Iterative Proportional Fitting Information and Code Iterative Proportional Fitting for a Two-Dimensional Table An informal description from May 2008 of two-dimensional IPF that I (Eddie Hunsinger) prepared for my coworkers and fellow researchers.Please feel
free to offer comments or corrections.
edyhsgr.github.io
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5.1 Weighting algorithms. A wide range of methods can be used to allocate individuals to zones in spatial microsimulation. As with the majority of procedures for statistical analysis, there are deterministic and stochastic methods. The results of deterministic
methods, such as IPF, never vary: no random or probabilistic numbers are used so the resulting weights will be the same every time.
spatial-microsim-book.robinlovelace.net
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Edward A. Sullivan, III
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On Feb 17, 2022, at 7:17 PM, Schroeder, Matt <Matt.Schroeder@metc.state.mn.us> wrote:
<ExampleIPF.R><seedData.csv>_______________________________________________Hi, Chuck – I’m not quite sure what the goal of this procedure is (perhaps because I’m more “census” than “transportation”). Based solely on the materials you sent, it looks like you’re trying to take the residence X workplace matrix and rake it to the row and column totals from the matrix itself. So the rows already sum to the row targets you defined, and the columns already sum to the column targets. I’m not sure why raking is appropriate here; do you have a different set of row/column targets you plan to use (like the “estimates of individual years” you mentioned)?In case it’s useful for your particular situation, I’ve attached some code used to develop population estimates for census blocks:
- The “seed data” is estimates of the population in households and the population in group quarters (in columns) for each census block (in rows). (These are fractional, not rounded.)
- The total population estimates have already been set; we just need to make sure that the household and group quarters populations add up to those totals (the row targets).
- We also have estimates of the household and GQ populations for cities and townships, and we want the block-level household and GQ population estimates to roll up to those as well (the column targets).
- The code uses the `mipfp` package to make these estimates roll up “vertically” (within columns) to city/township control totals, and roll up “horizontally” (within rows) to the block-level total populations.
I’m not a statistician – this is just something I worked out to solve a problem – so take all this with a grain of salt. Good luck!--Matt---Matt SchroederPronouns: he/him/hisPrincipal ResearcherMetropolitan Council Community DevelopmentP. 651-602-1513From: Charles Purvis <clpurvis@att.net>
Sent: Thursday, February 17, 2022 3:42 PM
To: The Census Transportation Products Program Community of Practice/Users discussion and news list <ctpp@listserv.transportation.org>
Subject: [CTPP News] frataring commuter matrices in RHelp!
I’m trying to fratar (iterative proportional fitting, raking) a 58 by 58 commuter matrix using R packages. I could use some help.
Attached are my initial scripts and input database to create the 58 by 58 “seed” matrix (2012-16 CTPP, Total Workers, Table A302100). I’m just a little mystified as to how to implement the raking/frataring given the different IPF packages available: Ipfp, mlfit, mipfp, rakeR, rake……
My goal is to rake the 58 by 58 county-to-county total commuters for California, using the 2012/2016 CTPP to estimates of individual years: 2012, 2013, 2014, 2015, 2016.
Chuck
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