Block2vec: An Approach for Identifying Urban Functional Regions by Integrating Sentence Embedding Model and Points of Interest

ISPRS International Journal of Geo-Information - Tập 10 Số 5 - Trang 339
Zhihao Sun1, Hongzan Jiao1, Hao Wu2, Zhenghong Peng2, Lingbo Liu1
1Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
2Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China

Tóm tắt

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs.

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ISPRS International Journal of Geo-Information

Tập 10 Số 5

339

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