Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Xiaowen Zhou, Hongwei Li, Jian Xu and Qingzhen Sun ()
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Xiaowen Zhou: College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Hongwei Li: School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
Jian Xu: School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
Qingzhen Sun: School of Civil Engineering and Environment, Zhengzhou University of Aeronautics, Zhengzhou 450046, China

Land, 2024, vol. 13, issue 6, 1-24

Abstract: This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city.

Keywords: urban spatial image; image elements; geographical big data; spatial analysis; urban spatial form and function (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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