Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)

Loidl, Martin; Traun, Christoph; Wallentin, Gudrun

doi:10.1016/j.jtrangeo.2016.02.008

Cited by 21 publications

(17 citation statements)

References 51 publications

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“…Each reference unit was overlaid with the geolocated crash reports and the total distance travelled. The high standard deviations for the number of crashes per reference unit in Table 1 indicate that crash locations and bicycle flows are unevenly distributed over space, what is in line with a previous study [30,31]. For the present study the following spatial reference units were tested.…”

Section: Conceptual Workflowsupporting

confidence: 78%

“…Figure 10 demonstrates the additional insight that is gained from risk mapping on a local scale. It reveals distinct spatial patterns of risk, which are neither captured in the analysis of absolute numbers of crashes [30] nor in aggregated statistics.…”

Section: Discussionmentioning

confidence: 94%

“…It might happen that these two locations are aggregated to the same spatial reference unit, indicating an area with high risk, although there is no relation in reality. Considering this argument, we also tested different network-based reference units (for details refer to [30,54]). Figure 8 shows the effect of network-based reference units on the emerging patterns.…”

Section: Resultsmentioning

confidence: 99%

“…In their study they demonstrate how spatial patterns of road mortalities in Europe change with the level of aggregation (NUTS0, NUTS1, and NUTS2). On the scale of a city, Loidl et al [30] made the high spatial variability and temporal dynamic of bicycle crash locations visible. Their study calls for risk calculations on the local scale that account for the high degree of spatial heterogeneity.…”

Section: Spatial Heterogeneitymentioning

confidence: 99%

“…In order to test the type of reference unit and the effect of the unit size, irregular and regular reference units were defined. Additionally, network-based reference units were generated (for the algorithm for the delineation of network-based units we refer to Loidl et al [30]). The crash data covered the years from 2002 to 2011.…”

Section: Conceptual Workflowmentioning

confidence: 99%

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Mapping Bicycle Crash Risk Patterns on the Local Scale

Loidl

Wallentin

Wendel

et al. 2016

Safety

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Currently, mainly aggregated statistics are used for bicycle crash risk calculations. Thus, the understanding of spatial patterns at local scale levels remains vague. Using an agent-based flow model and a bicycle crash database covering 10 continuous years of observation allows us to calculate and map the crash risk on various spatial scales for the city of Salzburg (Austria). In doing so, we directly account for the spatial heterogeneity of crash occurrences. Additionally, we provide a measure for the statistical robustness on the level of single reference units and consider modifiable areal unit problem (MAUP) effects in our analysis. This study is the first of its kind. The results facilitate a better understanding of spatial patterns of bicycle crash rates on the local scale. This is especially important for cities that strive to improve the safety situation for bicyclists in order to address prevailing safety concerns that keep people from using the bicycle as a utilitarian mode of (urban) transport.

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Section: Conceptual Workflowsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Spatial Heterogeneitymentioning

confidence: 99%

Section: Conceptual Workflowmentioning

confidence: 99%

See 3 more Smart Citations

Mapping Bicycle Crash Risk Patterns on the Local Scale

Loidl

Wallentin

Wendel

et al. 2016

Safety

Self Cite

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Spatial Statistics along Networks

Okabe

2018

Wiley StatsRef: Statistics Reference Online

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Spatial statistics along networks is a branch of spatial statistics. Traditional spatial statistics deals with events occurring on a plane, referred to as planar spatial statistics . By contrast, spatial statistics along networks, referred to in parallel as network spatial statistics , deals with events occurring along networks. The events occurring “along” a network are of two types: those directly occurring on a network, for example, traffic accidents, and those occurring alongside a network, for example, stores facing a street. Networks may potentially include corridors, roads, railways, gas and oil pipelines, sewer systems, rivers, canals, electric wires, neurons, the Internet, social networks (e.g., SNS), and so on. Network spatial statistics is applied to various studies, for instance, pedestrian, car, and bicycle accidents, roadkill, street crimes, contamination of rivers, floods, facility locations, tree falls on a railway, etc. This article first describes the most basic hypothesis used in network spatial statistics, called the completely spatial randomness ( CSR ) hypothesis. Next, the article introduces nine methods used for network spatial statistics, that is, Voronoi diagrams, nearest‐neighbor distance method, K function method, spatial autocorrelation, point cluster analysis, kernel density estimation, spatial interpolation, and software for network spatial statistics.

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