Andrew Lakin[ES1]

Lab 4 Answers

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

In general, the points and lines represent the data with the same level of abstraction. The dots representing cities are relatively proportional to the interstate system of roads mapped on the same data frame.[ES2]

  1. What happens when you use the identify tool? Is the option to change the layer(s) being identified useful?
    When clicking the map using the identify tool, a table would pop up with the cities and roads found closest to the point that was clicked. IT was useful to have an option to change the layers being identified because often times there are cities and roads very close together, and narrowing the search is useful.
  2. 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 field definition requires you to differentiate between text and numeric data types because this dictates how the attributes table will be created[ES3]. Defining the width of the field ensures the data being entered will fit into the spaces provided.

  1. What has changed in the table after joining?

There is now a column entitled “Weather” where the newly created data can be found for the 10 states.

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

The original attribute data from the states layer had a lot of data associated with all 50 states, whereas the data table that I created only had weather conditions for 10 states.

  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 I were to join the attributes from the States layer to the weather data, only data associated for the 10 states with weather data would appear. The others would not join successfully.[ES4]

  1. Print screen of selected record.

  1. Print screen of new attribute table.

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

Reclassifying the data ranges in step one creates a new ranking system which suits the purpose of the map better. For instance, now a location 0-60 meters from a major road would be the most suitable (with a score of 10).

  1. Please include a JPEG of roadscore (end of Step 1).

[ES5][ES6]

  1. JPEG of Hydro Score

[ES7][ES8][ES9]

  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 lowers scores represent?

In this case, the lower scores mean these locations are the least suitable to meet the developer’s needs, while the higher scores depict locations that are most suitable to the developer’s needs.[ES10]

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

This step helped narrow down the final location options for the developer’s building. Following Chapel Hill zoning ordinances, the building could only be placed in mixed use and or office/institutional zones.

  1. Please include a JPEG of final suitability layer

[ES11][ES12][ES13][ES14]

  1. Prepare a brief executive summary 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 whom it may concern,

Attached is a final analysis of the proposed development of an office building in Chapel Hill, North Carolina. The goal of the analysis was to find the most appropriate location for the building by adhering to the client’s preferences:

-Locations near major roads are desirable for transportation purposes

-Locations near streams are undesirable[ES15]

-The building must be located in the mixed use or industrial/office zones only

First, distances to major roads was analyzed. A map was created to depict how far any given location is from a major road. Suitability scores were assigned to different colors (1 being the least suitable, 10 being the most suitable). Next, a similar analysis was conducted in regards to location to nearby bodies of water. A map was created with assigned suitability scores in a similar fashion to the proximity to roads map.[ES16]

Finally, the zoning ordinance constraints were considered, and a final map was created that depicted the most suitable location options when all factors had been considered. This map also had associated suitability scores, with areas in red being the most ideal locations for the new building.[ES17]

Thank you for your business,

Andrew Lakin

[ES1]34.5/50

[ES2]I think I understand what you're trying to say here, but it’s important to note that the appropriate representation of some feature may be dependent on scale, and it’s even more important to note that that choosing among point, line, or polygon depends on what attributes you are trying to present of a feature. (-2)

[ES3]Not quite, 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. (-1)

[ES4]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)

[ES5]Need to clear selected major roads, because the selection covers the areas of high suitability, and roads needs to be added to legend, because it’s included in the layout. (-1)

[ES6]Need name, date, and data source. (-1.5)

[ES7]Need to include name, date, and data source. (-1.5)

[ES8]Scale bar text running together. (-0.5)

[ES9]There appears to be another layer in the background that should be deselected before making layout. (-0.5)

[ES10]More specifically, the higher scores are close to roads and far from streams, and the lower scores are far from roads and close to streams. (-1)

[ES11]Need to clear selected major roads, because the selection covers the areas of high suitability, and roads needs to be added to legend, because it’s included in the layout. (-1)

[ES12]Need name, date, and data source. (-1.5)

[ES13]Need to include road names and/or landmarks for orientation. (-0.5)

[ES14]I suggest flipping the values so that the highest suitability values are at the top of the legend and vice versa.

[ES15]Why?

[ES16]How was scoring handled for roads v. streams? (-0.5)

[ES17]Please name and describe the locations you would recommend for this project. (-2)