Spatial Autocorrelation Method for Local Analysis of The EU

Melecký, Lukáš

doi:10.1016/s2212-5671(15)00484-0

Cited by 23 publications

(15 citation statements)

References 1 publication

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“…The closer the I value is to −1, the more negative the correlation of C i value is in space. If I value is close to 0, this indicates that C i value has no correlation in space [37].…”

Section: Methodology (1) Iew and Topsismentioning

confidence: 99%

Research on Potential Evaluation and Sustainable Development of Rural Biomass Energy in Gansu Province of China

2018

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The development and utilization of renewable energy is an important way to solve the environmental dilemma. Biomass energy is a kind of renewable energy and one of the most widely distributed and easily accessible energy forms. It has currently become a main direction of renewable energy development. This paper took Gansu Province of China as the research object to calculate its theoretical reserves of biomass energy resources and then evaluate its potential of biomass energy development by using TOPSIS method under different agricultural development and geographical environmental conditions. Spatial autocorrelation analysis was also performed to reveal the spatial distribution and temporal evolution of the potential of biomass energy development in Gansu Province. The results show that: (1) The total reserves of biomass energy resources from agricultural wastes in Gansu Province reach 7.28 × 107 t/year, with equivalent biogas production of about 1.95 × 1010 m3/year. (2) In most counties of Gansu Province, the Ci value is smaller than 0.5000, indicating that the potential of biomass energy development is relatively low in Gansu Province. (3) The spatial agglomeration of biomass energy development potential occurs mainly in the Hexi area, the Gannan area and the Loess Plateau area of East Gansu Province. (4) There is an area with obvious high-low (H-L) agglomeration of biomass energy development potential to the north-west side of the Gannan area with low-low (L-L) agglomeration of biomass energy development potential. It is a key zone to help drive biomass energy development in the Gannan area. (5) The spatial range of positive correlation (high-high and low-low agglomeration) areas shrunk during the evaluation period.

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“…The closer the I value is to −1, the more negative the correlation of C i value is in space. If I value is close to 0, this indicates that C i value has no correlation in space [37].…”

Section: Methodology (1) Iew and Topsismentioning

confidence: 99%

Research on Potential Evaluation and Sustainable Development of Rural Biomass Energy in Gansu Province of China

2018

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“…If data clustering uses algorithms for extraction of the elements of potential statistical clusters, the existence of autocorrelation is one of the issues that may lead to bias. This point requires an earlier scheme of analytical models to determine if all of the spatial and temporal data are dependent or not (Melecky, 2015). The spatial correlation mechanism indicates whether related data exhibit similar behavior or not.…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Spatial Statistics Analysis to Identify Hot Spots Using Accidental Event Calls Services

Ghashghaie¹,

Behzadi²

2019

JSRI

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“…These formulas are commonly used to test the spatial association in demography [8,9], economics [18], diseases [19] and ecology [20].…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Geostatistics Analysis of Infant Mortality Rate in Ethiopia

Mohamed¹

2017

AJTAS

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Abstract:In this paper, spatial statistical analysis of infant mortality rate in Ethiopia is addressed. The analysis investigated of a significance spatial autocorrelation attendance as well as an adapting of a generalized linear mixed model with spatial covariance structure. The results showed the distribution is much spatially associated. Some geographical, economical and healthy variables are used to estimate the model. Several examined variables have a significant effect in the model contrast to other have an insignificant impact. The results highlight the role of improving education to decline the risk of infant mortality rate. Male and children with extra weight are higher exposed and the risk is highly different from one zone to another.

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Spatial Autocorrelation Method for Local Analysis of The EU

Cited by 23 publications

References 1 publication

Research on Potential Evaluation and Sustainable Development of Rural Biomass Energy in Gansu Province of China

Research on Potential Evaluation and Sustainable Development of Rural Biomass Energy in Gansu Province of China

Spatial Statistics Analysis to Identify Hot Spots Using Accidental Event Calls Services

Geostatistics Analysis of Infant Mortality Rate in Ethiopia

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