Abstract
BackgroundWhen analytical techniques are used to understand and analyse geographical events, adjustments to the datasets (e.g. aggregation, zoning, segmentation etc.) in both the spatial and temporal dimensions are often carried out for various reasons. The ‘Modifiable Areal Unit Problem’ (MAUP), which is a consequence of adjustments in the spatial dimension, has been widely researched. However, its temporal counterpart is generally ignored, especially in space-time analysis.MethodsIn analogy to MAUP, the Modifiable Temporal Unit Problem (MTUP) is defined as consisting of three temporal effects (aggregation, segmentation and boundary). The effects of MTUP on the detection of space-time clusters of crime datasets of Central London are examined using Space-Time Scan Statistics (STSS).Results and ConclusionThe case study reveals that MTUP has significant effects on the space-time clusters detected. The attributes of the clusters, i.e. temporal duration, spatial extent (size) and significance value (p-value), vary as the aggregation, segmentation and boundaries of the datasets change. Aggregation could be used to find the significant clusters much more quickly than at lower scales; segmentation could be used to understand the cyclic patterns of crime types. The consistencies of the clusters appearing at different temporal scales could help in identifying strong or ‘true’ clusters.
Highlights
In recent years, the advancement in geographical data collection techniques (e.g. Computer Aided Dispatch Systems (CAD), portable sensors etc.) has brought about exponential growth in the availability of geographic data at small space and time scales
This paper investigates the impacts of temporal aggregation and temporal segmentation and boundaries, which we will formally define as the three components of the Modifiable Temporal Unit Problem (MTUP)
4.1 Impacts of Temporal Aggregation For each of the three crime types, the clusters detected at each temporal scale are placed side-byside for comparison
Summary
The advancement in geographical data collection techniques (e.g. Computer Aided Dispatch Systems (CAD), portable sensors etc.) has brought about exponential growth in the availability of geographic data at small space and time scales. This trend of data availability is observed in many application domains including criminology, epidemiology, and transport, to mention but a few. When analytical techniques are used to understand and analyse geographical events, adjustments to the datasets (e.g. aggregation, zoning, segmentation etc.) in both the spatial and temporal dimensions are often carried out for various reasons. Its temporal counterpart is generally ignored, especially in space-time analysis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
