Using Local Moran's I to identify contamination hotspots of rare earth elements in urban soils of London

Yuan, Yumin; Cave, Mark; Zhang, Chaosheng

doi:10.1016/j.apgeochem.2017.07.011

Cited by 83 publications

(38 citation statements)

References 77 publications

(66 reference statements)

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“…Therefore, as previous studies found that GCMs in NMME generate skilful forecasts of ENSO (e.g. Kirtman et al, 2014;Saha et al, 2014;Zhang et al, 2017), Fig. 6 suggests that the skill, as is indicated by anomaly correlation, of GCM forecasts in NMME can also be related to ENSO.…”

Section: Frequency Of Case Hh In Son Djf and Mamsupporting

confidence: 59%

“…forecast-oriented low ocean resolution (Vecchi et al, 2014;Jia et al, 2015). Thirdly, there are substantial regional variations, possibly due to the predictability of seasonal precipitation (Doblas-Reyes et al, 2013;Becker et al, 2014;Zhang et al, 2017). For example, orange regions cover large parts of Australia, in particular south-western and south-eastern Australia.…”

Section: Spatial Clustering In Jjamentioning

confidence: 99%

“…As will be shown later in this paper, the technique of spatial clustering facilitates the identification of significant patterns of high, neutral, and low anomaly correlation and provides an objective approach to interpreting the predictive performance of GCM forecasts. For the purpose of inter-comparison, the examination of significant patterns in spatial plotting has been conducted for 10 sets of GCM seasonal precipitation forecasts in the North American Multi-Model Ensemble (NMME) experiment (Kirtman et al, 2014;Ma et al, 2016;Zhang et al, 2017). In the remainder of the paper, the dataset of GCM seasonal forecasts is illustrated in Sect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao

Zhang

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

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Section: Frequency Of Case Hh In Son Djf and Mamsupporting

confidence: 59%

Section: Spatial Clustering In Jjamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao

Zhang

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…In Eq. 6 Adding to the global Moran's I, the local Moran's I is obtained to test whether ri at a certain grid cell i significantly relates to surrounding rj at the local scale [Anselin, 2006;Hao et al, 2016;Yuan et al, 2018] ( ) ( )…”

Section: Methodsmentioning

confidence: 99%

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao¹,

Zhang²,

Zhang³

et al. 2019

Preprint

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Abstract. Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

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“…This means that the farther apart the two spatial units are, the weaker the spatial association is, which is consistent with Tobler's first law of geography. Therefore, this type of weight is widely used in the research of various spatial relationships [49,50]. In this study, an inverse distance weight was established, which added the limitation of K-nearest neighbors (K is a positive integer) to control the minimum number of neighbors in each province [51].…”

Section: Geographic Distance Weight (Geo-distance Weight)mentioning

confidence: 99%

A New Trend in the Space–Time Distribution of Cultivated Land Occupation for Construction in China and the Impact of Population Urbanization

Liu

2019

Sustainability

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Rapid urbanization has brought huge development dividends to China. At the same time, its negative effects have aroused people's attention. For example, a large amount of cultivated land has been occupied for urban expansion and construction. Using exploratory spatial data analysis (ESDA) and the spatial Durbin model (SDM), we analyzed the spatial distribution of cultivated land occupation for construction (CLOC) and its driving factors in 31 provinces in China from 2005 to 2016. The results indicated that (1) the CLOC rate presented a significant spatial clustering feature, and its distribution showed a new trend of "homogenization" after the year 2012; (2) as the core driving factor, the population urbanization rate significantly promoted the growth of the CLOC rate in the local province, while showing a negative effect on that rate in the neighboring provinces;(3) in addition, behind the new trend of the CLOC rate, there was a transformation from being "investment driven" to being "population and industry driven". Therefore, this paper suggests that the government should link each city's construction land supply to the constantly changing trend of population migrations in China. Further, promoting the tertiary industry can be a win-win strategy for easing the tension between cultivated land and construction land.Sustainability 2019, 11, 5089 2 of 23 construction, to ensure economic stability and improve domestic livelihood issues (narrowing the urban-rural gap and increasing employment in poor areas). Therefore, issues related to the game relationship between cultivated land and construction land are becoming the focus of scholarly attention, including the driving forces of urban expansion [4,7], the chain effect of cultivated land loss [8,9], and the transformation from cultivated land into construction land [10,11].Sustainability 2019, 11, x FOR PEER REVIEW 2 of 23 urban-rural gap and increasing employment in poor areas). Therefore, issues related to the game relationship between cultivated land and construction land are becoming the focus of scholarly attention, including the driving forces of urban expansion [4,7], the chain effect of cultivated land loss [8,9], and the transformation from cultivated land into construction land [10,11].

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Using Local Moran's I to identify contamination hotspots of rare earth elements in urban soils of London

Cited by 83 publications

References 77 publications

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

A New Trend in the Space–Time Distribution of Cultivated Land Occupation for Construction in China and the Impact of Population Urbanization

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