Dasymetric Mapping Some geographical distributions are best mapped as ‘volumes’ that represent surfaces characterized by plateaus of relative uniformity separated from one another by relatively steep slopes or escarpments where there is a marked change in statistical value. There are two techniques for defining this stepped surface, the choroplethic and the dasymetric. The choroplethic technique requires only grouping of similar values, and detail is constrained by the boundaries of enumeration units which rarely have to do with the variable being mapped. In choroplethic mapping, emphasis in the graphic statement is placed upon comparing relative magnitudes across the surface of the map. In contrast, the dasymetric method highlights areas of homogeneity and areas of sudden change and is produced by refining the values estimated by the choroplethic technique. Your task is to produce a dasymetric map of cropland in south central Ohio counties, using four variables to refine the enumerated data. Prepare this map and all related maps using ArcView GIS. It might be a good idea to keep a journal log while you are working through this exercise, annotated with hardcopy maps if you prefer, personal notes describing in your own words what the commands you performed in ArcView do, for later reference. Data for the initial distribution is given on the base map on page 4. Initial files for the assignment are in the Handouts folder in the usual Csiss folder on \\ubar\labs\. Copy the folder labeled dasy_ohio from the Handouts directory to your personal directory (for example to E:). Open the file dasy.apr in your directory with ArcView. When opening the project file you might be asked where certain missing files are. If so, select the files in your directory that have the same name as the missing ones, and click each time OK to continue. Once all the data is reassigned you should see an open view, labeled Cropland in Ohio 1959 with the five themes described below. Go to FILE: SET WORKING DIRECTORY... type in: your_local_directory:/your_personal _directory/dasy_ohio/ to set your working directory to your home directory and the lab folder you copied over. ArcView will produce a bunch of files and you want to make sure that are all stored in the lab directory. Make sure that any folder or file associated with this lab in your directory does not have any spacing in the label, nor Uppercase. Please Note: You might not be able to finish this lab in one sitting. So, don’t worry if you don’t. You can find the result maps at the end of this handout just in case. page: 2 For much of the exercise, the following classes will be used: 0 – 9% coverage of cropland 10 – 29% 30 – 49% 50 – 69% 70 – 89% 90 – 100% On any maps you print out, include the following information: • an appropriate title • a legend • a scale • your name • data source and year Use partial spectral progressions to indicate the classes of your map and for all worksheet maps except for economic activity which should be mapped using a full spectral progression. We have briefly touched on this concept in lectures. Part spectral progressions are generated as linear transects across or through the color wheel; full spectral progressions are generated as arcs of full or partial circumference. Think about the layer you are mapping (e.g. coverage by woodland, coverage by urban areas, terrain configuration, and coverage by cropland) and apply a meaningful progression. The following materials are provided to help you complete this exercise. The files are scanned or digitized from 1:100,000 scale copies of 1:24,000 maps. The stated resolution (.60 km per cell) is meaningful, and should guide your decisions about how much detail to include or simplify. This handout A discussion of the dasymetric technique and the methods to employ, including step-by-step procedures for preparing a dasymetric map. Maps • choropleth % crop A base map displaying the percentage of total area classed as cropland for each county. The single figure for each county includes several classes of cropland: cropland harvested, cropland used only for pasture, cropland not harvested and not pastured, areas in grasses and legumes for soil improvement, and idle cropland (fallow) and crop failure. For each county, these values have been summed and the total divided by total county acreage to give the percentage. The remaining area in each county consists of urban and rural non-agricultural landuses, woodland (pastured and non pastured), and non urban built-up land (e.g. farmsteads). Data are derived from the 1959 Census of Agriculture. The county names are included in the theme’s attribute table. In addition, there is a theme with county labels called county names. • towns A map of urban and rural non-agricultural land use areas. The areas outlined are occupied by residential, commercial, industrial, transportation, mining and similar land uses. Areas of many sizes have been shown, from the completely built-up city and metropolitan areas to crossroads settlements, hamlets, and galaxies of strip mines which dot the Ohio countryside. The page: 3 areas are shown as close to scale as possible, but crude reproduction techniques do involve some exaggeration. These data are derived from 1:24,000 topographic sheets. Maps from the 1960 Census were used to outline the urban areas. • woodland A map showing the amount of woodland cover throughout the area in six classes. It was derived from the same topographic sheets and reflects a series of estimates based on USGS 7.5 minute quadrangles. • terrain A ‘surface configuration’ map shows in four classes the nature of the terrain. This map was generalized using the work of Guy-Harold Smith, “The Relative Relief of Ohio”, Geographical Review vol.25, p. 272-284, 1935. • economy A map of ‘economic regions’ outlines areas by agricultural economic type. This is based on work by Alfred J. Wright, “Types of Farming Areas”, Economic Geography of Ohio Columbus Division of the Geological Survey of Ohio, Bulletin #50, 1953, Figure 13. It is important to realize that the concern in this exercise is with the dasymetric procedure and not with cropland in Ohio. Since this exercise marks the first time that you are presented with this particular mapping technique (map overlay) the methods are set down in considerable detail. After gaining understanding of the nature of the technique from the following section, the simplest way to proceed is simply to follow the steps in the order of presentation. Once accomplished here, map overlay techniques as used in conventional geographical information systems or for spatial modeling should be more readily understood. page: 4 page: 5 page: 6 page: 7 The Dasymetric technique The dasymetric technique provides one solution to the problem of mapping data gathered on the basis of enumeration areas whose boundaries bear no direct relation to the variable being mapped. It is based on certain assumptions. Assumption 1 the variable being mapped occurs non-uniformly over the statistical unit area (in this case, counties). Assumption 2 even though wide overall variations exist for the distribution, it basically consists of areas of relative uniformity separated by sudden changes in value. Assumption 3 other variables may be collected in association with the variable in question whose relation to the variable may be determined and expressed as a set of rules. These variables will enable the cartographer to adjust and refine the given data to form homogeneous regions whose boundaries are independent of the enumeration unit boundaries. The variable in question for this exercise is cropland (density). The four other variables are urban and non agricultural land use, woodland area, terrain, and economic activity. These variables may be characterized as being either “limiting variables” or “related variables”. We will tackle the limiting variables first. The limiting variables In some degree limiting variables restrict the possible occurrence of cropland. That is, a certain percentage of a limiting variable occurring in an area will set an absolute upper limit on the percentage of the mapped variable (cropland) that can occur in the same area. Two limiting variables are being employed in this exercise – urban landuse and woodland. An area devoted to urban landuse precludes the occurrence of cropland. In a categorical fashion, this may be expressed by the following rule: if urban landuses are present, then there can be no cropland. If there are no urban landuses, then cropland can exist (see Figure 1). In terms of the six categories of cropland, note that the presence of urban land use restricts an area to cropland category 1, namely, 0% cropland. All six classes are possible where no urban land use occurs. cropland classes (%) 123456 urban land use 0-9 10-29 30-49 50-69 70-89 90-100 present not present cropland not possible Figure 1: the limiting variable urban landuse It is also possible to compute a precise adjusted percentage for the cropland density, using a formula first published by J.K. Wright. It is called the computation of fractional parts of densities. In the formula below, D stands for Density, A stands for Area: DDA− 52−∗(.) 0 0 1 52 D = omm= ==57. 7 n − − 1 Am 101..09 page: 8 Thus, for a county with 52% cropland, 10% of the county covered by urban landuse, the percentage of cropland in the remaining county area must be approximately 58%. TASK 1 Revise estimates of county cropland density using this formula and the layer of urbanized areas. The first step is to generalize this layer, and the second is to do the computations. Tools You will be working with ArcView’s Spatial Analyst, a raster-based GIS module.