one would build in areas with a 15 or close to 15 for a score and steer far from areas with a 3 or close to 3 for a score.

(Q3) What does Step 4 accomplish towards producing the final suitability layer?

Step 4 allows the combination of road suitability and distance from potential flood areas to be combined with zoning, which pretty much shows where there could be land to build on. Once again, the highest values represent the most desirable place to build, and the lowest values represent the least desirable place to build.

(Q4) 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.

There are several areas that may be suitable for building. One area is Estes west of Airport, which has a very high suitability score. It is particularly away from water which would make insurance rates very low. There is also Franklin, but it is already very packed and may be difficult to find a place to build. There are two roads that goes south side by side until the merge: Columbia, which is longer and crosses Cameron, and Pittboro. These areas also have very high suitability scores. The location of the highest suitability score is near the intersection of Dobbins and Sage in northeastern Chapel Hill. Anywhere along Dobbins and Durham-Chapel Hill in this area could be a very good place for building. It is rather distant from bodies of water and would have low insurance rates.

The worst areas to build would be in central northern Chapel Hill. These areas have the worst suitability scores and are surrounded by water. There are many roads in this area, but the insurance would be very high. Northwest Chapel Hill also has very low suitability scores, and eastern Chapel Hill and southeastern Chapel Hill also have relatively low suitability scores. Southern Chapel Hill is rather mediocre, and there are not many roads, which means that the areas is not very well-traveled. Staying close to main roads could be a good indication of where to build. Central, western, and northeastern Chapel Hill seem to have the best suitability scores.

Maps: JPEG of roadscore (end of Step 1), JPEG of hydroscore (end of Step 2), JPEG of final suitability layer (end of Step 4). These should be completed maps (i.e. ready for display) and saved in your student folders. I’ve also decided to include print screens of the three maps here as follows:

I do not know why your final suitability map looks different from mine.. (-2.0)