Identification and prediction of mixed-use functional areas supported by POI data in Jinan City of China

Zheng, Minrui; Wang, Hongyu; Shang, Yiqun; Zheng, Xinqi

doi:10.1038/s41598-023-30140-x

Cited by 20 publications

(7 citation statements)

References 18 publications

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“…They can provide real-time descriptions of the spatial and attribute characteristics of these entities, such as name, latitude and longitude, address, type, etc. Notably, POIs are characterized by their large volume, high accuracy, and detailed classification, offering expanded possibilities for geographic spatial exploration 49 , 50 . In this study, we utilized the Gaode (Amap) API and employed Python web-crawling techniques to extract POI data pertaining to sports venues in China (excluding the regions of Hong Kong, Macao, and Taiwan).…”

Section: Methodsmentioning

confidence: 99%

Spatial distribution characteristics and causes of public sports venues in China

Zhang,

Yi Ming,

Shi

2023

Sci Rep

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Public sports venues serve as a crucial material medium for human athletic activities. Research on their spatial distribution holds profound implications for optimizing spatial layout and promoting human sports endeavors. This paper takes interest points of sports venues in China as research samples, employing GIS spatial analysis tools, mathematical statistics, and Geo-detector as research methods. It systematically investigates the spatial distribution characteristics and causes of sports venues in China, aiming to provide insights and references for the optimization of spatial arrangement, sustainable development, and relevant policy formulation of sports venues in the country. The results indicate that: (1) In terms of overall distribution characteristics, sports venues in China present a multi-centric agglomeration in geographical space. (2) From a regional distribution perspective, the spatial layout of these venues reveals a pattern of “more in the south, less in the north,” “dense in the southeast, sparse in the northwest,” and a coastal strip distribution. (3) Regarding spatial correlation, the hot and cold spot partitions of sports venues largely align with the “Hu-Line”, with their spatial distribution tending towards a positively correlated pattern of high–high clustering and low–low clustering. (4) There is evident heterogeneity in factors affecting sports venues’ spatial distribution. The distribution results from the interactive coupling of multiple factors, where the interaction between any two factors offers stronger explanatory power for the spatial layout of the venues.

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Section: Methodsmentioning

confidence: 99%

Spatial distribution characteristics and causes of public sports venues in China

Zhang,

Yi Ming,

Shi

2023

Sci Rep

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“…In a narrow sense, some scholars have defined land-use mix as the functional mixing of different types of urban buildable land [22,39]. However, in a broader sense, some scholars have argued that land-use mix should include the functional mixing of different types of facilities [40,41]. Furthermore, the geographical scale at which land-use mix is measured has also been found to account for the measurement of land-use mix [39].…”

Section: The Measurement Of Land-use MIX and Technological Innovation...mentioning

confidence: 99%

The Impact of Land-Use Mix on Technological Innovation: Evidence from a Grid-Cell-Level Analysis of Shanghai, China

Jiang,

Xiong

2024

Land

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While the benefits of land-use mix have been widely analyzed with regard to transportation, public health, and economic development, relatively little attention has been paid to empirically investigating the impact of land-use mix on technological innovation at the intra-urban level. Drawing upon a database of geo-coded patents that are used to reflect the capacity of technological innovation, this paper takes Shanghai as a case study and analyzes how the intra-urban distribution of technological innovation has been associated with land-use mix at the 1 km × 1 km grid cell level. Empirical results, which are robust when the grids are divided at the 2 km × 2 km level, show that the degree of land-use mix is positively associated with the number of patents for a given grid, suggesting that grids with a higher level of land-use mix are likely to have more patents, ceteris paribus. Moreover, the results demonstrate an inverted U-curve relationship between land-use mix and technological innovation, indicating that a too much higher level of land-use mix could lead to a smaller number of patents for a certain grid. In addition, the empirical results suggest the existence of spatial dependence in the effect of land-use mix on technological innovation.

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“…In view of the above results, Huang et al [21] integrated high-resolution multispectral and multi-angle ZY3-01 satellite images as well as JL1-07 nighttime light (NTL) images to obtain the UFA of Wuhan and Beijing in China. The above research attempts to separately add 3D attributes, social media data, POIs data and other similar geospatial information [22]- [29]. Although the classification accuracy has been improved to some extent, there are still some problems.…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

Chen,

He,

Guo

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The urban functional areas (UFA) are the basic spatial units of a city, identifying their category information and spatial distribution is of great significance for studying urban spatial structure and formulating scientific and reasonable urban planning policies. However, traditional classification systems are still limited due to the data processing costs and time consuming. With the development of high-resolution satellite, the two-dimensional image has been able to identify more ground object information on a fine scale, but it lacks the application of three-dimensional characteristics and social attributes. At the same time, machine learning and deep learning have shown great utility in extracting features, and they should be applied more to the classification of urban functional areas to improve efficiency. To solve these problems, we propose an efficient and accurate framework for mapping UFA using multi-source geospatial data. It can grasp the dynamic changes in the city more accurately, and provide reference for the study of urban functional areas. An improved frequency density (IFD) model was proposed to improve the overall classification accuracy by 4.4%. Besides, the nighttime light (NTL) data from SDGSAT-1 satellite glimmer imagers are also used to classify UFA. By combining NTL and urban building height data, a sky view enhanced nightlight index (SVENI) was proposed, which can improve the overall classification accuracy of UFA by 5.8%. This study systematically clarifies the role of data sources, methods, and automatically integration models in the classification framework for UFA, which is of great significance for urban planning and sustainable development.

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Identification and prediction of mixed-use functional areas supported by POI data in Jinan City of China

Cited by 20 publications

References 18 publications

Spatial distribution characteristics and causes of public sports venues in China

Spatial distribution characteristics and causes of public sports venues in China

The Impact of Land-Use Mix on Technological Innovation: Evidence from a Grid-Cell-Level Analysis of Shanghai, China

Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data

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