文章基本信息

标题：Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities
本地全文：下载
作者：Shen, Xiaoqi ; Shi, Wenzhong ; Liu, Zhewei 等
期刊名称：ISPRS International Journal of Geo-Information
电子版ISSN：2220-9964
出版年度：2022
卷号：11
期号：7
页码：1-35
DOI：10.3390/ijgi11070397
语种：English
出版社：MDPI AG
摘要：Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.
关键词：human activity; area extraction; large-scale spatial data; varying density; clustering algorithm