Quantifying the spatial nonstationary response of environmental factors on purse seine tuna vessel fishing.

To investigate the spatial and temporal patterns of environmental factors influencing the activity of purse seine tuna fishing vessels, data on fishing efforts of purse seine tuna fleets and environmental factors in the Western and Central Pacific Ocean (WCPO) from 2015 to 2020 were utilized to develop a geographically weighted regression (GWR) model. The results showed that fishing activity was primarily concentrated in the area between 140°E and 175°W, and between 10°S and 5°N. The GWR model showed excellent fitting performance and was suitable for correlation analysis. The environmental factors had a significant spatially heterogeneous effect on the fishing activity of purse seine tuna fishing vessels. The sea surface temperature, primary productivity at 200 m depth, and dissolved oxygen below the surface had the greatest spatially heterogeneous effect and are important environmental variables influencing the activity of purse seine tuna vessels in the WCPO. This study provides new methods for exploring the spatial distribution of fishing vessel activity to support science-based conservation and management.

Exploring urban compactness impact on carbon emissions from energy consumption: A township-level case study of Hangzhou, China.

Given that cities are the major contributors to carbon emissions, studying urban compactness (UC) and its impact on carbon emissions from energy consumption (CEECs) is crucial. This study calculated Hangzhou's township-level urban UC and CEECs using a hybrid subjective-objective weighted regression model on integrated panel datasets. By employing a geographically weighted regression (GWR) model, the spatio-temporal heterogeneity of the UC-CEEC relationship from 2006 to 2019 was uncovered. The results indicated an overall increase in UC, with significant variations across different counties. CEECs were higher in the central region, shifting eastward due to distinct urban development levels and policies. Moreover, the effects of various UC factors exhibited significant spatiotemporal inconsistency, with the impact intensity gradually diminishing. Additionally, the explanatory power of these factors declined and diversified over time. These findings emphasize the need for a comprehensive understanding of the relationship between UC and CEECs within the complex metropolitan environment and the importance of regulating their coordinated development. The research not only offers a more scientific approach to managing the growth of county-level cities and supporting balanced urbanization but also presents policy recommendations.

Spatial variation of short birth intervals and their determinant factors among reproductive women in Ethiopia using a geographically weighted regression model.

Background: In low- and middle-income nations, a significant proportion of maternal and infant deaths are caused by a short birth interval (SBI). In Ethiopia, it is the main factor contributing to maternal and infant mortality. Understanding the spatial distribution of SBIs, i.e., birth intervals of less than 33 months, and the factors that influence them is important for categorizing and promoting targeted interventions. This study used a geographically weighted regression model to evaluate the factors associated with SBIs in hot areas of Ethiopia. Methods: The 2019 Ethiopian Mini Demographic and Health Survey, which is nationally representative, provided the data for this study. The first step in the two-stage cluster design used to collect the data was enumeration areas, and the second stage was households. The survey was conducted between 21 March 2019 and 28 June 2019. A hot spot analysis (local Getis-Ord Gi* statistics) was initially used to investigate spatial variation in SBIs. Geographically weighted regression was used to examine the regional variation in the relationship between SBIs and the factors that cause them. Result: The study indicated that the overall proportion of SBIs among women in Ethiopia was 43.2%. The values for Global Moran's I (Moran's I = 0.773 and p < 0.001) showed the presence of significant SBIs clustering in Ethiopian administrative zones in Ethiopia. High-risk areas of the SBIs include Jarar, Doolo, Shabelle, Afder, Liben, Korahe, Nogob, West Harerge, Guji, Sidama, and Assosa zones. Conclusion: Living in a geographic region with a high proportion of uneducated women, women lacking breastfeeding practices, and followers of Orthodox religions increased the proportion of SBIs. Our full map of hot spots for short birth spacing and the factors that affect them helps in the implementation of precise public health measures for decision-makers.

Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study.

PURPOSE: Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest. METHODS: Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated. RESULTS: 140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR. CONCLUSION: Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.

Spatial heterogeneity of the effects of river network patterns on water quality in highly urbanized city.

River water quality deterioration is a serious problem in urban water environments. River network patterns affect water quality by influencing the flow, mixing, and other processes of water bodies. However, the effects of urban river network patterns on water quality remain poorly understood, thereby hindering the urban planning and management decision-making process. In this study, the geographically weighted regression (GWR) model was used to explore the spatial heterogeneity of the relationship between river network pattern and water quality. The results showed that the river network has a complex structure, high connectivity, and relatively even distribution and morphology. Important river structure indicators affecting water quality included the water surface ratio (Wp) and multifractal features (∆α, ∆f) while important river connectivity indicators included circuitry (α) and network connectivity (γ). River structure has a more complex effect on water quality than connectivity. This study recommends that the Wp should be increased in agricultural areas and appropriately reduced in urban built-up areas, and the number of river segments and nodes should be controlled within a rational configuration. Our study provides key insights for evaluating and optimizing the river network patterns to improve water quality of urban rivers. In the future, the land use intensity, hydrological processes, and human activities should be coupled with the river network pattern to deepen our understanding of urban river environment.

Geographically weighted regression analysis to assess hotspots of early sexual initiation and associated factors in Ethiopia.

Background: Early sexual initiation (ESI) causes unintended pregnancy, sexually transmitted infections (STI), high risk of depression and anxiety, developmental delays, lack of emotional maturity, and difficulty in pursuing education. This study aims to analyze the geographically weighted regression and associated factors of ESI of women in Ethiopia. Methods: The study utilized data from the Ethiopian Demographic and Health Survey, 2016. It included a weighted sample of 11,775 women. Spatial regression was carried out to determine which factors are related to hotspots of ESI of women. To identify the factors associated with ESI, a multilevel Poisson regression model with robust variance was conducted. An adjusted prevalence ratio (APR) with its 95 % confidence interval was presented. Results: The prevalence of ESI was 75.3 % (95%CI: 74.6 %, 76.1 %), showing notable spatial variation across different regions of Ethiopia. Areas of significant hotspots of ESI were identified in Western and Southern Tigray, most parts of Amhara, Southern, Central and Western Afar, Eastern Gambella, and North Western SNNPR. The significant variables for the spatial variation of ESI were; being single, rural residence, and having no formal education of the women. Factors including; wealth index, marital status, khat chewing, education level, residence, and region were associated significantly with ESI in the multilevel robust Poisson analysis. Conclusion: A higher proportion of ESI in women was found. Public health interventions must be made by targeting hotspot areas of ESI through increasing health care access and education (specifically among rural residents), developing a comprehensive sexual education, implementing policies and laws that outlaw early marriage, and mass community-based programs to increase awareness about the importance of delaying sexual activity.

[Spatiotemporal Evolution and Influencing Factors of Ecosystem Service Value in the Yellow River Basin].

The external spatiotemporal evolution and intrinsic impact mechanisms of ecosystem service value are of great significance for understanding regional ecosystem issues and enhancing human ecological well-being. Based on grid data, this study used the equivalent factor method and NDVI to measure the ecosystem service value of the Yellow River Basin, analyzed the spatial-temporal evolution of urban ecosystem service value along the basin, and established a GWR model to explore the spatial heterogeneity of each influencing factor on the basis of determining the main influencing factors via geographic detector. The results showed that:① The ecosystem service value of the Yellow River Basin increased first, then decreased, and finally increased from 2000 to 2020, showing a spatial distribution pattern of "the south was higher than the north;" "the lower reaches were lower, and the upper and middle reaches were higher;" and the regulation service contributed the most to the ecosystem service value of the basin. ② The results of geographical exploration showed that the degree of influence of various factors was different. Social factors played the strongest role in explaining the ecosystem service value of the Yellow River Basin, followed by economic factors, and natural factors played the weakest role. The high value areas in the upper reaches were mainly related to rivers and lakes, and the high value areas in the middle reaches were mainly related to mountains. ③ The results of the GWR model showed that population density and land reclamation rate were negatively correlated with ecosystem service value, whereas average annual precipitation was positively correlated, and the effects increased from east to west. The GDP per unit area was negatively correlated with the overall ecosystem service value but positively correlated in the upstream region.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities.

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Is air pollution exposure linked to household income? Spatial analysis of Community Multiscale Air Quality Model results for Madrid.

This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.

[Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM2.5 Concentration in Urban Agglomerations on the Northern Slope of Tianshan Mountains].

Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 µg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 µg·m-3) > spring (18.25 µg·m-3) > summer (12.47 µg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation.

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed.

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data.

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

[Spatial-temporal Evolution of Ecosystem Health and Its Influencing Factors in Beijing-Tianjin-Hebei Region].

Exploring ecosystem health and its influencing factors is of great significance for promoting regional sustainable development. An ecosystem health assessment model was constructed, and the spatial-temporal evolution characteristics of ecosystem health in the Beijing-Tianjin-Hebei Region in 2000, 2010, and 2020 were analyzed. The geographical detector and GWR were used to identify the dominant factors affecting ecosystem health. The main conclusions were as follows:during the study period, the index of ecosystem natural health in the Beijing-Tianjin-Hebei Region was generally better in the north and west than that in the southeast, and it showed an overall upward trend. The index of ecosystem services in the Beijing-Tianjin-Hebei Region presented as a spatial differentiation pattern of high in the north and low in the south, and it showed a downward trend. The ecosystem health level in the Beijing-Tianjin-Hebei Region showed a trend of rising first and then declining, showing significant heterogeneity in spatial distribution. The ecological health level in the central urban area of large cities was mostly poor, and the ecosystem health level in the Yanshan and Taihang Mountains and Bohai Rim was better. During the study period, the spatial pattern of ecosystem health in the Beijing-Tianjin-Hebei Region remained relatively stable. The hot spots and sub-hot spots were mainly distributed in the northern mountainous areas of Hebei Province and the Taihang Mountains, and the cold spots and sub-cold spots were mainly distributed in the southeast plain and the surrounding areas of some big cities. Population density, annual average temperature, per capita cultivated land area, and urbanization level were the dominant factors of ecosystem health in the Beijing-Tianjin-Hebei Region, and they were all negatively correlated with ecosystem health.

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.

Efecto de factores sociodemográficos en la mortalidad del COVID-19 en Costa Rica: un enfoque geográfico

Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.

Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.

Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China.

Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.

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.

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.

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.