Table 2: Predicting poor health with greenspace only.

From: Quantifying the Impact of Scenic Environments on Health

 All areasUrbanSuburbanRural
Greenspace−0.019***−0.0110.014−0.008
Income Deprivation1.696***1.797***1.418***1.024***
Employment Deprivation3.181***3.107***3.301***4.015***
Education Deprivation0.003***0.003***0.004***0.006***
Housing Deprivation−0.001***0.000−0.001***−0.001**
Crime0.010***−0.0030.007*0.015***
Living Deprivation0.000***0.001**0.000*−0.001*
AIC−10904−1301−5033−5443
No of observations16907394477815182
  1. A correlation analysis indicates that scenicness is significantly correlated with greenspace (τ  = 0.2, p < 0.001, N = 128,213). We therefore build another four CAR models to predict standardized rates of reports of poor health, using greenspace only. Here, we present the regression coefficients. As in Table 1, models are built for England as a whole, and for urban, suburban and rural areas separately. A range of socioeconomic deprivation variables are controlled for, and the analysis is carried out at the level of Lower Layer Super Output Areas. In this revised model, while less greenspace is significantly associated with reports of worse health, this effect no longer holds when the analysis is broken down into urban, suburban and rural areas. *p < 0.05, **p < 0.01, ***p < 0.001.