Journal Article

MCS Assisted Accurate Perception Framework for Urban POI Classification

Feng, Xiaorong, Yang, Yuchen, Zhang, Xudong, Guo, Dongsheng, Yang, Guisong

2025

Overview

The classification of urban points of interest (POI) reflects the development of various industries in a city, making their distribution analysis significant. Traditional mapping methods often face inefficiency and high costs, leading to limited data quality and inaccuracies in classification. To address this, a low-cost, high-quality method is essential. Mobile Crowd Sensing (MCS) technology offers an innovative solution for identifying urban POIs. This paper introduces a hybrid MCS perception framework (MCS-APF) that includes a data collection module and a clustering module. The data collection module combines traditional participatory and opportunistic methods, incorporating a new recruitment criterion considering workers’ abilities, reputations, and POI popularity to enhance data quality. The clustering module employs an improved version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN-H) algorithm using Haversine distance, which effectively analyzes the combined data for accurate POI classification. Experimental results show that POI classifications derived from DBSCAN-H feature significant intra-cluster tightness and inter-cluster separation, outperforming traditional techniques. Overall, MCS-APF provides more accurate, efficient, and cost-effective POI sensing outcomes.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Accuracy / Algorithms / Analysis / Clustering / clustering algorithm / Costs / Crowdsensing / Data collection / Data entry / Decision-making / Genetic algorithms / Geospatial data / Information management / mobile crowd sensing / Optimization / participatory MCS / Sustainable urban development / Traffic violations / urban POI / User behavior / User generated content / Workers