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Many studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.

BackgroundThere is now a substantial body of research suggesting that social cohesion, a collective characteristic measured by the levels of trust, reciprocity and formation of strong social bonds within communities, is an important factor in determining health. Of particular interest is the extent to which factors in the built environment facilitate, or impede, the development of social bonds. Severance is a characteristic of physical environments which is hypothesized to inhibit cohesion. In the current study we test a number of characteristics of spatial networks which could be hypothesized to relate either to severance, or directly to community cohesion. Particular focus is given to our most promising variable for further analysis (Convex Hull Maximum Radius 600 m).MethodsIn the current study we analysed social cohesion as measured at Enumeration District level, aggregated from a survey of 10,892 individuals aged 18 to 74 years in the Caerphilly Health and Social Needs Cohort Study, 2001. In a data mining process we test 16 network variables on multiple scales. The variable showing the most promise is validated in a test on an independent data set. We then conduct a multivariate regression also including Townsend deprivation scores and urban/rural status as predictor variables for social cohesion.ResultsWe find convex hull maximum radius at a 600 m scale to have a small but highly significant correlation with social cohesion on both data sets. Deprivation has a stronger effect. Splitting the analysis by tertile of deprivation, we find that the effect of severance as measured by this variable is strongest in the most deprived areas. A range of spatial scales are tested, with the strongest effects being observed at scales that match typical walking distances.ConclusionWe conclude that physical connectivity as measured in this paper has a significant effect on social cohesion, and that our measure is unlikely to proxy either deprivation or the urban/rural status of communities. Possible mechanisms for the effect include intrinsic navigability of areas, and the existence of a focal route on which people can meet on foot. Further investigation may lead to much stronger predictive models of social cohesion.