Application of integration of spatial statistical analysis with GIS to regional economic analysis

Fei, Chen; Daosheng, Du

doi:10.1007/bf02828549

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Where, f(s) is the kernel density estimation function at position s, and h is the path attenuation threshold, that is, bandwidth; ci is the spatial position of the i-th POI point in the bandwidth; n is the number of POI points whose path distance from position s is less than or equal to h; the λ function is a spatial weight function. In this paper, the quaternary weight equation is selected, and the formula is [5] 2…”

Section: The Methodsmentioning

confidence: 99%

Research on Charging Infrastructure Planning Based on Kernel Density Analysis

2021

jecet

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In order to effectively solve resource and environmental problems, the market of new energy vehicles, represented by electric vehicles, is growing rapidly. The convenience of charging infrastructure layout is an important factor affecting the development of electric vehicles, so how to reasonably add charging infrastructure has become a key issue to be solved. Starting from geography and planning, this paper uses kernel density analysis to explore the spatial distribution of different types of commercial service facilities in Zibo city, and obtains the suitable area for adding charging piles, so as to put forward suggestions for adding charging infrastructure in Zibo.

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Section: The Methodsmentioning

confidence: 99%

Research on Charging Infrastructure Planning Based on Kernel Density Analysis

2021

jecet

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“…During the process of conducting the spatial autocorrelation analysis [7], above of all, it needs to define the mutual adjacency relation of the spatial object and determine the weight of each space unit so as to understand the spatial linkage of the related data in the GIS database. The definition of the spatial weight matrix depends on the adjacency rule and the distance rule.…”

Section: A Spatial Weight Matrixmentioning

confidence: 99%

Spatial Disparity Analysis of Inner Mongolia Based on GIS

Liu¹,

Zhiyang²,

Liu³

2015

Proceedings of the AASRI International Conference on Industrial Electronics and Applications (2015)

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With the application of Geographic Information Systems (GIS) and Exploratory Spatial Data Analysis (ESDA) techniques, twelve criteria indicating the county economic strength of Inner Mongolia are selected to evaluate an analysis and discussed its causes on the spatial disparity. In order to make the factor analysis, principal component analysis (PCA) is performed by the SPSS software. Conclusions concerning global spatial autocorrelation analysis and local spatial autocorrelation analysis are studied. Some significant graphs and charts are demonstrated according to statistics of the results.

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“…The research of spatial distribution situation of GDP done by Tang Xiaoxu, Zhang Huaiqing and Liu Rui, et al, disclosed the effect of regional population and area to the economic growth. All these analyses that investigated the spatial law of regional economy are based on the data in different provinces or in one province nationwide [9] ; and there are many other scholars analyzed the spatial distribution mode and spatial relevancy relationship of economies in all provinces and counties by combining the spatial autocorrelation and the spatial statistical method of GIS [10][11][12][13][14][15][16][17] . Dai Hezhi and Zhao Xu utilized both single indicator and multi-index to construct the index of economy developing level, and further measured the distribution situation of economic growth level in Shandong regions [18][19] .…”

Section: Introductionmentioning

confidence: 99%

A spatial econometric analysis of county economic growth: A case study of 108 counties in Shandong province

Xie

Wang

2013

2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings

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County is the basic unit of studying regional economy, and the realization of a coordinating and harmonious development of county economy would be much easier if we can clarify and thoroughly understand the characteristics and key factors of county economic growth. This thesis first combs the achievements of spatial economic theories and methods that gained in recent years in the research of regional economic growth, describes the characteristics of economic spatial distribution in the 108 counties in Shandong province and makes an elaborate analysis of the spatial relativity of county economy by utilizing methods like global spatial autocorrelation and local spatial autocorrelation; and then, based on the fact that county economy has obvious space correlation, the thesis makes an spatial econometric analysis of the eleven factors that affect the county economic growth by adopting spatial lag model; and at last comes to the conclusion that the key to the county economic growth includes industrialization, government investment leading factor and farmers' own economic level.

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Application of integration of spatial statistical analysis with GIS to regional economic analysis

Cited by 9 publications

References 9 publications

Research on Charging Infrastructure Planning Based on Kernel Density Analysis

Research on Charging Infrastructure Planning Based on Kernel Density Analysis

Spatial Disparity Analysis of Inner Mongolia Based on GIS

A spatial econometric analysis of county economic growth: A case study of 108 counties in Shandong province

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