Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Zelong Xia · Hao Li · Yuehong Chen

30 citations0/0 files storedISPRS International Journal of Geo-Information

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Human Mobility and Location-Based Analysis Urban Design and Spatial Analysis Data Management and Algorithms

Abstract

Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 30 citations exist upstream.

References

2009Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data 2012Discovering Spatial Patterns in Origin‐Destination Mobility Data 2003Representation and Spatial Analysis in Geographic Information Systems 2009Mining time-dependent attractive areas and movement patterns from taxi trajectory data 2015Mining spatiotemporal patterns of urban dwellers from taxi trajectory data 2008Geocomputation and Urban Planning 2014The visualization and analysis of urban facility pois using network kernel density estimation constrained by multi-factors

Cited by

2021Urban hotspots detection of taxi stops with local maximum density 2021Characterization of Mobility Patterns With a Hierarchical Clustering of Origin-Destination GPS Taxi Data 2020Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing 2020Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation 2025Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.