Identification of Urban Functional Zones Considering Point of Interest Differences: A Case Study of Nanjing City

Yunpeng Zhang · A‐Xing Zhu · Yan Sun · Mengfan Guan

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Human Mobility and Location-Based Analysis Geographic Information Systems Studies Urban Design and Spatial Analysis

Abstract

ABSTRACT Identification of urban functional zones is of paramount significance to urban planning and construction. Point of Interest (POI) data, a type of geospatial data widely employed in recent years for urban functional zone identification, are often used in urban functional zone identification. However, the variability among different types of POIs is often not explored in existing research. This paper explores a novel approach for identifying urban functional zones that reflects the differential roles of various POIs. This approach involves identifying core POIs that represent significant features within a region and constructing spatial co‐location combinations based on these core POIs to identify urban functional zones. The experimental results demonstrate that this method achieves an accuracy of 90.57% in identifying urban functional zones in Nanjing, outperforming methods that do not consider POI differentiation under similar conditions. By considering and analyzing the differential roles of various POIs in the identification process, this study offers a new perspective for research on urban functional zone identification.

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2017Extracting urban functional regions from points of interest and human activities on location‐based social networks 2016Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method 2018Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs 2020Visualizing and exploring POI configurations of urban regions on POI-type semantic space 2022A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method 2021Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers 2021Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing 2022Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas

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