Planning Support Systems are spatially enabled computer based analytical tools. They are designed to process spatial data and model “what if” scenarios in support of planning analyses. This paper presents how an existing land-use planning software was customised to create the Walkability Planning Support System. The paper describes the tool features including: (i) automated calculation of built environment variables; (ii) “sketch planning” functionality; and (iii) suite of indicators including a walkability indicator that estimates the probability that an adult would walk for transport. We discuss how the Walkability PSS enables urban planners to explore built environment scenarios and visualise their potential impacts on walkability. We present a suburban case study where we compare a baseline scenario with an alternative scenario developed with local planners that incorporated possible built environment interventions. Finally, we discuss potential applications for the tool and present how it could be refined along with recommended research directions.

Introduction

Given rapid urbanization rates worldwide (United Nations et al., 2015), many cities are growing and transforming, putting enormous pressure on urban planning and decision-making (Giles-Corti et al., 2016; Stevenson et al., 2016). Cities are made up of complex and simultaneously occurring systems e.g., transport; land-use; social, physical and digital infrastructure as well as energy and utilities. Planners need to understand and capture the collective effects of these systems when planning for healthy, sustainable development (Ainsworth and Macera, 2012; Allen, 2001). To facilitate this, data driven systems approaches can help planners appreciate how patterns and issues for cities (e.g., public health risks, green-house gas emissions) emerge from the interplay of complex systems (e.g., suburban urban form, location of employment, public transport systems). Data driven systems approaches can also help capture opportunities for improvement, for example in recognizing leverage points in the urban systems where interventions could lead to improved health and environmental sustainability outcomes (Diez Roux, 2011). Importantly the human interactions in these complex systems can be modelled and simulated. Given places are designed for people this is a critical consideration when planning future neighbourhoods and cities. While it is established that regular participation in physical activity has positive impacts on individual physical and mental health and contributes to social cohesion (Van Dyck et al., 2015; World Health Organization and Calouste Gulbenkian Foundation, 2014), cities around the world face a dramatic increase in the rates of chronic disease, obesity and sedentary lifestyles. Over the last decade, there has been rapid growth in research into the built environment as an enabler or barrier to health and wellbeing. It appears that barriers to physical activity arise from the way cities are planned, designed, built or renewed and numerous studies have shown that city design influences walking behaviours (Heath et al., 2006; McCormack and Shiell, 2011; Ferdinand et al., 2012; Saelens and Handy, 2008; Saelens et al., 2012). Neighbourhoods are described as more “walkable” when they enable people to make walking their first transport mode of choice (Badland et al., 2013). Conversely, neighbourhoods that encourage motor vehicle dependency reduce opportunities for people to accumulate physically activity by way of active travel (Younger et al., 2008). Different design features influence different types of physical activity; key features found to be consistently associated with participation in transportwalking (Ainsworth and Macera, 2012), an active mode of transport, include: residential density, land use mix, street connectivity, proximity of destinations, presence of sidewalks, and access to public transport infrastructure (Adams et al., 2013). However, it is also understood that walkable environments arise from a combination of built environment attributes, typically described as a range of appropriate destinations easily accessible via connected street networks and supported by higher population densities (Christian et al., 2011; Frank et al., 2010; Grasser et al., 2013; Owen et al., 2007).