Measuring the impact of spatial network layout on community social cohesion: a cross-sectional study

Abstract

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.

Exploring associations between urban green, street design and walking: Results from the Greater London boroughs

“…The 0.50 m resolution Bluesky colour infrared image captured during aerial photography with the Vexcel UltraCamD and the Leica ADS4 has an accuracy of 1 m. Street network modelling with sDNA provides an innovative methodology to study the scalar and network effects of urban design and morphology upon walkability and physical activity. Compared to conventional measure of street connectivity, which is typically fixed in scale, sDNA can dynamically model urban morphometrics (morphological metrics) at multiple spatial scales, thereby capturing the impact of behaviourally-relevant spatial scales upon walkability (Cooper, Fone, & Chiaradia, 2014;Sarkar et al, 2014b). Since sDNA employs street network links as the fundamental unit of analyses, the urban morphometrics computed can be directly appended to each ITN street link which is then connected to a study participant's dwelling location through spatial GIS queries.…”

Natural and built environmental exposures on children's active school travel: A Dutch global positioning system-based cross-sectional study

“…The design of the street layout was measured as the proportion of cul-de-sacs, 3-way, 4-way, and 44-way intersections relative to all intersections within a cell (Schlossberg et al, 2006). Since these measures are descriptive, they do not provide insights into the local connectivity (accessibility) of street segments in regard to the surrounding street network (Cooper et al, 2014). Thus, we used the following two space syntax-based indices (Chiaradia et al, 2013): a) The closeness index, which describes the nearness/ farness by measuring how difficult it is to go from location i to all other locations on the street network (less accessible street segments should lower ATS); and b) the betweenness index, which quantifies which street segment will be busiest to move from location i to all other locations along the shortest path (well-connected street segments have a higher index value and should increase ATS).…”

Predictive spatial network analysis for high-resolution transport modeling, applied to cyclist flows, mode choice, and targeting investment

“…It has a history of use in the production of models to fit pedestrian and vehicle flows (Cooper, 2015;Haworth, 2014;Hillier & Iida, 2005;Jayasinghe, 2017;Lowry, 2014;Omer et al, 2017;Patterson, 2016;Serra & Hillier, 2017;Turner, 2007) but is not used in mainstream motor vehicle transport modeling for which the four-step model (Ort uzar & Willumsen, 2011) is ubiquitous. Due to their simplified nature, SpNA models have also been used in epidemiology to quantify built environment factors for individuals (Cooper, Fone, & Chiaradia, 2014;Fone et al, 2012;Sarkar et al, 2015;Sarkar, Gallacher, & Webster, 2013;Sarkar, Webster, & Gallacher, 2014). The aim of this article is not to challenge the highly developed four-step model, but to present a nascent alternative methodology based on the combination of SpNA with crossvalidated regression techniques which can handle collinear predictors.…”