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Updated: May 4, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Using simple agent-based modeling to inform and enhance neighborhood walkability.
Hannah Badland1, Marcus White, Gus Macaulay
1McCaughey VicHealth Centre for Community Wellbeing, School of Population and Global Health, University of Melbourne, Melbourne, Australia. hannah.badland@unimelb.edu.au.
This study introduces an open-source tool for assessing neighborhood walkability, enabling urban planners to model and enhance pedestrian-friendly environments. The tool helps optimize access to local destinations, promoting healthier communities.
Area of Science:
- Urban Planning and Design
- Geographic Information Systems (GIS)
- Public Health
Background:
- Pedestrian-friendly neighborhoods promote active living and reduce car dependency.
- Transit-Oriented Development (TOD) emphasizes proximity to transit nodes.
- Existing catchment modeling methods (circular, network buffers) have limitations in accuracy and accessibility.
Purpose of the Study:
- To develop an open-source agent-based tool for modeling walkable neighborhood catchments.
- To overcome limitations of existing GIS-based catchment analysis.
- To support urban designers, planners, and policymakers in scenario testing.
Main Methods:
- Developed a simplified agent-based model ported to an open-source GIS web tool.
- Utilized data from the Australian Urban Research Infrastructure Network (AURIN).
- Incorporated end-user stakeholder feedback during development and testing.
Main Results:
- The tool effectively assesses and optimizes neighborhood walkability around key destinations.
- Enables comparison of different scenarios using metrics like catchment area ratios and streets crossed.
- Models varying walking speeds and intersection wait times.
Conclusions:
- The open-source tool can inform urban planning and public health advocacy globally.
- It offers a flexible platform for assessing and improving neighborhood walkability.
- Future extensions could incorporate agent learning and environmental attributes for more complex modeling.
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