Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Spatiotemporal Characteristics of Typical Ecosystem Services and Their Spatial Responses to Driving Factors in Ecologically Fragile Areas in Upper Yellow River,China

The identification of dominant driving factors for different ecosystem services(ESs)is crucial for ecological conservation and sustainable development.However,the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated,particularly in ecologically fragile regions.This study employed the integrated valuation of ESs and trade-offs(InVEST)model to evaluate four ESs,namely,water yield(WY),soil conservation(SC),habitat quality(HQ),and carbon storage(CS),and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River,China from 2000 to 2020.The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region,while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas.The CS,WY,and SC exhibited decreasing trends over time.The most critical factors affecting WY,SC,HQ,and CS were the actual evapotranspiration,precipitation,slope,and normalized difference vegetation index,respectively.In addition,the effects of different factors on various ESs exhibited spatial heterogeneity.These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas.

Spatiotemporal Differentiation of Urban Spatial Form and Carbon Emissions in Poyang Lake City Group

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research,this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions.Based on generalized linear regression and geographically weighted regression models,this paper analyzed the spatiotemporal distribution characteristics of carbon emissions,the spatiotemporal relationship between urban form index and carbon emissions,and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020.The results showed that:①The carbon emissions of urban agglomerations around Poyang Lake are generally increasing,and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces;②The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area;③Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions.The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions,which could make the low-carbon land spatial planning in the Poyang Lake city group more reasonable and practical.

Spatial Downscaling of the Tropical Rainfall Measuring Mission Precipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China

Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams

Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R2 values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.

Spatio-temporal characteristics of population and economy in transitional geographic space at the southern end of"Hu Huan-yong Line"

"Hu Huan-yong Line(Hu Line)"depicts a geographical pattern of China’s population distribution.Its essence is the regionality of humanland relationship and reflects basic characteristics and laws of human beings’adaptation to the natural environment.With the development of the times and the progress of modern science and technology,especially the rapid urbanization and construction of transportation network system in China,the connection between economic and geographical space has been continuously strengthened.The geographical transition zones from mountain areas to plains,i.e.,transitional geographical space,have promoted the changes in human-land relationships through population migration and agglomeration.Taking Sichuan-Yunnan provinces at the southern end of Hu Line as study area,this study analyzed the spatial correlation between population distribution and economy in this region,explored the pattern of geographical agglomeration and deagglomeration,and explained the changing characteristics of humanland relationship in transitional geographic space using global Moran’s I index,global regression model(GRM)and geographically weighted regression(GWR).The results show that population and Gross Domestic Product(GDP)have significant spatial dependence to this region,with obvious aggregation in geographical distribution and positive autocorrelation;comparing with the general least square model,the GWR model incorporating spatial effect was more suitable for revealing the distribution characteristics of geographical elements,with fine results and better fitting;the spatial model of population and GDP as well as the spatio-temporal evolution model of their changes,all of them strongly indicated that Normalized Difference Vegetation Index(NDVI)and road density were important factors governing the spatial differentiation of population and economy;under the rapid development of regional economy and continuous evolution of urban-rural relations,rural transformation and spatial reconstruction promoted the change of population migration and agglomeration.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level:A study in a developing country context

Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels.However,built environment-related factors have primarily been examined in developed countries,resulting in a limited understanding of the phenomenon in the context of developing countries.Methodologically,these studies mostly used global regression models,which failed to incorporate spatial autocorrelation and spatial heterogeneity.Additionally,some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections.Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity,Dhaka,the capital of a developing country:Bangladesh.Using secondary pedestrian crash data,the study applied one global non-spatial model,two global spatial regression models,and two local spatial regression models following a comprehensive spatial regression modeling framework.The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density,mixed and recreational land use density,primary road density,major intersection density,and share of non-motorized modes.Except for the last factor,all the other ones were positively related to pedestrian crash density.Among the five models used in this study,the multiscale geographically weighted regression(MGWR)performed the best as it calibrated each local relationship with a distant spatial scale parameter.The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.

Covid-19 in West &East Africa, a Geographical Weighted Regression Exploration with http://mygeoffice.org/

Understanding the dynamics that affect the spread of Covid-19 is critical for the development of government measures to stop and reverse this nowadays disease propagation. Like in any epidemiological study, it is essential to analyze the spatial data to account for the inherent spatial heterogeneity within the data (spatial autocorrelation). This paper uses Geographically Weighted Regression (GWR) to identify the factors that influence the outbreak of Covid-19 in Western and Eastern countries of Africa. The analyses include traditional linear regression (including descriptive statistics, hierarchical clustering and correlations were not forgotten either) to reveal the importance of eight risk factors (population density, median age, aged over 65 years, GDP per capita, cardiovascular death rates, diabetes prevalence, female and male smokers) regarding Covid-19 dissemination. It is believed that this is the first attempt to explore possible causes associated with the spread of the Covid-19 pandemic in these disadvantage countries, where some intriguing clues are presented for further research such as the positive relationship between the financial purchase power of nations and the total number of infected people or the smoker’s gender impact on Covid-19.

Spatial variation of the relationship between transport accessibility and the level of economic development in Qinghai-Tibet Plateau, China

As an important component of China’ transportation systems, for a long time, the insufficient performance of transport in QinghaiTibet Plateau(QTP) was a bottleneck restricting the economic growth and social development in this area. Nevertheless, the implementation of the western development strategy has accelerated the preliminary construction of comprehensive transport network since 2000. Due to the large area and significant geographical heterogeneity, there is a growing need to understand the relationship between transportation and economic development based on the perspective of spatial difference. By using GIS-based raster analysis and Geographically Weighted Regression(GWR) model, we investigated the spatial-temporal distribution of highway, railway and airport accessibility, respectively, and estimated the correlation and heterogeneity between transport accessibility and the level of economic development. Results revealed that:(1) Transport accessibility in the QTP improved by 53.38% in the past 15 years, which is specifically embodied in the improvement of both highway and railway.(2) Accessibility presented prominent differentiation in the space, increasing from west to east and reducing with the rise of elevation, specifically, the best accessibility area of the highway is below 4000 m above sea level, while the area with an altitude of over 4000 m has the lowest aviation time cost.(3) In general, the long weighted average time cost to critical transport facilities posed significantly negative effect on county economic growth in QTP, more positively, the adverse effect gradually weakened over time.(4) Obvious heterogeneity exists at the influence of different transport accessibility factors on the level of economic development, reflecting both in the horizontal space and altitudinal belt. Therefore, region-specific policies should be addressed for the sustainable development of transport facilities as well as economy in the west mountain areas.

Modeling Monthly Spatial Distribution of Ommastrephes bartramii CPUE in the Northwest Pacific and Its Spatially Nonstationary Relationships with the Marine Environment

There are substantial spatial variations in the relationships between catch-per-unit-effort(CPUE) and oceanographic conditions with respect to pelagic species. This study examines the monthly spatiotemporal distribution of CPUE of the neon flying squid, Ommastrephes bartramii, in the Northwest Pacific from July to November during 2004–2013, and analyzes the relationships with oceanographic conditions using a generalized additive model(GAM) and geographically weighted regression(GWR) model. The results show that most of the squids were harvested in waters with sea surface temperature(SST) between 7.6 and 24.6℃, chlorophyll-a(Chl-a) concentration below 1.0 mgm^(-3), sea surface salinity(SSS) between 32.7 and 34.6, and sea surface height(SSH) between-12.8 and 28.4 cm. The monthly spatial distribution patterns of O. bartramii predicted using GAM and GWR models are similar to observed patterns for all months. There are notable variations in the local coefficients of GWR, indicating the presence of spatial non-stationarity in the relationship between O. bartramii CPUE and oceanographic conditions. The statistical results show that there were nearly equal positive and negative coefficients for Chl-a, more positive than negative coefficients for SST, and more negative than positive coefficients for SSS and SSH. The overall accuracies of the hot spots predicted by GWR exceed 60%(except for October), indicating a good performance of this model and its improvement over GAM. Our study provides a better understanding of the ecological dynamics of O. bartramii CPUE and makes it possible to use GWR to study the spatially nonstationary characteristics of other pelagic species.

Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.

Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018

To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.

Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy:A Case of Guangzhou City,China

This study applies multi-source datasets(i.e.,Baidu Heat Map data,points of interest(POIs)data,and floor area and land use data)and geographically and temporally weighted regression(GTWR)models to elaborate the spatiotemporal relationships between the built environment and urban vibrancy on both weekdays and weekends,using Guangzhou City as a case.First,we verified the spatially and temporally nonstationary nature of the built environment correlates,which have been largely ignored in previous studies based on local regression techniques.The spatially and temporally heterogeneous effects of the built environment on urban vibrancy are then presented and visualized,based on the GTWR results.We found that the elasticity of location(i.e.,distance),land use mix(i.e.,diversity),building intensity and numbers of POIs with various functions(i.e.,density)are different across time(2-h intervals within a day)and space(grids),due to people’s everyday lifestyle,time-space constraints,and geographical context(e.g.,spatial structure).The findings highlight the importance of a better understanding of the local geography on the spatiotemporal relationships for urban planners and local governments so as to put forward decision-making support for fostering and maintaining urban vibrancy.

Spatio-temporal Dynamic Simulation of Land use and Ecological Risk in the Yangtze River Delta Urban Agglomeration,China

Rapid urbanization leads to dramatic changes in land use patterns,and the land use/cover change(LUCC)can reflect the spatial impact of urbanization on the ecological environment.Simulating the process of LUCC and predicting the ecological risk future changes can provide supports for urban ecological management.Taking the Yangtze River Delta Urban Agglomeration(YRDUA),China as the study area,four developmental scenarios were set on the basis of the land use data from 2005 to 2015.The temporal land use changes were predicted by the integration of the system dynamic and the future land use simulation(SD-FLUS)model,and the geographically weighted regression(GWR)model was used to identify the spatial heterogeneity and evolution characteristics between ecological risk index(ERI)and socio-economic driving forces.Results showed that:1)From 2005 to 2015,the expansion of construction land(7670.24 km^(2))mainly came from the occupation of cultivated land(7854.22 km2).The Kappa coefficient of the SD-FLUS model was 0.886,indicating that this model could be used to predict the future land use changes in the YRDUA.2)Gross domestic production(GDP)and population density(POP)showed a positive effect on the ERI,and the impact of POP exceeded that of GDP.The ERI showed the characteristics of zonal diffusion and a slight upward trend,and the high ecological risk region increased by 6.09%,with the largest increase.3)Under different developmental scenarios,the land use and ecological risk patterns varied.The construction land is increased by 5.76%,7.41%,5.25%and 6.06%,respectively.And the high ecological risk region accounted for 12.71%,15.06%,11.89%,and 12.94%,correspondingly.In Scenario D,the structure of land use and ecological risk pattern was better compared with other scenarios considering the needs of rapid economic and ecological protection.This study is helpful to understand the spatio-temporal pattern and demand of land use types,grasp the ecological security pattern of large-scale areas,and provide scientific basis for the territory development of urban agglomeration in the future.

Spatial Pattern and Influencing Factors of Regional Ecological Civilisation Construction in China

Ecological civilisation construction is a strategy for regional sustainable development based on a regional system of human-land relations. The comprehensive measurement and regional differentiation in construction levels are the key issues of ecological civilisation construction. This study aims to build 35 index systems that coalesce on four aspects: ecological economic adjustment and operation, ecological and social development and progress, ecological resources and environmental security, and ecological institutional and cultural awareness. We measured and evaluated the level of ecological civilisation construction of 329 cities(prefecture-level cities, autonomous prefectures and leagues) in 2018 using a comprehensive evaluation system and a spatial autocorrelation method to assess spatial differences in the level of ecological civilisation construction across China. This approach takes ‘the humanities-economic geography’ comprehensive perspective and uses a GWR(geographically weighted regression) model to analyse 10 influencing factors. Results show that: 1) the level of ecological construction can be divided into five types: higher, high, medium, low, and lower levels, according to the evaluation score. The five types are spindle-shaped in quantity and there is a significant imbalance in their spatial distribution, mainly trending from the southeast coast to the northwest. The land is decreasing, and the southern region is higher in level than the northern region. 2) The results of the spatial autocorrelation method show obvious spatial differences in ecological civilisation construction across China and that the level of ecological civilisation construction is positively autocorrelated. From east to west, the hot zone gradually transitions to a cold zone. A high-high type is mainly distributed in eastern coastal cities of China, and the number of high-low and low-high types are small. The low-low type is mainly distributed in the northwestern and northeastern regions. 3) The effect of influencing factors is heterogeneous in their spatial distribution, and the abundance of ecological resources is the most influential factor. According to the main influencing factors, each region should adhere to the principle of differentiation according to local conditions when choosing its ecological civilisation construction path and establishing an assessment mechanism. This study provides a scientific basis for enriching the regional level measurement of ecological civilisation construction, clarifying the current level of ecological civilisation construction in China, and implementing the regional differentiation path of ecological civilisation construction.

Spatiotemporal variation of ecological environment quality and extreme climate drivers on the Qinghai-Tibetan Plateau

Protecting the ecological security of the Qinghai-Tibet Plateau(QTP)is of great importance for global ecology and climate.Over the past few decades,climate extremes have posed a significant challenge to the ecological environment of the QTP.However,there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP,and few researchers have made quantitative analysis.Hereby,this paper proposed the Ecological Environmental Quality Index(EEQI)for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020,and explored the effects of climate extremes on EEQI based on Geographically and Temporally Weighted Regression(GTWR)model.The results showed that the ecological environment quality in QTP was poor in the west,but good in the east.Between 2000 and 2020,the area of EEQI variation was large(34.61%of the total area),but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level(EEQI change range of 0.05-0.1).The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations,which may be attributed to climate extremes.Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality.Specifically,the effects of daily temperature range(DTR),number of frost days(FD0),maximum 5-day precipitation(RX5day),and moderate precipitation days(R10)on ecological environment quality were positive in most regions.Furthermore,there were significant temporal differences in the effects of consecutive dry days(CDD),consecutive wet days(CWD),R10,and FD0 on ecological environment quality.These differences may be attributed to variances in ecological environment quality,climate extremes,and vegetation types across different regions.In conclusion,the impact of climate extremes on ecological environment quality exhibits complex patterns.These findings will assist managers in identifying changes in the ecological environment quality of the QTP and addressing the effects of climate extremes.