lab 8: raster spatial analysis - sonoma state … 387 – fall 2011 lab 8 raster spatial analysis 1...

Geography 387 – Fall 2011 Lab 8 Raster Spatial Analysis

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Lab 8: Raster Spatial Analysis

1.0 Overview

In this laboratory exercise we will examine a spatial analysis problem and develop a GIS solution using the raster analytical capabilities of ArcGIS.

2.0 Introduction

The Scenario You have been hired in your first job as a GIS consultant. An executive from an energy supplier has presented you with an interesting problem. Her company would like to expand into the solar power market, but the company is not sure of which global regions to focus its efforts. The executive also hopes to introduce her company to GIS technology and its potential uses. She has been asked to present her boss with a ranking of global regions based on their potential for solar power generation. The executive has asked you to provide her with information to help in ranking potential new markets for solar power. Basic Information This work will involve some raster map algebra analysis. You will conduct a focal analysis across several raster layers with ArcMap's Raster Calculator tool. This is a very powerful and versatile tool, but for the purposes of your work, we are going to keep the analysis relatively simple. All of the data for this contract will be provided to you (although this is rarely the case in real-world consulting jobs). Each of the data layers used in this exercise were downloaded, re-projected to GCS, WGS84 datum and converted to ESRI’s raster dataset format within a file geodatabase. Data sources are given below. The layers used are those thought to relate to mapping the potential supply and demand for solar-generated power. On the supply side, layers include maps of estimated incoming solar radiation, the expected average cloud cover, and global elevation. On the demand side, a global population layer is provided to buffer the solution set and limit it to those areas located close to major global population centers. Your client has decided that solar power production should be a) close to populated areas, b) in low elevation areas, c) in relatively sunny areas, and d) near the equator so that solar intensity will be strong enough to efficiently produce electricity.


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Data Sources Insolation: Derived from a figure in Geosystems (Robert Christopherson) Units: Watts per square meter Population: Population density from NCGIA Units: persons per square kilometer More information: http://www.ciesin.org/datasets/gpw/globldem.doc.html Elevation: Data set GTOPO30, United States Geological Survey, EROS Data Center. This is a global digital elevation model (DEM) with a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). Units: meters More information: http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_info Cloudless: UNEP-GRID Units: percent cloudless, defined as the actual number of bright sunshine hours over the potential number. This means that a pixel with a lot of sunny days has a relatively HIGH percentage. More information: http://gcmd.nasa.gov

3.0 Add and display data

Download the datasets from the geog387_lab8.zip archive to the local drive or flash drive, and extract the lab8.gdb file geodatabase. Next, open ArcMap, and under Map Properties, set the default geodatabase to lab8.gdb and check the option to store relative paths. Save your map document.

There are four geodatabase raster datasets (i.e., layers, icon) provided: cloudless, elevation, insolation and population. Load all of these raster datasets into the TOC, like you would do for a feature class. Now we will change the symbology on each of the four raster layers so that they have reasonable display properties. This is just for display – we are not physically changing the data in the geodatabase.

Open the Properties Symbology window for the insolation layer. Choose "Unique Values" in the “Show” window and change the color scheme to a monochromatic ramp (e.g., shades of red) instead of random colors. Now change the display for the elevation layer. Change from "Stretched" classification to "Classified" classification. Change the number of class breaks to 10, but leave the classification scheme on Natural Breaks (the default). Select an appropriate color scheme for elevation. The Symbology window should look like the one below.

http://www.ciesin.org/datasets/gpw/globldem.doc.html

http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_info

http://gcmd.nasa.gov/


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Next, change your cloudless layer to show as "Classified" with 10 classes (natural breaks) and set it to an appropriate color ramp (e.g., shades of blue). Remember, high values are more cloudless – check to make sure that the spatial pattern of these data make sense to you. Go ahead and leave your population layer as the default gray-scale colors, or change its symbology to a different color scheme if desired. However, be sure to look at the map and make sure the patterns make sense. (It is always a good idea to look at a map of your spatial data before conducting any analysis!). Now that the symbology for each of the layers has been modified, remember to save your map document. Click on the minus sign to the left of the layer names to hide the classification schemes in the TOC. This will help keep your TOC organized and easy to navigate through.


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4.0 Analyze raster datasets using Raster Calculator

We will need the Spatial Analyst extension to work with raster data. Be sure this extension is

activated under Customize Extensions and check Spatial Analyst. We will use a tool called the Raster Calculator for raster “map algebra”. This tool is found in

ArcToolbox Spatial Analyst Tools Map Algebra Raster Calculator. First we will create a new raster dataset to demonstrate how the Raster Calculator works. In this example, we will select cells in population that have a value greater than 50 people/km2. In the “Layers and variables” window in Raster Calculator, double-click on population, then click on the ">" (i.e., greater than) button. Next, type in 50 in the text box after the > sign (or click on the number buttons). Give the output raster dataset an appropriate name – and be sure it is going into your lab8.gdb. Now run the tool by clicking OK.


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The Raster Calculator should create a new raster dataset and add it automatically to the TOC with a random color scheme. The raster shows all of the cells with a population density greater than 50 people/km2 with a value of 1 (expression was True). A cell value of 0 indicates a cell where the population density is less than 50 (the expression was False). The 0, 1 format is referred to as a "binary map" -- based on the query expression, every raster cell evaluates to either True or False. Read the ArcGIS help under the topics “How Raster Calculator works” and “Building Expressions in Raster Calculator”. Now let's use the Boolean function AND ("&") to write and evaluate queries that create the following new raster datasets: Raster 1 Population density is greater than 50 people/km2 Insolation is greater than 200 Watts/m2 Elevation is less than 1524 meters (5000 feet) Cloudless hours is greater than 60% cloud-free hours per year Note that you have to wrap all of your individual expressions in parentheses -- see screenshot below.


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Give the output name for the raster dataset cloudless_60pct, or some other name that makes sense to you. Then click OK to run the tool and create the new raster dataset. Now run the next two raster “models”, each raster with an appropriate name. Raster 2 Population density is greater than 50 people/km2

Insolation is greater than 200 Watts/m2 Elevation is less than 1524 meters (5000 feet) Cloudless hours is greater than 70% cloud-free hours per year Raster 3 Population density is greater than 50 people/km2 Insolation is greater than 200 Watts/m2 Elevation is less than 1524 meters (5000 feet) Cloudless hours is greater than 75% cloud-free hours per year

Question 1: (2)

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


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Question 2: (2)

Which of the 3 raster datasets (cloudless_60pct, cloudless_70pct, or cloudless_75pct) do you think is the most appropriate for use in your final analysis? Why?

Map 1 (8)

Make a map your 3 raster outputs. The map should have three panels, each with a

different a raster output. Rename the raster datasets and labels (e.g., 0, 1) to

something meaningful, so that the company understands your map. You can

change the name of the raster datasets and labels in each layer’s Properties

window (General tab for name, Symbology tab for colors and labels). Make the

colors of the 3 rasters the same for easy comparison. True values (1s) should be a

solid color, while False values (0s) should have no color. You can use the included

country boundary feature class in your map as a reference background layer.

Remember to add: a map title, your name, legend, scale bar, north arrow, neat line,

etc. Be sure to label the panels so that the client knows which panel belongs to each

model output. Use colors for the raster cells that are bright and obvious, and make

the sure countries are not the prominent part of the map (e.g., hollow and a light

gray, thin border works well).

Export the map to a PDF file with 150 dpi.

5.0 Buffer and select cities

Now that you have raster 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. As you learned in the previous lab, buffering calculates distances from vector features and then produces polygons that represent the area surrounding the features out to that distance. Begin by adding the world cities feature class to the ArcMap TOC. Use Select by Attributes from the Selection menu to find all of the cities with population greater than 1 million people. Export this selection into a new feature class. Call the exported feature class cities_selection or a similar, descriptive name. Be sure your layer is going to your lab8.gdb geodatabase and the output is a feature class (by default, ArcMap may select a shapefile as the output type – you want a geodatabase feature class).


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The new selected cities feature class will be added to your map document. Before moving on from here, choose Clear Selected Features from the Selection menu. This will clear the selected cities, and remove cities from the TOC. You should have cities in the new layer that look like the following (elevation shown for reference only):

Now create a 50-mile buffer around the cities_selection feature class. Refer back to Lab 7 if you forget how to do this vector spatial analysis. Make sure that your units are correct when filling in the distance setting! Also, leave the Dissolve Type as None. Save the buffers to a new feature class with an appropriate name (e.g., cities_selection_buffer50). Zoom in on a city to take a look at the 50-mile buffer – you can’t see the buffers at a global scale. Next you will need to find out which of these buffers intersect with the solar power production regions, identified by your raster model. Before doing the intersection, however, you must decide which of the power region raster datasets to use in your analysis. Based on your answer


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for Question 2 above, decide which of the output rasters (cloudless_60pct, cloudless_70pct, or cloudless_75pct) you should use for your project. The fact that potential power regions are cells in a raster layer and cities are points in a vector layer causes a complication in your analysis, since these are different data models. To remedy this problem, you need to convert the raster layer to a vector layer (do you remember doing this

in Lab 5?). Well, to do this use the Conversion Tools From Raster Raster to Polygon tool, and convert your chosen raster dataset (e.g., model) into a polygon feature class. Be sure to set the output path to your lab8.gdb geodatabase. Do not generalize the lines of your new feature class, so uncheck “Simplify polygons”. Name the output feature class solar_vector.

Classify the symbology on the solar_vector feature class to show the areas of potential use (GRIDCODE = 1).


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Now that the datasets are all vector layers, we can use the Selection Select By Location tool 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 potential area polygons are selected beforehand (i.e., solar_vector feature class, GRIDCODE is 1). Use Select by Attributes to select the polygons in solar_vector (be sure that is the target layer!) that have a value of GRIDCODE = 1. Next, use Select by Location to select the buffers that intersect your selected solar_vector polygons . Your final Select by Location window should look similar to the one below.


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You will now have city buffers selected that intersect the ideal solar power generation areas. Go ahead and clear the selected solar_vector polygons, by right-clicking on solar_vector in the TOC

and clicking Selection Clear Selected Features. If you don’t see the selected city buffers in light-blue (cyan), then make sure the layer is turned on in the TOC. Otherwise, ask your instructor for help. Now determine which cities fall within the selected city buffers. You are on your own for this part (hint: you need another Select by Location operation, and use the cities_selection feature class). Export your selected cities as a separate feature class – call it cities_final.

When completely finished, clear your selected buffer features with Selection Clear Selected Features. Turn off or remove the cities_selection feature class. At the end of this step, you will have a point feature class (cities_final) consisting of all cities of more than one million people that are within 50 miles of a region meeting your criteria for solar power production. This is a fairly sophisticated site suitability analysis!


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6.0 Summarize attribute table If you open the attribute table of your selected cities (cities_final), you can create summary statistics to learn more about the results. Here you will summarize the population of your selected cities by country. Open the attribute table for cities_final. Make the field Country active in your table. Right-click

on the field header and select Summarize. Select Population Sum as the summary statistic to be included in the output table. Specify the location and name for your output table, with the table in a text file format outside of the lab8.gdb geodatabase. Click OK. Go ahead and add the table to your TOC when prompted.

Now open the summary table from the TOC and examine the results (it will likely appear at the bottom of the TOC, as it is a table and not a layer). The "Count_COUNTRY" 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. Turn your output table in with your lab files. Note: it has to be a text file format so that you can easily send just the table. If you had output the table to a geodatabase table, then you would have to send the whole geodatabase – a very large directory of a lot of little files!


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7.0 Final analysis

Question 3: (3)

In which country would you advise the company is a good location for investment in solar power generation? Which is the most important city in that country? Explain your reasons for both answers.

Map 2 (8)

Make a map showing ALL of the potential cities that meet the criteria for solar power

generation. Use the included country boundary feature class in your map. Label the

city names and important countries. Include a text box that briefly explains the

criteria used in selecting these potential cities. The map also needs a map title, your

name, legend, scale bar, north arrow, neat line, etc.


For the final map, you will create a large-scale map for your selected city from Question 3. Your client wants you to show the area where the 50-mile buffer around the city and the potential solar generation area (GRIDVALUE = 1 polygons in solar_vector) intersect. Use your skills from Lab 7 and the Intersect tool to find the area where these two layers intersect. You only need to find the intersected area around your selected city. To do this, you should select the buffer and solar_vector polygons near your city before running the Intersect tool. The output features will thus be those intersected areas of selected features in each input layer. You can do the selection of your selected city’s buffer and solar_vector polygons manually with the Select tool, rather than through a Select by Attribute or Select by Location type of operation. First make sure that your solar_vector and cities buffer layers are the only ones selectable. You

do this in the TOC by clicking on the List by Selection button . Then click the light-blue little

icon next to each layer that you would like to be “Not Selectable”.


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Then use the selection tool to select the appropriate polygons from the solar_vector and buffer layers for your city. Just click on the area where the two layers intersect and both polygons will be selected. See the example below:

Notice in the List by Selection window of the TOC that the name of your city is shown under your city buffer layer. Also, this window should indicate that there is one feature selected from

solar_vector ( ) and one feature from the city buffer layer. Now use the Intersect tool in ArcTools to produce an output layer showing the intersected area (in green below).

Map 3 (8)

Make a large-scale map showing your final selected city and surrounding area.

Show potential site for the solar power station near your city (i.e., intersect polygon).

DO NOT show the buffers and solar_vector polygons -- just the intersect area of the

two layers around your city. Be sure to show the elevation raster in your map.

Include a text box that briefly explains the criteria used in selecting the best city and

explain what the intersect polygon represents. The map also needs a map title, your

name, legend, scale bar, north arrow, neat line, etc.



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Question 4: (3)

Is there uncertainty in this analysis? If you believe that there is uncertainty, which dataset or analytical step introduces the biggest uncertainty? Which dataset or analytical step was relatively certain?

Question 5: (3)

What other datasets do you think would be useful for this analysis? Why?

8.0 Conclusions This lab focused on a site-suitability spatial analysis. The analysis used a combination of vector and raster datasets to arrive at a final answer. Most of your new GIS skills have been used at some point in the analysis, including spatial selection, raster selection, attribute selection, vector overlay, attribute summary, and map design. In Advanced GIS (Geog 487), you will have the opportunity to strengthen these skills without the structure and help found in these more "cook book" labs. If you do not take Advanced GIS, your client and your instructor both wish you luck as you apply your new skills to real-world problems!

9.0 To turn in

The question sheet, with typed answers (Word document)

Table summarizing your potential cities by country

Map 1

Map 2

Map 3 Submit electronic files via email to your instructor, with the subject "G387, Lab 8, [your last name]".

Credits: The majority of the text in this lab was created by Sean Bennison in the Geography Dept. at UC Santa Barbara © 2000-2005 Regents of the University of California. Used with permission. This lab was modified for instruction at Sonoma State University using ArcGIS 9.x-10 by Matthew Clark.

lab 8: raster spatial analysis - sonoma state … 387 – fall 2011 lab 8 raster spatial analysis 1...

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