Weighted Regression Model Research Articles

Estimating test-day milk yields by modeling proportional daily yields: Going beyond linearity

Lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans (1986) model is a de facto method for estimating test-day milk yields in the USA, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans (1986) model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., LOESS) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including non-parametric models.

Analysis of spatial characteristics and influence mechanism of human settlement suitability in traditional villages based on multi-scale geographically weighted regression model: A case study of Hunan province

Traditional villages are invaluable cultural heritage sites in China, reflecting a rich historical and cultural legacy. However, rapid urbanization has led to the destruction and loss of numerous traditional villages. The remaining traditional villages also face challenges in preserving their original living environment. In order to assess human settlement suitability and identify potential factors, this paper proposes a village-scale model for assessing human settlement suitability. This paper used an eight-factor evaluation system, multi-criteria decision analysis and spatial visualization methods to evaluate the scores and spatial characteristics of human settlement suitability of 688 traditional villages in Hunan Province. This paper also examines the factors affecting cluster suitability using geographic detectors and multi-scale geographically weighted regression (MGWR) models. This study found that the quality of human settlement suitability varied widely. The overall quality is low and very few areas achieve medium or higher scores. Among them, the traditional villages in Huaihua city, Zhangjiajie junction, southern Hengyang city, northwestern Yongzhou city, central Chenzhou city and Changsha city exhibit higher scores. Secondly, the commercial facility density and normalized difference vegetation index (NDVI) are important factors driving the spatial changes. And there is a significant interaction effect between them. Additional factors impacting spatial variation include annual average temperature, per capita disposable income, and average house price. This comprehensive assessment reveals spatial characteristics and potential influencing factors. The results of the study provide an empirical basis and decision-making foundation for the protection and development of traditional villages, which will ultimately contribute to the sustainable development of China's rural areas.

Geographically weighted regression modelling using Poisson distribution for factors influencing good SPH at district level, South Africa

Geographically weighted regression modelling using Poisson distribution for factors influencing good SPH at district level, South Africa

Quantitative Precipitation Estimation in the Tianshan Mountains Based on Machine Learning

Precipitation in the Tianshan Mountains is abundant, and the quantitative estimation of precipitation in mountainous areas is important to the application and evaluation of regional water resources. With remote sensing technology, satellite inversion of precipitation can estimate precipitation in mountainous areas. However, the Tianshan Mountain terrain is complex, and the spatiotemporal variation in precipitation is large, so the accuracy of satellite precipitation inversion is not high. Here, precipitation data from around 1000 automatic weather stations in the Tianshan Mountains are used to study the correction technology of the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (GPM) mission’s (IMERG) monthly precipitation products using stepwise regression (STEP), geographically weighted regression (GWR), and random forest (RF). First, geographic information system technology was used to extract topographic variables from a digital elevation model, and vegetation indexes, which are important precipitation indicators, were introduced as explanatory factors to correct satellite precipitation data. Second, GPM IMERG precipitation was corrected by establishing the stepwise regression, the geographically weighted regression model, and the random forest model. The three correction methods can improve the GPM IMERG in terms of relative bias, root mean square error, correlation coefficient, and Nash–Sutcliffe efficiency, while the random forest method shows better corrections than the two traditional methods. For dense rainfall stations, the geographically weighted regression method is as effective as random forest. For different altitudes, the results show that RF has the best correction effect in the first three zones, but the correction effect in the last zone (over 3000 m) is worse than STEP. This study provides a practical reference method for estimating precipitation data in the non-rainfall observation area, which helps to deepen the scientific understanding of the water resource distribution in the Tianshan Mountains and provide scientific data support for regional hydrological and meteorological research.

ANALYZING LAND USE TYPES’ EFFECTS ON LST USING THE GWR MODEL AND CASE STUDIES IN BEIJING

The development of urbanization and the transformation of green lands into impermeable land increase temperature and create urban heat islands (UHIs). Our observations with remote sensing instruments of Landsat platforms show considerable changes in land use types in Beijing city with the shrinking of green lands, expansion of built environments, and a slight increase in the temperature during the recent four decades. Using remote sensing instruments of Landsat platforms and registered data from two meteorological stations in Beijing, this study finds the relationship between land surface temperature (LST) and the increasing conversion of cultivated lands into built-up areas. This article presents innovative research that shows the mutual correlation well and recommends revisions in the land use policies for better weather. The geographically weighted regression model (GWR) with a Gaussian weighting kernel function analyzes the impact of various urban land use types on the LST and the increase UHIs. In Beijing city, green lands show fewer standard deviations (SD) in the average temperatures equal to 0.109, while the industrial spaces exhibit a high SD equal to 0.212. The outcomes of this paper contribute to finding optimal land use policies everywhere in the world with the increasing urbanization through simulating its model for a more comfortable life.

Spatio-Temporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems in the Yellow River Basin

The accurate estimation of a regional ecosystem’s carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 109 t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase.

The Impacts of Climate and Human Activities on Grassland Productivity Variation in China

Grassland degradation is widespread and increasing globally, which is closely related to the sustainable development of the ecosystems and the well-being of human life in pastoral areas. Quantifying the factors influencing grassland ecosystems, specifically climate change and human activities, is of great significance for grassland restoration. However, due to the unpredictability of human activities, further research is still needed to distinguish and identify the factors affecting grasslands. In this study, we examined the changes in the gross primary productivity (GPP) of grassland cover in 10 provinces (autonomous regions) of China from 2000 to 2018 and selected three representative climate factors (temperature, precipitation, solar radiation) and six factors covering socioeconomic (primary industry production and population), animal husbandry (large livestock and sheep populations), and national policies (grazing areas, rodent, and pest control) to characterize human activities; then, we quantified the effects and contribution of climate and human factors using three analysis methods (partial correlation analysis, geographical and temporal weighted regression model, and Lindeman Merenda Gold method). The results indicated that the GPP of grassland presented an obvious uptrend (4.75 g C m−2 yr−1, p < 0.05). Among the nine factors, sheep, precipitation, and temperature were the primary factors affecting grassland dynamics. Additionally, the GPP dynamics of grassland were mainly dominated by human activities in seven provinces (autonomous regions). These findings provide decision support for protecting grassland ecosystems and implementing ecological restoration policies in China.

Spatiotemporal Evolution Characteristics and Driving Factors of Water Conservation Service in Jiangxi Province from 2001 to 2020

Water conservation services are key indicators of ecological services. Against the backdrop of frequent extreme weather events and water scarcity caused by global climate change and intensified human activities, assessing these services and their drivers are crucial tasks for regional ecological security and sustainable development. Jiangxi Province is one of the first national ecological civilization pilot zones in China, representing an important ecological barrier in southern China. Exploring the characteristics of spatial and temporal changes in water conservation and their driving factors can facilitate the rational development and utilization of regional water resources and the construction of ecological civilizations. Therefore, based on long time series data, the InVEST model was used to explore the spatiotemporal evolution characteristics of water conservation services, and to elucidate the trend of their change through the Theil–Sen median trend analysis and the Mann–Kendall test; then, the geographic detector and geographically weighted regression model were used to further analyze the drivers of spatial variability of water conservation services. The results showed the following: (1) The average depth of water conservation was 103.18 mm, showing a spatial pattern of “low in the middle, high in the surroundings, high in the north and low in the south”. (2) Slight improvements were primarily observed (77.49%), with only 1.60% of the area showing significant improvements. (3) Land use was the main driver of the spatial differentiation, and the interaction between precipitation and forestland had a significantly greater effect on spatial heterogeneity than any single factor. (4) Obvious spatial heterogeneity occurred in the driving factor impacts, with natural factors (precipitation, evapotranspiration, forestland, and grassland) having a positive impact on water conservation services, and land-use factors (construction land and cropland) and socioeconomic factors (population density and land area) having a negative effect. This study provides a reference for water-conservation-based ecosystem construction and policy formulation in Jiangxi Province.

Robust Geographically Weighted Regression Model on Poverty Data in South Sulawesi in 2019

Robust Geographically Weighted Regression Model on Poverty Data in South Sulawesi in 2019

Ecological Risk Assessment and Impact Factor Analysis of Ecological Spatial Patterns in Coastal Counties: Taking Dalian Pulandian District as an Example

A scientific foundation for the sustainable development of ecosystems and the improvement of the ecological spatial security pattern in the area is provided by carrying out a scientific assessment of ecological risk levels in coastal counties. An ecological risk assessment model was established in Pulandian district, Dalian, based on four periods of land use data from 1990 to 2020 combined with the PSR model. The spatial and temporal evolution of ecological risk in Pulandian was analyzed on this basis, and an exploratory regression analysis and a geographically weighted regression model were then used to explore the driving role of natural and social factors on comprehensive ecological risk in coastal counties. The findings demonstrate that there is an obvious ecological landscape type of conversion, with the majority of arable land being converted to forest land in northcentral and southwest areas, reflecting an improvement in the ecological environment and air quality, and the majority of coastal beach land being converted to construction land among the volumes transferred out and in. The area of high risk increased by 73.17% during the course of 30 years, with the majority of it concentrated in the research areas southern Fengrong, Tiexi, and Taiping regions in 1990 before expanding to the northeast, southeast, and central regions. The status index and response index both show a decline followed by an increase in change, while the pressure index shows a rising tendency with socioeconomic progress. The comprehensive ecological risk in the study area is significantly influenced by the urbanization rate, the ratio of environmental protection investment to GDP, the ecosystem service index, and the ecological space–land use ratio, with the urbanization rate displaying more overt negative correlation-driving characteristics, the ratio of environmental protection investment to GDP displaying significant spatial division characteristics, and the ecological space–land use ratio being an important factor. The findings serve as a foundation for decisions on ecological risk avoidance, control, and construction in Pulandian.

Spatiotemporal differentiation and mechanism of anthropogenic factors affecting ecosystem service value in the Urban Agglomeration around Poyang Lake, China

Clarifying the impact of anthropogenic factors on ecosystem service value (ESV) is crucial to achieving sustainable development. However, the dynamics of human-disturbed complex ESVs vary from region to region, and the specific mechanism of this remains unclear, confusing land use planning. Therefore, this study focuses on the Urban Agglomeration around Poyang Lake in a typical area of China, uses the improved equivalent factor to analyze ESV estimation and spatial–temporal change, and employs Geographically Weighted Regression model and Geographical Detectors to explore the regional differences and impact mechanisms of anthropogenic factors on ESV. The results indicate that ESV in the study area has increased from 2000 to 2020, with a slowed-down growth rate. Spatially, ESV shows a decreasing trend from the core area to the periphery area as the distance from the lake increases. Further analysis reveals significant spatial heterogeneity in the impact of anthropogenic factors on ESV, with more potent interaction effects than single effects. Specifically, there is a strong interaction between urban development factors and industrial/agricultural factors. Furthermore, the anthropogenic-driven forces affecting ESV become increasingly complex from the core area to the peripheral area. These findings provide valuable insights into the distribution, dynamics, and mechanisms of ESV.

Long-Term PM2.5 Exposure, Lung Function, and Cognitive Function Among Middle-Aged and Older Adults in China.

Long-term exposure to PM2.5 is related to poor lung function and cognitive impairment, but less is known about the pathway involved in this association. We aimed to explore whether the effect of PM2.5 on cognitive function was mediated by lung function. A total of 7 915 adults older than 45 years old were derived from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011 and 2015. PM2.5 exposure was estimated using a geographically weighted regression model. Lung function was measured by peak expiratory flow (PEF). Cognitive function was evaluated through a structured questionnaire with 4 dimensions: episodic memory, attention, orientation, and visuoconstruction. Under the counterfactual framework, causal mediation analysis was applied to examine direct and indirect associations. An interquartile range (IQR) increase in PM2.5 change was significantly related to an 8.480 (95% confidence interval [CI]: 3.116, 13.845) decrease in PEF change and a 0.301 (95% CI: 0.100, 0.575) decrease in global cognitive score change. The direct and indirect effects of PM2.5 exposure on global cognitive performance were -0.279 (95% CI: -0.551, -0.060) and -0.023 (95% CI: -0.041, -0.010), respectively. The proportion of the indirect effect was 7.48% (p = .010). The same significant association appeared in only 2 dimensions, episodic memory and attention, which were both mediated by PEF. Lung function played a partially mediating role in the association between long-term PM2.5 exposure and cognition. More clean air actions should be undertaken to improve lung function and cognitive function in older adults.

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China.

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Wearable Cardioverter Defibrillator Shortens the Lengths of Stay in Patients with Left Ventricular Dysfunction after Myocardial Infarction: A Single-Centre Real-World Experience.

The wearable cardioverter defibrillator (WCD) has been proven to be effective in preventing sudden cardiac death (SCD) in patients soon after acute myocardial infarction (AMI) and left ventricular ejection fraction (LVEF) ≤35%. The aim of this study was to assess whether a WCD may shorten the length of an initial hospital stay (total length, days in the intensive care unit (ICU) and in the acute cardiac care unit (ACCU)) among these patients. This was a single-centre, retrospective observational study of patients referred for the management of SCD risk post-AMI and LVEF ≤35%, in a tertiary care hospital. The clinical characteristics and length of index hospitalization of the group of patients discharged, with or without WCD, were compared. A propensity score analysis was performed, then weighted regression models were conducted. A total of 101 patients in the WCD group and 29 in the control group were enrolled in the analysis. In the weighted regression models, WCD significantly reduced the days spent in ACCU (p < 0.001). WCD patients had significantly fewer days spent in ACCU (5.5 ± 2.6 vs. 8.4 ± 12.8 days, p < 0.001) and shorter hospitalizations (10.2 ± 5.7 vs. 13.4 ± 17.6 days, p = 0.005), compared with the control group. It was concluded that the WCD appears to reduce the total length of hospitalization and lengths of stay in ACCU for patients post-AMI and with left ventricular dysfunction.

Prediction of meat yield in the Pacific whiteleg shrimp Penaeus vannamei

Meat yield, the percentage of net meat weight, is considered to be an important target trait for genetic improvement in the Pacific whiteleg shrimp Penaeus vannamei. The development of nondestructive methods for predicting meat yield in live shrimp holds great promise. With the objective of improving meat yield more efficiently in whiteleg shrimp, this study established prediction models for meat yield and net meat weight from body measurements (weight, height, length and width), and estimated genetic parameters for these traits. Forty full-sib families were produced and used for a 60-day growth-out test, and then 1560 shrimp were harvested for measurements. Individual meat yield ranged from 44.38% to 59.20%. Multiple linear regression models for meat yield and net meat weight were constructed through stepwise regression analysis. Models assumed for both traits shared the same independent variables (body weight, body length, total abdominal segment length and the third abdominal segment width), but the prediction accuracy reached 0.991 for net meat weight and only 0.450 for meat yield. Heritability estimates of observed (0.231 ± 0.059) and predicted (0.222 ± 0.057) meat yield were significantly lower than those of observed and predicted net meat weight (0.653 ± 0.108) and most of body traits (0.494 ± 0.094–0.656 ± 0.108) (P < 0.05). Genetic correlations of meat yield with other body traits were mainly moderate (0.370 ± 0.165–0.641 ± 0.124), and lower than those for predicted meat yield (0.844 ± 0.066–0.910 ± 0.039) and those for net meat weight (> 0.95). Our results indicate that simple body traits of whiteleg shrimp can be used to accurately predict net meat weight, but not meat yield. Without slaughter, still, relatively accurate meat yield can be indirectly obtained by measuring body weight and predicting net meat weight from body measurements. Although heritability of meat yield is not high, genetic improvement through direct selection on it is feasible.

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years.

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.

Analysis on spatial structure characteristics and influencing factors of Beijing-Tianjin-Hebei Region MICE cities.

Industrial upgrading and transfer is one of the three key areas in the coordinated development of Beijing-Tianjin-Hebei Region. Meetings, incentives, conferences and exhibitions (MICE) are important means of promoting industrial upgrading. Urban MICE industry, as an important carrier of event activities, become an important gripper for coordinated development of Beijing-Tianjin-Hebei Region. City exhibition space structure plays an dominant role in regional economic development, and it will greatly promote smooth implementation of the coordinated development strategy of Beijing-Tianjin-Hebei Region. In this paper, 13 prefecture-level cities in Beijing, Tianjin and Hebei were selected as research objects, and the data from 2012 to 2018 were selected to establish a gravity model of the attractiveness of MICE cities. With the help of UCINET software, the network density, centrality, cohesive subgroup and core-edge of nodes researches were obtained to analyze the spatial structure characteristics of the attractiveness of MICE cities in Beijing-Tianjin-Hebei Region. The influencing factors of the spatial characteristics of MICE city attractiveness are analyzed by using geographically weighted regression model. The results show that: (1) Beijing, as the overall core area, act as a prominent role. Beijing strengthens the attraction to the superior resources of the surrounding areas, but the network of MICE cities tends to be unbalanced. (2) Overall space forms a subgroup from a single independent subgroup to a subgroup that is spatially separated from each other and acts as an intermediary channel to connect each other, and the core region decreases from 3 to 2. Langfang was removed from the list, leaving Beijing and Tianjin as the core. (3) The influence of supporting facilities, urban environment and population factors on the MICE city attractiveness of Beijing-Tianjin-Hebei Region is increasing gradually. The influence of tourism development level on the MICE city attractiveness of Beijing-Tianjin-Hebei Region is decreasing gradually. The influence of economic development level and Internet development level on the MICE city attractiveness of Beijing-Tianjin-Hebei Region remains unchanged.

Evolution of farmland landscape fragmentation and its driving factors in the Beijing-Tianjin-Hebei region

Farmland landscape fragmentation has become one of the main manifestations of global land use changes during the last century. Since the reform and opening-up in China, the Beijing-Tianjin-Hebei region, as the representative region in China, has exhibited a continuous process of farmland landscape fragmentation. Scientifically revealing the spatiotemporal dynamics of farmland landscape fragmentation is of great significance for sustainable land management. Although the characteristics of farmland landscape fragmentation have been widely studied, measuring the dynamics of farmland landscape fragmentation from the perspectives of landforms and urban-rural variations remains poorly understood. This study relies on the comprehensively theoretical models to spatiotemporally identify farmland landscape fragmentation and applies a comprehensive index method that includes ArcGIS, FRAGSTATS landscape pattern analysis and the multiscale geographically weighted regression model to empirically analyse the spatial-temporal characteristics of farmland landscape fragmentation in China from 1980 to 2020. The results indicate that the value of the farmland landscape fragmentation index in the Beijing-Tianjin-Hebei region is characterised by process of ‘slight increase-rapid decrease-continuous increase’. High farmland landscape fragmentation index values were observed in the northern and western Beijing-Tianjin-Hebei regions. Moreover, significant differences in farmland landscape fragmentation were observed under different macro landforms. The farmland landscape fragmentation index in the mountainous regions is much higher than in the plains. Furthermore, as the distance to Beijing's centre increases, the farmland landscape fragmentation index decreases in suburban and peri-urban areas. Finally, the results also reveal the driving factors of farmland landscape fragmentation. The results showed that the natural environment is always the crucial driver of farmland landscape fragmentation. Besides, social activities play an important role in the past, but economic development has a more significant coefficient these days. The findings of this study not only verify that the proposed theoretical framework is valid and provides a reference for future policy measures to protect farmland but also provide a basis for farmland landscape fragmentation from a multiscale perspective by, more precisely, identifying the heterogeneity of drivers.

Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model.

Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space. The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data. MGWR was the best-fitting model (R2: 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water. Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control.

Spatial–Temporal Evolution Characteristics and Driving Factors of Rural Development in Northeast China

An assessment of rural development and its driving factors can effectively reflect the characteristics and transformation of rural areas and provide important information for the formulation and implementation of rural development strategies. Taking Northeast China as study area, a rural development index framework was constructed from three dimensions, i.e., basic rural conditions, the state of agricultural development, and farmers’ living standards, based on which the rural development level of each city in Northeast China for the years 2000, 2005, 2010, 2015 and 2020 was assessed. Then, an exploratory spatial data analysis was used to explore the spatial and temporal variations in the rural development level in Northeast China during the period 2000–2020. The driving factors were also analyzed using a geographically and temporally weighted regression model. The results showed that the rural development level showed an increasing trend overall, with a spatial pattern of “high in the central, low in the east and west” in most periods. The degree of spatial agglomeration of the rural development level also showed a strengthening trend overall. The hots spots of rural development were mainly distributed in the Southern and Northern regions, while the cold spots were mostly concentrated in the central, eastern and western regions. Urbanization processes, elevation, annual precipitation and other natural factors have weakened the level of rural development to a certain extent, while agricultural production upgrading, an increase in the general public budget expenditure per capita and the sound financial situation of the government can promote rural development in Northeast China. The effects of the natural environment and local economic conditions on rural development were different in different regions. To improve rural development in the future, we should scientifically grasp the basic conditions, such as rural resource endowment, location conditions, agricultural technology, policies, investment and other external conditions, and formulate regional rural development strategies according to local conditions and in light of local rural characteristics.