A Method to Identify Urban Fringe Area Based on the Industry Density of POI

Dong, Qi; Qu, Shuxue; Qin, Jiahui; Yi, Disheng; Liu, Yusi; Zhang, Jing

doi:10.3390/ijgi11020128

Cited by 30 publications

(29 citation statements)

References 11 publications

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“…Point of interest (POI) data have the advantages of being real time, high precision, with wide coverage and easy access. Based on this, a series of spatial analyses can be carried out, such as kernel density analysis, cluster analysis, buffer analysis, standard deviation ellipse analysis, location entropy analysis, and hot spot analysis, which can be applied to spatial pattern research, central hot spot identification, and urban functional planning [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Therefore, a series of spatial analyses can be carried out using the POIs of medical and health resources to evaluate the spatial allocation of medical and health resources in Taiyuan.…”

Section: Introductionmentioning

confidence: 99%

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Mao

Guo

2022

IJERPH

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COVID-19 has caused more than 500 million infections and 6 million deaths. Due to a continuous shortage of medical resources, COVID-19 has raised alarm about medical and health resource allocation in China. A balanced spatial distribution of medical and health resources is a key livelihood issue in promoting the equalization of health services. This paper explores the spatial allocation equilibrium of two-tier medical and health resources and its influencing factors in Taiyuan. Using extracted POIs of medical and health resources of AMAP, we evaluated the spatial quantitative characteristics through the Health Resources Density Index, researched the spatial distribution pattern by kernel density analysis, hot spot analysis, and service area analysis, and identified the influencing factors of the spatial distribution equilibrium by the Geodetector model. The findings are as follows. The overall allocation level of medical and health resources in Taiyuan is low. There are tiered and regional differences; the response degree of primary care facilities to external factors is greater than that of hospitals; and the comprehensive influence of economic and topographic systems is crucial compared with other factors. Therefore, in order to promote the rational spatial distribution of medical and health resources in Taiyuan and to improve the construction of basic medical services within a 15 min radius, it is important to continuously improve the tiered healthcare system, uniformly deploy municipal medical and health resources, and increase the resource allocation to surrounding counties and remote mountainous areas. Future research should focus on collecting complete data, refining the research scale, analyzing qualitative differences, and proposing more accurate resource allocation strategies.

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Section: Introductionmentioning

confidence: 99%

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Mao

Guo

2022

IJERPH

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“…We conducted nuclear density analysis on the POI data according to functional categories. According to the “Silverman rule of thumb”, we could determine that the search radius of this nuclear density analysis is 3781.70 m [ 49 ]. Through the nuclear density analysis tool in ArcGIS, we could generate a smooth contour surface of the density of interest points.…”

Section: Results and Analysismentioning

confidence: 99%

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

Ren

Sun

2023

IJERPH

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With the global spread of various human-to-human epidemics, public health issues have become a focus of attention. Therefore, it is of great importance to improve the quantitative risk assessment of the construction of resilient cities in terms of epidemic disasters. Starting with the dimensions of social activities and material space, this paper took Qingdao, China, with a population of 5 million, as an example, and took its seven municipal districts as the research scope. In this paper, five risk factors, including the Population density index, Night light index, Closeness index of roads, Betweenness index of roads and Functional mixed nuclear density index were selected for weighted superposition analysis. We conducted a quantitative assessment of the spatial risk of epidemic disaster so as to obtain the classification and spatial structure of the epidemic disaster risk intensity. The results show that: ① The roads with a large traffic flow are most likely to lead to the risk of urban spatial agglomeration, and the areas with a large population density and large mixture of infrastructure functions are also important factors causing the risk of epidemic agglomeration. ② The analysis results regarding the population, commerce, public services, transportation, residence, industry, green space and other functional places can reflect the high-risk areas for epidemic diseases with different natures of transmission. ③ The risk intensity of epidemic disasters is divided into five risk grade areas. Among them, the spatial structure of epidemic disasters, composed of the first-level risk areas, is characterized by “one main area, four secondary areas, one belt and multiple points” and has the characteristics of spatial diffusion. ④ Catering, shopping, life services, hospitals, schools and transportation functional places are more likely to cause crowd gathering. The management of these places should be focused on prevention and control. At the same time, medical facilities should be established at fixed points in all high-risk areas to ensure the full coverage of services. In general, the quantitative assessment of the spatial risk of major epidemic disasters improves the disaster risk assessment system in the construction of resilient cities. It also focuses on risk assessment for public health events. It is helpful to accurately locate the agglomeration risk areas and epidemic transmission paths that are prone to outbreak or cause epidemic transmission in cities so as to assist the relevant practitioners in containing the epidemic from the initial stage of transmission in a timely manner and prevent the further spread of the epidemic.

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“…The application of POI data in urban and regional economies helps us to understand the social and economic functions, vitality, and development of cities [38]. POI data can be used to identify urban land use types and functional areas [39], the spatial characteristics of human activity [40,41], industrial spatial development [42,43], the urbanization process [44], and population and economic distribution [39,45]. POI data are highly feasible for the above functions, which can enable the rapid identification of urban functional areas, population size, and business development.…”

Section: Research Background 21 Application Of Poi Data and Night Lig...mentioning

confidence: 99%

The Spatial Pattern and Influencing Factors of China’s Nighttime Economy Utilizing POI and Remote Sensing Data

Yan,

Zou,

Liu

2024

Applied Sciences

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The nighttime economy (NTE) is one of the primary measures used by the Chinese government to promote urban consumption and capital flow. Especially after COVID-19, more regulations were introduced by both the central and local governments to accelerate this commercial activity. However, the relationship between the NTE and urban development is controversial. There has been controversy over the relationship between the nighttime economy and urban development. We believe that organizations/individuals embedded in different regional contexts have different behavioral patterns, which, in turn, can make cities develop nighttime commercial activities differently. We wonder whether the nighttime economy’s large-scale development fits the diverse regional development. There is a lack of discussions of the spatial distribution of nighttime commercial activities from an urban geographical perspective, especially the differences and mechanisms of urban systems based on the nighttime economy. Based on existing research arguments, this article collects points of interest (POI) and nighttime light (NTL) remote sensing data (RSD) to spatialize nighttime economic activities in order to provide a reference for reasonable regional and urban economic planning. The nighttime economy (NTE) is one of the primary channels used by the Chinese government to promote urban consumption and capital flow, and the relationship between the NTE and urban development is controversial. Based on existing research, we selected the Yangtze River Delta (YRD) region as an example. We found that there are core–peripheral spatial patterns in nighttime commercial urban systems. The core is Shanghai, and provincial-level core cities form the second category, largely overlapping with the administrative urban system. Although the NTE is primarily concentrated in economically developed coastal areas, it spreads in the northwest–southeast direction, indicating that opportunities will arise in the geo-periphery. Although regulations encourage the growth of the NTE, infrastructure cannot fully support large-scale centralized expansion. The interaction of critical factors, such as urban policies, residents’ consumption, industrial structure, and economic foundations, may affect nighttime activities.

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A Method to Identify Urban Fringe Area Based on the Industry Density of POI

Cited by 30 publications

References 11 publications

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

The Spatial Pattern and Influencing Factors of China’s Nighttime Economy Utilizing POI and Remote Sensing Data

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