# Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics

## TL;DR

This paper shows how breaking down data on health behaviors and environmental factors can reveal local differences in community characteristics and inform better urban planning.

## Contribution

The novel use of data disaggregation methods to estimate small-area behavioral and environmental characteristics in Allegheny County.

## Key findings

- Disaggregated data revealed spatial disparities in behavioral risk factors, green space access, and walkability across zip codes.
- Higher-income zip codes did not necessarily have better green space access or walkability compared to the county median.
- The approach provides precise insights for understanding community-specific health and environmental interactions.

## Abstract

Background: Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods. Methods: We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania. Results: Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county’s median individual income, this does not lead them to have higher than its median green space access or walkability. Conclusion: We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.