Spatial Analysis: Map overlay and analysis

1- Download required datasets from Here.

2- Open ArcMap, and add the 4 raster layers provided: clouds, elevation, insolation and popdense.

3- Open the Properties -> Symbology window for the Insolation layer. Choose "Classified" in the Show column and change to a ramped color scheme (e.g., shades of red) instead of random colors.

4- Now change the display for the Elevation layer. Change from "Stretched" classification to "Classified" classification. Change the number of class breaks to 10. Select an appropriate color scheme for elevation.

5- Next, change your Clouds layer to show as "Classified" and set it to an appropriate color ramp (e.g., blue).

6- Open the Search Slider, Type 'Raster Calculator' at the search box, select the first item. This will open the Raster calculator GUI.

7- In the layers listing in the Raster Calculator window, double-click on Popdense, then click on the ">" button to the right. Next, type in 50 in the text box after the > sign. Specify the output and click OK.

*- In your new layer, a Value of 0 indicates that in that grid cell the population density is less than 50, and a Value of 1 indicates that the population density is greater than 50. The 0, 1 format is referred to as a "binary map;" with regard to the query expression, every grid cell is either true or false.

8-Now, using the Boolean function AND along with appropriate parentheses, write and evaluate queries to create the following new grids:

Grid 1

Grid 2:

Grid 3:

*- Question 1: Which of these 4 factors do you think is most important in determining potential solar power generation rates? Why?

*- Question 2: Which of the 3 outputs (cloud 60, cloud 70, or cloud 75) do you think is the most appropriate one to use in our analysis? Why?

 

Note- Now that you have layers with the potential areas for solar power generation, you also need to figure out which major cities could potentially benefit from solar power. Using buffers, we will determine which major cities are within 50 miles of potential power generation regions. Buffering calculates distances from spatial objects, and then produces polygons that represent the area surrounding the objects out to that distance.

9- Begin by adding the world cities layer (cities.shp) to the ArcMap display. Use Select by Attributes from the Selection menu to find all of the cities with population greater than 1 million people.

10- Export this selection into a new shapefile using  Data -> Export Data . Use cities_selection.shp or another similarly descriptive name. Add this new file to your layout. Before moving on from here, choose Clear Selected Features from the Selection menu to avoid potential problems later.

11- Now create a 50-mile buffer around the cities_selection shapefile. You will use the Buffer tool located in ArcToolbox -> Analysis -> Proximity to buffer the selected cities. Make sure that your units are correct when filling in the Distance setting.

*- Next we will need to find out which of these buffers intersect with the solar power production regions. Before doing the intersection, however, we must decide which of the power region rasters to use in our analysis. Based on your answer for Question 2 above, decide which of the output rasters (Grid, Grid2, Grid3) we should use for our project.

12- Open 'Raster to Polygon' tool to convert your chosen raster layer into a vector layer. Do not generalize the lines of your new dataset. Name the output file "solar_vector".

13- Classify the symbology on the solar_vector layer to show the areas of potential use (GRIDCODE = 1).

14- Now that the datasets are both vector layers, we can use the Selection -> Select By Location function to determine which buffers fall within the potential solar power locations. First, however, since you want to find which buffers intersect with areas of potential use only, you must make sure that these areas are selected beforehand. Use Select by Attributes to select the regions in solar_vector that have a value of GRIDCODE = 1. Next, use Select by Location to select from the buffers.

15- If you open the table of your selected cities, you can create summary statistics to learn more about the results. Open the attribute table for your selected cities. Make the field Country active in your table. Right-click on the field header and select Summarize. The Summarize window will pop up. Select Population -> Sum as the summary statistics to be included in the output table. Make sure that you are only summarizing the selected records. Specify the location and name for your output table. Click OK.

16- Now open the summary table and examine the results. The "Count" column shows the total number of cities in the given country that meet your criteria. If you wish to sort any column for easier reviewing, right-click on its heading and choose Sort Ascending or Sort Descending.

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Data Sources

Insolation data source: Derived from a figure in Geosystems (Robert Christopherson)
Units: Watts per square meter

Popden data source: NCGIA
Units: persons per square kilometer
More information: http://www.ciesin.org/datasets/gpw/globldem.doc.html

Elevation data source: Data set GTOPO30, United States Geological Survey, EROS Data Center
Units: meters
More information: http://edcdaac.usgs.gov/gtopo30/gtopo30.html

Clouds data source: UNEP GRID
Units: percent cloud cover (defined as the actual number of bright sunshine hours over the potential number)
More information: http://www.grid.unep.ch/data/grid/gnv13.html