2015/12/10 by Nik Lomax, Paul Norman · 1 citation
Decision Sciences · Social Sciences · #Health disparities and outcomes #Insurance, Mortality, Demography, Risk Management #demographic modeling and climate adaptation
paper · pdf · doi:10.1080/00330124.2015.1099449
crossref issued 2015/12/10 · crossref published 2015/12/10 · crossref published-online 2015/12/10 · openalex publication_date 2015/12/10 · crossref created 2015/12/10 · crossref published-print 2016/07/02 · crossref deposited 2023/01/05 · openalex created_date 2025/10/10 · crossref indexed 2026/07/28 · openalex updated_date 2026/07/28
Iterative proportional fitting (IPF) is a technique that can be used to adjust a distribution reported in one data set by totals reported in others. IPF is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample. Although widely applied, the IPF methodology is rarely presented in a way that is accessible to nonexpert users. This article fills that gap through discussion of how to operationalize the method and argues that IPF is an accessible and transparent tool that can be applied to a range of data situations in population geography and demography. It offers three case study examples where IPF has been applied to geographical data problems; the data and algorithms are made available to users as supplementary material.