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.

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.

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.

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.

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.

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.

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.

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。

一类变系数空间滞后的混合地理加权回归模型

为解决因变量空间滞后存在的局域性问题,对现有常系数空间滞后的混合地理加权回归模型作了具有更广泛形式的拓展,提出一类变系数空间滞后的混合地理加权回归(MGWR-VSLR)模型。MGWR-VSLR模型实现了空间相关性与空间异质性融合,涵盖了绝大多数地理加权回归的模型形式,基于重构参数化方法和似然比检验分别给出模型的系数估计方法与显著性检验以及选取变系数的判别检验。在蒙特卡罗模拟与实际应用中,MGWR-VSLR模型均表现出优异的因变量拟合与预测能力。MGWR-VSLR模型的提出为定量化研究空间效应问题设定适宜的模型形式提供了支撑依据。

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。

长三角城市群A级旅游景区时空异质性研究

城市群A级旅游景区演化研究对促进区域旅游业平衡充分发展具有重要价值.基于目的地多尺度、景区多等级及空间非稳定现实,运用多种空间分析方法,对长三角城市群A级旅游景区及影响因素的时空异质性进行研究.结果表明:(1)2001-2021年,3A、4A是长三角城市群A级旅游景区结构主体;上海、杭州、湖州、南京、安庆、金华、温州和台州的各级景区发展均较好.(2)长三角城市群A级旅游景区空间分布集聚性显著,逐渐形成“几”字形高密度景区带结构;各等级景区辐射范围不断扩大,形成东南-西北方向分布格局;城市尺度上,各等级景区都趋于均匀分布.(3)至2019年,影响A级旅游景区空间分布的因素包括旅游资源、旅游需求、旅游地交通及社会经济条件.各个因素的影响程度及其在空间上的异质性均随时间变化而变化.

湖南省城镇土地利用多样性对人口密度的影响研究

城镇人口密度降低已经成为制约部分城镇发展的重要因素之一。选取湖南省为案例区,在定量刻画湖南省城镇人口密度与土地利用多样性格局基础上,运用地理加权回归模型探索两者的关系,并构建压力-状态-响应模型探索湖南省城镇土地利用多样性对人口密度的影响路径。结果表明:①湖南省城镇人口密度、城镇建设用地土地利用多样性均存在明显的空间正相关性,但聚集趋势相反;②单一化的城镇建设用地利用方式既不利于湖南省城镇土地利用多样性的提升,也不利于湖南省城镇人口密度的提升,工业布局可以引导湖南省城镇人口密度的提升,注重经济发展、提高城镇公共服务能力是提高城镇土地利用多样性进而提高城镇人口密度的有效方法;③湖南省二、三产业增加值与城镇人口密度呈现弱负相关且呈现“南高北低”的格局;④湖南省城镇居民可支配收入与城镇人口密度的相关性呈现出“西北高、东南低”的格局,并在长沙市中心城区一带出现正相关与负相关的分界。最后,从优化城镇职能体系对应对历史遗留问题、促进人口密度有序提升方面提出了政策建议。

中国茶叶生产格局变迁及其影响因素研究

基于1985—2021年中国茶叶生产面板数据,采用GIS空间分析、多尺度地理加权回归模型探究中国茶叶生产格局的时空演变特征及影响因素。结果表明:(1)1985—2021年,中国茶叶生产规模不断扩大,生产重心持续“西移”,空间集聚存在显著正向规模效应,江南、华南和西南茶区聚集特征以“高高”型和“低高”型聚集为主,江北茶区未出现集聚特征,空间极化现象突出。(2)不同时期影响中国茶叶生产格局变迁的主导因素存在显著差异。1995年以有效灌溉面积和化肥投入量为主,2010年降水量、日照时数、受灾面积的影响程度逐步凸显,2020年后交通运输水平成为显著驱动因素,但是有效灌溉始终是影响茶叶生产格局变迁的主要驱动力。