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

Download Lab 8: Raster Spatial Analysis - Sonoma State   387 – Fall 2011 Lab 8 Raster Spatial Analysis 1 Lab 8: Raster Spatial Analysis 1.0 Overview In this laboratory exercise we will examine a spatial analysis problem and develop a GIS

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  • 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 ESRIs 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.htmlhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_infohttp://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 layers 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

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