Available online 24 March 2022, 100506

Virtual audit of observed neighborhood physical disorder via Google Street View

Spatio-temporal Regression Kriging to predict physical disorder

Model accuracy and concurrent validity with space-time lagged perceived disorder

Greater validity nearest spaces and times of perceived disorder responses

Using spatio-temporal model depends on space-time precision of epidemiologic data

This study tested spatio-temporal model prediction accuracy and concurrent validity of observed neighborhood physical disorder collected from virtual audits of Google Street View streetscapes. We predicted observed physical disorder from spatio-temporal regression Kriging models based on measures at three dates per each of 256 streestscapes (n=768 data points) across an urban area. We assessed model internal validity through cross validation and external validity through Pearson correlations with perceived physical disorder from a breast cancer survivor cohort. We compared validity among full models (both large- and small-scale spatio-temporal trends) versus large-scale only. Full models yielded lower prediction error compared to large-scale only models. Correlations between perceived and observed physical disorder predicted from the full model were higher compared to that of the large-scale only model, but only at locations and times closest to the respondent's exact residential address and questionnaire date. A spatio-temporal Kriging model of observed physical disorder is valid.

Built environment

observed neighborhood physical disorder

virtual neighborhood audit

spatio-temporal Universal Kriging

perceived neighborhood physical disorder

© 2022 The Authors. Published by Elsevier Ltd.