Accounting for spatial autocorrelation is needed to avoid misidentifying trade-offs and bundles among ecosystem services

Shaikh, Shaikh Fairul Edros Ahmad; See, Sin Ching; Richards, Daniel R.; Belcher, Richard N.; Grêt‐Regamey, Adrienne; Torres, Marcelo Galleguillos; Carrasco, Luis

doi:10.1016/j.ecolind.2021.107992

Cited by 24 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Spatial autocorrelation As an approach to analyze the distribution characteristics of data, spatial autocorrelation is helpful for testing the significance of an attribute value of variables and verifying the relevance of attributes between adjacent points. In this paper, spatial autocorrelation analysis is used to study the aggregation characteristics of the eco-environment conditions in Ibei Coalfield 45 , 46 . Global autocorrelation characterized the aggregation and dispersion degree of eco-environmental quality within the whole space and expressed using Global Moran's I ranging between − 1 and 1.…”

Section: Methodsmentioning

confidence: 99%

“…As an approach to analyze the distribution characteristics of data, spatial autocorrelation is helpful for testing the significance of an attribute value of variables and verifying the relevance of attributes between adjacent points. In this paper, spatial autocorrelation analysis is used to study the aggregation characteristics of the eco-environment conditions in Ibei Coalfield 45 , 46 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Jiang

Fan

Zhang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The contradiction between the exploitation of coal resources and the protection of the ecological environment in western China is becoming increasingly prominent. Reasonable ecological environment evaluation is the premise for alleviating this contradiction. First, this paper evaluates the eco-environment of Ibei coalfield by combining the genetic projection pursuit model and geographic information system (GIS) and using remote sensing image data and other statistical data of this area. The powerful spatial analysis function of GIS and the advantages of the genetic projection pursuit model in weight calculation have been fully used to improve the reliability of the evaluation results. Furthermore, spatial autocorrelation is used to analyze the spatial characteristics of ecological environment quality in the mining area and plan the specific governance scope. The geographic detector is used to determine the driving factors of the eco-environment of the mining area. The results show that Ibei Coalfield presents a spatially heterogeneous eco-environment pattern. The high-intensity mining area (previously mined area of Ili No.4 Coal Mine) has the worst ecological environment quality, followed by the coal reserve area of Ili No.4 Coal Mine and the planned survey area of Ili No.5 Coal Mine. The eco-environment quality (EEQ) of the study area is affected by both human and natural factors. Mining intensity and surface subsidence are the main human factors affecting the ecological environment in the study area. The main natural factors affecting the ecological environment in the study area are annual average precipitation, elevation, annual average evaporation, NDVI and land use type. Meanwhile, the interaction effect of any two indicators is greater than that of a single indicator. It is also indicated that the eco-environment of the mining area is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis shows three areas that should be treated strategically that are the management area, close attention area and protective area. Corresponding management measures are put forward for different regions. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Jiang

Fan

Zhang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Spatial autocorrelation divides the analysis results into random lower-low, clustered lower-high, and Clustered higher-high. Several spatial autocorrelations analyzes have been carried out and are useful for seeing spatial relationships (Balducci and Ferrara 2018; Sung and Liaw 2020; Moctezuma 2021; Shaikh et al 2021). Thus, the relationship and spatial distribution of ood events in Java will be well illustrated and used as the basis for disaster mitigation policies in Indonesia.…”

Section: Data Collecting and Analysis Methodsmentioning

confidence: 99%

A 50-years flood frequency in Coastal area of Java, Indonesia: Integrating historical hotspot and Spatial Autocorrelation Analysis

Sejati

Buchori

Lakshita

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper re-examines the facts of the influence of urban development that developed in Java on the occurrence of floods that started from 1970 to 2020. The purpose of this paper is to prove whether development policies in Indonesia in the last 50 years and to analyze the correlation between flood event, population growth, and urbanization on a wider regional scale. The Moran-I Spatial Autocorrelation Analysis method is applied to simulate the spatial pattern of flood events with combined results from satellite image interpretation with Spatial-temporal analysis from the 1970s to the 2020s. In more detail, the results of hotspot clusters show development from "lower-low" dispersion in the 70s to areas with a majority of "higher-high" clustered in 2000-2020. It proves that the burden of development in Java is in high category impact, so solutions for population distribution, land development, and spatial planning are needed to ensure environmental sustainability in Java.

show abstract

“…As an approach to analyse the distribution characteristics of data, spatial autocorrelation is useful for testing the signi cance of an attribute value of variables and verifying the relevance of attributes between adjacent points. In this paper, spatial autocorrelation analysis is used to study the aggregation characteristics of the eco-environment conditions in Ibei Coal eld 29,30 .…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Evaluation of Eco-environmental Quality of Coal Mining Area Using Multi-source Data – A Case Regarding Ibei Coalfield, Northwestern China

Jiang

Fan

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Eco-environmental evaluation is a prerequisite for balancing the relationship between coal resource recovery and eco-environmental protection. This paper divides the eco-environment system in coal mining area into 5 subsystems regarding geomorphology, climate, hydrology, land and vegetation, and human activity. Within the 5 subsystems, 13 indicators capable of reflecting eco-environment levels of coal mine fields are selected, weighed using genetic projection pursuit model, and applied to eco-environmental quality evaluation. Based on this, the spatial feature of the quality is analysed using spatial autocorrelation method, recognising the areas that need managements. Factors driving the eco-environment characteristics of coal mines are identified using geographic detector. The feasibility of the developed evaluation system is verified with Ibei Coalfield as a case. The results show that Ibei Coalfield sees a spatially heterogeneous eco-environment pattern. Geographic detector can quantify the impact of various indicators on ecological environment, and the indicator is of stronger interpretation ability as interacting with others. It is also indicated that mining area eco-environment is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis suggests three areas that should be treated strategically, that are the management area, close attention area and protective area. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.

show abstract

Accounting for spatial autocorrelation is needed to avoid misidentifying trade-offs and bundles among ecosystem services

Cited by 24 publications

References 34 publications

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

A 50-years flood frequency in Coastal area of Java, Indonesia: Integrating historical hotspot and Spatial Autocorrelation Analysis

Evaluation of Eco-environmental Quality of Coal Mining Area Using Multi-source Data – A Case Regarding Ibei Coalfield, Northwestern China

Contact Info

Product

Resources

About