Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of ß-amyloid in dementia.

Cortical gray to white matter signal intensity ratio (GWR) measured from T1-weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross-sectional dataset and a longitudinal dataset. The cross-sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross-sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with ß-amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with ß-amyloid deposition, MCI without ß-amyloid deposition, and controls. The spatial pattern of GWR-increased regions was not influenced by ß-amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with ß-amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR-increased regions was independent of ß-amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.

Spatiotemporal geographically weighted regression analysis for runoff variations in the Weihe River Basin.

In order to investigate the effects of vegetation changes on runoff and to obtain recommendations for improving runoff in the Weihe River Basin (. In this study, a spatiotemporal geographic autocorrelation weighted regression analysis (SGAWRA) approach was newly developed based on previous studies. This approach investigates spatial non-stationarity of the dynamic response from vegetation variations to climatic change and human activity. Implications of spatial non-stationarity related to runoff variability were also discussed, which in turn yield the effect that vegetation changes have on runoff. The method systematically analysed the spatial non-stationarity of vegetation variations and its associated effects on runoff. Therefore, more closely related results with less error were produced at each step, and results with more accuracy were obtained. These results indicated that the average trend rates of NDVI in the annual average, each season, and the growing season (Growing season refers to April to September) exceeded 0. Areas where NDVI show a growing trend cover more than 50%, which is greater than the area with a decreasing trend. The GWR regression parameters of precipitation, average temperature, and NDVI are all greater than 0. The GWR regression parameters of human activities and NDVI also have more than 50% of the area greater than 0. Based on the visual analysis of the calculation results, it can be seen that there are obvious spatial trends in the data, and the spatial data are significantly different between different regions. Therefore, WRB can be regarded as spatio-temporally non-stationary. In the WRB, the underlying surface change with vegetation change as the prominent feature is the leading cause (about 60%) of the runoff attenuation. The results showed that WRB has spatial and temporal non-stationarity. The spatial non-stationarity of vegetation has a greater effect on runoff changes. The results of this study support recommendations for improving runoff in the WRB.

Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China.

BACKGROUND: There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. METHODS: After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. RESULTS: The results show that the FVC local regression coefficients ranged from - 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. CONCLUSIONS: Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.

Contextual factors influencing incomplete immunization and investigation of its geospatial heterogeneity in Pakistan: a cross-sectional study based on PDHS (2017-18).

BACKGROUND: Immunization is one of the most effective public health initiatives, saving millions of lives and lowering the risk of diseases such as diphtheria, tetanus, influenza, and measles. Immunization saves an estimated 2-3 million lives per year. A study of the regional variations in incomplete immunization will be useful in identifying gaps in the performance of immunization programs that are not noticed by standard vaccination programs monitoring. The primary goal of this study was to identify factors influencing child immunization status and to examine regional variations in incomplete immunization among children aged 12 to 23 months in Pakistan. METHODS: For the current study, the data were taken from the Demographic and Health Survey for Pakistan (PDHS 2017-2018). Ever-married women who had children aged 12-23 months were included in this study. The immunization status of children was used as an outcome variable. In order to determine the effects of different factors on incomplete immunization, multilevel logistic model was used. To study the geographical variation of incomplete immunization, hotspot analysis was done using ArcGIS 10.7 and SaTScan software and to identify significant predictors of incomplete immunization, GWR 4 software was used. RESULTS: Place of delivery, gender of child, mother's educational level and region were identified as significant determinants of incomplete immunization of children in Pakistan. Chances of incomplete immunization of children were found significantly lower for educated mothers (AOR = 0.52, 95% CI 0.34-0.79) and mothers who had delivered children in the health facilities (AOR = 0.51, 95% CI 0.32-0.83). Female children were more likely (AOR = 1.44, 1.95% CI 1.04-1.99) to be incompletely immunized as compared to male children. FATA (AOR = 11.19, 95% CI 4.89-25.6), and Balochistan (AOR = 10.94, 95% CI 5.08-23.58) were found at the highest risk of incomplete immunization of children as compared to Punjab. The significant spatial heterogeneity of incomplete immunization was found across Pakistan. The spatial distribution of incomplete immunization was clustered all over Pakistan. The high prevalence of incomplete immunization was observed in Balochistan, South Sindh, North Sindh, South KPK, South FATA, Gilgit Baltistan, Azad Jammu Kashmir, South and East Punjab. Drang and Harcho were identified as hotspot areas of incomplete immunization in Gilgit Baltistan. Secondary clusters with a high risk of incomplete immunization were found in regions Balochistan, Sindh and FATA. CONCLUSION: Gender biasedness towards female children, regarding complete immunization of children prevailed in Pakistan. Spatial heterogeneity was also found for incomplete immunization of children. To overcome the problem access to health facilities is the foremost step. Government should target hotspot areas of incomplete immunization of children to provide primary health care facilities by opening health care units in these areas. The government in collaboration with the media should launch awareness campaigns in those areas to convince people that complete immunization is the right of every child regardless of gender.

Improving groundwater nitrate concentration prediction using local ensemble of machine learning models.

Groundwater is one of the most important water resources around the world, which is increasingly exposed to contamination. As nitrate is a common pollutant of groundwater and has negative effects on human health, predicting its concentration is of particular importance. Ensemble machine learning (ML) algorithms have been widely employed for nitrate concentration prediction in groundwater. However, existing ensemble models often overlook spatial variation by combining ML models with conventional methods like averaging. The objective of this study is to enhance the spatial accuracy of groundwater nitrate concentration prediction by integrating the outputs of ML models using a local approach that accounts for spatial variation. Initially, three widely used ML models including random forest regression (RFR), k-nearest neighbor (KNN), and support vector regression (SVR) were employed to predict groundwater nitrate concentration of Qom aquifer in Iran. Subsequently, the output of these models were integrated using geographically weighted regression (GWR) as a local model. The findings demonstrated that the ensemble of ML models using GWR resulted in the highest performance (R2 = 0.75 and RMSE = 9.38 mg/l) compared to an ensemble model using averaging (R2 = 0.68 and RMSE = 10.56 mg/l), as well as individual models such as RFR (R2 = 0.70 and RMSE = 10.16 mg/l), SVR (R2 = 0.59 and RMSE = 11.95 mg/l), and KNN (R2 = 0.57 and RMSE = 12.19 mg/l). The resulting prediction map revealed that groundwater nitrate contamination is predominantly concentrated in urban areas located in the northwestern regions of the study area. The insights gained from this study have practical implications for managers, assisting them in preventing nitrate pollution in groundwater and formulating strategies to improve water quality.

Urban expansion patterns and their driving forces analysis: a comparison between Chengdu-Chongqing and Middle Reaches of Yangtze River urban agglomerations.

Urban agglomerations have emerged as the primary drivers of high-quality economic growth in China. While recent studies have examined the urban expansion patterns of individual cities, a comparative study of the urban expansion patterns of urban agglomerations at two different scales is required for a more comprehensive understanding. Thus, in this study, we conduct a two-scale comparative analysis of urban expansion patterns and their driving factors of the two largest urban agglomerations in western and central China, i.e., Chengdu-Chongqing urban agglomeration (CCUA) and the Middle Reaches of Yangtze River urban agglomerations (MRYRUA) at both the urban agglomeration and city levels. We investigate the urban expansion patterns of CCUA and MRYRUA between 2000 and 2020 using various models, including the urban expansion rate, fractal dimension, modified compactness, and gravity-center method. Then we use multiple linear regression analysis and geographically weighted regression (GWR) to explore the magnitude and geographical differentiation of influences for economic, demographic, industrial structure, environmental conditions, and neighborhood factors on urban expansion patterns. Our findings indicate that CCUA experienced significantly faster urban growth compared to MRYRUA. There is an excessive concentration of resources to megacities within the CCUA, whereas there is a lack of sufficient collaboration among the three provinces within the MRYRUA. Additionally, we identify significant differences in the impacts of driving forces of CCUA and MRYRUA, as well as spatial heterogeneity and regional aggregation in the variation of their strength. Our two-scale comparative study of urban expansion patterns will not only provide essential reference points for CCUA and MRYRUA but also serve as valuable insights for other urban agglomerations in China, enabling them to promote sustainable urban management and foster integrated regional development.

Spatial and temporal distribution characteristics of typical pollution loads based on SWAT model across Tuojiang River watershed located in Sichuan Province, Southwest of China.

Tuojiang River watershed is an economically developed and densely populated area in Sichuan Province (southwest of China), which is also an important tributary of the Yangtze River. Nitrogen (N) and phosphorus (P) are the main pollutants affecting water quality, but there is still lack of study on the spatial and temporal distribution characteristics of these two pollutants. In this study, the typical non-point source pollution loads in the Tuojiang River watershed are simulated by Soil and Water Assessment Tool (SWAT) model, and the spatial autocorrelation method is used to reveal the spatial and temporal distribution characteristics of the pollution loads from the annual average and water periods. Combined with redundancy analysis (RDA) and geographically weighted regression (GWR) analysis, the main driving factors affecting the typical non-point source pollution loads in the Tuojiang River watershed are discussed from the global and local perspectives. The results show that (1) from different water periods, the pollution loads of total nitrogen (TN) and total phosphorus (TP) in three water periods show obviously different, is the highest in the abundant water period, with 323.4 kg/ha and 47.9 kg/ha, followed by the normal water period, with 95.7 kg/ha and 14.1 kg/ha, and the lowest in the dry water period, with 28.4 kg/ha and 4.2 kg/ha. The annual average value of TN pollution load is higher than that of TP, with 447.5 kg/ha and 66.1 kg/ha, respectively; (2) the TN and TP pollution loads are stable on the whole, and the overall level in the middle reaches is higher. The pollution loads of Shifang City and Mianzhu City are higher in all three water periods. (3) Elevation and slope are two main driving factors affecting the TN and TP pollution loads in the Tuojiang River watershed. Therefore, the visualization and quantification of temporal and spatial distribution characteristics of typical non-point source pollution loads in the Tuojiang River watershed are helpful to provide the basis for scientific prevention and control of pollution in the Tuojiang River watershed and are of great significance to promote the sustainable, coordinated, and healthy development of water environment and economy in the watershed.

Regional differences in health screening participation between before and during COVID-19 pandemic.

BACKGROUND: Health screening is a preventive and cost-effective public health strategy for early detection of diseases. However, the COVID-19 pandemic has decreased health screening participation. The aim of this study was to examine regional differences in health screening participation between before and during COVID-19 pandemic and vulnerabilities of health screening participation in the regional context. METHODS: Administrative data from 229 districts consisting of 16 provinces in South Korea and health screening participation rate of each district collected in 2019 and 2020 were included in the study. Data were then analyzed via descriptive statistics and geographically weighted regression (GWR). RESULTS: This study revealed that health screening participation rates decreased in all districts during COVID-19. Regional vulnerabilities contributing to a further reduction in health screening participation rate included COVID-19 concerns, the population of those aged 65+ years and the disabled, lower education level, lower access to healthcare, and the prevalence of chronic disease. GWR analysis showed that different vulnerable factors had different degrees of influence on differences in health screening participation rate. CONCLUSIONS: These findings could enhance our understanding of decreased health screening participation due to COVID-19 and suggest that regional vulnerabilities should be considered stringent public health strategies after COVID-19.

Understanding the shared working spaces: a Geography of Co-working supply in Delhi.

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China.

BACKGROUND: Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis. METHODS: Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence. RESULTS: In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM10, PM2.5, O3, NO2 and SO2 have impacts on TB incidence, and in different regions, the environmental factors show regional differences. CONCLUSIONS: Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.

Identifying geographical inequalities of maternal care utilization in Ethiopia: a Spatio-temporal analysis from 2005 to 2019.

INTRODUCTION: Inequalities in maternal care utilization pose a significant threat to maternal health programs. This study aimed to describe and explain the spatial variation in maternal care utilization among pregnant women in Ethiopia. Accordingly, this study focuses on identifying hotspots of underutilization and mapping maternal care utilization, as well as identifying predictors of spatial clustering in maternal care utilization. METHODS: We evaluated three key indicators of maternal care utilization: pregnant women who received no antenatal care (ANC) service from a skilled provider, utilization of four or more ANC visits, and births attended in a health facility, based the Ethiopian National Demographic and Health Survey (EDHS5) to 2019. Spatial autocorrelation analysis was used to measure whether maternal care utilization was dispersed, clustered, or randomly distributed in the study area. Getis-Ord Gi statistics examined how Spatio-temporal variations differed through the study location and ordinary Kriging interpolation predicted maternal care utilization in the unsampled areas. Ordinary least squares (OLS) regression was used to identify predictors of geographic variation, and geographically weighted regression (GWR) examined the spatial variability relationships between maternal care utilization and selected predictors. RESULT: A total of 26,702 pregnant women were included, maternal care utilization varies geographically across surveys. Overall, statistically significant low maternal care utilization hotspots were identified in the Somali region. Low hotspot areas were also identified in northern Ethiopia, stretching into the Amhara, Afar, and Beneshangul-Gumuz regions; and the southern part of Ethiopia and the Gambella region. Spatial regression analysis revealed that geographical variations in maternal care utilization indicators were commonly explained by the number of under-five children, the wealth index, and media access. In addition, the mother's educational status significantly explained pregnant women, received no ANC service and utilized ANC service four or more times. Whereas, the age of a mother at first birth was a spatial predictor of pregnant who received no ANC service from a skilled provider. CONCLUSION: In Ethiopia, it is vital to plan to combat maternal care inequalities in a manner suitable for the district-specific variations. Predictors of geographical variation identified during spatial regression analysis can inform efforts to achieve geographical equity in maternal care utilization.

Dynamic of land use, landscape, and their impact on ecological quality in the northern sand-prevention belt of China.

Changes in land use and landscapes have a direct impact on the regional eco-environment. It is of great importance to understand the change pattern of land use, landscapes, and their mechanism on the ecological quality, especially ecologically fragile areas. The northern sand-prevention belt (NSPB) is an important ecologically fragile area in China, which has a large influence on the ecological security of the entire country. Based on the land use data of the NSPB in 2000, 2010, and 2018, we studied the spatio-temporal characteristics of land-use change and change in landscape patterns. The ecological quality represented by the remote sensing-based desertification index (RSDI) was calculated using satellite images. The effects of land use and landscape patterns on RSDI were analyzed by geographic detector and geographically weighted regression. Important results include the following: (1) Land-use change in the study area was high during 2000-2010 but slower in 2010-2018. Grassland was the largest land-use type in the NSPB, and varied greatly in terms of total change and spatial location. The major change was the conversion between dense and moderate grass, with 64,860 km2 of dense grass turning into moderate grass, and 48,505 km2 changing the other way. (2) Among the four landscape metrics, patch density, area-weighted mean fractal dimension, and edge density increased, whereas the aggregation index decreased, which indicated that the landscape was developing towards heterogeneity, fragmentation, complexity, and aggregation. Spatially, the landscape metrics presented a strip distribution in the east of the NSPB. (3) The effects of various land-use types on ecological quality, from high to low, were unused land, woodland, dense grass, cropland, moderate grass, built-up land, sparse grass, and waterbody. The areas where the ecological quality was greatly affected by the landscape patterns were concentrated in the agro-pastoral ecotone and the forest-steppe ecotone. The results of this study reveal the trends of land use and landscape patterns in the NSPB over 18 years and can help to understand their mechanism on ecological quality, which is of significance for the management of this area.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization.

BACKGROUND: Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship. METHODS: This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored. RESULTS: GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists. CONCLUSIONS: Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.

Gender difference of geographic distribution of the stroke incidence affected by socioeconomic, clinical and urban-rural factors: an ecological study based on data from the Brest stroke registry in France.

BACKGROUND: Mapping the spatial distribution of disease occurrence is a strategy to identify contextual factors that could be useful for public health policies. The purpose of this ecological study was to examine to which extent the socioeconomic deprivation and the urbanization level can explain gender difference of geographic distribution in stroke incidence in Pays de Brest, France between 2008 and 2013. METHODS: Stroke cases aged 60 years or more were extracted from the Brest stroke registry and combined at the census block level. Contextual socioeconomic, demographic, and geographic variables at the census block level come from the 2013 national census. We used spatial and non-spatial regression models to study the geographic correlation between socioeconomic deprivation, degree or urbanization and stroke incidence. We generated maps using spatial geographically weighted models, after longitude and latitude smoothing and adjustment for covariates. RESULTS: Stroke incidence was comparable in women and men (6.26 ± 3.5 vs 6.91 ± 3.3 per 1000 inhabitants-year, respectively). Results showed different patterns of the distribution of stroke risk and its association with deprivation or urbanisation across gender. For women, stroke incidence was spatially homogeneous over the entire study area, but was associated with deprivation level in urban census blocks: age adjusted risk ratio of high versus low deprivation = 1.24, [95%CI 1.04-1.46]. For men, three geographic clusters were identified. One located in the northern rural and deprived census blocks with a 9-14% increase in the risk of stroke. Two others clusters located in the south-eastern (mostly urban part) and south-western (suburban and rural part) with low deprivation level and associated with higher risk of stroke incidence between (3 and 8%) and (8.5 and 19%) respectively. There were no differences in profile of cardiovascular risk factors, stroke type and stroke severity between clusters, or when comparing clusters cases to the rest of the study population. CONCLUSIONS: Understanding whether and how neighborhood and patient's characteristics influence stroke risk may be useful for both epidemiological research and healthcare service planning.

Relationship between COVID-19 infection rates and air pollution, geo-meteorological, and social parameters.

Like all infectious diseases, the infection rate of COVID-19 is dependent on many variables. In order to effectively prepare a localized plan for infectious disease management, it is important to find the relationship between COVID-19 infection rate and other key variables. This study aims to understand the spatial relationships between COVID-19 infection rate and key variables of air pollution, geo-meteorological, and social parameters in Dhaka, Bangladesh. The relationship was analyzed using Geographically Weighted Regression (GWR) model and Geographic Information System (GIS) by means of COVID-19 infection rate as a dependent variable and 17 independent variables. This study revealed that air pollution parameters like PM2.5 (p < 0.02), AOT (p < 0.01), CO (p < 0.05), water vapor (p < 0.01), and O3 (p < 0.01) were highly correlated with COVID-19 infection rate while geo-meteorological parameters like DEM (p < 0.01), wind pressure (p < 0.01), LST (p < 0.04), rainfall (p < 0.01), and wind speed (p < 0.03) were also similarly associated. Social parameters like population density (p < 0.01), brickfield density (p < 0.02), and poverty level (p < 0.01) showed high coefficients as the key independent variables to COVID-19 infection rate. Significant robust relationships between these factors were found in the middle and southern parts of the city where the reported COVID-19 infection case was also higher. Relevant agencies can utilize these findings to formulate new and smart strategies for reducing infectious diseases like COVID-19 in Dhaka and in similar urban cities around the world. Future studies will have more variables including ecological, meteorological, and economical to model and understand the spread of COVID-19.

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression.

BACKGROUND: Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). METHOD: This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. RESULTS: Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts. CONCLUSION: The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy.

Groundwater nitrate contamination has been the main water quality problem threatening the sustainable utilization of water resources in Jeju Island, South Korea. The spatially varying distribution of nitrate levels associated with complex environmental and anthropogenic factors has been a major challenge restricting improved groundwater management. In this study, we applied ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to determine the relationships between the NO3-N concentration and various parameters (topography, hydrology and land use) across the island. A comparison between the OLS regression and GWR prediction models showed that the GWR models outperformed the OLS regression models, with a higher R2 and a lower corrected Akaike Information Criterion (AICc) value than the OLS regression models. Interestingly, the GWR model was able to provide undiscovered information that was not revealed in the OLS regression models. For example, the GWR model found that orchards (OR) and urban (UR) variables significantly contributed to nitrate enrichment in the certain parts of the island, whereas these variables were ignored as a statistically insignificant factor in the OLS regression model. Our study highlighted that GWR models are a useful tool for investigating spatially varying relationships between groundwater quality and environmental factors; therefore, it can be applied to establish advanced groundwater management plans by reflecting the spatial heterogeneity associated with environmental and anthropogenic conditions.

Uncovering the spatial variability of recent deforestation drivers in the Brazilian Cerrado.

In recent years, the Cerrado deforestation has increased considerably, reaching rates higher than in the Amazonian realm. Although the effects of deforestation are well known, the understanding of its drives at regional levels is incipient. Most studies consider that a driver influences deforestation likewise in all regions. However, deforestation has a strong spatial structure that can lead drivers to vary their influence on deforestation in different regions. Here, we evaluated the spatial variability in the relationship between the recent Cerrado deforestation and socioeconomic, environmental, and structural drivers at a regional scale. We used a geographically weighted regression (GWR) to assess the spatial variability of predictor variables. We identified regions that respond similarly to the drivers by grouping municipalities, considering their GWR coefficients through hierarchical clustering. The analyses that consider the spatial variability of predictors are more appropriated to assess the causes of recent deforestation. Remnant natural vegetation influenced the recent deforestation in all defined regions. Greater access to rural credit concession was the main driving force of deforestation in the northeast region defined here. Distance to roads increased deforestation in the northeast and north regions, while it inhibited deforestation in the central-east and southeast regions. Rainfall inhibited deforestation in the northeast, north, and southwest regions. Steep slope prevented deforestation mainly in the northeast, north, and southwest regions. Our results highlight that, to effectively reduce Cerrado deforestation, public policies should integrate strategies focusing not only at national and biome levels but also at the regional spatial level.

Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China.

Effective strategies, policies and measures for carbon emission reduction need to be developed and implemented according to good understanding of both local conditions and spatial differentiation mechanism of energy consumption associated with human activities at high resolution. In the study, we first collected statistical yearbooks, high resolution remotely sensed imageries, and 3895 usable questionnaires for the urban areas of Kaifeng; then measured the carbon emissions from household energy consumption, using the accounting method provided in the IPCC GHG Inventory Guidelines; and finally applied both exploratory and explanatory statistical methods to characterize the spatial pattern of carbon emissions at high resolution, identify key influencing factors, and gain better understanding of the spatial differentiation mechanism of urban residential carbon emissions. Our study reached the following conclusions: (1) Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness; (2) Spatial clusters of high carbon emission areas were primarily located in the outer suburbs of the city, validated to some extent the hypothesis that urban sprawl has a driving effect on the increasing urban residential carbon emissions; (3) Factors like size of residential area, family structure, life style, personal preference and behavior rather than household income have significant impacts on household carbon emissions, implying that effective control of residential areas, promotion of family life and low-carbon lifestyle, and effective guidance of proper behaviors and preferences will play a crucial role in reducing urban residential carbon emissions; and (4) Most of the identified influencing factors exhibit clear and specific spatial patterns and gradients of impact, implying that measures for urban residential carbon emission reduction should be adapted to location conditions. The study has generated a set of concrete evidences and improved understandings of the spatially differentiated mechanisms upon which the formation and deployment of any effective strategies, policies and measures for reducing urban residential carbon emissions should be based.