Abstract
Demographic and geographic data are the foundation for redistricting and reapportionment. This chapter provides a step-by-step guide to collect, manage, analyze and report data (a process known as “CMAR”) to support redistricting initiatives. Demographic data come from: (1) the April 1 “full-count” Decennial Census, the official count of total and voting-age populations by race and Hispanic ethnicity for levels of census geography as small as the “census block”; and (2) the American Community Survey’s annually updated estimates of the Citizen Voting-Age Population during specific 1-year or 5-year periods, for levels of census geography as small as the “census block group”. Geographic data show the locations of populations within “census geography” (e.g., census blocks, cities, congressional districts), and their locations relative to one another. The redistricting process can be driven by a customized database developed to leverage the full capabilities of a Geographic Information System (G.I.S.) or off-the-shelf redistricting software. For many users today, the latter option may suffice.
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Notes
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This is known as the Census Bureau’s MAF/TIGER database, containing geographic features such as roads, railroads, rivers, as well as legal and statistical geographic areas.
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Professor Justin Levitt’s Guide to Drawing the Electoral Lines. http://redistricting.lls.edu/
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The U.S. Census Bureau standard for the confidence level around ACS estimates is 90% – the lowest of the recognized 90%, 95% or 99% standards of scientific certainty.
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In fact, MOEs can be so wide as to imply logically impossible estimates. For example, there are instances where very small ACS population estimates (“5” for example) are bounded by a MOE greater than the estimate itself (“+/−10” for example).
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In the Supreme Court of the United States Sue Evenwel, et al., Appellants, v. Greg Abbott, in his official capacity as Governor of Texas, et al., Appellees. On appeal from the United States District Court for the Western District of Texas. Amicus Brief of Former Directors of the U.S. Census Bureau - As amici curiae in support of Appellees. August 2015. Page 14
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Note: the issue of rounding is beyond the scope of this chapter.
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Nik Lomax & Paul Norman (2016) Estimating Population Attribute Values in a Table: “Get Me Started in” Iterative Proportional Fitting, The Professional Geographer, 68:3451–461, DOI: https://doi.org/10.1080/00330124.2015.1099449. http://eprints.whiterose.ac.uk/92147/8/Estimating%20Population%20Attribute%20Values%20in%20a%20Table%20VOR.pdf
- 16.
Cohen, M. 2008. Raking. In Encyclopedia of survey research methods, ed. P. Lavrakas, 672–74. Thousand Oaks, CA: Sage.
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Simpson, L., and M. Tranmer. 2005. Combining sample and census data in small area estimates: Iterative proportional fitting with standard software. The Professional Geographer 57 (2): 222–34.
- 18.
Johnston, R., and C. Pattie. 1993. Entropy-maximising and the iterative proportional fitting procedure. The Professional Geographer 45 (3): 317–22
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Deming, W. E., and F. F. Stephan. 1940. On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics 11 (4): 427–44.
- 20.
Deming,W.E. 1943. Statistical adjustment of data. New York: Wiley.
- 21.
Friedlander, D. 1961. A technique for estimating a contingency table, given the marginal totals and some supplementary data. Journal of the Royal Statistical Society: Series A (General) 124 (3): 412–20.
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Fienberg, S. E. 1970. An iterative procedure for estimation in contingency tables. Annals of Mathematical Statistics 41 (3): 907–14.
References
Cohen, M. 2008. Raking. In Encyclopedia of survey research methods, ed. P. Lavrakas, 672–674. Thousand Oaks: Sage.
Deming, W.E. 1943. Statistical adjustment of data. New York: Wiley.
Deming, W.E., and F.F. Stephan. 1940. On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics 11 (4): 427–444.
Fienberg, S.E. 1970. An iterative procedure for estimation in contingency tables. Annals of Mathematical Statistics 41 (3): 907–914.
Friedlander, D. 1961. A technique for estimating a contingency table, given the marginal totals and some supplementary data. Journal of the Royal Statistical Society: Series A (General) 124 (3): 412–420.
Johnston, R., and C. Pattie. 1993. Entropy-maximising and the iterative proportional fitting procedure. The Professional Geographer 45 (3): 317–322.
Lomax, N., and P. Norman. 2016. Estimating population attribute values in a table: “Get me started in” iterative proportional fitting. The Professional Geographer 68 (3): 451–461.
Simpson, L., and M. Tranmer. 2005. Combining sample and census data in small area estimates: Iterative proportional fitting with standard software. The Professional Geographer 57 (2): 222–234.
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Morrison, P.A., Bryan, T.M. (2019). Data Development and Management. In: Redistricting: A Manual for Analysts, Practitioners, and Citizens. Springer, Cham. https://doi.org/10.1007/978-3-030-15827-9_3
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