Mobility-Driven Early Warning for Candidate POI Updates Using Shared-Visitor Priors: Case Studies in Auckland and Queenstown, New Zealand
Year: 2026
Type: article
Abstract: Accurate and timely Point-of-Interest (POI) information is important for location-based services, tourism management, and digital map maintenance. However, newly opened or weakly documented POIs may be updated slowly because conventional map maintenance often relies on user reports, field surveys, and manual verification. This problem is especially relevant to tourism-centric towns, where POI update demand exists but large map-service providers may prioritize dense metropolitan areas with higher user activity and stronger commercial returns. This study addresses mobility-driven early warning for candidate POI updates under cold-start conditions, where candidate locations lack stable category labels and long-term visit histories. We propose MobiPOI, a framework that combines anonymized mobile location observations, map-derived spatial boundaries, and shared-visitor priors. Candidate visits are first constructed within geographic boundaries to reduce roadside contamination and spatial spillover. Behaviourally related mature POIs are then identified through shared visitors and activity-rhythm similarity. These mature peers provide dwell-time references for visit filtering and temporal priors for cold-start verification. A prior-guided interval gated recurrent unit (GRU) further combines the cleaned candidate sequence, peer-derived temporal prior, and shared-visitor acquisition velocity to estimate dynamic visitor-volume intervals. Experiments are conducted in two New Zealand case-study regions, Auckland and Queenstown, representing metropolitan and tourism-centric mobility settings. Using delayed temporal OpenStreetMap (OSM) snapshots and manual map-based inspection for post hoc validation, the results show that MobiPOI improves prediction-interval reliability and candidate POI warning efficiency compared with spatial clustering, fixed-threshold, time-series, and prior-transfer baselines.
DOI: 10.3390/ijgi15100472
Authors: Zhengduo Xiao, Chiu-Wing Sham, Ka-Shing Cheung
Institution: University of Auckland
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,292
OpenAlex ID: W7222494438
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