revengc: Reverse Engineering Censored, Decoupled Residential Data for Population Density Estimation

A wealth of open source information is available that points to building usage including floor area of building size and likely occupancy. In the case of residential structures, census data provides a number of attributes including two values important to interior density estimation: household size (hhs) and area. If a national census revealed the raw data or provided a full uncensored contingency table (hhs x area), computing interior density as people/area would be straightforward. However, agencies rarely report this contingency table. Rather hhs and area are often decoupled and reported as separate univariate frequency tables, average values, or a combination of the two. In addition, the decoupled or contingency tables provided are typically left (<, <=), right (>, >=), and interval (-) censored. This type of information becomes problematic in estimating interior residential occupancy for numerous reasons. How can the people/area ratio be calculated when no affiliation between the variables exist? If a census reports a hhs average of 5.3, then how many houses are there with 1 person, 2 people,..., 10 people? If a census reports that there are 100 houses in an area of 26-50 square meters, then how many houses are in 26, 27,..., 50 square meters? The challenge therefore is to infer the people/area ratio when given decoupled and summarized data. The statistical package 'revengc' was designed to reverse engineer censored, decoupled census data into a likely hhs x area uncensored contingency table for estimating interior residential occupancy.

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