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# Population Density Mapping using the Dasymetric Method

Talk given at Wisconsin Land Information Association Annual Meeting, Feb 2015.
Published on: Mar 4, 2016
Published in: Data & Analytics

#### Transcripts - Population Density Mapping using the Dasymetric Method

• 1. Problem: Mapping units may be a poor match to the spatial distribution of the phenomenon being mapped. Example: Distribution of cropland variable within a county. Solution: Reapportion the variable spatially based on knowledge (or assumptions) about its spatial distribution. Example: Reapportion total county cropland to zones based on land use. Limiting or related variable: Variable that controls, that we think has an effect on, or is statistically related to, the phenomenon being mapped. Example: Land use (for cropland). Why do this? Should produce a better map, with more accurate spatial distribution of the phenomenon. What is Dasymetric Mapping?
• 2. Robinson Example Source: Arthur H. Robinson et al, Elements of Cartography, 6th Edition, New York: Wiley, 1995.
• 3. Early Example Source: John K. Wright, “A Method of Mapping Densities of Population: With Cape Cod as an Example,” Geographical Review, Vol. 26, No. 1 (Jan., 1936), pp. 103-110.
• 4. Favorite Example
• 5. Goal: Create dasymetric map of Wisconsin population based on census tract populations. ~1400 census tracts in Wisconsin Tracts can be quite large Population density not uniform within tracts We’d like a better map Wisconsin Case Study
• 6. Average area of tracts: > 10 sq. miles
• 7. Why not used census blocks? Blocks are much smaller than tracts, on average. But in rural areas, blocks can still be very large. A problem when there are population concentrations in rural areas, such as unincorporated communities. Wisconsin Case Study
• 8. Blocks are much smaller on average: > 1/10 sq. mile
• 9. Controlling variable: Landcover NLCD 2006…most current at the time Resolution of 30 meters Maybe not the best choice…more later Starting point is to intersect the tract layer and the land cover layer Wisconsin Case Study
• 10. Let Pi,j be the population of polygon “i,j” (formed by intersection of tract i and land cover polygon j) Dasymetric Equation
• 11. Then Pi,j = dj x ai,j where dj = population density of land cover polygon j ai,j = area of polygon “i,j” Dasymetric Equation
• 12. Note that dj (population density of land cover polygon j) depends on the land cover class of polygon j. The most complex part of dasymetric mapping is estimating population densities for each land cover class. To generate estimates, use the polys created by intersected tract and land cover layers to get the ai,j values. Sum all areas within each tract that belong to the same land cover class. Density Estimation
• 13. Set up the following regression model: Pi = ď1 x ai,1 + ď2 x ai,2 + … + ďK x ai,K Where: Pi = observed population of tract i ai,k = observed area of all land cover polys within tract i for which the land cover class = k ďk = pop density for cover class k (unknown coefficients) K = number of unique land cover classes Density Estimation
• 14. Analogous to “hedonic” regression, the classic example of which is: estimate the increase in market value (in \$) of specific characteristics of a home (bathroom, deck, garage…). In our case, estimate the increase in population of a tract associated with a unit-area increase in each land cover class. Many ways to implement this. We use Generalized Reduced Gradient (GRG) optimization to constrain ďk > 0. Regression Analysis
• 15. Final Map
• 16. Map Detail: Legend
• 17. Map Detail: Pipe
• 18. Map Detail: Explanation
• 19. Land cover data not a good proxy for population density.  NLCD includes transportation in “low intensity developed” causing population to be reapportioned from census tracts to the transportation network.  NLCD mixes residential and non-residential in “high intensity developed” causing high-population areas to be mixed with low-population areas such as malls and parking lots.  Should try to combine land cover with land use, zoning, parcels… Data-intensive. Even after simplification, the intersection of tracts and land cover generates about 3 million polys. Small-scale depiction of the population distribution over the state; not accurate for large-scale mapping. Issues
• 20. CREDITS RESEARCH TEAM Blaine Hackett Co-Founder and President, Flat Rock Geographics Tom Cox Minnesota Power; formerly a UW-Madison student Howard Veregin Wisconsin State Cartographer PHOTO CREDITS Making a map for the blind. Stefan Kühn (http://hdl.loc.gov/loc.pnp/ggbain.19023) [Public domain], via Wikimedia Commons. Image of an officer and soldier making maps in France, 1917-1918. US Army Signal Corps (US Army Center of Military History, Carlisle, PA) [Public domain], via Wikimedia Commons Staff Sergeant Blake Ellis, Sheel Creek, Tennessee, inking in the pencil tracings. Culture, Hydrography, and Contours are shown. England , 01/11/1943. Department of Defense. Department of the Army. Office of the Chief Signal Officer. (09/18/1947 - 02/28/1964) [Public domain], via Wikimedia Commons