Weighted Regression Model Research Articles

ANALISIS SPASIAL BERBASIS PEMETAAN MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA SEBARAN JUMLAH WISMA DI KABUPATEN LOMBOK BARAT

This research aims to analyze the effect of population density, number of food stalls, and population on the number of homesteads using the Geographically Weighted Regression (GWR) model. GWR modeling is carried out based on spatial data using secondary data sourced from the Central Statistics Agency (BPS). First, multiple linear regression analysis was carried out to evaluate the relationship between variables. The results of multiple linear regression show that only the variable number of food stalls has a significant effect on the number of homesteads. Next, spatial aspects were tested, including spatial autocorrelation tests and spatial heterogeneity tests. Testing spatial aspects shows the existence of spatial autocorrelation and spatial heterogeneity in the data. Therefore, we continued with GWR modeling to obtain a more in-depth picture of the distribution of the number of homesteads in West Lombok Regency. The results of modeling with GWR show that the variables used have a spatially varying influence throughout the West Lombok Regency area. Mapping GWR results produces better modeling of the factors that influence the number of homesteads in each village. A comparison between the multiple linear regression model and the GWR shows that the GWR model is able to explain variations in the number of homesteads with a higher R-square and a smaller AIC value

The spatially differentiated impact of high-speed railway on accessibility and socio-economic development of developing regions: A study in southwest China

The high-speed railway (HSR) plays a crucial role in bolstering regional connectivity and economic and social progress. Nevertheless, there is an ongoing debate around the impact of HSR on the economic and social development of the developing regions. On one hand, developing regions necessitate the implementation of HSR systems to facilitate their progress. On the other hand, they must also mitigate the potential drawbacks and adverse consequences associated. By employing accessibility analysis, urban potential analysis, and geographically-temporal weighted regression modeling, this study aims to comprehensively elucidate the spatially variable impact of HSR on developing regions such as Southwest China. The findings indicate that the implementation of HSR has yielded significant benefits in terms of accessibility, economic growth, and social development in developing regions. However, there is a growing disparity between cities that have HSR connections and those that do not. Additionally, smaller and medium-sized cities situated along the middle section of the HSR route experience relatively less pronounced impacts. This study posits that there is a necessity to enhance the expansion of the HSR network in developing regions, concurrently with the reinforcement of inter-city cooperation and the establishment of urban agglomerations, thereby promoting high-quality and balanced development of the developing regions.

Changes in ecosystem services supply–demand and key drivers in Jiangsu Province, China, from 2000 to 2020

AbstractHuman activities and regional land development have considerably contributed to the degradation of ecosystems and the growing contradiction between the supply and demand of ecosystem services (ESs) in Jiangsu Province, China. However, few studies have applied a comprehensive approach to elucidate the patterns and evolutionary characteristics of ESs over long periods. This study investigated the spatial–temporal evolution of the supply, demand, and supply–demand relationships of six individual ESs and comprehensive ESs in Jiangsu Province from 2000 to 2020. It revealed the key drivers of the changes in comprehensive ecosystem services supply–demand relationship (CESSD) using a geo‐detector model and a geographically and temporally weighted regression model to address the shortcomings of previous studies. The results showed that at the provincial level, the CESSD presented a surplus while the supply–demand state tended to be imbalanced. At the 1‐km2 grid scale, the spatial heterogeneity of CESSD was obvious, with surplus supply–demand zones primarily distributed in the Taihu Lake watershed, hilly areas, riverside zones, the watershed from Hongze Lake to Gaoyou Lake, and the eastern coastal zone, while deficit supply–demand zones were mainly clustered in areas with concentrated and contiguous construction land. From 2000 to 2020, the area of deficit in CESSD increased from 626 to 3257 km2 and the degree of deficit gradually deepened, with socioeconomic factors having the greatest influence on changes in CESSD. Our findings reveal the balance/imbalance between human society and natural ecology in Jiangsu Province and highlight the need for effective management of regional ecosystems.

Home sharing or crime sharing? Evidences of the relationship between Airbnb, crime and structural factors in Malaga, Spain

ABSTRACT Negative externalities stemming from overtourism have been widely reported in the literature. However, a consequence less explored in scientific research is the increase in crime rates derived from high levels of tourism. This article analyses the relationship between the concentration of Airbnb accommodation and crime density in the city of Malaga (Spain); both the social disorganization and the routine activities theories are used as the theoretical basis. The region under study has been defined by removing the non-urbanized part of the city and the unit of spatial analysis is the census tract. The relationship between Airbnb and crime has been measured by controlling for other factors traditionally related to crime. Two models that take into account the spatial nature of the data have been employed: a two-stage spatially weighted spatial least squares model and a geographically weighted regression model. Results show that Airbnb is significantly related to an increase in crime density in the census tract, as is the proportion of people born in countries with a Human Development Index below .825. Nightlife likewise shows a significant relationship with crime in the city, but this relationship is inverse. Nevertheless, the variable that shows the strongest link is crime itself in neighboring census tracts. An increase in crime in a given region leads to an increase in crime in the surrounding census tracts. However, the local model reveals that the impact of crime in adjacent units is not related in the same way to local crime in region analyzed as a whole. The policy implications of these results and other findings contrary to the original criminological theories are discussed.

Evolvement of Spatio-Temporal Pattern and Driving Forces Analysis of Ancient Trees Based on the Geographically Weighted Regression Model in Guangzhou and Foshan, China

Ancient trees play an important ecosystem service role in high-density cities, revealing the zonal distribution characteristics of vegetation under climate influence. The ancient trees in Guangzhou and Foshan in 2018 and 2023 were taken as study objects to explore the evolution of their spatio-temporal patterns and to analyze the spatial differentiation characteristics of their driving factors using the geographical weighted regression (GWR) model. The results showed the following: (1) The ancient trees in Guangzhou and Foshan were composed of typical subtropical evergreen broad-leaved forest communities, mainly represented by broad-leaved species of evergreen dicotyledonous plants. The dominant species mainly included Litchi chinensis, Ficus microcarpa, Canarium pimela, Ficus virens, and Dimocarpus longan. However, there was a significant difference between Guangzhou and Foshan. (2) The number of ancient trees in Guangzhou showed negative growth, while Foshan saw a significant increase. However, species diversity in both areas increased, with the highest diversity in the northeast, higher diversity in the south-central part, and lower diversity in the western and northwestern parts. (3) The maximum kernel density of ancient trees in Guangzhou and Foshan differed 22-fold, indicating a spatial distribution pattern of multiple clusters. (4) The GWR model effectively explained the driving factors of the heterogeneity of the spatial distribution of ancient trees. The results showed that artificial disturbance was the most important factor affecting the spatial distribution of ancient trees in high-density urban agglomerations in the same vegetation zone. The study clarified the characteristics of the spatial distribution and species diversity of ancient trees in the region, revealed the driving factors for the evolution of the spatial pattern of ancient trees in highly urbanized areas, and provided guidelines for policies and measures for enhancing biodiversity and conserving germplasm resources in the region.

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.

In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (β=-0.016) and average annual income of employees (β=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (β=1.416) and kindergarten (β=0.412) was a risk factor. HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

Ecological zoning and ecosystem management based on landscape ecological risk and ecosystem services: A case study in the Wuling Mountain Area

Land use and cover changes have substantially altered surface landscape patterns, resulting in landscape ecological risk (LER) and jeopardizing the continued supply of ecosystem services (ES). Although ES and LER are positive and negative aspects of ecological security representation, respectively, the spatiotemporal mechanism of their interaction still needs to be further clarified. This study focused on the Wuling Mountain Area (WMA), a critical ecological functional zone. First, multi-source data and multi-models were used to evaluate LER and ES and analyze their spatiotemporal characteristics. Subsequently, a geographically and temporally weighted regression model was applied to uncover the spatiotemporal heterogeneity of the impacts of LER on various ES. Finally, ecological zones were delineated based on the LER-ES quadrant, a driving force analysis was conducted, and ecological management strategies are proposed. The main results are as follows: (1) LER and various ES showed overall decreasing and increasing trends, respectively. (2) Except for water yield, the impact of LER on various ES showed clear spatiotemporal non-stationarity, with a significant negative impact, which was most pronounced for habitat quality. (3) The WMA was divided into four ecological zones: ecological risk prevention zone, ecological conservation zone, ES enhancement zone, and ecological reshaping zone. Elevation, normalized difference vegetation index, and human footprint were identified as the main drivers of ecological zoning. This research lays a solid foundation for in-depth understanding of the impacts of LER on ES. The proposed zoning scheme provides strong support for the ecologically sustainable development of the WMA.

Exploring the spatio-temporal driving mechanism of multi-dimensional urbanization on urban residential buildings based on GTWR: a case study of China

ABSTRACT Examining how urbanization affects carbon emissions in the construction industry is crucial for China's efforts to achieve environmentally friendly and low-carbon growth. Nevertheless, current research predominantly employs a unidimensional approach to gauge urbanization, overlooking the nuanced characteristics of novel urbanization. This study explored the spatial and temporal characteristics of the impact of urbanization on carbon emissions from urban residential buildings from a multidimensional perspective, starting from five dimensions: population, economy, land, life, and ecology, using a spatial–temporal geographically weighted regression model (GTWR) and panel data from 30 provinces in China for the period 2010–2020. The findings of this study revealed that, on the whole, population urbanization, land urbanization, and lifestyle urbanization exert a significant facilitating influence on carbon emissions from urban residential buildings. Conversely, economic urbanization exhibits a notable inhibitory effect on carbon emissions from urban residential buildings. Furthermore, ecological urbanization transitions from an inhibitory effect to a promoting effect on carbon emissions from urban residential buildings. The impact of urbanization across various dimensions on carbon emissions from urban residential buildings displays spatiotemporal heterogeneity across different regions and periods. In addition, the structure of energy consumption is a contributing factor to the increase in carbon emissions from urban residential buildings, while there is a notable U-shaped relationship between the level of foreign investment, the industrial structure, and carbon emissions originating from urban residential areas. This study offers profound insights into the relationship between urbanization and carbon emissions, providing valuable guidance for policymakers.

Impact of repositioning on brain injury following transcatheter aortic valve replacement with a self-expanding valve

Repositionable self-expanding valves allow for repositioning during deployment to achieve optimal valve placement. However, the risk of brain injury associated with repositioning, as detected by diffusion-weighted magnetic resonance imaging (DW-MRI), is unknown. Consecutive patients undergoing transcatheter aortic valve replacement (TAVR) with repositionable self-expanding valves and receiving DW-MRI before and within 7 days post-TAVR procedure were included. The primary outcomes were incidence, number, total volume, and volume per lesion of the cerebral ischemic lesion in DW-MRI after TAVR. Univariate and multivariate logistic regression assessed the association between repositioning and bigger total lesion volume (> 1 cm3 or > 0.5 cm3). Negative binomial regressions were performed to explore the association between repositioning and number of lesions. A propensity score matching was performed to adjust the potential confounders. Moreover, inverse probability of treatment weighted regression model with nonstabilized weights was used as sensitivity analysis. Among 243 included patients, repositioning was performed in 116 (47.7%) patients. The incidence of overt stroke (1.7% vs. 1.6%, p = 0.927) and silent stroke (86.2% vs. 85.8%, p = 0.932) were comparable between two groups. However, the number of new lesions (5.0 [2.0–9.0] vs. 3.0 [2.0–6.0], p = 0.048), and total lesion volume (275.0 [90.0–947.5] mm3 vs. 180.0 [50.0–440.0] mm3, p = 0.022) were significantly higher in the repositioned group. Moreover, the proportion of patients with lesion size greater than 0.5 cm3 was higher in the repositioned group (37.9% vs. 22.0%, p = 0.007). The similar results were observed after propensity score matching. In both multivariate regression model and sensitivity analysis, the repositioning was the independent predictor of number of lesions and bigger total lesion volume after TAVR. The utilization of the repositioning feature may increase the number and volume of silent brain infarcts in DW-MRI in patients who underwent TAVR. (Transcatheter Aortic Valve Replacement Single Center Registry in Chinese Population [TORCH]; NCT02803294)

Unveiling the Impact of Transportation Infrastructure Construction on Rurality: A Case Study from Guangdong, China

Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of “high in the north and low in the south, high in the west and low in the east”, while TA was on the rise, maintaining a stable pattern of “high in the middle and low in the periphery”. (2) Both TA and the CRI in Guangdong had a Moran’s I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development.

Geospatial Analysis of Lumpy Skin Disease Outbreaks among Cattle in Uttar Pradesh, India, 2021-2022.

The emergence of lumpy skin disease (LSD) among cattle in India is concerning. District-level data on LSD cases in Uttar Pradesh between 2021 and 2022 were analyzed. A stepwise spatial analytical approach was followed by first constructing yearly and monthly disease maps for LSD incidence rates (IRs), then spatially interpolating the LSD IRs, followed by evaluating the global and local clustering of LSD IRs and finally conducting spatial regression modeling. Overall, 5784 LSD cases from 6 districts and 112,226 cases from 33 districts were detected in 2021 and 2022, respectively. In the incremental spatial autocorrelation analysis, the highest global clustering of LSD IRs for the 2022 outbreak was detected at 196.49 km. For the 2021 LSD outbreak, one district with high-low and nine districts with low-high LSD IRs were identified in the eastern region of the state. For the 2022 LSD outbreak, 13 districts with high-high and 7 districts with low-high LSD IRs were identified in the western part of the state. A geographically weighted regression model identified the impact of climate (temperature and humidity) and land cover (pasture, fallow, and non-agricultural land) on LSD IRs. The study results can aid animal health authorities in developing LSD prevention and control programs.

Exploration of the Impact of Built Environment Factors on Morning and Evening Peak Ridership at Urban Rail Transit Stations: A Case Study of Changsha, China

<div>To identify the influences of various built environment factors on ridership at urban rail transit stations, a case study was conducted on the Changsha Metro. First, spatial and temporal distributions of the station-level AM peak and PM peak boarding ridership are analyzed. The Moran’s I test indicates that both of them show significant spatial correlations. Then, the pedestrian catchment area of each metro station is delineated using the Thiessen polygon method with an 800-m radius. The built environment factors within each pedestrian catchment area, involving population and employment, land use, accessibility, and station attributes, are collected. Finally, the mixed geographically weighted regression models are constructed to quantitatively identify the effects of these built environment factors on the AM and PM peak ridership, respectively. The estimation results indicate that population density and employment density have significant but opposite influences on the AM and PM peak ridership, which can be attributed to the opposite travel directions of commuters in the AM and PM peak. The recreational facility density, road density, and 10-min walking accessibility could significantly positively affect the PM peak ridership, and their influences vary greatly over space. Besides, the operating time of stations significantly positively affects both the AM and PM peak ridership, transfer stations have significantly larger PM peak ridership and terminal stations have significantly larger AM peak ridership. The findings are expected to provide new insights for agencies to formulate appropriate measures to improve the ridership of urban rail transit.</div>

Exploring the spatial relationship between urban built environment and green travel: An improved semi-parametric GWR approach

Public transit and non-motorized travel offer numerous environmental, social and public health benefits. Existing literature focuses on the correlation between the built environment and green travel in metropolitan centers, with limited studies on suburban new towns, essential for comprehensive urban expansion and sustainable development. This paper examines the effect of the built environment on public transit and non-motorized travel by using point of interest data and household economic data from Jiading District, Shanghai. An innovative framework is introduced, based on the geographically weighted regression model, which captures spatial heterogeneity while balancing the inequality and directionality of different areas in the transportation network. The results demonstrate that the proposed improved semi-parametric geographically weighted regression outperforms the traditional geographically weighted regression model in evaluation capabilities. A distinct spatial heterogeneity in travel patterns is identified between central and peripheral areas. The densely populated central zones exhibit a preference for intra-district travel, often opting for non-motorized modes due to their convenience and health benefits while peripheral areas serve as crucial connectors, facilitating long-distance travel within the metropolitan area through public transit and multimodal transportation. Regional transportation infrastructure construction and socioeconomic factors also affect the choice of green travel. The findings could provide theoretical references and policy implications to the development of green transportation and urban sustainability.

Sensing the Environmental Inequality of PM2.5 Exposure Using Fine-Scale Measurements of Social Strata and Citizenship Identity

Exposure to PM2.5 pollution poses substantial health risks, with the precise quantification of exposure being fundamental to understanding the environmental inequalities therein. However, the absence of high-resolution spatiotemporal ambient population data, coupled with an insufficiency of attribute data, impedes a comprehension of the environmental inequality of exposure risks at a fine scale. Within the purview of a conceptual framework that interlinks social strata and citizenship identity with environmental inequality, this study examines the environmental inequality of PM2.5 exposure with a focus on the city of Xi’an. Quantitative metrics of the social strata and citizenship identities of the ambient population are derived from housing price data and mobile phone big data. The fine-scale estimation of PM2.5 concentrations is predicated on the kriging interpolation method and refined by leveraging an advanced dataset. Employing geographically weighted regression models, we examine the environmental inequality pattern at a fine spatial scale. The key findings are threefold: (1) the manifestation of environmental inequality in PM2.5 exposure is pronounced among individuals of varying social strata and citizenship identities within our study area, Xi’an; (2) nonlocal residents situated in the northwestern precincts of Xi’an are subject to the most pronounced PM2.5 exposure; and (3) an elevated socioeconomic status is identified as an attenuating factor, capable of averting the deleterious impacts of PM2.5 exposure among nonlocal residents. These findings proffer substantial practical implications for the orchestration of air pollution mitigation strategies and urban planning initiatives. They suggest that addressing the wellbeing of the marginalized underprivileged cohorts, who are environmentally and politically segregated under the extant urban planning policies in China, is of critical importance.

Disparities in prevalence of screening/monitoring in children with intellectual and developmental disabilities: culturally sensitive provider can mitigate effects

IntroductionAbout one in six children in the US, about 17% of the population, have one or more intellectual or developmental disabilities. Increases in disability due to neurodevelopmental or mental health conditions have increased by 21% in the last decade. Early intervention based on developmental screening and provider-initiated monitoring can significantly improve long-term health and cognitive outcomes. This paper assesses whether differences in receipt of developmental screening or monitoring are associated with access to a high-quality primary care medical home and having a provider who shows sensitivity to a family’s customs and values among neurotypical children and children with intellectual and developmental disabilities (IDD).MethodsWe used cross-sectional data from the National Survey of Children’s Health (NSCH) from 2017 to 2019. The NSCH is a nationally representative, parent-completed annual survey of children under 18. Children between 9 months and 5 years with IDD (n = 2,385) and neurotypical children (n = 20,200) were included in the analysis.ResultsUptake of developmental screening/monitoring in neurotypical children and children with IDD conditions was associated with belonging to minority race/ethnic backgrounds, specifically Black, Asian, and AIAN/NHPI, and single-parent households with lower incomes, being publicly insured or uninsured and not having access to a high-quality medical home. Weighted regression models showed that the odds of neurotypical children receiving developmental monitoring/screening were 53% higher when their healthcare provider always or usually demonstrated cultural sensitivity to the family’s values and customs (OR 1.53, 95% CI, 1.08–2.18, p < 0.05). For children with IDD, the odds of receipt of monitoring/screening increased by 2.1 times when the provider always/usually demonstrated an understanding of the family’s cultural norms (95% CI, 0.99–4.43, p = 0.053). Being female was significantly associated with a lack of screening/surveillance (OR 0.73, 95% CI, 0.58–0.91, p < 0.05).DiscussionWith the rising prevalence of children with IDD conditions, early identification of developmental delays and subsequent access to interventions are crucial steps in supporting children and children with IDD to receive preventive care, services, and reduce disparities in accessing quality care. Implementing culturally sensitive approaches can be a low-cost and effective intervention in improving rates of provider-initiated monitoring and parent-completed screening.

Study on the spatial relationship between road network and the diversity of urban public facilities: the case of the central area of Changsha City

In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity.

Spatial distribution of inadequate meal frequency and its associated factors among children aged 6-23 months in Ethiopia: Multilevel and spatial analysis.

More than two-third of global child death is occurred due to inappropriate feeding practice that happened during early childhood period. Evidence on meal frequency status among infant and young children at national level can be used to design appropriate interventions to improve the recommended feeding frequency. Therefore, this study was aimed to explore the spatial distribution and identify associated factors of inadequate meal frequency among children aged 6-23 months in Ethiopia. Secondary data analysis was conducted using the 2019 mini Ethiopian Demographic and Health Survey data. A total weighted sample of 1,532 children aged 6-23 months were included. To identify significant factors associated with of inadequate meal frequency, multilevel binary logistic regression model was fitted. Variables with p-value < 0.25 from the bi-variable model were exported to multivariable analysis. In the multivariable model, variables with p-value < 0.05 were declared as significantly associated factors and adjusted odds ratio (AOR) with its 95% confidence interval were reported. Multilevel models were compared using deviance and log-likelihood. Spatial analysis tools were utilized to visualize the distribution of inadequate meal frequency. Bernoulli model was fitted using SaTScan V.9.6 to identify most likely clusters and ArcGIS V.10.8 was used to map the hotspot areas. Ordinary least square and geographic weighted regression models were used and compared using information criteria and adjusted-R2. Local coefficients of factors associated with hotspots of inadequate meal frequency were mapped. The prevalence of inadequate meal frequency was 47.03% (95% CI: 44.54%, 49.53%) in Ethiopia. Age of the child, sex of the household head, timely initiation of breastfeeding, current breastfeeding status, number of antenatal care visit, maternal education, and region were significantly associated with inadequate meal frequency. The spatial distribution of inadequate meal frequency was showed significant variation across Ethiopia (Global Moran's I = 0.164, p-value <0.001). A total of 38 significant clusters were detected through SaTScan analysis, from these the 22 primary clusters were located in Somali and Harari. The prevalence of inadequate meal frequency was high in Ethiopia and had significant clustering patter. Significant hotspot clusters were located in Somali, northern Afar, Harari, Amhara, Gambela, and eastern South nation nationalities and peoples' region. Therefore, public health interventions which enhance breastfeeding practice, optimal number of antenatal care visits, educational empowerments should target hotspot areas to decrease inadequate meal frequency practice.

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

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. 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. 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. 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.

An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

Evolution and Drivers of Production Patterns of Major Crops in Jilin Province, China

Exploring the evolution of the production patterns of major crops and their driving factors can provide new ideas to ensure the security of supply of important agricultural products. This study selected four major crop types in Jilin Province—grain, oil crop, vegetable, and fruit—as research subjects. Using the center of gravity migration model, spatial autocorrelation analysis, and comparative advantage index, the spatial and temporal evolution of these crops’ production patterns from 2000 to 2020 was examined. A geographically weighted regression model was employed to analyze driving factors. The results showed that the planting area of grain was the most extensive; the percentage of production of grain, oil crop, and fruit increased to different degrees; the unit area yield of the four crops increased. The centers of gravity of all four crops’ productions were located in the central-northwestern region of Jilin Province. The global Moran’s I was positive for all four crops’ productions. The comparative advantage characteristics of the four crops were closely related to the natural geographical environment. Grain, vegetable, and fruit production were significantly and positively correlated with fertilizer application, and the most significant factor affecting oil crop production was land input intensity.