Functional data analysis approach for mapping change in time series: A case study using bicycle ridership patterns

Abstract

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.