Hi Chuck-coming at this from a different perspective I took a look at the 11 Places in Illinois where Tracts nest within their place for Part 2 table B202100, Total Workers. Other than the loss of data what I found interesting is that when I did the same analysis for the 2016-2020 data, my town of Berwyn only had an 11% percent loss of trips. This is not conclusive enough to say that the issue is getting worse but with the stricter disclosure rules applied to the workplace data for the 2017 data this was not a surprise.
 
Place (No. Tracts) Place Totals Sum of Tracts Change
EST MOE EST MOE Nos. Percent
Berwyn (10)    12,705 1,133     11,135   -1,570 -12%
Burnham (1)          930 432          785 399 -145 -16%
Cicero (16)    18,010 1,228     14,775   -3,235 -18%
Elmwood Park (5)             3,460 548       3,115   -345 -10%
Evanston (19)            48,935 1,644     45,250   -3,685 -8%
Hometown (1)                  435 176          345 137 -90 -21%
Lincolnwood (3)               7,105 649       6,470   -635 -9%
Oak Park (14)    22,745 1,149     20,360   -2,385 -10%
River Forest (2)       5,530 557       5,175   -355 -6%
Stickney (1)               2,795 580       2,665 577 -130 -5%
Venice (1)                  320 95          295 91 -25 -8%
Totals  122,970     110,370   -12,600 -10%

I also took at look at B202105 which allowed for a slightly deeper dive into the Tract loss issue. It is important at this point to note that when working with Census-based CTPP tabular data, the total for something is a completely separate published value. That means that it is possible to sum all the components of a variable and compare that total to the total. The figure below shows the results by summing all the modal components for the Tracts in each Place and comparing it to the sum of Tract totals for each Place. In other words, how does the sum of the modes for each Tract sum, in Berwyn compare to the published sum of the total workers in each Tract. Although these differences could be attributed rounding, there are other disclosure methods at play that obscure where this noise is getting introduced. This is evident when you consider the the number Tracts in the places, identified in the parenthesis.
 
Place (Nos. Tracts) Sum of the Tracts Difference
Totals  Modes 
Berwyn (10)    11,135    11,126 -9
Burnham (1)          785          784 -1
Cicero (16)    14,775    14,783 8
Elmwood Park (5)             3,115       3,114 -1
Evanston (19)         45,250    45,259 9
Hometown (1)         345          345 0
Lincolnwood (3)         6,470       6,453 -17
Oak Park (14) 20,360    20,385 25
River Forest (2) 5,175       5,153 -22
Stickney (1)         2,665       2,659 -6
Venice (1)         295          298 3
Totals 110,370  110,359 -11
Source: Table B202105 Means of Transportation to Work (17)

Sorry I do not have any R script to share. I used the online extraction software and a lot of Excell ;-)


On 8/27/2025 6:20 PM, Charles Purvis wrote:
I’ve finished an analysis of the CTPP 2017-21 data for Table B202105 (workers at workplace by detailed means of transportation). This is for California counties, and California census tracts, summarized to the county-level. The purpose is to ascertain the level of missing data due to absence of secondary allocation (imputation) to the tract-level database.

To reiterate, “primary allocation” of missing values for workplace location is always produced (by the Census Bureau) to the county, place and MCD levels. “Secondary allocation” was used in previous CTPP products, but was discontinued in the 2012-2016 CTPP. Secondary allocation imputes workplace location down to the TAZ or census tract or block group levels. (There may be better ways of stating this. I’m using my human powered AI to construct these statements.)

Results for the San Francisco Bay Area are included in this inserted graphic:
Screenshot 2025-08-27 at 3.47.13 PM.png
Overall, at least in the Bay Area, the least amount of missing secondary allocation are for the bicycle-to-work and walk-to-work modes, at 9 to 11 percent missing values. The most (worst) secondary allocation is for 3-or-more person carpools, 2-person carpools, and other (3) (motorcycle + taxicab + other). 

A possible explanation is that bicycle and walk commuters are more savvy and address-conscious than carpoolers?

As should be expected, workers working at home should never have workplace allocation issues, since the block-tract-place-county of workplace for at-home workers is identical to their home location. Only non-home workers should be factored up in Part 2 tables. 

The weirdness in the work-at-home totals (593,489 from tract data; 593,510 from county summary level) is due to rounding issues at the tract vs county level. Moral of this story: don’t expect tract-level data from the CTPP to aggregate neatly up to the county level. It’s all due to rounding. But tract-level data from standard ACS five-year tables *should* aggregate neatly up to county level.

I’m thinking that the most needed set of “county correction factors” will be for part 3: factoring tract-to-tract commuters based on county-of-residence, county-of-work, and means of transportation. 

Here is my fully fleshed R script to analyze Table B202105 from both tract summary level and county summary level file.

r_scripts.png
r_scripts/ctpp1721_california_b202105_1.R at main · chuckpurvis/r_scripts
github.com


I hope this is of use to the community!

Chuck Purvis
Hayward, CA
clpurvis@att.net




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