The spatial statistic trinity: A generic framework for spatial sampling and inference

Wang, Jinfeng; Gao, Bingbo; Stein, Alfred

doi:10.1016/j.envsoft.2020.104835

Cited by 37 publications

(10 citation statements)

References 47 publications

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“…Geodetector, put forward by Wang Jinfeng [50,51], is a statistical method to detect spatial heterogeneity and reveal its driving force, and it has been widely used in medicine [52][53][54][55][56], geography [57][58][59][60][61][62], sociology [63,64], economics [65][66][67], ecology [68,69], environmental science [70][71][72], landscape architecture [73,74], urban and rural planning [75][76][77][78][79][80], and other fields. In the working principle of geodetector, it is assumed that if a certain independent variable has an important influence on the dependent variable, the independent variable and the dependent variable should have the similar heterogeneity of spatial differentiation or stratification [81,82]. Geodetector involves differentiation and factor detection, interaction detection, risk area detection, and ecological detection, which can be achieved by means of Geodetector (http://www.geodetector.cn, accessed date 1 November 2020).…”

Section: Methodsmentioning

confidence: 99%

The Key Factors Driving the Development of New Towns by Mother Cities and Regions: Evidence from China

Zhao

Zhang

2021

IJGI

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As an important carrier of expanded urban spatial growth, new towns have been a “policy tool” for spatial production in the new era and have received long-term and constant attention from circles such as geography, planning, and economics. National new districts constitute a new regional space for China to implement the national strategy and promote the transformation of urban development mode. They are mutually reinforcing with their mother cities and hinterland provinces. Based on the geodetector method, this paper reveals the key factors driving the development of national new districts by mother cities and hinterland provinces and their interaction effects, which provides a basis for municipal and provincial governments to accurately formulate policies to promote the development of new towns by classification. The study shows that, firstly, there are five types of driving factors, that is, all-round driving factors, scale-increasing factors, expansion and quality-improving factors, expertise driving factors, and non-driving factors. The strength and dimension of the driving factors are characterized by prominent heterogeneity; R&D personnel, export and import trade are the key factors to expand the increment, optimize the inventory, and improve the quality; the overall development driving forces are in the order of innovation > opening > industry > investment > population. Secondly, the pairwise interaction between different factors exhibits two-factor enhancement, and the population shows a nonlinear increase in the driving force of investment, openness, and innovation on a provincial scale. Thirdly, according to the driving force of the factors and the interaction between them, suggestions are put forward based on the development stage and key demands for city and provincial governments to make policies for the development of national new districts, to support the establishment of scientific competition and cooperation between new towns and mother cities or regions, and to build a long-term collaborative development mechanism.

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Section: Methodsmentioning

confidence: 99%

The Key Factors Driving the Development of New Towns by Mother Cities and Regions: Evidence from China

Zhao

Zhang

2021

IJGI

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“…A of sampling aims high efficiency: small error given a sample size or a small sample given a required error of estimation. The efficiency can be achieved when the sampling way and the estimation method are conditioned by the property of population, which is called spatial statistic trinity (Wang et al, 2020).…”

Section: Assessment and Applicability Of The Sampling Methodsmentioning

confidence: 99%

“…Although sensor technology provides a near real-time manner for indicator data collection, it is still challenging to deploy a large number of sensing equipment in a large study area (Wang et al, 2020;Yao et al, 2020;Ye et al, 2016Ye et al, , 2018. This is because of excessive operation and maintenance costs, especially in the northeast of China with frozen soils during cold winters.…”

mentioning

confidence: 99%

A soil sampling design for arable land quality observation by using SPCOSA–CLHS hybrid approach

et al. 2021

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Arable land quality has been evaluated through weighted average of indicators related to soil properties and tillage technics to present the most basic function of cropland: food production potential. A hybrid sampling method spatial coverage sampling and random sampling-conditioned Latin hypercube sampling (SPCOSA-CLHS) was designed for arable land quality observation network in this paper. The SPCOSA-CLHS integrates the uniform spatial partitioning results generated by the spatial coverage sampling and random sampling (SPCOSA) into the conditioned Latin hypercube sampling (CLHS) method along with other arable land quality indicators such as field slope, soil bulk density, organic matter content, thickness of plough layer and irrigation. Then, SPCOSA, CLHS, SPCOSA-CLHS, CLHS with x and y coordinates as covariates (XY-CLHS), random sampling method (RSM) were compared using the example of Heilongjiang Province. The sample population covers 12,147,008 grid cells and 17 arable land quality indicators. Five parameters: information entropy, Kullback-Leibler divergence, similarity distance, expression ability to local spatial heterogeneity of arable land quality and distribution homogeneity of sampling results were used to compare the applicability of these methods for overall arable land quality. The SPCOSA-CLHS can better trade off sampling result's representative ability to the population and spatial heterogeneity of arable land quality, as well as its samples have advantages of spatially uniform distribution. When the sample size is between 5000 and 20,000, all methods show good applicability. When the sample size is below 5000, however, the differences among these methods become significant. SPCOSA and random sampling method offset most dramatically. Based on this detailed comparison of the five sampling strategy approaches, we strongly recommend to use SPCOSA-CLHS to design arable land quality observation.

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“…The global Moran's I statistic for PM2.5 at multiple sites was 0.815 ( p < 0.001). This indicated that the experimental data showed significant spatial positive correlation and the kriging method was appropriate and effective after handling heterogeneity (Wang, Gao, & Stein, 2020). On this basis, the STCF‐DBSCAN algorithm was used to divide the study area into homogeneous sub‐areas for handling this heterogeneous situation.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous spatiotemporal copula‐based kriging for air pollution prediction

Wang

Deng

et al. 2021

Transactions in GIS

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Air pollutants have a significant negative impact on human health, especially cardiovascular and respiratory. An efficient and effective spatiotemporal model for air pollution prediction is urgently needed to help strengthen air pollution control and improve air quality. Empirical statistical models have been widely applied for spatiotemporal prediction. However, they are not capable of dealing well with space‐time heterogeneity and dependency, thereby achieving lower accuracy. Here, a hybrid frame named heterogeneous spatiotemporal copula‐based kriging is proposed for fine particulate matter (PM2.5) concentration prediction; it is a multi‐time, multi‐site kriging model based on spatiotemporal clustering. The proposed model is capable of addressing spatiotemporal heterogeneity effectively and accounting for the spatiotemporal dependence by copula‐based covariance functions. To evaluate the effectiveness of the proposed approach, an experiment on daily PM2.5 data in China in 2016 was carried out. The results (MAE = 6.48, RMSE = 10.68, R2 = 0.919) show that the proposed model can achieve good performance in PM2.5 prediction, which is of great significance in regional air quality management.

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The spatial statistic trinity: A generic framework for spatial sampling and inference

Cited by 37 publications

References 47 publications

The Key Factors Driving the Development of New Towns by Mother Cities and Regions: Evidence from China

The Key Factors Driving the Development of New Towns by Mother Cities and Regions: Evidence from China

A soil sampling design for arable land quality observation by using SPCOSA–CLHS hybrid approach

Heterogeneous spatiotemporal copula‐based kriging for air pollution prediction

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