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Using crowdsourced data to monitor change in spatial patterns of bicycle ridership
被引:53
|作者:
Boss, Darren
[1
,5
]
Nelson, Trisalyn
[2
]
Winters, Meghan
[3
]
Ferster, Colin J.
[4
]
机构:
[1] Univ Victoria, 3800 Finnerty Rd, Victoria, BC V8P 5C2, Canada
[2] Arizona State Univ, 975 S Myrtle Ave, Tempe, AZ 85287 USA
[3] Simon Fraser Univ, Fac Hlth Sci, Burnaby, BC V5A 1S6, Canada
[4] Univ Victoria, Dept Geog, POB 1700 STN CSC, Victoria, BC V8W 2Y2, Canada
[5] POB 948, Cumberland, BC V0R1S0, Canada
基金:
加拿大自然科学与工程研究理事会;
关键词:
Cycling;
Crowdsource;
Spatial analysis;
Networks;
Infrastructure;
ROUTE CHOICE MODEL;
TRANSPORTATION;
HEALTH;
D O I:
10.1016/j.jth.2018.02.008
中图分类号:
R1 [预防医学、卫生学];
学科分类号:
1004 ;
120402 ;
摘要:
Cycling is a sustainable mode of transportation with numerous health, environmental and social benefits. Investments in cycling specific infrastructure are being made with the goal of increasing ridership and population health benefits. New infrastructure has the potential to impact the upgraded corridor as well as nearby street segments and cycling patterns across the city. Evaluation of the impact of new infrastructure is often limited to manual or automated counts of cyclists before and after construction, or to aggregate statistics for a large region. Due to methodological limitations and a lack of data, few spatially explicit approaches have been applied to evaluate how patterns of ridership change following investment in cycling infrastructure. Our goal is to demonstrate spatial analysis methods that can be applied to emerging sources of crowdsourced cycling data to monitor changes in the spatial-temporal distribution of cyclists across a city. Specifically, we use crowdsourced ridership data from Strava to examine changes in the spatial-temporal distribution of cyclists in Ottawa-Gatineau, Canada, using local indicators of spatial autocorrelation. Strava samples of bicyclists were correlated with automated counts at 11 locations and correlations ranged for 0.76 to 0.96. Using a local indicator of spatial autocorrelation, implemented on a network, we applied a threshold of change to separate noise from patterns of change that are unexpected given a null hypothesis that processes are random. Our results indicate that the installation or temporary closure of cycling infrastructure can be detected in patterns of Strava sample bicyclists and changes in one location impact flow and relative volume of cyclists at multiple locations in the city. City planners, public health professionals, and researchers can use spatial patterns of Strava sampled bicyclists to monitor city-wide changes in ridership patterns following investment in cycling infrastructure or other transportation network change.
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页码:226 / 233
页数:8
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