融合区域嵌入表示的城市功能区识别方法  

A Method for Identifying Urban Functional Regions by Integrating Regional Embedding Representations

作　　者：韦烨娜 吴升[1] WEI Yena;WU Sheng(The Academy of Digital China(Fujian),Fuzhou University,Fuzhou 350003,China)

机构地区：[1]福州大学数字中国研究院(福建),福州350003

出　　处：《地球信息科学学报》2025年第2期424-440,共17页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2023YFB3906804)。

摘　　要：【目的】城市功能区是城市规划和人类活动共同作用、相互影响的结果,其准确识别对于优化配置公共资源和高效组织商业活动具有重要意义。目前,许多研究利用新兴的社会感知大数据进行城市功能区识别,但往往未能挖掘这些数据中蕴含的深层次特征,或者未能充分捕捉和利用不同特征之间的相互关系和关联性,导致识别精度较低。【方法】针对这些问题,本研究提出了一种融合区域嵌入表示的城市功能区识别框架。该方法基于手机定位数据和兴趣点数据(Point of Interest,POI),采用Node2vec算法提取工作日与周末6个时段的区域间空间交互特征,并利用GloVe模型提取区域的语义特征。随后,通过多头注意力机制进行特征融合,并结合部分人工标注的功能区进行分类识别,在福州市三环以内地区进行了实证研究。【结果】实验结果表明,本方法生成的区域表示特征具有较高区分度,能够有效识别6类功能区,总体精度(OA)为81%,Kappa系数为0.77。【结论】与DTW_KNN和Word2Vec方法相比,精度分别提高了30%和20%,能够充分挖掘具有全局性质的空间交互特征和语义特征。此外,消融实验进一步表明,与单一数据源或简单融合方法相比,本方法在捕捉区域内部和区域间复杂关系的同时,对重要特征赋予更高的权重,使得模型的整体OA值相较于单源数据提高了约18%和6%,相较于简单融合方法提高了约13%,尤其在住宅区和混合区的识别方面表现出了显著优势。[Objectives]Urban functional regions result from the complex interactions and mutual influences of urban planning and human activities.Accurately identifying these regions is crucial not only for optimizing the allocation of public resources,such as infrastructure and services,but also for improving the efficiency of commercial and economic activities within the city.In recent years,the rise of social sensing big data has opened up new possibilities for identifying urban functional regions.While many studies have leveraged this emerging data for identification purposes,they often fall short in fully exploiting the deep features within the data.Additionally,they frequently fail to capture and utilize the complex interrelationships and correlations between different features,leading to lower identification accuracy.[Methods]To address these problems,this study proposes a framework for identifying urban functional regions by integrating regional embedding representations using a multi-head attention mechanism.The framework leverages mobile phone location data and Point of Interest(POI)data,employing the Node2vec algorithm to extract spatial interaction features across six time periods(weekdays and weekends),and utilizing the GloVe model to extract semantic features of regions.Subsequently,multi-head attention mechanisms are applied to effectively integrate these features,enabling the classification and identification of functional areas with the assistance of partially labeled data,thereby ensuring greater accuracy and reliability.An empirical study was conducted within the Third Ring Road of Fuzhou City.[Results]The results demonstrate that the proposed method generates regional representations with high discriminative power,effectively identifying six types of functional areas.The Overall Accuracy(OA)of the model is 81%,with a Kappa coefficient of 0.77.[Conclusions]Compared to the DTW_KNN and Word2Vec methods,the proposed approach improves accuracy by 30%and 20%,respectively,through fully exploiting the global spa

关 键 词：城市功能区识别 区域嵌入表示 多头注意力机制 出行有向图 手机定位数据 POI 

分 类 号：P208[天文地球—地图制图学与地理信息工程]