Bay Area PopulationSim Documentation

2023 TM2 Synthetic Population — Full Reference

Executive summary

Document updated: 2025-11-19 14:28:20 with data from model run dated 2025-11-10

Document updated: 2025-11-19 14:27:12 with data from model run dated 2025-11-10

This single consolidated reference documents the inputs, outputs, processing steps, aggregate summaries, and theoretical underpinnings of the 2023 TM2 synthetic population workflow based on PopulationSim. It is intended as the canonical briefing for modelers, data engineers, and reviewers who need a single place to understand: what data feed the pipeline, what files are produced and where to find them, how marginal controls are defined and applied, and what the main statistical algorithms are that guarantee marginal consistency.

This document builds on the existing repository documentation (docs/TM2_OUTPUTS.md, docs/TM2_INPUT_FIELDS.md, and docs/TM2_OUTPUT_SUMMARIES.md) and consolidates the technical material into a narrative report ready for export to Word/PDF for distribution.

Executive summary — main outputs & quick modeler guidance

This integrated executive summary combines the short, modeler-focused one-page synthesizer notes with the numeric highlights from the full dataset analysis. Use this to quickly understand the canonical outputs, what they mean, and where to look for more detail.

Snapshot: canonical totals (complete synthetic population)

Total households (all geographies): 3,032,138
Total persons: 7,642,976
Average household size: 2.52 persons
Average household income: $124,024
Average age: 39.9 years

Key county totals (households):

San Francisco: 418,196 (13.8%)
San Mateo: 288,250 (9.5%)
Santa Clara: 704,160 (23.2%)
Alameda: 646,592 (21.3%)
Contra Costa: 431,991 (14.2%)
Solano: 165,589 (5.5%)
Napa: 56,033 (1.8%)
Sonoma: 208,981 (6.9%)
Marin: 112,346 (3.7%)

Household size distribution (selected):

1 person: 899,396 (29.7%)
2 persons: 934,694 (30.8%)
3 persons: 488,574 (16.1%)
4 persons: 423,747 (14.0%)
5+ persons: 257,294 (8.5%)

Person age groups (selected totals):

0–19 (aggregated 0–4 + 5–17): ~1,536,551 (~20.1%)
20–34: 1,745,985 (22.8%)
35–64: 3,103,775 (~40.6%)
65+: 1,256,209 (~16.4%)

(For exact, column-level counts see output_2023/FULL_DATASET_ANALYSIS.md.)

What the outputs include (quick reference)

Geographies

COUNTY (9-county Bay Area) — used for regional controls and reporting.
TAZ_NODE — primary mapping unit for TAZ-level marginals and Tableau exports; TAZ-level summaries live under output_2023/populationsim_working_dir/output/.
MAZ_NODE — micro/parcel-like zones used when MAZ marginals are available.
Group quarters (GQ) are tracked separately and often included in hh_size_1 at the TAZ level in our marginals.

Canonical control variables (examples)

Households: numhh_gq, hh_size_1..hh_size_6_plus, hh_gq_university, hh_gq_noninstitutional, hh_wrks_0..hh_wrks_3_plus, hh_kids_yes/no.
Persons: pers_age_00_19, pers_age_20_34, pers_age_35_64, pers_age_65_plus, plus occupation buckets.
Income bins: inc_lt_20k, inc_20k_45k, … inc_200k_plus.

Controls vs weights (important modeler note)

control_value: raw supplied marginal. May be unbalanced or sourced from a different base; good for reporting but may not sum exactly to final totals.
COUNTY_balanced_weight / COUNTY_integer_weight: these are the balanced (float) and integerized versions that sum consistently to final county totals. Use them when you need additive consistency across aggregations.
TAZ/MAZ results appear as *_control and *_result columns in the final_summary_* CSVs.

Practical guidance (short checklist)

If you need additive consistency (TAZ → county → region), prefer the balanced/integer weight fields over raw control_value marginals.
Check whether your TAZ marginals already include GQ in hh_size_1 before adding GQ separately.
When rerunning synthesis after changing controls, always verify:
1. numhh_gq totals by geography,
2. whether hh_size_1 includes GQ in those marginals,
3. that you select balanced vs raw controls according to the comparison you intend.

Where to find the detailed artifacts (quick links)

Full dataset numeric summary: output_2023/FULL_DATASET_ANALYSIS.md (generated by the analysis pipeline; includes complete household and person breakdowns).
Per-TAZ final summary: output_2023/populationsim_working_dir/output/final_summary_TAZ_NODE.csv.
County performance & detailed breakdowns and charts: output_2023/charts/county_analysis/ (contains county_performance_summary.csv and county_detailed_results.csv plus PNGs).
TAZ analysis charts & summaries: output_2023/charts/taz_analysis/ (PNG charts and taz_analysis_summary.csv).
MAZ household comparison: output_2023/charts/MAZ_household_comparison_table.csv and MAZ_household_comparison.png.

Top takeaways (one-liners for quick consumption)

Regional totals are consistent: 3.03M households and ~7.64M persons in the current synthetic outputs.
Some control breakdowns (raw control_value) may not sum to the canonical numhh_gq totals; use balanced/integer county weights when you require exact summation.
TAZ-level hh_size_1 often includes group quarters in our marginals—this is the most common source of confusion when comparing household counts across processing steps.

For more detail, see the per-file appendices and the charts section in this document. If you want the executive summary shortened to a single page with the same content, I can produce a one-page PDF/DOCX trimmed to the top bullets.

Background and scope

PopulationSim is a micro-synthesis engine that creates synthetic households and persons by sampling microdata (PUMS) and reweighting samples to match aggregate control marginals. The TM2 adaptation of PopulationSim targets the nine-county San Francisco Bay Area and produces final CSVs consumed by activity-based travel models (CT-RAMP and related workflows).

This reference covers the following:

Raw inputs used to build the seed population and controls (PUMS, 2020 Census PL tables, 2023 ACS 1- and 5-year tables, and NHGIS 2020→2010 crosswalks).
The generated control/marginal files at MAZ / TAZ / County / Region levels and their canonical filenames.
Final synthetic outputs (households and persons) and sample schemas.
Aggregate results reporting and where to find the authoritative summary CSVs in the repo.
Theoretical background: iterative proportional fitting (IPF), balancing/weighting, integerization, and specific handling for group quarters (GQ).

Data sources and canonical years

2020 Decennial PL (Public Law 94-171) — used for block-level authoritative counts (CENSUS_EST_YEAR = 2020). Key variables: H1_002N (households), P1_001N (total population), P5_xxx (group quarters subcategories).
2023 American Community Survey (ACS) — ACS 5-year for many demographic and socio-economic marginals; ACS 1-year used for county-level targets where timeliness is prioritized (ACS_EST_YEAR = 2023).
2023 PUMS (ACS 5-year Public Use Microdata Sample) — seed microdata for sampling households and persons; filtered to Bay Area PUMAs.
NHGIS 2020→2010 geographic crosswalks — used to interpolate 2020 Census geographies to 2010 block geometry so MAZs (which reference 2010 blocks) can be aligned with 2020 PL counts.

Geographic framework

MAZ (Micro Analysis Zone): 2010 block-based microzones used for final household placement.
TAZ (Traffic Analysis Zone): aggregated travel-model zones used in CT-RAMP.
County: nine Bay Area counties with local numeric identifiers used in the repo.
PUMA: Public Use Microdata Areas used to select PUMS seeds.

File map — inputs and outputs (where to find them in the repo)

Important repository locations (relative to repo root):

Primary outputs directory: output_2023/populationsim_working_dir/
- data/ — generated upstream marginal files and crosswalks (e.g., maz_marginals_hhgq.csv, taz_marginals_hhgq.csv, county_marginals.csv, geo_cross_walk_tm2_maz.csv).
- output/ — final PopulationSim outputs and run summaries (e.g., synthetic_households.csv, synthetic_persons.csv, households_2023_tm2.csv, persons_2023_tm2.csv, final_summary_TAZ_NODE.csv, final_summary_COUNTY_*.csv).
Documentation: docs/TM2_OUTPUTS.md, docs/TM2_INPUT_FIELDS.md, docs/TM2_OUTPUT_SUMMARIES.md (supporting reference files already in the repo).

Canonical controls and marginals (summary)

Controls are defined in tm2_control_utils/config_census.py. Source rules:

Any control declared with the 'pl' prefix uses 2020 Decennial PL tables and is aggregated from 2020 block geography. NHGIS crosswalks convert those 2020 block values to 2010 block geometry where necessary.
Controls declared with 'acs5' or 'acs1' use ACS 5-year or 1-year tables (2023). The source geography (tract, block group, county) depends on the control and is encoded in the control tuple.

Representative control variables by geography (high level):

MAZ marginals: num_hh, total_pop, hh_gq_university, hh_gq_military, hh_gq_other_nonins, numhh_gq.
TAZ marginals: income bins (inc_*), household workers (hh_wrks_*), age buckets (pers_age_*), household size (hh_size_*), hh_size_1_gq.
County marginals: occupational splits (pers_occ_*) and county targets such as num_hh_target_by_county (ACS1).

For complete control tuples and programmatic details, see tm2_control_utils/config_census.py (the CONTROLS dictionary and INCOME_BIN_MAPPING).

Seed data and synthetic outputs — canonical fields

Rather than rerun extraction, this reference imports the canonical field lists from existing docs. See docs/TM2_INPUT_FIELDS.md for per-field descriptions. In brief:

Seed household fields: HHID/unique_hh_id, PUMA, SERIALNO, WGTP, ADJINC/HHinCADJ, hh_income_2023, hh_income_2010, NP, VEH, HHT, BLD, TYPE, hhgqtype, integer_weight/initial weight.
Seed person fields: PERID, HHID, SERIALNO, SPORDER, PWGTP, AGEP, SEX, ESR, SCHL, OCCP, WKHP, WKW, HISP, PINCP, person_type, hhgqtype.
Synthetic household fields: unique_hh_id, PUMA, TAZ_NODE, MAZ_NODE, integer_weight, SERIALNO, ADJINC, WGTP, NP, TYPEHUGQ, ACR, BLD, HHT, HINCP, TEN, VEH, hh_workers_from_esr, hh_income_2023, hhgqtype, hh_income_2010.
Synthetic person fields: PUMA, TAZ_NODE, MAZ_NODE, integer_weight, unique_hh_id, SERIALNO, SPORDER, PWGTP, AGEP, COW, MIL, SCHG, SCHL, SEX, WKHP, WKWN, ESR, HISP, PINCP, POWPUMA, INDP, OCCP, occupation, employed, employ_status, student_status, person_type, hhgqtype.

(The full headers and representative sample rows are embedded in docs/TM2_OUTPUTS.md Appendix D and saved as docs/sample_synthetic_households.csv and docs/sample_synthetic_persons.csv.)

Aggregate results summary (what exists in the repo now)

This section summarizes the aggregate outputs you already have in the repository. I have not run any new code — this points to the authoritative CSVs and describes what they contain so you can find concrete numbers.

Primary summary files and how to read them:

output_2023/populationsim_working_dir/output/final_summary_TAZ_NODE.csv — final comparison table at the TAZ level showing control names, control vs result counts, and difference metrics. Use this file to understand per-TAZ control adherence.
output_2023/populationsim_working_dir/output/final_summary_COUNTY_*.csv (one per county) — county-level summary tables (control vs result). Look for COUNTY_balanced_weight and COUNTY_integer_weight columns which reconcile to authoritative numhh_gq totals.
output_2023/populationsim_working_dir/data/county_summary_2020_2023.csv — county-level diagnostic combining 2020 PL and ACS 2023 scaling considerations.
output_2023/populationsim_working_dir/output/households_2023_tm2.csv and persons_2023_tm2.csv — these are PopulationSim-compatible household/person exports (full files; large)

Interpreting key aggregate checks (procedural guidance):

To check MAZ-level household totals: aggregate maz_marginals_hhgq.csv numhh_gq by county and compare to county ACS targets (county_targets_2023.csv or county_summary_2020_2023.csv).
To check TAZ-level breakdowns: compare taz_marginals_hhgq.csv size/income buckets to final_summary_TAZ_NODE.csv to inspect where differences exceed tolerances.
To reconcile unexpected mismatches: check whether the summary uses raw control breakdowns (control_value) versus balanced weights (COUNTY_balanced_weight) or integerized weights (these latter two are additive and generally align to numhh_gq).

Theoretical background (IPF, balancing, integerization, GQ)

Iterative proportional fitting (IPF)

IPF is used to adjust microdata sample weights so that aggregated sample marginals match the target control marginals. IPF iteratively rescales weights for each margin (e.g., income, household size, age) until the differences fall below tolerance or a maximum iteration count is reached.
In a multi-geography setting (MAZ/TAZ/County) IPF is either run hierarchically or via constrained approaches that respect MAZ totals as authoritative and adjust TAZ-level distributions proportionally when necessary.

Balancing and integerization

PopulationSim first computes real-valued balanced weights (floating point) via IPF.
Integerization converts these fractional weights into whole households (integer weights) because a household is an indivisible unit in downstream models; common approaches include deterministic rounding with redistribution, combinatorial optimization (e.g., TRS), or probabilistic rounding. The repo stores both COUNTY_balanced_weight (float) and COUNTY_integer_weight (integerized) in final summaries for auditability.

Group quarters (GQ) handling

TM2 uses a person-as-household approach for certain non-institutional GQ populations (for example, university dorm residents) by converting each GQ person into a 1-person household equivalent. This produces numhh_gq which replaces num_hh for the purposes of synthesis where GQ populations must be represented.
MAZ-level GQ counts are derived from 2020 PL P5 variables and aggregated to MAZs; TAZ-level analyses may include hh_size_1_gq to allow apples-to-apples comparisons when GQ persons are present.

Validation and QA recipes (no code run here)

Recommended checks (high level):

Referential integrity: ensure every person in synthetic_persons.csv has a matching household id in synthetic_households.csv.
TAZ vs MAZ totals: aggregate maz_marginals_hhgq.csv by TAZ using geo_cross_walk_tm2_maz.csv and compare to taz_marginals_hhgq.csv.
County reconciliation: sum MAZ numhh_gq by county and compare to county_targets_2023.csv and to final_summary_COUNTY_*.csv balanced/integer weight columns.

Standard acceptance tolerances:

Hierarchical differences: within ±1 per MAZ after rounding (this repo sets HIERARCHICAL_TOLERANCE = 1.0)
Relative differences for bucketed controls: domain-specific thresholds (commonly ±0.5% to ±2% depending on the control’s variance and population size)

How to extend and maintain this document

This reference intentionally points to the live CSV artifacts in output_2023/ rather than embedding large numeric tables. To refresh numeric aggregates or add charts, re-run the analysis scripts in analysis/ (for example, analysis/analyze_taz_controls_vs_results.py and analysis/analyze_county_results.py) and update the summary sections in this document.
When control definitions change, update the authoritative tm2_control_utils/config_census.py and regenerate marginals via create_baseyear_controls.py.

Export and sharing

To export this Markdown to Word or PDF use pandoc (installed in our environment):

pandoc docs/TM2_FULL_REFERENCE.md -o docs/TM2_FULL_REFERENCE.docx
pandoc docs/TM2_FULL_REFERENCE.md -o docs/TM2_FULL_REFERENCE.pdf

If you prefer a formatted Word document with title page and TOC, we can add a pandoc template and re-export; say the word and I will add a template and produce a refined docx.

References and pointers

Primary control definitions: tm2_control_utils/config_census.py
Seed generation: create_seed_population.py, create_seed_population_tm2_refactored.py
Baseyear control generation: create_baseyear_controls.py and create_baseyear_controls_23_tm2.py
Analysis scripts: analysis/analyze_taz_controls_vs_results.py, analysis/analyze_county_results.py, analysis/compare_synthetic_populations.py
Outputs: output_2023/populationsim_working_dir/data/ and .../output/
Samples and embedded snippets already saved: docs/sample_synthetic_households.csv, docs/sample_synthetic_persons.csv and docs/TM2_OUTPUTS.docx (previous export)

Appendix A — Quick checklist before a formal delivery

Confirm geo_cross_walk_tm2_maz.csv covers all MAZs referenced in maz_marginals_hhgq.csv.
Confirm maz_marginals_hhgq.csv contains a numhh_gq column and that county sums match county_targets_2023.csv when using balanced/integer weights.
Run hierarchical enforcement (if re-generating marginals) and check reported HIERARCHICAL_TOLERANCE.

Appendix B — Contact and versioning

Repo: https://github.com/BayAreaMetro/populationsim (branch: tm2)
If you’d like, I can (a) convert this file to a styled Word doc with TOC, (b) embed selected numeric aggregate tables here (if you provide the CSV extracts), or (c) produce a short 1-page summary slide for stakeholders.

Aggregate snapshots (selected summaries)

Below are verbatim headers and the first few rows from key summary CSVs found under output_2023/ — pasted here for easy inspection and review. These are snapshots (headers + sample rows) and not the full files. Use the CSVs in output_2023/ for complete data.

County performance summary — `output_2023/charts/county_analysis/county_performance_summary.csv`

county_id,county_name,total_control,total_result,total_diff,total_diff_pct,mae,rmse,perfect_matches,perfect_pct,variable_count

1,San Francisco,396119.2400090425,395670,-449.2400090424926,-0.1134102976245833,4128.383483444326,11627.743040182844,0,0.0,33

2,San Mateo,275368.1982865932,275352,-16.19828659319319,-0.0058824100582357,2719.5355327764355,7130.955833395954,0,0.0,33

3,Santa Clara,687503.2012797081,687199,-304.2012797080679,-0.0442472528334169,6348.458270235131,16323.822382170883,0,0.0,33

4,Alameda,635505.1036905675,634945,-560.1036905675428,-0.088135199436614,6520.067806705379,17369.62139651412,0,0.0,33

5,Contra Costa,413812.0586755095,413706,-106.05867550952826,-0.0256296725254916,4276.81502485539,11017.16468471024,0,0.0,33

6,Solano,158311.5759314681,158276,-35.57593146810541,-0.0224720973553481,1336.9747255488733,3891.411409747325,0,0.0,33

7,Napa,52721.18162235967,52718,-3.181622359668836,-0.0060348085186305,344.71558252225725,916.25953664135,0,0.0,33

8,Sonoma,193976.6604102071,194051,74.33958979288582,0.0383239868320642,1677.1455929603758,4232.679303059822,0,0.0,33

9,Marin,106932.58211472387,106762,-170.58211472387484,-0.1595230484015281,1000.7055042238642,2677.3647004427853,0,0.0,33

County detailed results (San Francisco sample rows) — `output_2023/charts/county_analysis/county_detailed_results.csv`

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight,county_id,county_name,result,control

numhh,371859.0,374115.95384263335,374152.5044130808,373789.0,374152.5044130808,373769,374152.5044130808,374080,1,San Francisco,373769,374115.95384263335

numhh_gq,395670.0,396119.2400090425,396096.9901883293,395670.0,396096.99018832936,395670,396096.99018832936,395698,1,San Francisco,395670,396119.2400090425

hh_gq_university,6486.0,5909.324024243913,5851.982889952319,5847.0,5851.982889952318,5841,5851.982889952318,5826,1,San Francisco,5841,5909.324024243913

hh_gq_noninstitutional,17325.0,16093.962142165174,16092.502885296264,16034.0,16092.502885296264,16060,16092.502885296264,15792,1,San Francisco,16060,16093.962142165174

hh_size_1,170765.0,145705.08065269142,145903.40908029611,145772.0,145903.40908029611,145321,145903.40908029611,145512,1,San Francisco,145321,145705.08065269142

hh_size_2,150447.0,119586.42220594992,119288.9351768422,119153.0,119288.9351768422,119262,119288.9351768422,119327,1,San Francisco,119262,119586.42220594992

hh_size_3,58164.0,47923.226966056594,47797.88383291751,47757.0,47797.88383291751,47899,47797.88383291751,47916,1,San Francisco,47899,47923.226966056594

hh_size_4,43583.0,35852.686253426255,36048.57426440453,36013.0,36048.57426440453,36220,36048.57426440453,36236,1,San Francisco,36220,35852.686253426255

hh_size_5,14967.0,13419.932390770911,13494.382656704496,13482.0,13494.382656704496,13516,13494.382656704496,13543,1,San Francisco,13516,13419.932390770911

hh_size_6_plus,13288.0,11628.605373738266,11619.319401915893,11612.0,11619.319401915893,11551,11619.319401915893,11546,1,San Francisco,11551,11628.605373738266

Final TAZ-level summary (header + first row) — `output_2023/populationsim_working_dir/output/final_summary_TAZ_NODE.csv`

geography,id,numhh_control,numhh_gq_control,hh_gq_university_control,hh_gq_noninstitutional_control,hh_size_1_control,hh_size_2_control,hh_size_3_control,hh_size_4_control,hh_size_5_control,hh_size_6_plus_control,hh_wrks_0_control,hh_wrks_1_control,hh_wrks_2_control,hh_wrks_3_plus_control,pers_age_00_19_control,pers_age_20_34_control,pers_age_35_64_control,pers_age_65_plus_control,hh_kids_yes_control,hh_kids_no_control,inc_lt_20k_control,inc_20k_45k_control,inc_45k_60k_control,inc_60k_75k_control,inc_75k_100k_control,inc_100k_150k_control,inc_150k_200k_control,inc_200k_plus_control,numhh_result,numhh_gq_result,hh_gq_university_result,hh_gq_noninstitutional_result,hh_size_1_result,hh_size_2_result,hh_size_3_result,hh_size_4_result,hh_size_5_result,hh_size_6_plus_result,hh_wrks_0_result,hh_wrks_1_result,hh_wrks_2_result,hh_wrks_3_plus_result,pers_age_00_19_result,pers_age_20_34_result,pers_age_35_64_result,pers_age_65_plus_result,hh_kids_yes_result,hh_kids_no_result,inc_lt_20k_result,inc_20k_45k_result,inc_45k_60k_result,inc_60k_75k_result,inc_75k_100k_result,inc_100k_150k_result,inc_150k_200k_result,inc_200k_plus_result,numhh_diff,numhh_gq_diff,hh_gq_university_diff,hh_gq_noninstitutional_diff,hh_size_1_diff,hh_size_2_diff,hh_size_3_diff,hh_size_4_diff,hh_size_5_diff,hh_size_6_plus_diff,hh_wrks_0_diff,hh_wrks_1_diff,hh_wrks_2_diff,hh_wrks_3_plus_diff,pers_age_00_19_diff,pers_age_20_34_diff,pers_age_35_64_diff,pers_age_65_plus_diff,hh_kids_yes_diff,hh_kids_no_diff,inc_lt_20k_diff,inc_20k_45k_diff,inc_45k_60k_diff,inc_60k_75k_diff,inc_75k_100k_diff,inc_100k_150k_diff,inc_150k_200k_diff,inc_200k_plus_diff

TAZ_NODE,1,621.0,626.0,0.0,5.0,389.0,287.0,63.0,24.0,0.0,0.0,201.0,261.0,236.0,65.0,109.0,561.0,538.0,179.0,55.0,708.0,65.0,153.0,12.0,32.0,45.0,30.0,189.0,237.0,622,626,0,4,301,239,56,26,0,0,159,207,198,58,84,427,407,137,45,577,54,122,9,26,37,24,158,192,1.0,0.0,0.0,-1.0,-88.0,-48.0,-7.0,2.0,0.0,0.0,-42.0,-54.0,-38.0,-7.0,-25.0,-134.0,-131.0,-42.0,-10.0,-131.0,-11.0,-31.0,-3.0,-6.0,-8.0,-6.0,-31.0,-45.0

TAZ_NODE,2,887.0,1225.0,0.0,338.0,376.0,167.0,179.0,150.0,13.0,5.0,169.0,331.0,268.0,123.0,222.0,543.0,1077.0,267.0,92.0,798.0,146.0,73.0,108.0,36.0,55.0,72.0,111.0,289.0,905,1225,0,320,396,168,177,144,13,7,173,343,269,120,229,584,1166,288,98,807,147,73,105,38,56,72,117,297,18.0,0.0,0.0,-18.0,20.0,1.0,-2.0,-6.0,0.0,2.0,4.0,12.0,1.0,-3.0,7.0,41.0,89.0,21.0,6.0,9.0,1.0,0.0,-3.0,2.0,1.0,0.0,6.0,8.0

TAZ_NODE,3,1333.0,1371.0,0.0,38.0,656.0,455.0,209.0,38.0,52.0,0.0,372.0,483.0,435.0,119.0,202.0,1036.0,993.0,331.0,143.0,1267.0,108.0,108.0,126.0,76.0,94.0,143.0,163.0,592.0,1335,1371,0,36,621,427,198,36,53,0,352,457,414,112,210,1003,977,324,137,1198,102,101,115,71,91,134,159,562,2.0,0.0,0.0,-2.0,-35.0,-28.0,-11.0,-2.0,1.0,0.0,-20.0,-26.0,-21.0,-7.0,8.0,-33.0,-16.0,-7.0,-6.0,-69.0,-6.0,-7.0,-11.0,-5.0,-3.0,-9.0,-4.0,-30.0

TAZ_NODE,4,641.0,1210.0,0.0,569.0,120.0,153.0,126.0,127.0,63.0,28.0,94.0,219.0,131.0,172.0,271.0,506.0,555.0,45.0,169.0,448.0,77.0,118.0,51.0,4.0,122.0,35.0,60.0,150.0,642,1210,0,568,204,187,120,88,30,13,122,243,128,149,370,813,842,61,198,444,77,129,53,2,125,38,64,154,1.0,0.0,0.0,-1.0,84.0,34.0,-6.0,-39.0,-33.0,-15.0,28.0,24.0,-3.0,-23.0,99.0,307.0,287.0,16.0,29.0,-4.0,0.0,11.0,2.0,-2.0,3.0,3.0,4.0,4.0

TAZ_NODE,5,856.0,873.0,0.0,17.0,502.0,288.0,118.0,0.0,0.0,0.0,157.0,513.0,214.0,23.0,86.0,430.0,740.0,142.0,59.0,849.0,59.0,90.0,10.0,0.0,170.0,135.0,39.0,405.0,858,873,0,15,480,270,108,0,0,0,149,486,202,21,74,418,726,141,55,803,56,83,11,0,163,126,37,382,2.0,0.0,0.0,-2.0,-22.0,-18.0,-10.0,0.0,0.0,0.0,-8.0,-27.0,-12.0,-2.0,-12.0,-12.0,-14.0,-1.0,-4.0,-46.0,-3.0,-7.0,1.0,0.0,-7.0,-9.0,-2.0,-23.0

TAZ_NODE,6,324.0,329.0,0.0,5.0,480.0,184.0,17.0,0.0,10.0,0.0,120.0,391.0,163.0,18.0,66.0,327.0,563.0,108.0,49.0,642.0,10.0,22.0,0.0,0.0,8.0,79.0,174.0,398.0,327,329,0,2,216,93,10,0,8,0,55,179,83,10,37,143,247,47,23,304,5,10,0,0,4,37,84,187,3.0,0.0,0.0,-3.0,-264.0,-91.0,-7.0,0.0,-2.0,0.0,-65.0,-212.0,-80.0,-8.0,-29.0,-184.0,-316.0,-61.0,-26.0,-338.0,-5.0,-12.0,0.0,0.0,-4.0,-42.0,-90.0,-211.0

TAZ_NODE,7,713.0,714.0,0.0,1.0,366.0,164.0,18.0,35.0,0.0,6.0,102.0,333.0,139.0,15.0,56.0,279.0,480.0,92.0,43.0,546.0,106.0,48.0,26.0,23.0,41.0,65.0,119.0,162.0,713,714,0,1,443,198,21,42,0,9,122,403,169,19,73,342,595,115,53,660,128,57,30,27,50,78,147,196,0.0,0.0,0.0,0.0,77.0,34.0,3.0,7.0,0.0,3.0,20.0,70.0,30.0,4.0,17.0,63.0,115.0,23.0,10.0,114.0,22.0,9.0,4.0,4.0,9.0,13.0,28.0,34.0

TAZ_NODE,8,393.0,393.0,0.0,0.0,130.0,161.0,57.0,22.0,22.0,5.0,73.0,153.0,154.0,17.0,156.0,141.0,466.0,139.0,49.0,348.0,49.0,0.0,0.0,0.0,26.0,121.0,42.0,159.0,393,393,0,0,125,154,55,24,29,6,70,150,157,16,152,135,452,136,53,340,49,0,0,0,25,117,44,158,0.0,0.0,0.0,0.0,-5.0,-7.0,-2.0,2.0,7.0,1.0,-3.0,-3.0,3.0,-1.0,-4.0,-6.0,-14.0,-3.0,4.0,-8.0,0.0,0.0,0.0,0.0,-1.0,-4.0,2.0,-1.0

TAZ_NODE,9,472.0,486.0,0.0,14.0,102.0,223.0,52.0,37.0,0.0,0.0,76.0,160.0,161.0,18.0,163.0,147.0,485.0,145.0,67.0,347.0,14.0,31.0,46.0,12.0,16.0,40.0,30.0,225.0,470,486,0,16,112,249,60,49,0,0,85,181,184,20,159,162,520,161,77,393,16,34,51,13,19,45,36,256,-2.0,0.0,0.0,2.0,10.0,26.0,8.0,12.0,0.0,0.0,9.0,21.0,23.0,2.0,-4.0,15.0,35.0,16.0,10.0,46.0,2.0,3.0,5.0,1.0,3.0,5.0,6.0,31.0

TAZ_NODE,10,460.0,460.0,0.0,0.0,104.0,187.0,58.0,86.0,28.0,0.0,85.0,178.0,180.0,20.0,182.0,164.0,543.0,162.0,177.0,286.0,0.0,9.0,14.0,23.0,12.0,51.0,33.0,321.0,460,460,0,0,105,187,57,83,28,0,83,178,178,21,190,172,586,174,175,285,0,8,14,23,12,50,34,319,0.0,0.0,0.0,0.0,1.0,0.0,-1.0,-3.0,0.0,0.0,-2.0,0.0,-2.0,1.0,8.0,8.0,43.0,12.0,-2.0,-1.0,0.0,-1.0,0.0,0.0,0.0,-1.0,1.0,-2.0

Expanded county rows (first 10 rows each)

Below are the first 10 data rows (header + 10 rows) from each final_summary_COUNTY_*.csv located in output_2023/populationsim_working_dir/output/ — included for quick reference.

final_summary_COUNTY_1.csv (San Francisco)

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight

numhh,371859.0,374115.95384263335,374152.5044130808,373789.0,374152.5044130808,373769,374152.5044130808,374080

numhh_gq,395670.0,396119.2400090425,396096.9901883293,395670.0,396096.99018832936,395670,396096.99018832936,395698

hh_gq_university,6486.0,5909.324024243913,5851.982889952319,5847.0,5851.982889952318,5841,5851.982889952318,5826

hh_gq_noninstitutional,17325.0,16093.962142165174,16092.502885296264,16034.0,16092.502885296264,16060,16092.502885296264,15792

hh_size_1,170765.0,145705.08065269142,145903.40908029611,145772.0,145903.40908029611,145321,145903.40908029611,145512

hh_size_2,150447.0,119586.42220594992,119288.9351768422,119153.0,119288.9351768422,119262,119288.9351768422,119327

hh_size_3,58164.0,47923.226966056594,47797.88383291751,47757.0,47797.88383291751,47899,47797.88383291751,47916

hh_size_4,43583.0,35852.686253426255,36048.57426440453,36013.0,36048.57426440453,36220,36048.57426440453,36236

hh_size_5,14967.0,13419.932390770911,13494.382656704496,13482.0,13494.382656704496,13516,13494.382656704496,13543

hh_size_6_plus,13288.0,11628.605373738266,11619.319401915893,11612.0,11619.319401915893,11551,11619.319401915893,11546

| control_name | control_value | COUNTY_preliminary_balanced_weight | COUNTY_balanced_weight | COUNTY_integer_weight | TAZ_NODE_balanced_weight | TAZ_NODE_integer_weight | MAZ_NODE_balanced_weight | MAZ_NODE_integer_weight | |:———————–|—————-:|————————————-:|————————-:|————————:|—————————:|————————–:|—————————:|————————–:| | numhh | 371859 | 374116 | 374153 | 373789 | 374153 | 373769 | 374153 | 374080 | | numhh_gq | 395670 | 396119 | 396097 | 395670 | 396097 | 395670 | 396097 | 395698 | | hh_gq_university | 6486 | 5909.32 | 5851.98 | 5847 | 5851.98 | 5841 | 5851.98 | 5826 | | hh_gq_noninstitutional | 17325 | 16094 | 16092.5 | 16034 | 16092.5 | 16060 | 16092.5 | 15792 | | hh_size_1 | 170765 | 145705 | 145903 | 145772 | 145903 | 145321 | 145903 | 145512 | | hh_size_2 | 150447 | 119586 | 119289 | 119153 | 119289 | 119262 | 119289 | 119327 | | hh_size_3 | 58164 | 47923.2 | 47797.9 | 47757 | 47797.9 | 47899 | 47797.9 | 47916 | | hh_size_4 | 43583 | 35852.7 | 36048.6 | 36013 | 36048.6 | 36220 | 36048.6 | 36236 | | hh_size_5 | 14967 | 13419.9 | 13494.4 | 13482 | 13494.4 | 13516 | 13494.4 | 13543 | | hh_size_6_plus | 13288 | 11628.6 | 11619.3 | 11612 | 11619.3 | 11551 | 11619.3 | 11546 |

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight
numhh_gq,288250.0,288146.11301593675,288146.4604158015,288250.0,288146.4604158015,288250,288146.4604158015,288250
hh_gq_university,942.0,964.2236408501045,964.9480411541948,964.0,964.9480411541947,927,964.9480411541947,923
hh_gq_noninstitutional,5283.0,5366.708816736122,5369.7115048111555,5380.0,5369.7115048111555,5331,5369.7115048111555,5128
hh_size_1,71671.0,77335.29617934089,77643.0910946521,77668.0,77643.09109465212,77444,77643.09109465212,77508
hh_size_2,87899.0,92158.15668272613,92338.12054458798,92362.0,92338.12054458796,92288,92338.12054458796,92321
hh_size_3,48432.0,49865.00006332666,49864.07562406577,49882.0,49864.075624065765,49961,49864.075624065765,49949
hh_size_4,42043.0,42257.89695554165,42090.26639106725,42103.0,42090.26639106725,42229,42090.26639106725,42197
hh_size_5,16326.0,16105.134354115116,15978.801139928191,15990.0,15978.801139928193,16061,15978.801139928191,16008
hh_size_6_plus,11122.0,10424.628780886362,10232.105621500195,10245.0,10232.105621500197,10267,10232.105621500197,10267
hh_wrks_0,53000.0,56643.547847730224,57200.42551181885,57215.0,57200.425511818845,57302,57200.425511818845,57499

final_summary_COUNTY_3.csv (Santa Clara)

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight

numhh,656068.0,657044.5299128868,657097.7167459886,656821.0,657097.7167459887,657080,657097.7167459887,657483

numhh_gq,687199.0,687503.2012797081,687498.8269491679,687199.0,687498.8269491679,687199,687498.826949168,687199

hh_gq_university,19440.0,19108.58832847633,19082.22017575558,19063.0,19082.22017575558,18822,19082.22017575558,18782

hh_gq_noninstitutional,11691.0,11350.083038344814,11318.890027423698,11315.0,11318.890027423698,11297,11318.890027423698,10934

hh_size_1,152017.0,133662.29212545996,133637.42673974964,133582.0,133637.42673974964,133607,133637.42673974964,133647

hh_size_2,219225.0,201434.8559289799,201417.36066657296,201336.0,201417.36066657296,201520,201417.36066657296,201267

hh_size_3,129808.0,124328.25115618316,124380.295534527,124308.0,124380.295534527,124424,124380.295534527,124705

hh_size_4,117669.0,117416.48879287227,117470.91368616773,117434.0,117470.91368616773,117461,117470.91368616773,117654

hh_size_5,46136.0,47898.13125356879,47889.1394149117,47870.0,47889.1394149117,47869,47889.1394149117,47930

hh_size_6_plus,29898.0,32304.510655822847,32302.58070405964,32291.0,32302.58070405964,32199,32302.58070405964,32280

| control_name | control_value | COUNTY_preliminary_balanced_weight | COUNTY_balanced_weight | COUNTY_integer_weight | TAZ_NODE_balanced_weight | TAZ_NODE_integer_weight | MAZ_NODE_balanced_weight | MAZ_NODE_integer_weight | |:———————–|—————-:|————————————-:|————————-:|————————:|—————————:|————————–:|—————————:|————————–:| | numhh | 656068 | 657045 | 657098 | 656821 | 657098 | 657080 | 657098 | 657483 | | numhh_gq | 687199 | 687503 | 687499 | 687199 | 687499 | 687199 | 687499 | 687199 | | hh_gq_university | 19440 | 19108.6 | 19082.2 | 19063 | 19082.2 | 18822 | 19082.2 | 18782 | | hh_gq_noninstitutional | 11691 | 11350.1 | 11318.9 | 11315 | 11318.9 | 11297 | 11318.9 | 10934 | | hh_size_1 | 152017 | 133662 | 133637 | 133582 | 133637 | 133607 | 133637 | 133647 | | hh_size_2 | 219225 | 201435 | 201417 | 201336 | 201417 | 201520 | 201417 | 201267 | | hh_size_3 | 129808 | 124328 | 124380 | 124308 | 124380 | 124424 | 124380 | 124705 | | hh_size_4 | 117669 | 117416 | 117471 | 117434 | 117471 | 117461 | 117471 | 117654 | | hh_size_5 | 46136 | 47898.1 | 47889.1 | 47870 | 47889.1 | 47869 | 47889.1 | 47930 | | hh_size_6_plus | 29898 | 32304.5 | 32302.6 | 32291 | 32302.6 | 32199 | 32302.6 | 32280 |

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight
numhh_gq,646592.0,647057.2825438137,647048.0619520488,646592.0,647048.0619520489,646592,647048.0619520489,646592
hh_gq_university,17772.0,17237.854884824184,17118.219049011797,17114.0,17118.2190490118,17049,17118.2190490118,16936
hh_gq_noninstitutional,26158.0,23890.6778627441,23840.94372610789,23832.0,23840.943726107886,23734,23840.943726107886,23359
hh_size_1,213823.0,194117.76544990143,194087.75104196844,193978.0,194087.75104196847,193663,194087.75104196847,193626
hh_size_2,208072.0,187052.29965902085,187053.72641183136,186901.0,187053.72641183145,186925,187053.72641183145,187078
hh_size_3,118565.0,106407.0877810976,106433.707859005,106348.0,106433.707859005,106418,106433.707859005,106414
hh_size_4,105906.0,95585.23162904622,95628.51408185843,95562.0,95628.51408185843,95747,95628.51408185843,95698
hh_size_5,41599.0,38613.12804487417,38605.987922074346,38589.0,38605.987922074346,38655,38605.987922074346,38584
hh_size_6_plus,26614.0,25281.769979873425,25238.374635311233,25214.0,25238.374635311233,25184,25238.374635311233,25192
hh_wrks_0,127535.0,130119.44252959885,127150.58761787383,127072.0,127150.58761787383,127224,127150.58761787383,127618

final_summary_COUNTY_5.csv (Contra Costa)

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight

numhh,406978.0,407170.2537242574,407173.55773921526,407072.0,407173.5577392153,407126,407173.5577392153,407309

numhh_gq,413706.0,413812.0586755095,413812.20228500926,413706.0,413812.2022850093,413706,413812.2022850093,413706

hh_gq_university,1461.0,1463.1438385048727,1463.4226055531458,1463.0,1463.422605553145,1420,1463.422605553145,1405

hh_gq_noninstitutional,5267.0,5178.661112747144,5175.22194024079,5171.0,5175.221940240791,5160,5175.221940240791,4992

hh_size_1,92726.0,88761.82802198837,88643.76374036803,88628.0,88643.76374036803,88538,88643.76374036803,88765

hh_size_2,131287.0,126613.66812727354,126492.29058593744,126459.0,126492.29058593744,126472,126492.29058593744,126501

hh_size_3,75114.0,72888.28558088455,72868.63218565722,72855.0,72868.63218565722,72967,72868.63218565722,72911

hh_size_4,68285.0,67067.83808695879,67178.46132996383,67162.0,67178.46132996381,67219,67178.46132996381,67139

hh_size_5,31358.0,30982.20650146981,31046.796894316,31034.0,31046.796894315998,31078,31046.796894315998,31055

hh_size_6_plus,20845.0,20856.427405682447,20943.61300297285,20934.0,20943.61300297285,20852,20943.61300297285,20938

| control_name | control_value | COUNTY_preliminary_balanced_weight | COUNTY_balanced_weight | COUNTY_integer_weight | TAZ_NODE_balanced_weight | TAZ_NODE_integer_weight | MAZ_NODE_balanced_weight | MAZ_NODE_integer_weight | |:———————–|—————-:|————————————-:|————————-:|————————:|—————————:|————————–:|—————————:|————————–:| | numhh | 406978 | 407170 | 407174 | 407072 | 407174 | 407126 | 407174 | 407309 | | numhh_gq | 413706 | 413812 | 413812 | 413706 | 413812 | 413706 | 413812 | 413706 | | hh_gq_university | 1461 | 1463.14 | 1463.42 | 1463 | 1463.42 | 1420 | 1463.42 | 1405 | | hh_gq_noninstitutional | 5267 | 5178.66 | 5175.22 | 5171 | 5175.22 | 5160 | 5175.22 | 4992 | | hh_size_1 | 92726 | 88761.8 | 88643.8 | 88628 | 88643.8 | 88538 | 88643.8 | 88765 | | hh_size_2 | 131287 | 126614 | 126492 | 126459 | 126492 | 126472 | 126492 | 126501 | | hh_size_3 | 75114 | 72888.3 | 72868.6 | 72855 | 72868.6 | 72967 | 72868.6 | 72911 | | hh_size_4 | 68285 | 67067.8 | 67178.5 | 67162 | 67178.5 | 67219 | 67178.5 | 67139 | | hh_size_5 | 31358 | 30982.2 | 31046.8 | 31034 | 31046.8 | 31078 | 31046.8 | 31055 | | hh_size_6_plus | 20845 | 20856.4 | 20943.6 | 20934 | 20943.6 | 20852 | 20943.6 | 20938 |

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight
numhh_gq,165589.0,165557.96290131172,165558.10820404647,165589.0,165558.10820404647,165589,165558.10820404647,165589
hh_gq_university,861.0,872.5295891717775,873.0338690716457,873.0,873.0338690716458,851,873.0338690716458,846
hh_gq_noninstitutional,1616.0,1635.0139445221857,1635.6778810542796,1636.0,1635.6778810542796,1608,1635.6778810542796,1557
hh_size_1,41313.0,42709.28184967466,42831.26499775176,42840.0,42831.26499775176,42649,42831.26499775176,42693
hh_size_2,50813.0,51816.26050712724,51870.31229563909,51881.0,51870.31229563908,51833,51870.31229563908,51814
hh_size_3,27305.0,27819.234082106625,27807.833233207704,27814.0,27807.8332332077,27868,27807.833233207704,27890
hh_size_4,22135.0,22351.343420036876,22312.648204528672,22315.0,22312.648204528676,22415,22312.648204528676,22386
hh_size_5,12439.0,12510.593716673256,12469.164588157479,12473.0,12469.164588157479,12536,12469.164588157479,12534
hh_size_6_plus,8498.0,8351.249325693017,8266.884884761783,8266.0,8266.884884761785,8288,8266.884884761785,8272
hh_wrks_0,39588.0,41430.2870499374,41558.94956776177,41565.0,41558.94956776176,41536,41558.94956776177,41597

final_summary_COUNTY_7.csv (Napa)

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight

numhh,49768.0,49779.50341254168,49779.73140011068,49777.0,49779.73140011067,49796,49779.73140011067,49946

numhh_gq,52718.0,52721.18162235967,52721.28337071059,52718.0,52721.28337071059,52718,52721.28337071058,52718

hh_gq_university,620.0,618.2504684037604,618.2271169907685,618.0,618.2271169907685,608,618.2271169907685,576

hh_gq_noninstitutional,2330.0,2323.427741414224,2323.324853609141,2323.0,2323.324853609141,2314,2323.324853609141,2196

hh_size_1,12705.0,12593.055760554842,12595.58016981024,12595.0,12595.58016981024,12561,12595.58016981024,12586

hh_size_2,17819.0,17685.48529585163,17686.307372392806,17684.0,17686.307372392806,17696,17686.307372392806,17757

hh_size_3,7408.0,7360.307586358826,7359.558196962868,7360.0,7359.558196962869,7381,7359.558196962869,7408

hh_size_4,6876.0,6839.203436183069,6838.18215711565,6837.0,6838.182157115649,6870,6838.182157115649,6891

hh_size_5,3279.0,3265.276713489788,3264.523553782009,3265.0,3264.523553782009,3264,3264.523553782009,3269

hh_size_6_plus,2034.0,2036.174620103535,2035.5799500471023,2036.0,2035.579950047103,2024,2035.5799500471028,2035

| control_name | control_value | COUNTY_preliminary_balanced_weight | COUNTY_balanced_weight | COUNTY_integer_weight | TAZ_NODE_balanced_weight | TAZ_NODE_integer_weight | MAZ_NODE_balanced_weight | MAZ_NODE_integer_weight | |:———————–|—————-:|————————————-:|————————-:|————————:|—————————:|————————–:|—————————:|————————–:| | numhh | 49768 | 49779.5 | 49779.7 | 49777 | 49779.7 | 49796 | 49779.7 | 49946 | | numhh_gq | 52718 | 52721.2 | 52721.3 | 52718 | 52721.3 | 52718 | 52721.3 | 52718 | | hh_gq_university | 620 | 618.25 | 618.227 | 618 | 618.227 | 608 | 618.227 | 576 | | hh_gq_noninstitutional | 2330 | 2323.43 | 2323.32 | 2323 | 2323.32 | 2314 | 2323.32 | 2196 | | hh_size_1 | 12705 | 12593.1 | 12595.6 | 12595 | 12595.6 | 12561 | 12595.6 | 12586 | | hh_size_2 | 17819 | 17685.5 | 17686.3 | 17684 | 17686.3 | 17696 | 17686.3 | 17757 | | hh_size_3 | 7408 | 7360.31 | 7359.56 | 7360 | 7359.56 | 7381 | 7359.56 | 7408 | | hh_size_4 | 6876 | 6839.2 | 6838.18 | 6837 | 6838.18 | 6870 | 6838.18 | 6891 | | hh_size_5 | 3279 | 3265.28 | 3264.52 | 3265 | 3264.52 | 3264 | 3264.52 | 3269 | | hh_size_6_plus | 2034 | 2036.17 | 2035.58 | 2036 | 2035.58 | 2024 | 2035.58 | 2035 |

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight
numhh_gq,208930.0,208845.0474710793,208844.8308795519,208930.0,208844.830879552,209020,208844.830879552,208981
hh_gq_university,3097.0,3126.0715972976227,3128.4913102272717,3129.0,3128.491310227272,2978,3128.491310227272,2966
hh_gq_noninstitutional,3590.0,3647.7560962028792,3651.149074567115,3653.0,3651.149074567116,3619,3651.1490745671153,3536
hh_size_1,59503.0,65436.7584167128,65432.13923768363,65453.0,65432.13923768363,65166,65432.13923768363,65137
hh_size_2,67854.0,71880.20569720367,71899.59553686972,71931.0,71899.59553686972,72074,71899.59553686972,72058
hh_size_3,29419.0,30102.880719500674,30124.055418754662,30135.0,30124.055418754655,30247,30124.055418754655,30201
hh_size_4,26016.0,25679.71787150244,25674.492525666126,25688.0,25674.492525666126,25787,25674.492525666126,25801
hh_size_5,10634.0,10158.007016912385,10143.494580066363,10145.0,10143.49458006636,10178,10143.49458006636,10205
hh_size_6_plus,6102.0,5587.477749247371,5571.053580511484,5578.0,5571.053580511483,5568,5571.053580511483,5579
hh_wrks_0,52285.0,55647.19210156741,56447.42527147926,56470.0,56447.42527147927,56592,56447.42527147927,56604

final_summary_COUNTY_9.csv (Marin)

control_name,control_value,COUNTY_preliminary_balanced_weight,COUNTY_balanced_weight,COUNTY_integer_weight,TAZ_NODE_balanced_weight,TAZ_NODE_integer_weight,MAZ_NODE_balanced_weight,MAZ_NODE_integer_weight

numhh,104158.0,104539.53578777864,104543.49881855394,104377.0,104543.49881855394,104409,104543.49881855394,104468

numhh_gq,106762.0,106932.58211472387,106927.74404928667,106762.0,106927.74404928667,106762,106927.74404928667,106762

hh_gq_university,628.0,593.3487589122124,591.7852394446792,593.0,591.7852394446791,572,591.7852394446791,571

hh_gq_noninstitutional,1976.0,1799.6975680330352,1792.4599912880512,1792.0,1792.4599912880517,1781,1792.4599912880522,1723

hh_size_1,38033.0,28553.92265217998,28536.410555230377,28490.0,28536.41055523038,28529,28536.41055523038,28586

hh_size_2,45202.0,36279.41067540563,36263.77220781914,36200.0,36263.77220781914,36204,36263.77220781914,36225

hh_size_3,19387.0,16361.263287360784,16382.254051139384,16358.0,16382.254051139384,16361,16382.254051139384,16357

hh_size_4,16836.0,14997.750110314271,15018.381445167428,14999.0,15018.38144516743,15009,15018.38144516743,14989

hh_size_5,6069.0,5824.703306254201,5820.716057524535,5811.0,5820.716057524534,5821,5820.716057524534,5839

hh_size_6_plus,2500.0,2522.485756263781,2521.9645016730697,2519.0,2521.96450167307,2485,2521.96450167307,2472

| control_name | control_value | COUNTY_preliminary_balanced_weight | COUNTY_balanced_weight | COUNTY_integer_weight | TAZ_NODE_balanced_weight | TAZ_NODE_integer_weight | MAZ_NODE_balanced_weight | MAZ_NODE_integer_weight | |:———————–|—————-:|————————————-:|————————-:|————————:|—————————:|————————–:|—————————:|————————–:| | numhh | 104158 | 104540 | 104543 | 104377 | 104543 | 104409 | 104543 | 104468 | | numhh_gq | 106762 | 106933 | 106928 | 106762 | 106928 | 106762 | 106928 | 106762 | | hh_gq_university | 628 | 593.349 | 591.785 | 593 | 591.785 | 572 | 591.785 | 571 | | hh_gq_noninstitutional | 1976 | 1799.7 | 1792.46 | 1792 | 1792.46 | 1781 | 1792.46 | 1723 | | hh_size_1 | 38033 | 28553.9 | 28536.4 | 28490 | 28536.4 | 28529 | 28536.4 | 28586 | | hh_size_2 | 45202 | 36279.4 | 36263.8 | 36200 | 36263.8 | 36204 | 36263.8 | 36225 | | hh_size_3 | 19387 | 16361.3 | 16382.3 | 16358 | 16382.3 | 16361 | 16382.3 | 16357 | | hh_size_4 | 16836 | 14997.8 | 15018.4 | 14999 | 15018.4 | 15009 | 15018.4 | 14989 | | hh_size_5 | 6069 | 5824.7 | 5820.72 | 5811 | 5820.72 | 5821 | 5820.72 | 5839 | | hh_size_6_plus | 2500 | 2522.49 | 2521.96 | 2519 | 2521.96 | 2485 | 2521.96 | 2472 |

variable,total_control,total_result,total_diff,total_diff_pct,mae,rmse,mape,r_squared,perfect_matches,perfect_pct,taz_count
numhh_gq,3032012.0,3032146,134.0,0.004419507574508281,0.028438030560271648,1.459411235566984,0.0010244036045110572,0.9999885650036583,4710,99.95755517826825,4712
hh_gq_university,52288.0,50802,-1486.0,-2.841952264381885,1.034804753820034,14.765038663114288,4.919743033221721,0.994980653630686,4553,96.62563667232598,4712
hh_gq_noninstitutional,76791.0,73764,-3027.0,-3.9418681876782435,2.4267826825127337,11.86092119579686,8.412914357934476,0.9730913672362441,3189,67.67826825127334,4712
hh_size_1,1043229.0,1023945,-19284.0,-1.8484915584210178,41.684634974533104,121.57735595280637,21.163147969939555,0.9181221028345171,144,3.0560271646859083,4712
hh_size_2,972432.0,934422,-38010.0,-3.9087566020040474,40.0606960950764,124.33003651421681,19.78459708452401,0.5699461242316931,81,1.7190152801358234,4712
hh_wrks_0,610138.0,640123,29985.0,4.914461974176334,20.064728353140918,32.17656417073793,23.01606715702099,0.9064976897039876,123,2.6103565365025467,4712
pers_age_00_19,1693504.0,1699288,5784.0,0.3415403801821549,48.946519524618,89.83677662647395,20.326388501727763,0.9009780813061613,50,1.061120543293718,4712

Charts (visual outputs)

This section references the chart images and includes small CSV snapshots (headers + sample rows) for key chart data files. Images are linked relative to this docs/ folder so you can open them in the repository or include them in exports.

TAZ analysis charts

TAZ total population analysis

TAZ overall summary

TAZ numhh_gq analysis

TAZ hh_size 1-6

TAZ hh_wrks 0-3+

TAZ age 0-19

TAZ age 20-34

TAZ age 35-64

TAZ age 65+

TAZ income lt 20k

TAZ income 20k-45k

TAZ income 45k-60k

TAZ income 60k-75k

TAZ income 75k-100k

TAZ income 100k-150k

TAZ income 150k-200k

TAZ income 200k+

Note: there are many TAZ charts; open output_2023/charts/taz_analysis/ to view the full set.

County analysis charts

County performance overview

County variable heatmap

County workers analysis

County income analysis

County household size analysis

County group quarters analysis

County age groups analysis

Note: open output_2023/charts/county_analysis/ for the full set of county charts and the CSVs (county_performance_summary.csv, county_detailed_results.csv).

MAZ comparison chart and table

MAZ household comparison

MAZ household comparison table (header + first 30 rows) — output_2023/charts/MAZ_household_comparison_table.csv:

MAZ_NODE,control_total_hh,syn_total_hh,diff_total_hh,pct_diff_total_hh,control_regular_hh,syn_regular_hh,diff_regular_hh,pct_diff_regular_hh,control_gq_hh,syn_gq_hh,diff_gq_hh,pct_diff_gq_hh,control_uni_hh,control_noninst_hh,diff_uni_hh,pct_diff_uni_hh,diff_noninst_hh,pct_diff_noninst_hh
10001,58.0,58.0,0.0,0.0,58.0,58.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10002,68.0,68.0,0.0,0.0,68.0,68.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10003,73.0,73.0,0.0,0.0,73.0,73.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10004,76.0,76.0,0.0,0.0,76.0,76.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10005,66.0,66.0,0.0,0.0,66.0,66.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10006,101.0,101.0,0.0,0.0,101.0,101.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10007,185.0,185.0,0.0,0.0,185.0,185.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10009,42.0,42.0,0.0,0.0,42.0,42.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10012,320.0,320.0,0.0,0.0,320.0,320.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10018,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10021,52.0,52.0,0.0,0.0,10.0,43.0,33.0,330.0,42.0,9.0,-33.0,-78.57142857142857,0.0,42.0,9.0,0.0,-33.0,-78.57142857142857
10022,54.0,54.0,0.0,0.0,34.0,25.0,-9.0,-26.47058823529412,20.0,29.0,9.0,45.0,0.0,20.0,29.0,0.0,9.0,45.0
10023,15.0,15.0,0.0,0.0,15.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10024,57.0,57.0,0.0,0.0,57.0,57.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10025,31.0,31.0,0.0,0.0,31.0,31.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10030,203.0,203.0,0.0,0.0,203.0,203.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10038,795.0,795.0,0.0,0.0,660.0,641.0,-19.0,-2.878787878787879,135.0,154.0,19.0,14.074074074074074,0.0,135.0,154.0,0.0,19.0,14.074074074074074
10039,91.0,91.0,0.0,0.0,91.0,91.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10044,54.0,54.0,0.0,0.0,54.0,54.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
10045,58.0,58.0,0.0,0.0,58.0,58.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0