Design and Application of an Attractiveness Index for Urban Hotspots Based on GPS Trajectory Data

Li Cai · Wei Zhou · Keqin Li

39 citations0/0 files storedIEEE Access

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

Urban hotspots refer to regions where flourishing shopping centers are located, the travel volume is very large, and there is high traffic. The formation of hotspots is strongly correlated with many features, i.e., time, space, and the distribution of points of interest; however, most studies have used qualitative analyses to describe the relevance of these features, and there is a lack of quantitative analyses. Therefore, we propose the concept and a model of the attractiveness index of a hotspot that is used to quantify the spatio-temporal distribution of the hotspots and determine the degree of attractiveness to the residents. In addition, a novel algorithm of hotspot similarity is designed and implemented to improve the efficiency of summarizing the hotspot data, which is usually performed manually. We mine the data and determine different hotspots using a one-week GPS trajectory dataset collected from 6599 taxis in Kunming. Furthermore, we calculate the attractiveness index of hotspots and visualize their characteristics. The research results provide a scientific basis and reference for public infrastructure planning, land-value evaluation, store location planning, consumption recommendations, and other applications.

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 — 39 citations exist upstream.

References

2012Discovering regions of different functions in a city using human mobility and POIs 2014From mobile phone data to the spatial structure of cities 2012Land-Use Classification Using Taxi GPS Traces 2014B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces 2014Detection of dynamic activity patterns at a collective level from large-volume trajectory data

Cited by

2021Tourist hot spots prediction model based on optimized neural network algorithm 2024Evaluation of urban nightlife attractiveness for Millennials and Generation Z 2021Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning Approach 2023Urban railway network expansion on transit oriented development: Improvement in accessibility in four Asian developing cities 2023Which Spatial Elements Influence Waterfront Space Vitality the Most?—A Comparative Tracking Study of the Maozhou River Renewal Project in Shenzhen, China 2019A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior 2022Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process

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.