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Mapping Density

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1GIS II - Contemporary Analysis with GIS Mitchell Chapter 4 - Mapping Density I. Introduction - Why map density? A. Mapping density shows you the concentration of features per area B. Density maps are useful for general patterns, rather than for individual features Note fig P.70 upper left - on the left is a map showing indiv locations of businesses, while on the right is a concentration (density) of businesses per unit something (square mile?) C. To map density, you need a denominator, such as: # of crimes or # of residents mile2 city block D. Mapping density is particularly useful when you are trying to compare data in areas of greatly varying sizes (like counties or census tracts) - for example, larger census tracts may have more people in total, but smaller census tracts may have higher population density (more people per square mile) - Fig p.70, upper right E. Map Gallery - i. Crime map with density of arrests per sq. mile (fig p.70 lwr right) ii. Population density map of Detroit to find best places for new Secretary of State service branches (Fig p. 71, left) iii. Fish density (fig. p.71, upper right) in Lake Norman, NC was mapped in order to determine what changes will occur in the future (where fish will flourish, where they will die iv. Cubic meters of timber produced per hectare in Oregon helps foresters keep track of - and manage - deforestation II. Deciding what to map - A. what is your data source? What kind of data do you have? i. You can map densities of points or lines with a “density surface” (fig p. 72 upper left) - here, you must: 1. define your area, 2. create a Z-value that represents the number of features (businesses, logging roads, etc) within each of these areas 3. create a continuous surface (contour map) from the Z-values ii. you can also map densities by using defined areas, such as: 1. census tracts or blocks 2. counties 3. school district, city boundaries, other “administrative” boundaries B. do you map features, or feature values? i. Example: 1. no. of businesses per square mile (the feature) 2. no. of employees per square mile (feature value) III. two ways of mapping density A. mapping density for defined areas i. you can use dot density maps, where a dot represents a specific value, like 1000 people, or 10 burglaries. BE CAREFUL, because the actual locations of these features are NOT accurate ii. you determine the number of features each dot represents2iii. to create a Density Value, you divide the total number (or total value) of features by the area of the polygon that contains the features. Once this is done, a density value chloropleth map can be created, where the polygons are shaded according to the number of features contained in each polygon (p.73, upper right) B. creating a density surface - the density value layer can be created as a raster layer i. here, each cell in the layer receives a density value (such as number of businesses) based upon the number of features that are found within a certain radius of the cell (p.73, lwr right) ii. density values can be created from points or lines. If the features are lines, the density will be in units of length per unit area (like miles per square mile) iii. compare the methods on p. 74 iv. to summarize the choice: 1. map by defined area if your data is already summarized by area 2. create a density surface (through TIN or raster creation) if you desire to “see” the concentrations of point or line features IV. mapping density for defined areas (the details) A. Procedure to calculate a density value for a defined area i. add a new field (column) to the feature table (attribute table), title it. Make sure you set the properties of this field to be numeric (NOT text). MAKE SURE YOU THINK ABOUT THE UNITS OF MEASUREMENT (e.g., people per mi2) and note them on paper somewhere, especially if you are not going to include units in the column title ii. for Pop. Density, book gives a map and example (p.75 fig lwr left). The attribute table’s 1st three columns are: census tract number, census tract area (in square feet) population in each census tract. The 4th column is the new pop_density column (in units of number of people per sq. mile: Note: my problem setup is more detailed than that in the book, because you need to see how the area units are changed in the multiplication process pop_density (no. of people per sq. mile) = pop. in census tract “X” x 27,878,400 ft2 census tract “X” area (ft2) 1 mi2 Map output can be displayed, for example, by using “graduated colors” in “Symbology” in ArcMap Properties to assign diff colors to the census tract features, based upon the pop_density attribute Also note that Mitchell points out that you can also calculate pop_density “on the fly” in ESRI software. Here, you would use the attribute of population, then “normalize” population by area. B. creating a dot density map (the details) i. this type of map can give you a more detailed visual sense of density, or “a quick sense of density in a place” because a “dot map simply represents density graphically” ii. you define, say, that a dot represents 200 people, and a census tract has 6000 people living in it: the GIS places 30 dots within the boundary of the census tract3V. creating a density surface A. what the GIS does to create a density surface (a raster process) is explained on p.78-80 - a good explanation of how a “neighborhood search” is performed using a “moving cell analysis” to create a certain number of dots assigned per cell B. how to create a density surface map from data already summarized by area (RH side of p.81): i. this looks to be a useful (and not particularly difficult) way to create a density surface map, once you create a pop_density field. In the example, census tract population density is tied to the “centroid” (center of gravity) point in each tract, essentially creating a “Z”-value that can be used to create a TIN (or raster) grid, depending upon the how unevenly (good for TIN) or evenly (good for raster) the points are distributed ii. the TIN or raster grid can be modeled / mapped via 3D modeling or Contouring. iii. You can see that there is a dramatic difference in this example between “census tracts shaded by population” vs “density surface of population” C. Displaying a density

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