ACS Data Users Group

We are trying to create tables showing the percent of income spent on housing costs( e.g. Table B25101) for unincorporated areas in Colorado. I was wondering if anyone has tried to do this.

One approach we thought of was a "top-down" method of taking the difference between the county estimates and the estimates derived from the incorporated area (taking municipalities that are in more than one county into account through the use of the "Place/Remainder" geography from data.census.gov)

Another approach would be to use a "bottom-up" method that classified block groups according to their incorporated or unincorporated status. The issue here is developing the criteria for classifying "partially incorporated" block groups.

I would appreciate any ideas about how one might approach this problem.

TIA

AB

One other thing that might help if you haven’t already tried it, is the CDP designation on places (state-place sumlev) that means it’s a named but unincorporated area — usually a post office name or something like that for unincorporated areas. Places I’ve worked, they generally named all the unincorporated areas of a county but I honestly don’t know if that’s true everywhere.

Another quirk might be townships which are another sumlev -- 060 -- county subdivisions I believe. In some areas they include village and cities which are incorporated, but the townships or towns themselves are not usually considered incorporated although they are a subcounty level of government with elected officials. Can cause confusion.

To identify the percent of income spent on housing costs in unincorporated areas and unincorporated communities in Colorado, consider a hybrid method. Combine the "top-down" approach, adjusting county estimates for incorporated areas, with a "bottom-up" strategy. For the latter, classify block groups based on incorporated or unincorporated status, allowing for a nuanced classification for "partially incorporated" groups, including those within unincorporated community. This hybrid model leverages both macro and micro perspectives, offering a comprehensive view. Ensure criteria for classifying block groups are well-defined to maintain accuracy. This integrated approach can enhance the accuracy and relevance of your housing cost analysis for unincorporated areas and communities.

Please discuss the data processing you would use to do this.