International Journal of Health Geographics | |
Using simple agent-based modeling to inform and enhance neighborhood walkability | |
Billie Giles-Corti3  Christopher Pettit1  Suzanne Mavoa3  Serryn Eagleson5  Gus MacAulay2  Marcus White4  Hannah Badland3  | |
[1] Australian Urban Research Infrastructure Network & Faculty of Architecture, Building, and Planning, University of Melbourne, Melbourne, Australia;Computing and Information Systems, University of Melbourne, Melbourne, Australia;McCaughey VicHealth Centre for Community Wellbeing, School of Population and Global Health, University of Melbourne, Melbourne, Australia;Melbourne School of Design, Faculty of Architecture, Building, and Planning, University of Melbourne, Melbourne, Australia;Centre for Spatial Data Information and Land Administration, Faculty of Engineering, University of Melbourne, Melbourne, Australia | |
关键词: What-if; Spatial data; Schools; Public transport; Liveability; Health; Catchment modeling; AURIN; | |
Others : 814333 DOI : 10.1186/1476-072X-12-58 |
|
received in 2013-09-02, accepted in 2013-12-02, 发布年份 2013 | |
【 摘 要 】
Background
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.
Methods
This 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.
Results
The 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.
Conclusions
The 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).
【 授权许可】
2013 Badland et al.; licensee BioMed Central Ltd.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20140710032216627.pdf | 2080KB | download | |
Figure 2. | 153KB | Image | download |
Figure 1. | 218KB | Image | download |
【 图 表 】
Figure 1.
Figure 2.
【 参考文献 】
- [1]Transportation Research Board and Institute of Medicine of the National Academies: Does the built environment influence physical activity? Examining the evidence. Washington D.C: Transportation Research Board, Institute of Medicine of the National Academies; 2005.
- [2]Department of Health Physical Activity Health Improvement and Promotion: At least five a week: Evidence on the impact of physical activity and its relationship to health. A report from the Chief Medical Officer. London: Department of Health; 2004.
- [3]Beaglehole R, et al.: Priority actions for the non-communicable disease crisis. Lancet 2011, 377:1438-1447.
- [4]World Health Organization: Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization; 2009.
- [5]Frank L, et al.: The development of a walkability index: application to the neighborhood quality of life study. Br J Sports Med 2010, 44:924-933.
- [6]Owen N, et al.: Neighborhood walkability and the walking behavior of Australian adults. Am J Prev Med 2007, 33:387-395.
- [7]Witten K, et al.: Neighbourhood built environment is associated with residents’ transport and leisure physical activity: findings from New Zealand using objective exposure and outcome measures. Env Health Persp 2012, 120:971-977.
- [8]Badland H, Schofield G, Garrett N: Travel behavior and objectively measured urban design variables: associations for adults traveling to work. Health Place 2008, 14:85-95.
- [9]Badland H, et al.: Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study: research design and methodology. BMC Public Health 2009, 9(224): . 10.1186/1471-2458-9-224
- [10]Leslie E, et al.: Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes. Health Place 2007, 13:111-122.
- [11]Van Dyck D, et al.: Neighbourhood walkability and its particular importance for adults with a preference for passive transport. Health Place 2009, 15:496-504.
- [12]Giles-Corti B, Ryan K, Foster S: Increasing density in Australia: Maximising the health benefits and minimising harm. Canberra: National Heart Foundation; 2012.
- [13]Sugiyama T, et al.: Destination and route attributes associated with adults’ walking: a review. Med Sci Sport Exerc 2012, 44:1275-1286.
- [14]Ewing R: Building environments to promote health. J Epi Comm Health 2005, 59:536-537.
- [15]Handy S, et al.: How the built environment affects physical activity: views from urban planning. Am J Prev Med 2002, 23(2S):S64-S73.
- [16]Renne J: From transit-adjacent to transit-oriented development. Local Environ 2009, 14:1-15.
- [17]Sander H, et al.: How do you measure distance in spatial models? An example using open-space valuation. Enviro Plann B 2010, 37:874-894.
- [18]Andersen J, Landex A: GIS-based approaches to catchment area analyses of mass transit. In Proceedings of the ESRI Users Group Conference. San Diego: ESRI; 2009.
- [19]Zhao F, et al.: Forecasting transit walk accessibility: regression model alternative to buffer method. Transportation 1835, 2003:34-41.
- [20]Schlossberg M: Visualizing accessibility with GIS. Elec J Geog Math 2002, 13:1-18.
- [21]Goode S: Something for nothing: management rejection of open source software in Australia’s top firms. Info Manage 2005, 42:669-681.
- [22]Macal C, North M: Tutorial on agent-based modelling and simulation. J Simul 2010, 4:151-162.
- [23]Klügl F, Rindsfüser G: Large-scale agent-based pedestrian simulation. In Proceedings of Multi-agent systems technologies. Leipzig: Springer; 2007.
- [24]Langton C: Interdisciplinary workshop on the synthesis and simulation of living systems (ALIFE '87). In Proceedings of Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Los Alamos: ALIFE '87; 1989.
- [25]Yang Y, Diez Roux A: Using an agent-based model to simulate children’s active travel to school. Int J Behav Nutr Phys Act 2013, 10: . 10.1186/1479-5868-10-67
- [26]Yang Y, et al.: A spatial agent-based model for the simulation of adults’ daily walking within a city. Am J Prev Med 2011, 40:353-361.
- [27]Crooks A, Castle C, Batty M: Key challenges in agent-based modelling for geospatial simulation. Comp Enviro Urban Syst 2008, 32:417-430.
- [28]Zheng X, Zhong T, Liu M: Modeling crowd evacuation of a building based on seven methodological approaches. Build Enviro 2009, 44:437-445.
- [29]Rajabifard A, Eagleson S: Spatial data access and integration to support liveability: A case study in North and West Melbourne. Melbourne: Centre for Spatial Data Information and Land Administration, the University of Melbourne; 2013.
- [30]Giles-Corti B, et al.: Understanding physical activity environmental correlates: increased specificity for ecologial models. Exerc Sport Sci Rev 2005, 33:175-181.
- [31]Mavoa S, et al.: GIS based destination accessibility via public transit and walking in Auckland, New Zealand. J Trans Geog 2012, 20:15-22.
- [32]White M: Future of cities impact: Indicators and implementations. In Proceedings of the 51st IFHP World Congress. Copenhagen; 2007.
- [33]White M: Implementing the rhetoric. In Proccedings of the Now and When. Venice: Australian Urbanism; 2010.
- [34]White M: Informing an integrated and sustainable urbanism through rapid, defragmented analysis and design. In Proceedings of the Spatial Information Laboratory. Melbourne: RMIT; 2008.
- [35]Owen N, et al.: Understanding environmental influences on walking: review and research agenda. Am J Prev Med 2004, 27:67-76.
- [36]Aspelin K: Establishing pedestrian walking speeds. Portland: Portland State University; 2005.
- [37]Fitzpatrick K, Brewer M, Turner S: Another look at pedestrian walking speed. Transp Res Record 1982, 2006:21-29.
- [38]Hart P, Nilsson N, Raphael B: A formal basis for the heuristic determination of minimum cost paths. Trans Syst Sci Cybern 1968, 4:100-107.
- [39]Jackson R, Dannenberg A, Frumkin H: Health and the built environment: 10 years after. Am J Public Health 2013, 103:e1-e3.
- [40]Kerr J, et al.: Advancing science and policy through a coordinated international study of physical environments: IPEN methods. J Phys Act Health 2013, 10:581-601.
- [41]Sallis J, et al.: Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation 2012, 125:729-737.
- [42]Lee I-M, et al.: Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012, 380:219-229.
- [43]Hallal P, et al.: Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 2012, 380:247-257.
- [44]Strategic Review of Health Inequalities in England post-2010: Fair society, healthy lives. London: Department of Health; 2010.
- [45]Sugiyama T, et al.: Associations of neighbourhood greenness with physical and mental health: do walking, social coherence and local social interaction explain the relationships. J Epi Comm Health 2008, 62:1-6.
- [46]Woodcock J, et al.: Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport. Lancet 2009, 374:1930-1943.
- [47]Giles-Corti B, et al.: The co-benefits for health of investing in active transportation. N S W Public Health Bull 2010, 21:122-127.
- [48]Dodson J, Sipe N: Unsettling suburbia: The new landscape of oil and mortgage vulnerability in Australian cities. In Proceedings of the Griffith University Urban Research Program. Brisbane; 2008.
- [49]World Health Organization: Urbanization and health. Bull WHO 2010, 88:245-246.
- [50]Ewing R, Schieber R, Zegeer C: Urban sprawl as a risk factor in motor vehicle occupant and pedestrian fatalities. Am J Public Health 2003, 93:1541-1545.
- [51]Badland H, et al.: Socio-ecological predictors of the uptake of cycling for recreation and transport in adults: longitudinal results from the RESIDE study. Prev Med 2013, 57:396-399.
- [52]Frank L, et al.: Stepping towards causation: Do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Soc Sci Med 2007, 65:1898-1914.
- [53]McCormack G, Shiell A: Search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act 2011, 8(125): . 10.1186/1479-5868-8-125
- [54]Mason P, Kearns A, Livingston M: “Safe going”: the influence of crime rates and perceived crime and safety on walking in deprived neighbourhoods. Soc Sci Med 2013, 91:15-24.
- [55]Waddell P: UrbanSim: modeling urban development for land use, transportation and environmental planning. J Am Plann Assoc 2002, 68:297-314.
- [56]Waddell P: Integrated land use and transportation planning and modelling: addressing challenges in research and practice. Transport Rev 2011, 31:209-229.
- [57]Hatzopoulou M, Hao J, Miller E: Simulating the impacts of household travel on greenhouse gas emissions, urban air quality, and population exposure. Transportation 2011, 38:871-887.
- [58]Giles-Corti B, et al.: Development and trial of an automated, open-source walkability index tool through the Australian Urban Research Infrastructure Network’s open source portal. In Proceedings of the State of Australian Cities. Sydney: Department of Infrastructure and Regional Development; 2013.