ABSTRACT INTRODUCTION

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1 Iterative Proportional Fitting and Population Dynamics using SAS Himanshu Joshi, Houston-Galveston Area Council, Houston, TX Dmitry Messen, Houston-Galveston Area Council, Houston, TX ABSTRACT For doing small area socioeconomic forecast Metropolitan Planning Organizations (MPOs) often need demographic data at individual person level. There no such single data source available and often iterative proportional fitting (IPF) procedure is used to generate synthetic population using census datasets. This paper demonstrates how the Socioeconomic Modeling group at the Houston-Galveston Area Council (H-GAC) uses SAS to create synthetic population data and how this data is also used to forecast population using aging-birthing-migration. INTRODUCTION At H-GAC, Socioeconomic modeling group s main task is to prepare long range growth forecast of population and employment for 8-county region. The primary purpose of the Regional Growth Forecast is to support Travel Demand Modeling which is used in Regional Transportation Planning. H-GAC also uses the forecast for other long range planning purposes. We use UrbanSim modeling package for doing the small area forecast ( UrbanSim is a software-based simulation model for integrated planning and analysis of urban development, incorporating the interactions between land use, transportation and public policy. It is intended for use by Metropolitan Planning Organizations and others needing to interface existing travel models with new land use forecasting and analysis capabilities ( This paper explains how we are using SAS to write our own module for demographic input for the model. UrbanSim is an agent based model and requires detailed input information regarding population, employment and land use. Among the several input data tables, information about current detailed demographic data and future control totals, is required. Data preparation for these two tables is explained in following sections. A. Iterative Proportional Fitting (IPF) IPF procedure is carried out to produce synthetic population. The data sources for creating synthetic population are 2000 census summary tape file 3 (STF3) and census Public Use Microdata Sample (PUMS) data. The PUMS file has 5% sample from long form census records at relatively large geographic areas called PUMA. A PUMA consists of several census tracts or block groups. PUMS data consists of 2 tables, one for households and one for persons. Records from both tables can be related using unique household ID given for each household. While PUMS data consists of entries at individual level, STF3 data consists of population counts by different geography level. Following example shows a simple 2-dimensional IPF procedure: Suppose we have total counts of people by age and sex from STF3 table as shown in table 1 in a block group. And we have corresponding PUMS data as shown in table 2. The goal here is to use IPF procedure to fill in the? signs in table 1 such that the marginal totals are preserved.

2 Male????? 53 Female????? 47 Total Table 1: Hypothetical age by sex block group distribution Male Female Total Table 2: Hypothetical PUMS data for the example block group First step is to calculate the adjustment factors for first variable, say sex. This is done in following manner. Adjustment Factor Male /723 = Female /762 = Total Table 3: Calculate first adjustment factor In the second step, each cell in the table 2 is multiplied by the adjustment factor. The results are shown in table 4. Male 56 * = Female 61 * = Total Table 4: First step adjustments Next step is to calculate the adjustment factors for the second variable, age as shown in table 5.

3 15/ = / = / = / = / = Table 5: Calculate second adjustment factor In the last step of first iteration, the cells in the table 4 are multiplied by respective adjustment factors in table 5. The results are shown in table 6. Sex >75 Total Male Female Total Table 6: Second step adjustments The above procedure of adjusting one dimension at a time is repeated until adjustment factors are close to 1. Although it can become real complex, this procedure can be extended for 3 or more dimensions. This is well documented in TRANSIMS population synthesizer (Hobeika, Antoine 2005). Basically, idea is to fit one dimension at a time until the multi-dimensional matrix is converged. Example code: %do %until (matrix is converged) %do l = 1 %to 2; dimension */ /* For first iteration. Repeat for each data synth; set input data file; frac = sf3 /pums; /* Calculate the adjustment factor */ pumsf1 = frac * pumsf; /* Calculate the cell values */ /* Calculate new marginal totals */ create table syntha as select *, sum(pumsf1) as pumss from synth group by sex; create table synthb as select *, sum(pumsf1) as pumsa from synth group by age; create table synth1 as select x.*,y.pumsa,z.pumsi,z1.pumsr from syntha as x,synthb as y where x.sex = y.sex and x.age = y.age and order by sex,age; data psynthesis; set synth1; pop = int(pumsf1); /* Take only the integer portion of the number */ %

4 % This is an example code for a 2 X 2 matrix. The actual macro is done for 4-dimensional matrix. B. Population Dynamics This module attempts to predict the demographic change over the period of time. Once we have the base year synthetic population ready we can use it to project for future years. This is done using simple probabilistic simulation with one year increment. The data sources for this module are, persons data created using IPF as described above and survival rates, fertility rates and net migration rates. Survival rates, fertility rates and net migration rates were obtained from Texas State Data Center (TSDC). These rates are year, county, age, race and sex specific. Following section illustrates the basic concept of the procedure along with some sample code. At first, year 2000 persons file is tabulated by county, age, race and sex. In the first iteration all people are aged by 1 and a random number, between 0 and 1, is assigned to each person. This variable is called as rs. The survival rates are then joined to this table by county, age, year, race and sex. The rates are then compared with variable rs. If rs is greater than the survival rate then that person is termed as dead. These persons are separated out to a different table. data persons_2001; set persons_2000; age+1; create table persons_2001a as select x.*,y.survival_p from persons_2001 as x left join survival_p as y on x.county = y.county and x.age = y.age and y.year = 2001 and x.race = y.race and x.sex = y.sex; data persons_2001c deaths_2001; set persons_2001b; if rs > survival_p then do; person_status = 'P00'; output deaths_2001; else output persons_2001c; After this, a females table is created for birthing. Since fertility rates are for females of age between 9 and 50, only females between these age categories are selected and a random number rb, between 0 and 1, is assigned to each person. This variable is called as rb. The fertility rates are then joined to this table by county, age, year and race. The rates are then compared with variable rb and then birth event is determined including sex of the baby. A new person id is assigned to the new baby. data females_2001(keep = county person_id family_id age race hisp race); set persons_2001c; if sex = 'S02'; if 9 < age < 50;

5 data females_2001; set females_2001; RB = ranuni(2); create table females_2001a as select x.*,y.birth_event,y.p0_birth,y.p1_birth from females_2001 as x left join birthing_p as y on x.county = y.county and y.year = 2001 and x.race = y.race and x.age = y.age; Once survival and fertility modules are done a new persons table is created adding survived people and new born babies. Persons are then aggregated by county, age, race and sex. To this new base table migration rates are applied by county, age, race and sex. The migration rates are net migration rates. So a negative migration rate denotes out migration and a positive migration rate denotes in migration. A unique person id is applied to the in migrants. A separate table for in and out migrants is created. create table mig1 as select distinct county,age,race,sex,count(person_id) as people from persons_2001 group by county,age,sex,race; create table mig2 as select x.*,y.migrate,round(people*migrate) as mpop from mig1 as x,migrates as y where x.county = y.county and x.race = y.race and x.sex = y.sex and x.age = y.age and calculated mpop ~= 0; proc sort data = persons_2001; by person_id; create table mig3 as select x.*, y.mpop from persons_2001 as x,mig2 as y where x.county = y.county and x.age = y.age and x.race = y.race and x.sex = y.sex order by county,race,age,sex,rand; data imig omig; retain n;set mig3;by county race age sex; if first.county then n = 0; if first.race then n = 0; if first.age then n = 0; if first.sex then n = 0; if mpop > 0 then do; n+1; if n < = mpop then output imig; if mpop < 0 then do; n=n-1; if n < 0 then if n > = mpop then do; output omig; delete; After all three modules are done a final persons file for 2001 is created. This procedure was repeated for desired number of years.

6 CONCLUSION The household synthesis module in UrbanSim was written in Java before and it s now written in python. This was kind of a black box for a person who is not familiar with these programming languages. Population synthesis module is currently not available for UrbanSim. SAS gave us an opportunity to write and test our own modules. REFERENCES Hobeika, Antoine TRANSIMS Fundamentals. Virginia Polytechnic University. UrbanSim website. H-GAC website. CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: Himanshu Joshi Houston-Galveston Area Council 3555 Timmons Lane, Suite#120 Houston, TX himanshu.joshi@h-gac.com SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies.

Exercise 13. Accessing Census 2000 PUMS Data

Exercise 13. Accessing Census 2000 PUMS Data Purpose: The goal of this exercise is to extract some 2000 PUMS data for Asian Indians for PUMAs within California. You may either download the records for