Marcus White

Using simple agent-based modeling to inform and enhance neighborhood walkability

BackgroundPedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory ‘what-if’ scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate.MethodsThis study sought to overcome these limitations by developing an open-source simple agent-based walkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments. A simplified version of an agent-based model was ported to a vector-based open source GIS web tool using data derived from the Australian Urban Research Infrastructure Network (AURIN). The tool was developed and tested with end-user stakeholder working group input.ResultsThe resulting model has proven to be effective and flexible, allowing stakeholders to assess and optimize the walkability of neighborhood catchments around actual or potential nodes of interest (e.g., schools, public transport stops). Users can derive a range of metrics to compare different scenarios modeled. These include: catchment area versus circular buffer ratios; mean number of streets crossed; and modeling of different walking speeds and wait time at intersections.ConclusionsThe tool has the capacity to influence planning and public health advocacy and practice, and by using open-access source software, it is available for use locally and internationally. There is also scope to extend this version of the tool from a simple to a complex model, which includes agents (i.e., simulated pedestrians) ‘learning’ and incorporating other environmental attributes that enhance walkability (e.g., residential density, mixed land use, traffic volume).

Using an Online Data Portal and Prototype Analysis Tools in an Investigation of Spatial Livability Planning

This paper introduces an online spatial data portal with advanced data access, analytical and visualisation capabilities which can be used for evidence based city planning and supporting data driven research. Through a case study approach, focused in the city of Melbourne, the authors show how the Australian Urban Infrastructure Network AURIN portal can be used to investigate a multi-facetted approach to understanding the various spatial dimension of livability. While the tools explore separate facets of livability employment, housing, health service and walkability, their outputs flow through to the other tools showing the benefits of integrated systems.

Designing with Data for Urban Resilience

The growing availability of spatial data heralds extensive opportunities for urban planning and design. Planning for resilience and enabling positive design outcomes requires transliterate methods of working with data and instigation of systems which can be quickly and iteratively adapted to complex multiple criteria and across multiple geographies. As such, planning support systems are critical to assist decision-makers navigate increasingly large repositories of (big) data, and develop evidence-based, replicable methodologies and easily communicated scenarios that can inform both the planning process and increase community buy-in for behavioural augmentation. To do this, we need to bring together data and information sets in a dynamic way, from disparate and vastly divergent disciplines and sources. This chapter will present a series of exemplars for environmental analysis, predictive modelling and planning support systems, particularly, the Australian Urban Research Infrastructure Network (AURIN): a federated data platform supporting urban research, design and policy formulation.

BIM-PIM-CIM: The Challenges of Modelling Urban Design Behaviours Between Building and City Scales

This paper discusses the challenges that designers face when modelling the anticipated behaviours of people: their movement and transactions around and within precinct scale development. Building Information Modelling (BIM) software philosophy contrasts with that of City Information Modelling (CIM)—the route by which we consider how precinct scale development, being somewhere between the two (BIM and CIM), requires a wholly different approach to information and behaviour modelling. The authors offer evidence of the value of augmenting the planners’ analytical approach with the architects’ synthesis from data leading to meaningful speculations on otherwise unanticipated future scenarios for the precinct far beyond expectation. Novel approaches to modelling behaviour at precinct scale suggest alternative readings of precincts, which require a wider set of approaches to Precinct Information Modelling (PIM) software development than simply an expansion of BIM.