Figure 3 - uploaded by Avipsa Roy
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Bubble chart showing how the incidents normalized by street length varies with the number of Strava riders for each streetscape category. The bubble number lists the category and the size of the bubble represents the total length in kilometers.
Source publication
Cities are making infrastructure investments to make travel by bicycle safer and more attractive. A challenge for promoting bicycling is effectively using data to support decision making and ensuring that data represent all communities. However, ecologists have been addressing a similar type of question for decades and have developed an approach to...
Context in source publication
Context 1
... bicycling destinations category is closer to natural features than the other categories, while further than average to shopping and entertainment, and commercial and retail, and incomes are higher than average (Figure 2). This category has the most Strava activities, while incidents per kilometer or per trip are less than half of category 3 ( Figure 3). The bicycling destinations region is often located along rivers and canals. ...
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Citations
... The classes are subjective and we mapped the change clusters to further visually detect these categories. Existing literature (Brum-Bastos et al., 2019;Nelson et al., 2022) on classifying bicycling regions based on ridership patterns using clustering techniques are used to further support the categorization schemes based on temporal patterns. Further, differentiating classes by magnitude of change was useful for an end goal of stratified sampling and identifying areas for future bike infrastructure planning. ...
Monitoring change is an important aspect of understanding variations in spatial–temporal processes. Recently, 'big data' on mobility, which are detailed across space and time, have become increasingly available from crowdsourced platforms. New methods are needed to best utilize the high spatial and temporal resolution of such data for monitoring purposes. These data can be considered mappable time series but are challenging to use owing to varying sampling rates and issues of temporal misalignment. We present a methodological framework for change detection from big data captured by crowdsourced fitness app Strava, which addresses misalignment issues in the underlying ridership patterns and maps temporal clusters of bicycling ridership change in the city of Phoenix, AZ between 2017 and 2018 at the street-segment level. Hourly and monthly changes were classified into four clusters for each time period - mapped along with crash density to highlight variations in bicycling ridership. Using spatially and temporally continuous data our study advances the existing approaches to mobility analysis, by using a functional data analysis approach. Our method is reproducible and can be used to expand studies in other cities for monitoring changes directly from crowdsourced ridership data thereby facilitating the decision-making process by practitioners to assess and plan safe bicycle infrastructure.
