Attracting and Recruiting the Right People Presented by: Clint Vawser 26 th July 2007.
13 th TRB Application Conference, Reno, NV May 11 th , 2011 Wu Sun Clint Daniels
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Transcript of 13 th TRB Application Conference, Reno, NV May 11 th , 2011 Wu Sun Clint Daniels
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Comparisons of Synthetic Populations Generated From Census 2000 and American Community Survey
(ACS) Public Use Microdata Sample (PUMS)
13th TRB Application Conference, Reno, NVMay 11th, 2011
Wu SunClint Daniels
& Ziying Ouyang, SANDAGPeter Vovsha
& Joel Freedman, PB Americas
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Presentation Outline Project Background SANDAG PopSyn
– Feature– Scenarios– Methodology– Geographies– Key steps– Control variables
Data Sources Validations Results Analysis Conclusions
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Project Background SANDAG & SANDAG Travel Models SANDAG PopSyn & ABM
– What is a PopSyn?– What role does a PopSyn play in an ABM?
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SANDAG PopSyn Development
PopSyn II
PopSyn I PopSyn I• Based on Atlanta PopSyn• Updated controls and
programming• No person level controls
PopSyn II
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PopSyn II Features Formulated as an entropy-maximization problem Balance person and household controls
simultaneously Applicable to both Census 2000 and ACS data Updated household weight discretizing step Added household allocation from TAZ to small
geography Database-driven and OOD
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PopSyn Scenarios Year 2000 PopSyn Year 2008 PopSyn Future year PopSyn(s)
2000 Census Base Year 2010
2008 ACS Base Year 2050
Future Years
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An entropy-maximization problem by Peter Vovsha Subject to constraints:
αi
Where i = 1, 2….I Household and person controls Set of households in the PUMA
A priori weights assigned in the PUMA Zonal controls
αi Coefficients of contribution of household to each control
Methodology
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PopSyn Geographies
MGRA (33,000)
TAZ (4,605)
PUMA (16)
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SANDAG PopSyn Key Steps
Create Sample HHs
Balance HH Weights
Discretize HH Weights
Allocate HHs
Validate PopSyn
Create control targets
Create validation measures
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Control Variables Household level controls
– Household size (1,2,3,4+)– Household income (5 categories)– Number of workers per household (0, 1, 2, 3+)– Number of children in household (0, 1+)– Dwelling unit type (3 categories)– Group quarter status (4 categories)
Person level controls– Age (7 categories)– Gender (2 categories)– Race (8 categories)
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Data Sources Census and ACS PUMS
– Household and person level microdata Census and ACS summary data
– Source for base year control targets– Source for base year validation data
SANDAG estimates and forecasts– Source for future year control targets
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ACS Vs. CensusACS Census
Frequency Every year Every 10 years
Data Collected
Both SF1 and SF3 data
oSF1: number of people, age, race, gender, etc.oSF3: income, education, disability status, etc.
Estimates Period estimates "Point-in-time" estimates
Sample Size 1 in 40 households
o Short form SF1: 100% counto Long form SF3: 1 in 6 households
o 1-year PUMS: 1%o 3-year PUMS: 3%o 5-year PUMS: 5%
PUMS: 5% sample
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Why ACS? Advantages
• Timeliness: a new set of data every year for areas that are large enough (population > 65,000).
Disadvantages• Based on a smaller sample associated with increased
error compared with decennial Census. • ‘Period estimates’ vs. ‘Point in time’. Which year does
the ACS PUMS data represent?
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Validations Objectives
– Compare PopSyn against Census or ACS Number of validation measures
– Year 2000: 96– Year 2008: 86
Variables used as universes– Number of households– Number of persons
Controlled variables Non-Controlled variables
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Validation Statistics Mean percentage difference Standard Deviations Absolute values vs. percentage values Geography: PUMA
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Results
HHID HH Serial # GeoType GeoZone Version SourceID
…
HH Serial # PUMA Attributes
Allocated Household Table
PUMS Person TablePerID HH Serial # Attributes
PUMS Household Table
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Results-Validation Excerpt
Label Description PopSyn CensusMean Diff.
Standard Dev.
1 number of HHs 985938 992681 -0.6% 0.9%6 size 1 24.2% 24.2% -0.4% 1.5%7 size 2 32.3% 32.0% 0.8% 1.0%8 size 3 15.9% 16.1% -1.8% 2.0%9 size 4 27.7% 27.7% -0.7% 3.3%
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Census 2000 Population Density
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Results-Examples(I)
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Results-Examples(II)
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Results-Examples(III)
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Results-Examples(IV)
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Results-Household Characteristics
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Results-Person Characteristics
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Results-Summary(I)
Mean Diff. Range by PUMA Census 2000
ACS2005-2009
>-2% & <2% 40/96 28/86>-5% & <5% 59/96 50/86>-10% & <10% 78/96 67/86>-20% & < 20% 87/96 84/86
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Results-Summary(II) ACS-Based vs. Census-Based PopSyn(s)
– Both produced acceptable results– Census PopSyn performed better than ACS PopSyn
in validation measures– Consistency between targets and validation data
• Census PopSyn: both from Census summary• ACS PopSyn: targets from estimates, validation data
from ACS summary– Target accuracy at small geography is the key
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Results-Software Performance Test environment
– Dell Intel Xeon PC with dual 2.69 GHz processors and 3.5 GB of RAM
Performance
Year 2000 Year 2008Runtime 11.8 min 14.1 minSynPop Pop 2.77mil 2.95milSynPop HHs 0.99mil 1.05mil
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Issues and Future Work Issues
– Consistency of various geographies• Census/ACS geography• Transportation modeling geography• Land use modeling geography
– Accuracy of land use estimates and forecasts at small geographies
Future Work– Add worker occupations as controls– Improve control target accuracy– Automate control target generations
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Conclusions Closed form formulation provides a sound
theoretical basis Balance household and person controls
simultaneously Applicable to both ACS and Census data An early application using 2009 ACS 5-year data Database-driven and OOD makes software easy to
maintain, expand, and transfer
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AcknowledgementsThe authors thank SANDAG staff:
– Daniel Flyte, – Ed Schafer, – Eddie Janowicz,
For their help in this project, especially in providing control target data.
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Questions & Contacts Questions? Contacts
– Wu Sun: [email protected]– Ziying Ouyang: [email protected]– Clint Daniels: [email protected]