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.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

Association between Macroscopic-factors and Identified HIV/AIDS Cases among Injecting Drug Users: An Analysis Using Geographically Weighted Regression Model

Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.

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.

基于GTWR模型的济南都市圈生态系统服务价值对城市扩张时空响应

【目的】随着我国城镇化发展进入到以中心城市引领都市圈、城市群的发展阶段,如何促进都市圈城镇化与生态环境协调发展成为高质量城镇发展的重要议题。生态系统服务价值对城市扩张的时空响应研究有助于把脉城市发展与生态系统服务的时空演进特征,推动城市与生态系统的协同发展,助力可持续规划以及建设策略的拟定与实施。【方法】本文以济南都市圈为研究对象,基于城镇扩展指数的计算,定量描述各城市扩张的时空特征。采用生态系统服务当量因子法,从多个角度刻画研究区生态系统服务的时空分异特征,并分析生态系统权衡与协同效应。在此基础上,运用时空地理加权回归(GTWR)模型,探究城市扩张对生态系统服务功能变化的驱动方向与驱动强度。【结果】(1)1980—2020年间济南都市圈内城市扩张显著,具有时序阶段性与区域分异性两方面特征,城市空间扩展速率与强度由高到低依次为小城市、大城市、特大城市、中等城市。(2)都市圈内整体生态服务价值量呈逐年下降趋势,黄河干流、东平湖及周边区域与鲁中山区等地区是重要的生态系统服务价值高值聚集区,都市圈内协同关系占比略低于权衡关系,其中特大城市协同关系占比最高。(3)济南都市圈内城市扩张对生态系统服务价值整体具有负面影响,随着时间的推进,影响强度有所下降。城市扩张对各亚类生态系统服务功能的影响作用具有显著差异,对供给服务与支持服务价值量变化具有负面影响,其中对供给服务变化的驱动强度不断增强,对调节服务价值量变化具有正向作用且影响力整体变化不大,对文化服务价值量变化的影响具有两面性,在不同地区的驱动方向与强度差异性较大。【结论】本研究明确了研究期限内济南都市圈中不同等级城市空间扩展的时空分异规律以及生态系统服务逐渐劣化的发展状态,所构建的GTWR模型在空间层面上量化了城市扩张对生态系统服务总量及各亚类变化量的不同驱动特征与驱动强度,研究成果可为都市圈高质量可持续发展提供决策依据。

基于GTWR模型的安徽省气候对水稻生产力影响的时空分布规律

以气温和降水量作为气候变化的2个因素,以安徽省为研究区域,基于2001—2020年气温、降水量及水稻产量数据,使用时空地理加权回归(GTWR)模型分析气温与降水量2个因素对水稻产量的作用机制。结果表明,2001—2020年,安徽省各市水稻年平均产量在时间上出现持续波动的现象,在空间上也存在特定的集聚现象;安徽省西北部地区气温、降水量与水稻产量呈正相关关系,其中蚌埠市正相关关系最为显著;在安徽省所有城市中,淮南市和六安市的水稻产量受气温和降水量影响最为明显,而淮北市水稻产量受气温和降水量的影响相对较小,说明该地区其他因素对水稻产量具有更深影响。

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.

基于GTWR的站域建成环境对城市轨道交通客流量的时空影响

轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点属性3个方面因素在时间和空间维度上对天津市轨道交通客流量的影响。结果表明:相较于传统的地理加权(geographically weighted regression,GWR)模型和最小二乘法(ordinary least squares,OLS)模型,GTWR具有更好的拟合优度;公交站点密度对轨道交通客流产生促进作用,尤其在工作日的早晚高峰时段和中心城区位置;市中心的商业设施在工作日晚高峰吸引更多的地铁乘客,而在近郊区它们在早高峰吸引更多的地铁乘客;人口密度促进轨道交通的客流量;充足的停车场设施数量可以吸引更多的轨道交通乘客。

Spatial non-stationary characteristics between temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass,and their accuracy has important practical significance for the stable management of grasses and livestock.To this end,the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China(August 2020),combined with hydrometeorology,elevation,net primary productivity(NPP),and other auxiliary data over the same period.Accordingly,non-stationary characteristics of the spatial scale,and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression(MGWR)model.The results showed that the model was suitable for correlation analysis.The spatial scale of ratio resident-area index(PRI)was the largest,followed by the digital elevation model,NPP,distance from gully,distance from river,average July rainfall,and daily temperature range;whereas the spatial scales of night light,distance from roads,and relative humidity(RH)were the most limited.All influencing factors maintained positive and negative effects on grass yield,save for the strictly negative effect of RH.The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield.Regression parameters revealed that the results of Ordinary least squares(OLS)(Adjusted R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=0.889)models.Based on the results of the RMSE and radius index,the simulation effect also was MGWR>GWR>OLS models.Ultimately,the MGWR model held the strongest prediction performance(R^(2)=0.8306).Spatially,the grass yield was high in the south and west,and low in the north and east of the study area.The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.

基于GTWR的广东省乡村聚落规模时空演变研究

乡村聚落在聚落体系中具有重要地位,但长期以来缺乏对乡村聚落规模时空演变内在机理的深入研究,导致乡村聚落的规划、建设和管理缺乏有效的理论指导。本研究基于遥感影像获取1980年、1995年、2005年、2015年4期广东省乡村聚落空间分布数据,以乡村聚落斑块为单元,使用时空地理加权回归模型,揭示乡村聚落规模演变影响因素的时空差异,总结影响机制的阶段性规律。研究结论如下:(1)广东省乡村聚落规模演变呈现出增加–平稳–减少的趋势,35 a间总规模略有减少但区域结构性差异明显。珠三角地区乡村聚落演化与全省趋势一致;粤东地区原有乡村聚落不断扩张导致总规模持续上升,但趋势逐渐减弱;粤西北乡村聚落以部分聚落原地扩张和部分聚落日渐收缩并行为主,新增乡村聚落和乡村聚落城市化幅度均不高。(2)各影响因素对乡村聚落规模演变的作用力存在明显时空差异,地形条件、路网密度、县域人均GDP和基期聚落面积对乡村聚落规模的影响普遍呈现“倒U型”趋势,其它自然环境本底、区位可达性、社会经济因素的驱动方向稳定,但影响力随阶段变化。(3) 35 a间广东省经历了城乡分割、城乡冲击和城乡融合3个发展阶段,各阶段乡村聚落规模演变的动力呈现出显著差别,在城乡分割阶段,乡村聚落发展存在明显的路径依赖;而在城乡冲击阶段,社会经济发展水平高、自然条件优越、交通基础设施建设完备的聚落更易发生城市化;在城乡融合阶段,自然环境本底的影响逐渐减弱,而区位可达性的重要性逐渐凸显。

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.

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.

基于GTWR模型的中国城乡收入差距时空分异及影响因素分析

在掌握省域尺度下我国城乡收入差距时空演变特征及影响因素的基础上,探究不同地区差异化问题解决路径,有助于促进我国城乡协调发展,从而实现共同富裕。文章基于2014—2020年31个省份城乡居民收入比数据,通过构建城乡收入差距指标,从空间自相关性中考察了中国城乡收入差距的空间格局;运用时空地理加权回归模型(GTWR),探讨了自然地理因素与社会经济因素对城乡收入差距的影响及空间差异。研究结果表明:中国东西部省份在城乡收入差距方面的差异巨大,形成西高东低的空间结构;受自然地理因素与社会经济因素的影响,中国城乡收入差距存在显著区域差异。

京津冀地区PM_(2.5)与CO_(2)的协同控制效应及调控因素研究

京津冀地区面临着二氧化碳(CO_(2))减排和细颗粒物(PM_(2.5))浓度降低的双重挑战,研究该地区CO_(2)与PM_(2.5)的协同控制效应及调控因素演变特征,对其经济高质量发展与环境改善具有重要意义。文章基于2005-2020年人口栅格数据、夜间灯光数据以及统计年鉴数据,利用协同控制效应坐标系法和时空地理加权回归模型评估了京津冀地区CO_(2)排放量和PM_(2.5)浓度的协同控制效应及调控因素作用机制的时空异质性。结果表明:(1)京津冀地区实现CO_(2)和PM_(2.5)协同控制效应的区域呈现上升-下降-上升的变化趋势,在2019年达到最高,最高占比68.15%。(2)第三产业发达、清洁能源丰富或产业布局合理的城市更易实现CO_(2)排放量与PM_(2.5)浓度的共控。(3)产业结构、研发支出、对外开放程度和降雨量为京津冀地区协同控制CO_(2)排放量和PM_(2.5)浓度的主要因素,但这些调控因素的作用存在时空异质性。

可达性对城市群多模式交通碳排放的空间异质性影响

当前,在交通行业碳减排存在巨大挑战以及未来中国城市群交通发展长远规划双重背景下,如何通过改善交通可达性提高居民出行效率和减少碳排放是亟待解决的关键问题之一。本文基于城际出行手机信令数据,从居民出行的角度提出城际多模式交通客运碳排放量方法,并采用梯度提升决策树(GBDT)模型及多尺度地理加权回归(MGWR)模型探讨可达性对区域碳排放量的空间异质性影响。以关中平原城市群为例进行验证,结果表明:城际公路客运碳排放量远大于铁路,呈现沿交通基础设施线路分布的特征;在整体区域范围内,可达性指标对碳排放水平具有一定的正向边际效应;MGWR能够刻画碳排放与可达性指标关系的空间异质性及尺度差异;经济潜能可达性、介数中心性及接近中心性对城际碳排放具有显著的正向空间异质性影响,但影响尺度不同;公路客运碳排放对介数中心性及接近中心性要素较为敏感,经济潜能对碳排放的影响较为平稳;铁路出行可达性的提升对中心城市的影响效应低于周边区县城市。

长江经济带居民共同富裕与旅游发展水平耦合关系及影响因素研究

实现居民共同富裕是中国式现代化的本质要求,旅游业作为幸福产业,如何促进居民共同富裕与旅游发展水平协调共进成为当前研究的重点问题.该文以长江经济带为研究区域,构建了居民共同富裕与旅游发展水平指标体系,并基于耦合协调模型和时空地理加权回归模型(GTWR),考察分析2011—2020年居民共同富裕与旅游发展水平关系及其影响因素.结果发现:1)长江经济带旅游发展水平研究期内整体呈波动上升趋势,2020年因受疫情影响而出现跌落,居民共同富裕水平则呈整体逐年上升趋势;两者均呈现出显著空间异质性,但中上游地区旅游发展水平相近.2)居民共同富裕与旅游发展水平耦合协调水平呈波动上升趋势,重庆和贵州两地上升较快,2020年各省市受到疫情影响有所下降;下游地区高于其他地区.3) GTWR模型表明,人口密度、环境治理投入、旅游收入占比、人均GDP因素存在明显的时空差异,不同时期各因素对地区影响强度和波动方向不同,其中人均GDP影响为正向且影响作用较强.

黄河流域陆地生态系统碳储量测算及其影响因素

准确估算陆地生态系统碳储量,科学制定生态环境保护和土地利用政策,对促进区域低碳可持续发展,实现“碳中和”目标具有重要意义。基于大量碳密度样点数据,将其与生态地理分区和土地利用类型图空间叠置,采用克里金插值法得到黄河流域碳密度空间分布数据集。应用InVEST模型对2000年、2010年和2020年黄河流域陆地生态系统碳储量的时空演变测度,提高了碳储量估算结果的准确性。利用Pearson相关性分析和多尺度地理加权回归模型(MGWR)对自然、社会经济和景观格局指数等因素对县级行政单元尺度单位面积碳储量的影响进行分析。主要结论如下:(1)黄河流域碳密度空间分布呈西部大于东部、东部地区自东南向西北递减的格局;(2)2000—2020年黄河流域陆地生态系统碳储量增加0.02%(7.011×10~9—7.012×10~9t),空间分布与碳密度相同,空间集聚特征显著,“高高集聚区”主要分布在黄河上游西南部的青藏高原地区,“低低集聚区”主要分布在黄河上游北部和黄河下游大部分地区;(3)Pearson相关性分析得出与碳储量呈正相关的影响因素为Pr(降水)、NDVI(归一化植被指数)和Slope(坡度);呈负相关的影响因素为TEM(温度)、HAI(人类影响指数)、SHDI(香农多样性指数)、DN(夜间灯光数据像素值)和PPOD(人口密度)。(4)MGWR模型得出TEM、Pr、NDVI和SHDI空间异质性强,HAI在2010年后异质性强;Slope空间异质性中等;DN和PPOD为全局尺度变量,空间影响平稳;(5)MGWR模型得出NDVI对黄河流域县级单位面积碳储量作用强度最大。NDVI、Slope对县级单位面积碳储量的影响呈正效应,TEM、HAI、DN和PPOD呈负效应,Pr、SHDI呈正、负双向效应。

基于“潜力⁃弹性⁃稳定性”模型的温州市生态韧性时空变化及影响因素研究

城市生态韧性为城市应对长期发展积累的内在压力,以及外界不确定性风险的冲击提供了新思路,对城市的可持续发展具有重要意义。基于城市生态学视角,从生态韧性的抵御力、恢复力及适应力三个方面特性,构建基于“潜力⁃弹性⁃稳定性”的生态韧性评价模型,分析了1990—2020年温州市生态韧性时空变化特征,并运用时空地理加权回归模型(GTWR),探究了生态韧性影响因素的时空异质性。结果表明:(1)1990—2020年,温州城市生态韧性指数总体呈现先上升后下降的趋势,潜力、弹性、稳定性呈现出相似的变化趋势,研究后期稳定性的提升使生态韧性的下降得到短暂缓解。(2)温州市东部沿海地区生态韧性较差,西部与北部山地生态韧性较好;低韧性水平区呈现出区域中心韧性水平降低,并且向外围扩张的趋势。(3)温州市城市生态韧性影响因素之间存在显著的时空差异,空间上也存在波动方向与强度的差异,这种差异性集中分布于城市边缘县(区)。研究以期为温州市及沿海同等级城市,提升城市生态韧性、促进区域可持续发展决策提供参考。

太行山复杂地形下华北暖季极端降水的时空分布特征

基于2012—2021年5—9月华北五省的逐日降水资料和台站地形高度数据,统计分析了华北全区及各子区域极端降水事件的降水量及其强度和频次的时空分布特征;并运用地理加权回归(GWR)模型分析得到极端降水事件的降水量、强度及频次与海拔高度之间的关系。结果表明:1)华北区域极端降水量的时间变化均呈多波动特征且区域差异性显著,太行山以西高原和以东平原降水频次多、波动明显且强度较弱,太行山南段以南平原降水频次少、变化平缓而强度明显偏强。2)极端降水量的空间分布呈现南北少、中间多的型态分布,降水量大值区分别位于燕山东南侧和太行山南段晋冀豫三省交界处;极端降水高频站点主要聚集在晋东南地区;日最大降水量超过300 mm的站点主要集中在太行山脉和燕山山脉与华北平原的过渡地带。3)华北区域38°N以北,极端降水量、降水频次、强度和日最大降水量均随海拔高度的升高而减小;38°N以南,山西南部临运地区降水量随海拔高度的升高而显著增加。由于降水频次和强度与地形均存在正相关而导致,太行山附近降水量随海拔高度的升高而减小的贡献主要在于降水强度而非降水频次。