SAMPLID: A New Supervised Approach for Meaningful Place Identification Using Call Detail Records as an Alternative to Classical Unsupervised Clustering Techniques

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ISPRS International Journal of Geo-Information Pub Date : 2024-08-17 DOI:10.3390/ijgi13080289
Manuel Mendoza-Hurtado, Juan A. Romero-del-Castillo, Domingo Ortiz-Boyer
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Abstract

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods.
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SAMPLID:一种利用通话详细记录进行有意义地点识别的新监督方法,可替代经典的无监督聚类技术
手机提供的数据已成为识别个人常去的有意义场所的基础。在本研究中,我们介绍了 SAMPLID,这是一种新的有意义地点识别监督方法,其基础是针对我们要解决的特定问题(如家庭/工作地点识别)提供一个知识库。这种方法可以从监督的角度来解决地点识别问题,为无监督聚类技术提供了一种替代方案。这些聚类技术依赖于数据特征,而这些特征可能并不总是与分类目标直接相关。我们使用米兰呼叫详情记录(CDR)提供的流动性数据得出的结果表明,与应用聚类技术相比,我们的结果具有更优越的性能。对于所有类型的 CDR,使用 20 × 20 子网格都能获得最佳结果,这表明当模型获得来自空间关系密切的相邻小区的信息时,其性能会更好。考虑到一个地点或单元格同时被标记为多个类别的情况很常见,这种有监督的方法为从多标签角度识别有意义的地点打开了大门,而这是传统的无监督方法难以实现的。
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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