Functional Area Recognition and Use-Intensity Analysis Based on Multi-Source Data: A Case Study of Jinan, China

Mingyang Yu,Jingqi Li,Yongqiang Lv,Huaqiao Xing,Huimeng Wang

doi:10.3390/ijgi10100640

Mingyang Yu, Jingqi Li + Show 3 more

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https://doi.org/10.3390/ijgi10100640

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Abstract

This paper proposes a GIS-based field model for hot-spot extraction based on POI data and analyzes the use intensity of functional areas by using Tencent location data to identify and describe the morphological characteristics and dynamic use intensity of facilities in urban functional areas. Taking the four districts of Jinan City Center as an example, we used the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu to extract facility hot spots. Tencent location data were then applied to quantify differences in the use intensity of functional areas between workday and weekend, as well as between daytime and nighttime. Finally, refined research on functional areas was realized from a dynamic point of view. Results showed that (1) the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu can identify and express the characteristics of the spatial distribution and hierarchical structures of urban facility hot spots at the horizontal and vertical levels, respectively; (2) overall, the distribution of all types of functional areas presents the characteristics of “circular structures,” which form a spatial pattern of “multi-center” groups and “single/mixed” functional areas; (3) aside from residential facilities, green space and square land facilities have the highest use intensity; this finding highlights the tourism characteristics of Jinan. Low-use intensity areas are distributed at the periphery of the four districts, while high-use intensity areas, the functional type of which is mainly business facilities, are mainly distributed around the urban area. These results are helpful to the development strategy of the city’s efforts to adapt to economic change and provide a scientific basis for the functional orientation of Jinan City.

Highlights

The present paper proposes a GIS-based field model for hot-spot extraction based on point of interest (POI) data and analyzes the use intensity of functional areas by using Tencent location data [17]
The POI data were superimposed on the fishnet data of the four central districts, frequency density (FD) was used to calculate the ratio of each type of POI within the unit to the total number of POIs in the category, and the category ratio was constructed to identify urban functional areas
Where i is the type of POI, ni is the number of units in the POI category i, Ni is the first category of the respective POI, Fi is the first category of the POI accounting for the total number of POI categories, which is used to calculate the FD, and Ci is the first category of the POI FD that proportionally accounts for the FDs of all POIs

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A city is characterized by various social activities and material resources. Continuous developments encourage cities to undergo changes in their structure and function; these changes gradually form different functional regions, including commercial, residential, and industrial regions [1]. The accurate and timely identification and analysis of urban functional areas and structures are essential to measure the status of urban development and support the planning and management of cities accurately

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Results

Conclusion