I ran across this paper that describes an interesting approach to combining survey and sociodemographic data:
As stated, their main contribution seems to be "a new method of estimating pedestrian and cyclist activity levels at a fine geographic scale."
A quote from the document best explains their method:
(1) First, we use cluster analysis to assign census tracts to neighborhood types based on built environment characteristics, and (2) we calculate miles biked and miles walked for each travel survey respondent. All survey respondents are included, and those who do not report cycling or walking are assumed to walk and bike zero miles. (3) We then assign each survey respondent to a category based on their age, gender, and home neighborhood type. (4) Finally, we calculate average miles biked and miles walked for each category, and use census data to expand these average distances walked and biked to represent population totals.
Step labels in parentheses were added by myself.
They used this equation to calculate total miles for each tract:
TotalMiles$_{tract}=\Sigma_{i=1}^{10} $SurveyAvgMiles$_i\times $2010CensusTractPopulation$_i$
where $i$ indexes gender-age group categories and each tract is classified as a neighborhood type.
The authors alude to several other studies in their domain that used a similar approach.
However, the only justification that I could find for the validity of their methodology was that the results obtained using two different surveys, after adjusting for "differences in survey response" were "broadly consistent."
It seems to me that they are still lacking a source of truth against which their census tract estimates can be evaluated.
How do we know that this new methodology is sound?