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

Zhihao Sun,Hao Wu,Hongzan Jiao,Zhenghong Peng,Lingbo Liu

doi:10.3390/ijgi10050339

Zhihao Sun, Hao Wu + Show 3 more

Open Access

https://doi.org/10.3390/ijgi10050339

Copy DOI

Abstract

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.

Highlights

Cities are composed of various functions that describe human social activities and their employment of land [1,2], and can be divided into various functional regions, such as commercial, residential, industrial and open space
The fast and accurate identification of urban functional regions has become essential for improving urban planning and management [11,12,13]
Based on the nearest neighbor method, the Point of Interest (POI) sequence and further sequence group were constructed for each parcel in Block2vec

Summary

Introduction

Cities are composed of various functions that describe human social activities and their employment of land [1,2], and can be divided into various functional regions, such as commercial, residential, industrial and open space. The fast and accurate identification of urban functional regions has become essential for improving urban planning and management [11,12,13]. There are extremely strict requirements for its update speed and update frequency, which is obviously not conducive to our real-time understanding of the urban land use structure. It may be difficult for them to distinguish the categories closely related to human social activities, because these data cannot capture functional interaction pattern, nor can they understand socioeconomic environments [21,22,23,24,25]. The land cover categories for impervious surfaces usually include commercial, residential and industrial land

Objectives

Results

Discussion

Conclusion