Validating a spatio-temporal model of observed neighborhood physical disorder

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 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 respondent-reported perceptions of physical disorder from a breast cancer survivor cohort. We compared validity among full models (both large- and small-scale spatiotemporal trends) versus large-scale only. Full models yielded lower prediction error compared to large-scale only models. Physical disorder predictions were lagged at uniform distances and dates away from the respondentreported perceptions of physical disorder. 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.

Automated summary

This study tested the accuracy and validity of spatio-temporal model prediction of observed neighborhood physical disorder collected from virtual audits. The results showed that the full models had lower prediction error compared to large-scale models and had higher correlations with respondent-reported perceptions of physical disorder. The study demonstrates the validity of a spatio-temporal Kriging model for predicting observed physical disorder.