Development of a novel walkability index for London, United Kingdom: cross-sectional application to the Whitehall II Study

Abstract

BackgroundPhysical activity is essential for health; walking is the easiest way to incorporate activity into everyday life. Previous studies report positive associations between neighbourhood walkability and walking but most focused on cities in North America and Australasia. Urban form with respect to street connectivity, residential density and land use mix—common components of walkability indices—differs in European cities. The objective of this study was to develop a walkability index for London and test the index using walking data from the Whitehall II Study. MethodsA neighbourhood walkability index for London was constructed, comprising factors associated with walking behaviours: residential dwelling density, street connectivity and land use mix. Three models were produced that differed in the land uses included. Neighbourhoods were operationalised at three levels of administrative geography: (i) 21,140 output areas, (ii) 633 wards and (iii) 33 local authorities. A neighbourhood walkability score was assigned to each London-dwelling Whitehall II Study participant (2003–04, N = 3020, mean ± SD age = 61.0 years ± 6.0) based on residential postcode. The effect of changing the model specification and the units of enumeration on spatial variation in walkability was examined.ResultsThere was a radial decay in walkability from the centre to the periphery of London. There was high inter-model correlation in walkability scores for any given neighbourhood operationalisation (0.92–0.98), and moderate-high correlation between neighbourhood operationalisations for any given model (0.39–0.70). After adjustment for individual level factors and area deprivation, individuals in the most walkable neighbourhoods operationalised as wards were more likely to walk >6 h/week (OR = 1.4; 95 % CI: 1.1–1.9) than those in the least walkable.ConclusionsWalkability was associated with walking time in adults. This walkability index could help urban planners identify and design neighbourhoods in London with characteristics more supportive of walking, thereby promoting public health.

A Reliability Check of Walkability Indices in Seoul, Korea

“…By measuring walkability to identify the spatial distribution characteristics of the walkability level, the city government will be equipped to establish urban planning and transportation policies to enhance citizens' walking. For example, it is expected that various urban planning policies (e.g., the improvement of pavements on pedestrian roads, the installation of pedestrian-only streets, and improvements in the traffic signal system for pedestrians) could be developed to improve neighborhood environment of the area by identifying the vulnerable area of walking.Among the studies on building pedestrian-friendly environments, some have attempted to develop walkability indices that measure pedestrian urban environments [24][25][26][27][28]. However, only a few studies of walkability have been conducted in Korea [28].…”

“…Among the studies on building pedestrian-friendly environments, some have attempted to develop walkability indices that measure pedestrian urban environments [24][25][26][27][28]. However, only a few studies of walkability have been conducted in Korea [28].…”

Modelling walking accessibility: A case study of Ljubljana, Slovenia

“…Although walkability indices in general are found to be a reliable and valid measure of estimating access to walkable amenities (Carr et al, 2010;Duncan et al, 2011), and have also performed quite well in describing pedestrian behaviours (Manaugh and El-Geneidy, 2011;Stockton et al, 2016;Weinberger and Sweet, 2012) or vehicle miles travelled (Kuzmyak et al, 2006), they have conceptual and computational limitations, as Vale et al (2015) argued in their extensive review of operational measures of active accessibility. For example, they can be less accurate in certain areas (Koschinsky et al, 2017), partly because they can mask within-buffer variations (Gutiérrez et al, 2011), or if they use Euclidean distance instead of the street network (Kozina, 2010).…”

Optimizing Pedestrian-Friendly Walking Path for the First and Last Mile Transit Journey by Using the Analytical Network Process (Anp) Decision Model and Gis Network Analysis

“…(Carr, Dunsiger and Marcus, 2010;Foda and Osman, 2010;Lwin and Murayama, 2011;Giles-Corti et al, 2014;Murekatete and Bizimana, 2015;Stockton et al, 2016). These studies focuses on the diversity of land uses that can be accessed by the walking path.…”