2008—2018年中国手足口病时空分异特征

手足口病是一种在婴幼儿中多发的常见传染病,对儿童的身体健康具有重要影响。为揭示我国手足口病的时空分异特征,为手足口病的防控提供科学依据,本文选取自2008年手足口病被列为丙类传染病以来至2018年中国手足口病发病率为研究数据,运用全局莫兰指数、Getis-Ord G_i ^*、新兴时空热点分析和标准差椭圆等空间统计方法对中国手足口病的整体和局部时空分布模式、特征和趋势进行了分析。结果显示：① 2008—2018年我国手足口病发病率呈现显著的空间聚集性,且聚集的强度显著增高;② 我国手足口病发病的热点区域主要集中在东南沿海地区,且较明显向内陆以及北部沿海地区扩张,而冷点区域主要集中在西北内陆以及东北地区;③ 我国手足口病的新兴时空热点模式以振荡的热点为主,新增热点主要出现在云南、重庆和四川,而冷点分布区域相对稳定,且冷点大部分以加强的、持续的模式出现;④ 手足口病高发病率区域在2008—2018年期间主要向西南方向移动,但在2008—2009年、2013—2014年以及2017—2018年存在向北部移动的趋势,同时2018年手足口病在东—西方向上的分布范围显著增大。总体来说,我国手足口病在南方地区高发。     

中国艾滋病空间格局和时空演化分析

探索艾滋病疫情的空间格局和时空演化特征,发现其分布和流行规律,对促进艾滋病防控工作具有重要意义。本文基于1997—2016年中国艾滋病省级发病率资料,利用空间自相关技术探测艾滋病疫情的空间格局,并使用重心轨迹迁移算法进行时空演化分析。结果表明：中国艾滋病疫情在省级尺度上具有较强的空间依赖性。1997—2016年,中国艾滋病疫情的全局空间关联程度从弱变强,而且存在进一步强化的趋势;疫情总体呈现“南重北轻,中部过渡”的空间格局,可将中国艾滋病发病率在局部发生空间自相关的区域划分为以内蒙为代表的北方疫情冷点片区和以广西为代表的南方疫情热点片区;中国艾滋病疫情的总体流行程度不断加深,且具有明显的地域差异性,在空间上表现为非均衡增长。因此,今后的艾滋防控工作必须在传统预防手段的基础上注重疫情扩散的时空规律,重点加强对疫情热点区域和高风险传播方向的管控,以达到区域协同、精准防控的目的。     

中国县域农村贫困的空间模拟分析

以中国县级行政区划为研究单元,从自然和社会经济因素中选取贫困的影响因子,建立评价指标体系,利用GIS空间分析和 BP人工神经网络,模拟各县域的自然致贫指数和社会经济消贫指数,并在分析贫困内在形成原因的基础上,明晰了空间贫困的分布特征。结果显示：自然因素是现阶段中国县域主要的致贫原因,全国县域自然致贫指数的分布呈现出明显随纬度和经度地带性分布的规律,自北而南、自西而东逐次呈带状排列分布。社会经济因素对贫困起到一定的缓解作用,全国县域社会经济消贫指数的空间分布较为破碎,各省区内部县域社会经济消贫指数的变异系数均大大高于自然致贫指数的变异系数。全国贫困压力指数以“黑河-百色”一线为界,东中西差异显著,呈现“大分散、小聚集”的空间分布格局。本文识别的贫困县与国家确定的重点扶贫县在空间上具有较高的重合性。     

基于BP神经网络的区域贫困空间特征研究——以武陵山连片特困区为例

随着国家新一轮区域发展和扶贫攻坚战略的实施,连片特困地区成为新时期扶贫开发工作的主战场。本文以武陵山连片特困区县级行政区划为例,从自然和社会因素中选取主要贫困影响因子,构建评价指标体系,利用GIS和BP神经网络,模拟区域自然致贫指数、社会经济消贫指数,分析贫困的内在成因,探究贫困的空间分布特征,旨在为扶贫开发政策的制定和区域协调发展提供辅助决策。结果表明,研究区自然因素是主要的致贫原因,而社会因素在一定程度上起到了缓解作用。大部分县的自然致贫程度在中等以上,其中,铜仁、湘西地区程度较为严重,绝大多数贫困地区的社会经济水平不高,缓解贫困的能力不强;黔江地区、张家界地区的贫困程度较低,铜仁地区和湘西地区的贫困程度较高。各县的贫困状况和贫困程度存在较大差异,古丈、龙川,务川、正安,隆回、新化及道通、城步共同构成武陵山片区"大分散、小聚集"的贫困分布格局。今后的扶贫开发过程中,应充分考虑自然致贫因素,深入挖掘区域资源优势,加强区域间的交流与协作。     

中国旅游产业科技创新能力的时空动态和驱动因素分析

从科技创新的基础、投入、产出和潜力4方面构建了旅游产业科技创新能力结构模型和综合评价指标体系,利用熵值法、线性加权法以及ArcGIS空间分析工具分析评价了2004、2008、2014年中国旅游产业科技创新能力的总体水平、时空动态演化及驱动因素。结果表明：① 2004-2014年,中国旅游产业科技创新能力总体上呈不断提高态势,但空间分布极不均衡,存在明显的地区差异,在趋势上基本表现出东西方向递增,南北方向倒“U”型分布态势;② 中国旅游产业科技创新能力在地理空间上存在着显著而稳定的集聚特征和一定的极化特征,毗邻的区域在旅游科技创新方面存在一定的空间外溢效应;③ 中国旅游产业科技创新能力热点区主要分布在北京、天津和少数东部沿海省份和中部省份,冷点区主要集中在中西部内陆地区的省份;④ 空间残差回归和地理加权回归研究表明,旅游产业基础、空间外溢效应、政策制度因素是驱动中国旅游产业科技创新能力时空变化3个核心因素。     

多尺度视角下城市住宅价格空间格局与影响机制研究

城市住宅价格的空间分异受到多重资源因素的影响,其影响机制在不同尺度上表现出的空间分异格局各不相同.以2015—2018年16个季度成都市中心城区商品住宅小区住宅价格为基本数据,利用空间自相关分析法、多尺度视角分析法探索成都市住宅价格空间分异格局,利用地理加权回归模型(GWR)构建成都市住宅价格特征变量指标体系,分析导致住宅价格空间差异的影响机制,研究发现:在2015—2018年16个季度中,成都市住宅价格先持续上升后出现小幅下降,中心城区各区住宅价格空间自相关性高,表明近年来住宅价格空间邻近联动性强.500 m×500 m栅格尺度下成都市中心城区住宅价格分布不均衡,住宅价格总体呈现中心区域高、边缘区域低分布格局,且南北住宅价格存在一定差异,中心城区住宅价格局部呈现"多中心+边缘化"的空间分布格局.多尺度视角分析法不仅能够考虑尺度效应及分区效应对住宅价格空间格局的影响,而且能够定量分析多资源因素对住宅价格的影响机制,解析多资源因素指标对住宅价格空间分异产生的影响.     

黄河流域山东段县域绿色发展效率时空分异及影响因素研究

基于DEASBM模型,运用空间自相关分析和地理探测器软件研究黄河流域山东段县域绿色发展效率的时空分异特征及影响因素。结果表明：2010—2020年黄河流域山东段整体绿色发展效率呈现下降趋势,从最优下降至中等。从时序特征来看,黄河流域山东段县域绿色发展效率变化差异显著,可分为持续降低、持续增长、稳定不变、先升后降、先降后升5种变化类型。从空间分布来看,2010年县域绿色发展效率空间分布呈随机态势,2015年和2020年空间集聚特征较为显著。高—高集聚区与热点区分布具有一致性,低—低集聚区与冷点区分布具有一致性,均主要分布在济南都市圈,且面积不断增大,济南市都市圈县域间绿色发展效率高低分化显著。黄河流域山东段县域绿色发展效率影响因子交互作用呈非线性增强效应。     

时空统计学在贫困研究中的应用及展望

消除贫困是人类社会的共同目标.贫困分布具有明显的空间特征,同时呈现出空间异质性和空间相关性.时空统计学以时空分析为优势,在贫困的时空分布及形成机制研究中发挥了重要作用.本文综述了不同时期我国贫困分布的空间特征、贫困数据的空间类型和特征以及贫困时空分布的影响因素,并总结了时空统计学方法在贫困空间研究中的4类应用,包括:探...     

基于地理神经网络加权回归的中国PM2.5浓度空间分布估算方法

中国空气污染问题日益严重,为获得连续的PM_2.5浓度空间分布,现有研究建立了多种基于统计回归的PM_2.5估算模型。然而,由于PM_2.5回归关系显著的空间非平稳性和复杂的非线性特征,如何实现高精度、高合理性的PM_2.5浓度空间大面估计仍然面临挑战,尤其在地形变化复杂、覆盖范围广阔的中国地区更为突出。本文引入了一种将普通线性回归（OLR）和神经网络结合的地理神经网络加权回归（GNNWR）模型,通过集成遥感数据、气象数据和地理信息数据建立了基于GNNWR的PM_2.5浓度空间估算方法。文章以中国2017年PM_2.5年平均浓度估算为例,开展了该模型与OLR、地理加权回归（GWR）的比较实验。实验结果表明,基于GNNWR的PM_2.浓度估算性能指标均明显优于OLR和GWR,且预测精度显著高于GWR。此外,GNNWR获得的PM_2.5浓度空间分布也更为合理,较为细致地刻画了中国地区PM_2.5浓度的局部空间变化和细节层次。     

县域尺度下湖南省碳排放空间分异特征与影响因素

开展县域尺度下的碳排放空间分异与影响因素研究,是助推实现县域等多尺度区域“双碳”目标的重要环节。以湖南省122个县(市、区)为研究对象,通过夜间灯光数据估算湖南省各区县的碳排放量,采用探索性空间数据分析方法刻画了县域碳排放时空格局,运用地理探测器和时空地理加权回归(GTWR)模型,从政策、经济、能源、社会和产业维度对县域碳排放空间分异与影响因素进行了定量研究。结果表明：(1)2012～2020年,湖南省县域碳排放总体呈减弱趋势,且差异较为明显,空间格局分布为北高南低,东西差距较小;(2)莫兰指数(Moran’s I)逐年下降,空间集聚特征较为显著,总体成正相关关系;(3)财政支出、人口规模、城镇化率、能源碳排放强度和农业发展水平是影响县域碳排放的主导因子;(4)通过时空地理加权回归模型对主导因子进行分析,发现同一指标对不同区县碳排放的影响存在显著的时空差异。     

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.     

Regional Equity and Influencing Factor of Social Assistance in China

Social assistance is the last safety net in the social security system and plays a vital role in poverty alleviation in countries around the world. Promoting the equal financial assistance is meaningful to achieve equalization of social assistance. Based on the provincial panel data from 2002 to 2017, this paper analyzes the dynamic characteristics and main influencing factors of the equity of social assistance in China, using the Theil index and geographically weighted regression(GWR) model. The results suggest that the level of per capita social assistance expenditure(PSAE) in China keeps increasing year by year, but the changes in different regions and provinces are quite different. These changes not only significantly changed the spatial pattern of PSAE in China, but also greatly improved its spatial coupling with the deeply impoverished areas. Further analysis shows that the regional inequality of PSAE between provinces is obvious during the study period, and the inter-regional inequality is significantly higher than the intra-regional inequality.This makes inter-regional inequality become the main source of the regional inequality of PSAE in China for a long time. According to GWR results, there is obvious spatiotemporal heterogeneity in the influence intensity and direction of the per capita financial revenue,urbanization rate, urban unemployment rate, natural disaster-affected area, and transfer payment intensity on the PSAE. The urbanization rate and per capita financial revenue are the main driving factors of PSAE, and the impact intensity of per capita financial revenue tends to strengthen. The remaining three factors have a positive effect on PSAE, but the effect intensity is not high.     

Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison

Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity(EC) estimation systematically. Additionally, the performance of Multiple Linear Regression(MLR), Geographically Weighted Regression(GWR), and Random Forest regression(RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.     

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.     

Measurement of self-development capacity in the contiguous destitute mountainous areas,China

Affected by the mountainous location and ecological vulnerability, the incidence of poverty in contiguous destitute mountainous areas is higher than that in other regions of China. Regional selfdevelopment capacity is an internal driving force for poverty reduction in contiguous destitute mountainous areas. This study selects 17 indicators from the four dimensions: industrial capacity, market capacity, spatial capacity and soft power to measure the overall self-development capacity of 658 counties in 14 different mountain areas in China. The results show that self-development capacity is at a low level and the development of the different regions is unbalanced. The self-development capacity is low in the southwest and high in the east and "low in the middle, high around" in each study region. It has achieved a certain degree of improvement in 2011, 2013 and 2015. From the perspective of the four dimensions, industrial capacity is the constraint on the promotion of self-development capacity. Based on this, we should develop green industries in line with local realities and achieve industrial poverty eradication, but the expansion of space capacity should take into account the resources and environmental carrying capacity in these areas and should not be blindly expanded. Local leaders should be made to improve the local education level and public service level, improve social infrastructure, develop reserve strength for the future, and enhance future development potential.     

东北地区TRMM数据降尺度的GWR模型分析

利用东北地区2000-2010年93个气象站点观测数据作为“真实值”,对TRMM降水数据进行精度验证,发现研究区TRMM降水数据与观测数据之间具有明显的线性相关性,且TRMM降水数据数值偏大于观测值,表明TRMM降水数据在东北地区具有一定的可信度。对东北地区多年平均、2001、2010年的TRMM数据,进行GWR模型降尺度研究,得到1 km的新降水数据,并与全局OLS回归模型进行对比。结果表明：（1）相比全局OLS回归模型,GWR模型的降尺度结果可获得更好的R与RMSE,说明GWR模型更适用于东北地区TRMM数据的降尺度研究;（2）东北地区GWR模型的降尺度分析结果与观测数据之间的相关系数在0.44-0.97之间,且分布较分散;（3）经过降尺度的TRMM降水数据,在空间分辨率上有较大提高,能更真实地反映研究区的降水特征,为该数据小尺度的应用研究奠定基础。     

武汉市中心城区住宅价格空间分布格局及其影响因素研究

利用网页爬虫技术从安居客网站抓取了武汉市中心城区2016年7月共3 188个小区的平均房价数据。运用GIS空间分析方法和GWR模型对武汉市中心城区房价空间分布模式及其影响因素进行探讨。研究发现武汉市中心城区的房价具有聚类分布特征;新盘房价与片区位置(汉口、武昌、汉阳)、环线位置(内环、二环、中环)、距湖泊距离、距商圈距离、小区附近学校个数显著相关;楚河汉街商圈对二手房房价的影响力度高于其他商圈,东湖和沙湖对二手房房价的影响效应也很显著。     

Land use intensity dynamics in the Andhikhola watershed,middle hill of Nepal

Land use intensity is a valuable concept to understand integrated land use system, which is unlike the traditional approach of analysis that often examines one or a few aspects of land use disregarding multidimensionality of the intensification process in the complex land system. Land use intensity is based on an integrative conceptual framework focusing on both inputs to and outputs from the land. Geographers' non-stationary data-analysis technique is very suitable for most of the spatial data analysis. Our study was carried out in the northeast part of the Andhikhola watershed lying in the Middle Hills of Nepal, where over the last two decades, heavy loss of labor due to outmigration of rural farmers and increasing urbanization in the relatively easy accessible lowland areas has caused agricultural land abandonment. Our intention in this study was to ascertain factors of spatial pattern of intensity dynamism between human and nature relationships in the integrated traditional agricultural system. High resolution aerial photo and multispectral satellite image were used to derive data on land use and land cover. In addition, field verification, information collected from the field and census report were other data sources. Explanatory variables were derived from those digital and analogue data. Ordinary Least Square(OLS) technique was used for filtering of the variables. Geographically Weighted Regression(GWR) model was used to identify major determining factors of land use intensity dynamics. Moran's I technique was used for model validation. GWR model was executed to identify the strength of explanatory variables explaining change of land use intensity. Accordingly, 10 variables were identified having the greatest strength to explain land use intensity change in the study area, of which physical variables such as slope gradient, temperature and solar radiation revealed the highest strength followed by variables of accessibility and natural resource. Depopulation in recent decades has been a major driver of land use intensity change but spatial variability of land use intensity was highly controlled by physical suitability, accessibility and availability of natural resources.     

基于土地利用类型的村级人口空间分布模拟——以湖北鹤峰县为例

在人口分布及其相关研究中,常常会遇到小尺度人口数据部分缺失的问题。本文以湖北省鹤峰县为例,在分析土地利用与人口分布关系的基础上,从全局与局部、线性回归与非线性回归考虑,基于土地利用类型,分别利用地理加权回归(GWR)方法、格网方法、BP神经网络方法对缺失数据的行政村人口数据进行模拟,并进行了多角度精度对比验证。研究结果表明:(1)各种土地利用类型中,耕地、林地、城镇村及工矿用地、交通用地是影响研究区村级人口分布的主要因素;(2)30个调查村中,3种方法模拟的人口总数误差小于3%,通过每个村的模拟值与实际值相比,BP神经网络方法能更好地模拟研究区村级人口的分布,格网方法次之,GWR方法最差;(3)研究区各村人口分布呈现较高的空间正相关性,各乡镇的人口密度在空间上并不独立,而是呈现紧密的集聚特征。