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. 2023 Feb 15;18(2):e0279906.
doi: 10.1371/journal.pone.0279906. eCollection 2023.

Tracking stolen bikes in Amsterdam

Titus Venverloo  1   2 , Fábio Duarte  1 , Tom Benson  1 , Pietro Leoni  1 , Serge Hoogendoorn  2 , Carlo Ratti  1
Affiliations

Tracking stolen bikes in Amsterdam

Titus Venverloo et al. PLoS One. .

Abstract

Crime has major influences in urban life, from migration and mobility patterns, to housing prices and neighborhood liveability. However, urban crime studies still largely rely on static data reported by the various institutions and organizations dedicated to urban safety. In this paper, we demonstrate how the use of digital technologies enables the fine-grained analysis of specific crimes over time and space. This paper leverages the rise of ubiquitous sensing to investigate the issue of bike theft in Amsterdam-a city with a dominant cycling culture, where reportedly more than 80,000 bikes are stolen every year. We use active location tracking to unveil where stolen bikes travel to and what their temporal patterns are. This is the first study using tracking technologies to focus on two critical aspects of contemporary cities: active mobility and urban crime.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Reported bike thefts in the Netherlands, Amsterdam, Utrecht (municipality), and Rotterdam (2010 = 1).
Data from 2020 and 2021 are preliminary. Data by [15].
Fig 2
Fig 2. The number of reported bike thefts per neighborhood in Amsterdam (2019).
Data from [15], base map data from OpenStreetMap.
Fig 3
Fig 3. The brands of the bikes and the quality of each bike.
Fig 4
Fig 4. Two of the bikes used in the study, on the right an older model Batavus, on the left a newer bike from the brand Lekker.
Fig 5
Fig 5
left: Placement of tracker as reflector, right: Placement of tracker underneath the seat.
Fig 6
Fig 6. Schematic of the panoramic image processing.
Original images: Municipality of Amsterdam.
Fig 7
Fig 7. Stolen bikes and average number of bikes in the images.
The linear model is represented by the red line.
Fig 8
Fig 8. Deployment locations and likelihood indicator, base map data from OpenStreetMap.
Fig 9
Fig 9. Planned deployment locations and realized parking locations of the 100 bikes.
Base map data from OpenStreetMap.
Fig 10
Fig 10. Count of the number of bikes stolen per day.
Fig 11
Fig 11. Count of the number of start times of a trip per hour.
Purple indicates the start time of the first trip, right after it was stolen.
Fig 12
Fig 12. The number of bikes that started at the same time (could be a different date) for each of the first five trips the stolen bikes made.
The start times of trip one are the times the bikes were stolen at.
Fig 13
Fig 13. Visits of stolen bike to 4-digit postal code, base map data from OpenStreetMap.
Fig 14
Fig 14. Hierarchical clustering results, visualized as the deviation of the cluster from the overall mean providing information about the differences between all clusters.
Fig 15
Fig 15. Betweenness, and therefore importance for the movements of stolen bikes, of the grid cells.
Base map data from OpenStreetMap.
Fig 16
Fig 16. Weighted betweenness identifying grid cells with few functions and amenities yet a high importance for the movements of stolen bikes.
Base map data from OpenStreetMap.
Fig 17
Fig 17. Identified communities of stolen bikes based on the Louvain algorithm.
See this image and copyright information in PMC

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