Government 98dn Mapping Social and Environmental Space LAB EXERCISE 5: The Analysis of Fields Objectives of this lab: Visualizing raster data Using Spatial Analyst functions to create new data Analysis with raster data Visualization in (almost) 3D Estimated Time: 5-6 hours Some suggestions for handing in lab exercises: 1. Insert all images(including jpegs) into the Word file (use main menu Insert, Picture, then From File). 2. Type your name on every page if possible (use the insert header facility in Word). 3. Convert your word document to a pdf document (the Word version in the HMDC lets you do this by clicking on the convert to pdf button). The pdf document that you create is usually much smaller in size and can easily be emailed using your Harvard/ FAS email software. 4. Email your lab as (preferably) one document to me (ssrinivasan@cga.harvard.edu) as a backup even if you give me a printed version. 1.0 Mapping Rasters Open ArcMap. Add the raster lichildden from massgis (in Gov1008, GISdata) to the map document. This shows the low-income children population density for the year 2000 in the region distributed in 500-meter square grid cells. You should get an automatic classification of density using the default stretched classification. You might want to change the classification type for the layers. Right click mouse against the raster in the TOC and then select Properties followed by Symbology. Click on Classified instead of Stretched. Try and experiment with the different classification types by clicking on the Classify button and then choose some of the options available like Quantile, Standard Deviation, Equal Interval, Natural Breaks, etc. (See the screens on the next page). Review: What kind of classification is best for depicting this data? Is there one? Question 1 (45 points): Create maps for three types of classifications (with the same number of classes) and compare in a few paragraphs the resulting spatial patterns of low-income children s population density in Massachusetts (or a region of your choice within Massachusetts). Add these maps to a well-designed layout.
Add the shapefile: lus35, which shows the land use Boston or lus49 which shows land use in Cambridge. To understand what the numbers mean (when you click a cell and try to get information on it) refer to the 21-category classification shown below. Add the layer file Land Use (21 classes).lyr from the massgis folder (or go to http://www.mass.gov/mgis/ftplus.htm and then click on ). To set the data source right click on the layer in the TOC and then select Properties and then Source and then set the data source to the location that interests you (Boston lus35.shp or Cambridge lus49.shp). Note: Save your map document in your workspace for this lab exercise. The 21 category classification of land use 1 AC Cropland Intensive agriculture 2 AP Pasture Extensive agriculture 3 F Forest Forest 4 FW Wetland Nonforested freshwater wetland 5 M Mining Sand; gravel & rock 6 O Open Land Abandoned agriculture; power lines; areas of no vegetation 7 RP Participation Recreation Golf; tennis; Playgrounds; skiing 8 RS Spectator Recreation Stadiums; racetracks; Fairgrounds; drive-ins 9 RW Water Based Recreation Beaches; marinas; Swimming pools 10 R0 Residential Multi-family 11 R1 Residential Smaller than 1/4 acre lots 12 R2 Residential 1/4-1/2 acre lots 13 R3 Residential Larger than 1/2 acre lots 14 SW Salt Wetland Salt marsh 15 UC Commercial General urban; shopping center 16 UI Industrial Light & heavy industry 17 UO Urban Open Parks; cemeteries; public & institutional greenspace; also vacant undeveloped land 18 UT Transportation Airports; docks; divided highway; freight; storage; railroads 19 UW Waste Disposal Landfills; sewage lagoons 20 W Water Fresh water; coastal embayment 21 WP Woody Perennial Orchard; nursery; cranberry bog 22 - No Change Code used by MassGIS only during quality checking 2
1.1 Creating Rasters You can create a new raster dataset showing the land use in Boston. Click on the Spatial Analyst pull down menu get a new window. and then Convert and then Features to Raster to If you do not have the Spatial Analyst menu button go to the main menu, Tools and then Extensions and check against Spatial Analyst. Then go to main menu, View and Toolbars and make sure Spatial Analyst is checked. Follow the screen shown below to create a raster from the lus35 (or lus47) shapefile with a 21 category classification for land use in the year 1999. Make sure that the resolution is the same as your other data for the density of low-income children in Massachusetts. Review: When is a raster more useful? When is a vector more useful? Compare the new raster data you created to your original land use data, which were in vector shapefiles. What happened to the quality of the data? Why? What do you need to know about how the original data were created? What happens when you decrease the pixel size of the raster? Background: Low-income child density is a raster that was derived from the total population of low-income children in a census block group (by adding the appropriate fields in the table income_poverty_levels_by_age.dbf). This population variable was used to create a new field that indicated the density of low-income children (LOWINCCHILD/ DRY_SQKM) over a 500x500m cell converted from the census block group data (to which income_poverty_levels_by_age.dbf was joined). 1.2 Raster analysis or Map Algebra Now we can use the map calculator to generate new data layers. First, zoom into Boston (or Cambridge) or wherever you created the new raster data. Click on Spatial Analyst and then select Raster Calculator to get a Raster Calculator window. In the layer lu99 select all locations in Boston where there is waste disposal related land use (19) or industrial land use (16) 3
see below. Use the calculator rather than typing the expression. Here = = means equal to and means Or. Then click on Evaluate to get a new raster layer showing such land uses as 1 and other land uses as 0. You could also add other land uses that you think are hazardous. You might want to save it later with a more meaningful name. (Right click on the mouse next to the new layer and then click on Data and then Make Permanent, in the new Make Calculation Permanent window input the name you want for this raster). 1.3 Proximity ArcGIS allows you to create raster distance maps to vector features. Each cell in the output grid will show the distance from the closest vector feature. First, open the calculation attribute table and select only the pixels with value 1. Then, from the Spatial Analyst menu, select Distance, then Straight Line... Specify the active layer as the new raster that you created in the dialog box that pops out. You can make this new layer transparent to see the child density raster. Right-click on the layer in the TOC and click on Properties followed by Display to get a Transparency variable that you can manipulate. Try adjusting the colors, classification, etc as well so you can better see the relationships between where low income children live and how close they are to hazardous land uses. Add EOT_MajorRoads_Class.lyr, Spills, Environmental_Justice_Populations.lyr, etc. from your previous lab exercise(s) to explore any interesting hypotheses you may have developed with respect to low income children, land value, poverty, spills and proximity to such land uses. 4
1.4 Creating an attribute table (Optional) Click on the Spatial Analyst toolbar, then select Zonal Statistics to get a new window as shown below. Let census2000blockgroups_poly (from your previous lab) be your zone data set and the field you are interested is the number of low income children. In other words you want to find the average proximity to hazardous land uses for each low income children category (the number of children varying from 0 to the maximum number of low income children). Save this data to an output table in your folder. Click OK to get a new table and a chart. The graph does not seem to indicate any trend. However the table may be opened in Excel where you can examine the relationships more carefully. It is difficult to say very much from the data alone. It may be worth looking at the relationship between low income child density and spills as well or comparing the proximity of low income children versus all/ high income children. Question 2: Do you notice any patterns in proximity of low-income children to waste disposal land uses? Explain the spatial patterns you notice in a few paragraphs. Refer to the map(s) that you created in this section to explain the spatial patterns that you noticed. Attach the map (s) that you refer to. Also see Question 3 before you answer this question. Screen dumps rather than layouts are fine but avoid visual clutter. (40 points) Save this map document. 2.0 Visualization in ArcGIS: 3D Analyst and ArcScene ArcGIS 3D Analyst enables users to effectively visualize and analyze surface data. Using 3D Analyst, you can view a surface from multiple viewpoints, query a surface, determine what is visible from a chosen location on a surface, and create a realistic perspective image draping 5
raster and vector data over a surface. ArcGIS 3D Analyst extends the capabilities of ArcGIS in three ways: 1. it creates models of continuous geographic surfaces (such as the elevation of the earth), 2. it displays these models, along with the features found on them, in three-dimensional perspective, and 3. it analyzes various properties of these models. The core of the 3D Analyst extension is the ArcScene application. ArcScene provides the interface for viewing multiple layers of (almost) 3D data and for creating and analyzing surfaces. But you can also use it in ArcMap in a 2D view, to create and analyze continuous geographic surfaces. A geographic surface, or surface model, represents a spatial quantity or phenomenon that can be measured continuously over some part of the earth. Attributes for thematic data can be used for heights to create a three-dimensional block diagrams. Data can also be overlaid on a surface to make it acquire Z values. 2.1 Navigating ArcGIS 3D Analyst To display surfaces in 3D, you can use the application ArcScene. ArcMap need not be open (but 3D Analyst is accessible within ArcMap as well). Start ArcScene by clicking the button in ArcMap or from start menu, ArcGIS and then ArcScene. Adding data into ArcScene is the same as adding data into a view in ArcMap. You can add raster layers like the DEM and other rasters in the previous section, feature themes (points, lines and polygons) or TIN (a vector representation of elevations). Add the shapefile, census2000blockgroups_poly from your lab4.gdb or from massgis. Right click census2000blockgroups_poly in the TOC to get Properties and then under Extrusion choose Extrusion value or expression and then click on the calculator symbol, click on [TOTAL_POP] / [DRY_SQKM] or [TOTAL_POP] / [DRY_SQMI] and click OK. The block groups are now extruded according to the number in that field and indicate density of the block group per sq km (or sq mile). 6
3D scenes are used like an ArcMap view to display and query spatial data. Adding a third dimension enables viewing 3 dimensional datasets from different angles. Rotate the data set by using the Navigate button and move the mouse (right button down) in any direction in the ArcScene window. With the left button you have a zoom in/ out tool. These three button are also useful to navigate. Move you mouse on them to find what they are used for: There are many others options under View and then View properties or Scene Properties. Try Scene Properties and Illumination. Change the position of the sun from the default in the system. Use the same technique to do some multivariate mapping. Add transparency to the layers using the Display tab in the Properties dialog box (right-click on TOC against the layer). Save the scene document in your folder. Review: What are the best ways to map multivariate data? How can you track patterns in many variables without getting confused? Is it better to have a few hypotheses before you start your analysis? Question 3 (15 points): Make a basemap in ArcScene showing some aspect of the Census block groups relevant to Question 2 by joining it with an appropriate attribute data file as you did in Lab 4. Possible candidates to join include: housing and income related tables. (For example in the figure that follows I extruded density and also mapped the number of persons who commuted by public transit to indicate that there was a possible correlation between density and commuting by transit (I joined transportation_commute_means to the census2000blockgroups_poly shapefile). Note that my example is not relevant to Question 2 but your map should be. 7