Title |
Using simple agent-based modeling to inform and enhance neighborhood walkability
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Published in |
International Journal of Health Geographics, December 2013
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DOI | 10.1186/1476-072x-12-58 |
Pubmed ID | |
Authors |
Hannah Badland, Marcus White, Gus MacAulay, Serryn Eagleson, Suzanne Mavoa, Christopher Pettit, Billie Giles-Corti |
Abstract |
Pedestrian-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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Australia | 3 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Turkey | 1 | <1% |
Australia | 1 | <1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
Thailand | 1 | <1% |
Japan | 1 | <1% |
United States | 1 | <1% |
Unknown | 211 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 41 | 19% |
Student > Ph. D. Student | 38 | 17% |
Researcher | 30 | 14% |
Student > Doctoral Student | 20 | 9% |
Student > Bachelor | 10 | 5% |
Other | 41 | 19% |
Unknown | 38 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 32 | 15% |
Design | 21 | 10% |
Medicine and Dentistry | 18 | 8% |
Engineering | 18 | 8% |
Environmental Science | 14 | 6% |
Other | 55 | 25% |
Unknown | 60 | 28% |