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Table 3 Linear regressions that predict physical activity from traditional proxies for physicial activity (lnGDP, clemency, greenery, walkability, and landmarks), and from our street entropy metric. Z-scored transformed predictors are noted with ‘z’, and significant predictors with p values < 0.05 are marked in bold. The best predictive model (\(M_{IG+E+W+L}\)) suggests that both street entropy and the interaction between lnGDP and clemency are good predictors of physical activity

From: Imagine a Walkable City: Physical activity and urban imageability across 19 major cities

Predictor β std. error p-value
\(\boldsymbol{M_{lG+C}}\): \(\boldsymbol{\mathit{Adj} R^{2} = 0.14}\), Durbin-Watson = 0.77, AIC = 54.51
Intercept 4.86 11.87 0.68
lnGDP −0.41 0.85 0.63
Clemency −0.04 0.12 0.7
lnGDPxClemency −0.74 0.65 0.27
\(\boldsymbol{M_{lG+C+EU}}\): \(\boldsymbol{\mathit{Adj} R^{2} = 0.26}\), Durbin-Watson = 0.88, AIC = 52.29
Intercept −2.47 11.68 0.83
lnGDP 0.11 0.83 0.89
Clemency −0.04 0.11 0.72
lnGDPxClemency −0.57 0.61 0.36
EU 1.09 0.58 0.08
\(\boldsymbol{M_{lG+C+E+G}}\): \(\boldsymbol{\mathit{Adj} R^{2} = 0.37}\), Durbin-Watson = 2.33, AIC = 50.24
Intercept 12.05 11.37 0.31
lnGDP −0.93 0.81 0.27
Clemency 0.01 0.11 0.89
lnGDPxClemency −1.33 0.62 0.05
z-street Entropy 0.64 0.22 0.01
z-Greenery −1.20 3.11 0.7
lnGDPxz-Greenery 0.09 0.24 0.71
\(\boldsymbol{M_{lG+C+E}}\): \(\boldsymbol{\mathit{Adj} R^{2} = 0.46}\), Durbin-Watson = 1.32, AIC = 46.55
Intercept 10.17 9.61 0.31
lnGDP −0.79 0.68 0.26
Clemency 0.004 0.09 0.96
lnGDP×Clemency −1.26 0.54 0.03
z-street Entropy 0.61 0.19 0.00
\(\boldsymbol{M_{lG+E+W+L}}\): \(\boldsymbol{\mathit{Adj} R^{2} = 0.52}\), Durbin-Watson = 1.19, AIC = 44.69
Intercept 10.17 9.61 0.31
lnGDP −0.58 0.36 0.13
lnGDP×Clemency −0.97 0.37 0.02
z-street Entropy 0.57 0.18 0.00
Walkability 1.60 1.87 0.40
Landmarks −5.72 5.36 0.30