Validating a spatio-temporal model of observed neighborhood physical disorder

Abstract

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 spatio-temporal 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 respondent-reported 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.

Keywords: Built environment; Observed neighborhood physical disorder; Perceived neighborhood physical disorder; Spatio-temporal universal Kriging; Virtual neighborhood audit.

Figure 1.

Observed neighborhood physical disorder values by location and date, Essex County, New Jersey, n=768

Figure 2.

Estimated observed neighborhood physical disorder from a model of large-scale trend, Essex County, New Jersey, n=768

Figure 3.

Empirical and fitted theoretical spatio-temporal semivariograms of observed neighborhood physical disorder, Essex County, New Jersey, n=768

Figure 4.

Differences between empirical and fitted theoretical spatio-temporal semivariograms of observed neighborhood physical disorder, Essex County, New Jersey, n=768

Figure 5.

Predicted observed neighborhood physical disorder across Essex County, New Jersey 2010-2018

Figure 6.

Differences in correlation coefficients of perceived neighborhood physical disorder and predicted observed neighborhood physical disorder by time and space lags^{1 1} Observed neighborhood physical disorder at time lag 0 indicates a model prediction at the exact date when perceived neighborhood physical disorder was assessed. Negative time lags indicate observed neighborhood physical disorder values predicted at times occurring before the date when perceived neighborhood physical disorder was assessed. Positive times indicate observed neighborhood physical disorder values predicted at times occurring after the date when perceived neighborhood physical disorder was assessed. Distance at lag 0 indicates that observed neighborhood physical disorder was predicted at the exact address where perceived neighborhood physical disorder was assessed. Distances > 0 indicate observed neighborhood physical disorder values estimated at locations ‘X km’ away from the address where perceived neighborhood physical disorder was assessed. 16 different locations were selected along an arc with radius = distance_x. The 16 locations were uniformly distributed along the arc and all points were within the study region. Correlations were then calculated under a multiple imputation framework where correlations from the 16 distance realizations were considered an imputed dataset (distance lag of 0 contain no imputation realizations). Correlation coefficient differences were from two sets of correlations: those using predictions of observed physical disorder from the full model and those using predictions of observed physical disorder from the large-scale only model.