Creating neighbourhood groupings based on built environment features to facilitate health promotion activities

Detailed assessments of the built environment often resist data reduction and summarization. This project sought to develop a method of reducing built environment data to an extent that they can be effectively communicated to researchers and community stakeholders. We aim to help in an understanding of how these data can be used to create neighbourhood groupings based on built environment characteristics and how the process of discussing these neighbourhoods with community stakeholders can result in the development of community-informed health promotion interventions.

We used the Irvine Minnesota Inventory (IMI) to assess 296 segments of a semi-rural community in Alberta. Expert raters "created" neighbourhoods by examining the data. Then, a consensus grouping was developed using cluster analysis, and the number of IMI variables to characterize the neighbourhoods was reduced by multiple discriminant function analysis.

The 296 segments were reduced to a consensus set of 10 neighbourhoods, which could be separated from each other by 9 functions constructed from 24 IMI variables. Biplots of these functions were an effective means of summarizing and presenting the results of the community assessment, and stimulated community action.

It is possible to use principled quantitative methods to reduce large amounts of information about the built environment into meaningful summaries. These summaries, or built environment neighbourhoods, were useful in catalyzing action with community stakeholders and led to the development of health-promoting built environment interventions.