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The ecology of mining areas has been one of the most crucial components in terrestrial ecosystems. However, the over-exploitation of mineral resources has posed a great threaten to the ecosystem in recent years, leading to frequent environmental issues, such as land degradation, vegetation loss, and water scarcity. Therefore, accurate monitoring of mining ecology is of great importance to protect the ecological environment and balance. Remote sensing technology can be expected to provide an effective means for the ecological monitoring in mining areas. In this study, Remote Sensing Ecological Index (RSEI) was improved to realize the overall spatial average on the indicator weights for the ecological monitoring of mining sites. The factor of coal dust pollution was also added into the conventional greenness, wetness, dryness and heatness. The indicator weights were then determined using Geographically Weighted Principal Component Analysis (GWPCA). The indicator was finally selected to construct Geographically Weighted-Remote Sensing Ecological Index (GW-RSEI). Taking the Datong coal field in Shanxi Province as an example, the validity and applicability of GW-RSEI were verified for monitoring mining area ecology using multi-phase remote sensing images from 2000 to 2020. The results showed that the indicator weights of GW-RSEI were varied continuously over the space, indicating the spatial heterogeneity within different local surface areas. GW-RSEI shared the average correlation coefficients with universal normalized vegetation index (UNVI), normalized difference moisture index (NDMI), soil index (SI), temperature vegetation dryness index (TVDI) and index-based coal dust index (ICDI) of 0.76, 0.78, -0.77, -0.82 and -0.41, respectively, (P<0.05). Furthermore, the GW-RSEI was integrated each indicator to fully reflected the ecological environment of mining areas. The overall monitoring that obtained by GW-RSEI was similar to that by RSEI over the past two decades. However, the GW-RSEI was focused on the real ecological impacts at the surface, indicating the more reliable at local scales. Whether in areas with single or complex land use types, GW-RSEI exhibited the higher correlations with soil moisture (SM), net primary productivity (NPP) and particulate matter 2.5 (PM2.5) than RSEI. The GW-RSEI monitoring was more consistent with the actual surface, in order to effectively reflect the ecological environment of the mining areas. Therefore, the GW-RSEI was successfully used to monitor the severe pollution of coal dust in local coal mining activity areas. The grade of GW-RSEI was lower than that of RSEI in the areas with the severe pollution of coal dust. The trend was the gradually increasing from the area with the high level of coal dust pollution as the center to the surrounding areas, indicating the spatial pollution in mining areas. The GW-RSEI values were higher than RSEI ones in urban areas of the county with the medium to high vegetation cover, and in rural areas where the land type was mainly arable land and grassland. There were significantly more medium to low GW-RSEI value in the mining area and surroundings, compared with the RSEI. Furthermore, the gradual trend was highlighted the spatial continuity in ecological environment quality. GW-RSEI averages were 0.51, 0.48, 0.46, 0.59, and 0.56, indicating that the overall ecological environment experienced a process of first deterioration and then improvement. The trend in the southeastern region of Datong Coal field was consistent with the overall trend, while the northwestern region showed a trend of first improvement and then deterioration. The GW-RSEI can provide the more effective way to accurately monitor the ecology of the mining area. [ABSTRACT FROM AUTHOR]

Background: Urban areas are disproportionately affected by multiple pressures from overbuilding, traffic, air pollution, and heat waves that often interact and are interconnected in producing health effects. A new synthetic tool to summarize environmental and climatic vulnerability has been introduced for the city of Rome, Italy, to provide the basis for environmental and health policies. Methods: From a literature overview and based on the availability of data, several macro-dimensions were identified on 1,461 grid cells with a width of 1 km2 in Rome: land use, roads and traffic-related exposure, green space data, soil sealing, air pollution (PM2.5, PM10, NO2, C6H6, SO2), urban heat island intensity. The Geographically Weighted Principal Component Analysis (GWPCA) method was performed to produce a composite spatial indicator to describe and interpret each spatial feature by integrating all environmental dimensions. The method of natural breaks was used to define the risk classes. A bivariate map of environmental and social vulnerability was described. Results: The first three components explained most of the variation in the data structure with an average of 78.2% of the total percentage of variance (PTV) explained by the GWPCA, with air pollution and soil sealing contributing most in the first component; green space in the second component; road and traffic density and SO2 in the third component. 56% of the population lives in areas with high or very high levels of environmental and climatic vulnerability, showing a periphery-centre trend, inverse to the deprivation index. Conclusions: A new environmental and climatic vulnerability indicator for the city of Rome was able to identify the areas and population at risk in the city, and can be integrated with other vulnerability dimensions, such as social deprivation, providing the basis for risk stratification of the population and for the design of policies to address environmental, climatic and social injustice.

This paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web application to perform techniques from a subarea of spatial Statistics known as Geographically Weighted (GW) models, such as Geographically Weighted Regression (GWR) and its extensions: Robust GWR, Generalized GWR, Heteroskedastic GWR, Mixed GWR, and “Scalable​ GWR), Geographically Weighted Principal Component Analysis, and Geographically Weighted Discriminant analysis. It also allows calculating a basic and robust Geographically weighted summary. The main goal of GeoWeightedModel package was to make the workflow easier to use, especially for those who are not familiar with the R environment. With GeoWeightedModel, analyses can be performed interactively (point-and-click way) in a web browser, making the applications easier for many more researchers. In addition with this tool, the results of the analyses can be mapped providing a valuable tool for exploring the spatial heterogeneity of the data.

Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted principal component analysis (GWPCA), respectively. We assessed spatial variation in the loadings using a Monte Carlo randomization test. The results indicated that there was statistically significant variation (p = 0.004) in the loadings of the spatial index. The variability of the census variables explained by the spatial index ranged from 60 to 90%. We found that the first geographically weighted principal component explained the most variability in the census variables in counties in the Northeast and the West, and the least variability in counties in the Midwest. We also tested the two measures by assessing the associations with county-level diabetes prevalence using data from the CDC's US Diabetes Surveillance System. While associations of the two NSES measures with diabetes did not differ for this application, the descriptive results suggest that it might be important to consider a spatial index over a global index when constructing national county measures of NSES. The spatial approach may be useful in identifying what factors drive the socioeconomic status of a county and how they vary across counties. Furthermore, we offer suggestions on how a GWPCA–based NSES index may be replicated for smaller geographic scopes. [ABSTRACT FROM AUTHOR]

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model's performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a widely used multivariate data analysis technique for variable reduction and analysis of variable relationships, among other purposes. In this current study set in New England, PCA is used together with local indicators of spatial autocorrelation (LISAs) to examine regional patterns of spatial dependence between population density and synthetic variables (i.e. principal components) derived from mobility-related socio-economic variables. A secondary analysis using geographically weighted principal component analysis (GWPCA) is used to identify which of the original contributing variables are most important for different locations within the region. Using a local bivariate indicator of spatial autocorrelation, overall results show that most resulting principal components are positively spatially correlated with population density, although this correlation is not a direct point-to-point one and instead relates the principal component to the spatial lag of density for a given location. These findings are relevant for understanding the relationship between agglomeration (as represented by density) and the triad of mobility, accessibility, and connectivity. The results of this study could aid and inform future research and efforts at modeling travel behavior. [ABSTRACT FROM AUTHOR]

The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation.

Bangladesh-China-India-Myanmar economic corridor, a critical part of the Belt and Road Initiative program, is subject to the impact of various natural disasters and intense human activities, which have led to serious ecological vulnerability. This study proposed a prototype model using geographically weighted principal component analysis to quantify ecological vulnerability at the grid level, and an analysis was conducted on the dynamic changes of ecological vulnerability along Bangladesh-China-India-Myanmar economic corridor during 2005–2015. An indicator system for 23 spatial variables was established based on Driver-Pressure-State-Impact-Response framework to calculate the ecological vulnerability index. The cluster principle was adopted to split the ecological vulnerability into five vulnerability levels, namely, potential, light, medium, heavy, and very heavy. Given the spatial recognition of ecologically vulnerable areas along Bangladesh-China-India-Myanmar economic corridor, several suggestions on ecological management were offered. As revealed from the results, the ecological vulnerability has been rising progressively, particularly in the mountainous areas, and most of the protected areas are at medial to very heavy vulnerability level. The ecological vulnerability was tightly correlated with vulnerability events and impacts. As suggested from the results, ecological restoration and protection measures should be strictly implemented to minimize the adverse impact on the protected areas under the construction of economic corridor. Our results indicated that the geographically weighted principal component analysis can effectively quantify environmental vulnerability, and these space management policies has implications for ecological protection, resource utilization, and sustainable development in other similar regions.

The development of small towns in Germany in terms of their economic, demographic and social endowment is a subject area that has been rather neglected so far. What is more, research often focuses exclusively on small towns in rural and peripheral regions, because this is the only place where they are assigned a certain role. Small towns in central areas, on the other hand, are subsumed under suburbia in research and are thus rarely considered in terms of their specific functions. In this paper, all small towns in Germany are classified with regard to important functions. The typification is done by means of a cluster analysis based on central factors, which in turn are tied to a broad set of indicators. Reference points are the classical method of principal component analysis and, as an alternative method, to geographically weighted principal component analysis. The methodological comparison shows that the alternative method – also due to the high computational effort – is not yet a suitable method for describing spatial relationships between the variables and for small towns with regard to existing functions in their surrounding areas. On the other hand, the approach of the principal component analysis, cluster analysis and discriminant analysis, which is found in the literature, produces well defined and explainable solutions for the formation of different types of small towns in Germany.

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India.

In the present article, the patterns of the geographic variability in yields of rye within Polesia and the Forest-Steppe zone of Ukraine are presented and the correlation of the factors and dynamics of an agroeconomic and agroecological nature was determined. The dynamics of rye yields in the study area over time were determined as being characterized by three extreme points: two local maxima and one local minimum. Specific terms of the polynomial curve of the fourth order can be meaningfully interpreted and applied to describe the dynamics of productivity. Free members of the polynomial indicate culture productivity in the starting period. Dynamics of the productivity that can be explained by the regression indicate that agrotechnological and agrecological conditions of agricultural production are a pervasive factor that determines the presence of a general trend. The determination coefficient of the regression total trend can be interpreted as an indicator of the role of the agrotechnological and agroeconomic factors in the dynamics of productivity. The residue of the trend regression model can be interpreted so as to include the agroecological component of the rye yields dynamics. Their analysis revealed seven key components that together explained 58.4% of the total variability of the space feature. The principal components of vibrational patterns reflect the specific nature of variation of rye yields over time, which are spatially defined. Vibrational effects are environmental in nature. Geographically weighted principal component analysis showed the transience of environmental spatial modes which determine the oscillating component of rye yield variation over time. Spaces within which the structure of ecological interactions remains unchanged can be considered as the basis of agroecological zoning areas.

Socio-economic inequalities may affect coronavirus disease 2019 (COVID-19) incidence. The goal of the research was to explore the association between deprivation of socio-economic status (SES) and spatial patterns of COVID-19 incidence in Chennai megacity for unfolding the disease epidemiology. This is an ecological (or contextual) study for electoral wards (subcities) of Chennai megacity. Using data of confirmed COVID-19 cases from May 15, 2020, to May 21, 2020, for 155 electoral wards obtained from the official website of the Chennai Municipal Corporation, we examined the incidence of COVID-19 using two count regression models, namely, Poisson regression (PR) and negative binomial regression (NBR). As explanatory factors, we considered area deprivation that represented the deprivation of SES. An index of multiple deprivations (IMD) was developed to measure the area deprivation using an advanced local statistic, geographically weighted principal component analysis. Based on the availability of appropriately scaled data, five domains (i.e., poor housing condition, low asset possession, poor availability of WaSH services, lack of household amenities and services, and gender disparity) were selected as components of the IMD in this study. The hot spot analysis revealed that area deprivation was significantly associated with higher incidences of COVID-19 in Chennai megacity. The high variations (adjusted R2: 72.2%) with the lower Bayesian Information Criteria (BIC) (124.34) and Akaike's Information Criteria (AIC) (112.12) for NBR compared with PR suggests that the NBR model better explains the relationship between area deprivation and COVID-19 incidences in Chennai megacity. NBR with two-sided tests and P <0.05 were considered statistically significant. The outcome of the PR and NBR models suggests that when all other variables were constant, according to NBR, the relative risk (RR) of COVID-19 incidences was 2.19 for the wards with high housing deprivation or, in other words, the wards with high housing deprivation having 119% higher probability (RR = e0.786 = 2.19, 95% confidence interval [CI] = 1.98 to 2.40), compared with areas with low deprivation. Similarly, in the wards with poor availability of WaSH services, chances of having COVID-19 incidence was 90% higher than in the wards with good WaSH services (RR = e0.642 = 1.90, 95% CI = 1.79 to 2.00). Spatial risks of COVID-19 were predominantly concentrated in the wards with higher levels of area deprivation, which were mostly located in the northeastern parts of Chennai megacity. We formulated an area-based IMD, which was substantially related to COVID-19 incidences in Chennai megacity. This study highlights that the risks of COVID-19 tend to be higher in areas with low SES and that the northeastern part of Chennai megacity is predominantly high-risk areas. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area deprivation. Image 1 • COVID-19 cases was modelled using Poisson regression and negative binomial regression. • An IMD was devised using geographically weighted principal component analysis. • Area deprivation in Chennai has both positive and inverse associations with the COVID-19. [ABSTRACT FROM AUTHOR]

Water poverty, defined as insufficient water of adequate quality to cover basic needs, is an issue that may manifest itself in multiple ways. Extreme seasonal variation in water availability, such as in Laos, located in Monsoon Asia, results in large differences in water poverty conditions between dry and wet seasons. In this study, seasonal Water Poverty Indices (WPI) are developed for 8215 villages in Laos. WPI is a multidimensional composite index integrating five dimensions of water: resource availability, access to safe water, capacity to manage the resource, its use and environmental requirements. Principal Component Analysis (PCA) and Geographically Weighted PCA (GWPCA) were used to examine drivers of water poverty and to derive different weighting schemes. Three major drivers were identified: poverty, commercial/subsistence agriculture and village location. The least water poor areas are located around the capital city and along the Mekong River Valley while the highest water poverty is found in sparsely populated mountainous areas. Wet season WPI is on average more than 12 index points higher than in the dry season, but in some villages monsoon rain does not improve the situation. The results indicate large spatial and temporal differences in WPI within Laos. In analysis of WPI components, a mean-variance scaled PCA is recommended due to its capacity for uncovering processes driving water poverty. Extending to GWPCA is recommended when information on local differences is of interest. [ABSTRACT FROM AUTHOR]

• Environmental risks in urban areas are important determinants of population health. • Multiple environmental dimensions were integrated in a composite spatial indicator. • 56% of Rome residents live in areas with high-very high environmental vulnerability. • The highest environmental and climatic vulnerability was found in the city centre. • There are some spatial clusters with both environmental and social vulnerability in the eastern area of Rome. Urban areas are disproportionately affected by multiple pressures from overbuilding, traffic, air pollution, and heat waves that often interact and are interconnected in producing health effects. A new synthetic tool to summarize environmental and climatic vulnerability has been introduced for the city of Rome, Italy, to provide the basis for environmental and health policies. From a literature overview and based on the availability of data, several macro-dimensions were identified on 1,461 grid cells with a width of 1 km2 in Rome: land use, roads and traffic-related exposure, green space data, soil sealing, air pollution (PM 2.5 , PM 10 , NO 2 , C 6 H 6 , SO 2), urban heat island intensity. The Geographically Weighted Principal Component Analysis (GWPCA) method was performed to produce a composite spatial indicator to describe and interpret each spatial feature by integrating all environmental dimensions. The method of natural breaks was used to define the risk classes. A bivariate map of environmental and social vulnerability was described. The first three components explained most of the variation in the data structure with an average of 78.2% of the total percentage of variance (PTV) explained by the GWPCA, with air pollution and soil sealing contributing most in the first component; green space in the second component; road and traffic density and SO 2 in the third component. 56% of the population lives in areas with high or very high levels of environmental and climatic vulnerability, showing a periphery-centre trend, inverse to the deprivation index. A new environmental and climatic vulnerability indicator for the city of Rome was able to identify the areas and population at risk in the city, and can be integrated with other vulnerability dimensions, such as social deprivation, providing the basis for risk stratification of the population and for the design of policies to address environmental, climatic and social injustice. [ABSTRACT FROM AUTHOR]

Understanding the spatial structure of regional economic development is of importance for regional planning and provincial development strategy making. Taking Jiangsu Province in the economically richest Yangtze Delta as a case study, this paper aims to explore regional economic development level on a provincial scale. Using the data sets from provincial statistical yearbook of 2010, eleven variables are selected for statistical and spatial analyses at a county level. Both the traditional principal component analysis (PCA) and its local version-geographically weighted PCA (GWPCA)-are employed to these analyses for the purpose of comparison. The results have confirmed that GWPCA is an effective means of analyzing regional economic development level through mapping its local principal components. It is also concluded that the regional economic development in Jiangsu Province demonstrates spatial inequality between the North and South. [ABSTRACT FROM AUTHOR]

Dimensionality reduction methods such as principal components analysis (PCA) provide a means of identifying trends in soil characteristics which may be represented by a wide range of variables. However, these characteristics may be highly spatially variable and so the results from PCA represent, in some sense, an 'average' of locally distinct characteristics. One approach to account for these local differences is to introduce a geographical weighting scheme into the PCA process. In this paper, such an approach is assessed in the exploration of soil characteristics in the state of Pennsylvania, USA. Data from 878 georeferenced soil profiles which include different soil parameters ( n = 12) were extracted from the National Soil Survey Center database. Where data are parts of compositions (e.g., percentages of sand, silt, and clay), analysis using raw data is not appropriate and such data were transformed using log ratios (specifically, balances). Single variables (i.e., those which are not parts of compositions) were logged. The first two principal components explain over 50% of the variance. The mapped values suggest marked spatial variation in soil characteristics, but it is not possible to assess which of these variables explain most variation in particular regions from the simple maps of raw variables. Geographically weighted PCA (GWPCA) provides additional information which is obscured by PCA, and it also provides a set of component scores and loadings at all data locations. The soil variable with the largest loading at most locations of Pennsylvania is the logged base saturation (BSln), and this supports the findings of the conventional PCA analysis. While BSln loads most highly in most of the eastern third, the middle and the south west of the state, the northwest is less spatially consistent in terms of the variables which explain most variation. For GWPC 1, the variable with the second largest loading at most locations (i.e., primarily the south and west) is CEC.B1 (the log ratio of Ca, Mg, and Na to K and EXACID), while CEC.B2 (the log ratio of Ca and Mg to Na), pHln (logged pH) and BSln dominate in other areas. The GWPCA results suggest that there is marked spatial variation in multivariate soil characteristics across Pennsylvania state and that results from standard PCA obscure this considerable variation. [ABSTRACT FROM AUTHOR]

Globally, processes that drive urbanization have mostly evolved within economic downturns. Economic crises have been more severe and frequent, particularly in the Mediterranean region. However, studies on the recession effects on urbanization are limited. The present study explores possible differences in spatial direction and intensity of land-use change trajectories at two time intervals (2006–2012, 2012–2018) using high-resolution Copernicus Land Urban Atlas images in the Rome metropolitan area. To this aim, a landscape ecology classical approach based on land-use metric analysis combined with a multivariate spatial analysis has been carried out. Results have identified different land-use change patterns during expansion and recession. "Greening", defined as the conversion of urban marginal areas into croplands and forests, increased during the recession. At the same time, the rate of urban expansion into rural areas decreased, thus indicating a beneficial effect of economic downturns in reducing urban sprawl. [ABSTRACT FROM AUTHOR]

• A novel assessment method for SFQI was proposed based on GWPCA (i.e., SFQI GWPCA). • Spatially varying relationships among soil indicators were revealed by GWPCA. • Spatially varying indicator weights were determined by GWPCA rather than PCA. • SFQI GWPCA is an effective method for SFQI assessment in large-scale areas. • The spatial uncertainty of SFQI GWPCA was revealled by the simulated realizations. Principal component analysis (PCA) has been widely used in the integrated soil fertility quality index (SFQI) (SFQI PCA) assessment. However, the traditional PCA, only established in variable space, does not consider the spatially varying relationships among soil fertility indicators in large-scale areas, and thus cannot appropriately determine the spatially varying relative importance (i.e., weight calculated based on communality) of soil indicators in the SFQI assessment. Moreover, uncertainty inevitably exists in the spatial distribution pattern of SFQI due to the limited sample points, which is critical to the precision management of soil fertility. To address these limitations, this study first proposed a novel assessment method for SFQI based on geographically weighted principal component analysis (GWPCA) (SFQI GWPCA). Secondly, SFQI GWPCA was assessed in Shayang County, China, and then compared with the traditional SFQI PCA. Finally, the spatial uncertainty of SFQI GWPCA was assessed based on the 1000 realizations generated by sequential Gaussian simulation (SGS). Results showed that (i) spatially varying relationships among soil indicators were revealed by Monte Carlo test and GWPCA outputs (i.e., the winning variable and the local percentage of total variation), while traditionally-used PCA was conducted in the assumption of spatially stationary relationships among soil indicators; (ii) in SFQI assessment, the spatially varying indicator weights were determined by GWPCA, but could not be determined by PCA; (iii) the areas with higher threshold-exceeding probability (≥ 0.95) mainly located in the northwest of this county, and the areas with lower threshold-exceeding probability (≤ 0.05) mainly located in the east of this county. It is concluded that GWPCA adequately considers the spatially varying relationships among soil indicators, and SFQI GWPCA is an effective tool in the SFQI assessment in large-scale areas. [ABSTRACT FROM AUTHOR]

The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors. Published by Elsevier Ltd.)

• This study aimed to evaluate ecological vulnerability and understand its dynamic changes. • We proposed a grid level prototype model to obtain the weights varying with location. • The results revealed that ecological vulnerability has been rising progressively, particularly in the mountainous areas. • Spatial management policies conforming to regional characteristics should be strictly enforced. Bangladesh-China-India-Myanmar economic corridor, a critical part of the Belt and Road Initiative program, is subject to the impact of various natural disasters and intense human activities, which have led to serious ecological vulnerability. This study proposed a prototype model using geographically weighted principal component analysis to quantify ecological vulnerability at the grid level, and an analysis was conducted on the dynamic changes of ecological vulnerability along Bangladesh-China-India-Myanmar economic corridor during 2005–2015. An indicator system for 23 spatial variables was established based on Driver-Pressure-State-Impact-Response framework to calculate the ecological vulnerability index. The cluster principle was adopted to split the ecological vulnerability into five vulnerability levels, namely, potential, light, medium, heavy, and very heavy. Given the spatial recognition of ecologically vulnerable areas along Bangladesh-China-India-Myanmar economic corridor, several suggestions on ecological management were offered. As revealed from the results, the ecological vulnerability has been rising progressively, particularly in the mountainous areas, and most of the protected areas are at medial to very heavy vulnerability level. The ecological vulnerability was tightly correlated with vulnerability events and impacts. As suggested from the results, ecological restoration and protection measures should be strictly implemented to minimize the adverse impact on the protected areas under the construction of economic corridor. Our results indicated that the geographically weighted principal component analysis can effectively quantify environmental vulnerability, and these space management policies has implications for ecological protection, resource utilization, and sustainable development in other similar regions. [ABSTRACT FROM AUTHOR]

Composite Indicators (CIs) recently earned popularity as decision-support tool in policy-making for their ability to give concise measures of complex phenomena. Despite growing diffusion of the use of CI in policy-making, current research has barely addressed the issue of the spatial dimension of input data and of final indicator scores. Nowadays the spatial dimension of data plays a crucial role in analysis, thanks to recent developments in spatial data infrastructures which has enabled seamless access to a large amount of geographic information. In addition, recent developments in spatial statistical techniques are facilitating the understanding of the presence of spatial effects among data, spatial dependence and spatial heterogeneity. These advances are improving our ability to understand the spatial dimension of information, which is crucial to obtain a more robust representation of the territorial reality and insights of territorial dynamics in order to inform decisions in spatial planning and policy-making. This paper proposes an original method for the integration of spatial multivariate analysis and the use of spatial data to extend existing state of the art methods for CIs, as a step towards the construction of Spatial Composite Indicators. The method was successfully tested on a landscape planning case study. [ABSTRACT FROM AUTHOR]