中东地区恐怖主义指数时空演变及其影响因素的时空异质性

恐怖主义指数是评判恐怖主义风险的重要指标,剖析中东地区恐怖主义指数时空演变及其影响因素的时空异质性,对推理与解析恐怖主义发展趋势及其成因的时空分异具有重要意义。基于GTD数据库,计算恐怖主义指数,对中东地区各国恐怖主义风险进行量化评估,并结合统计指标进一步分析该地区恐怖主义指数的时空演变特征,采用OLS、GWR和GTWR模型解析诸多因素对该地区恐怖主义指数的作用模式。研究表明：（1）1995年至2016年期间,中东地区大部分国家恐怖主义风险逐步提高,且恐怖主义风险表现出一定的空间扩散趋势。（2）相比于OLS模型及GWR模型,GTWR模型解释度及拟合效果均明显增高,说明时空效应对各类因素均存在一定的影响,即各影响因素对中东地区恐怖主义指数均表现为时空非平稳作用模式。（3）通过综合分析各模型回归结果,民族宗教因素为导致中东地区恐怖主义指数发生时空变化的主导因素。     

基于地理加权和最小二乘线性回归模型的气温与NDVI聚集密度的相关分析

基于2015年广州地区1 km空间分辨率的MOD13A 3月合成NDVI数据以及春夏秋冬4个季节的气象站点近地表气温,首先利用聚集密度计算方法计算NDVI的聚集密度,构建不同季节近地表气温与NDVI聚集密度的最小二乘线性回归模型(OLS)和地理加权回归模型(GWR),分析广州市近地表气温与NDVI聚集密度的相关关系,探讨不同季节NDVI聚集密度回归系数的空间分布,并利用AICc信息准则、拟合优度和Sigma指标对GWR与OLS的结果进行比较分析。结果表明:NDVI聚集密度较好地反映了研究区建设用地、植被和水体等下垫面的综合信息;与OLS模型相比,GWR模型的拟合效果更显著,最小的拟合度从0.02提高到0.464,GWR模型的拟合度最大值达到了0.724;GWR模型回归残差的Moran’s I显著减少,如1月份Moran’s I指数从0.383减少到0.022;NDVI聚集密度对气温的影响具有空间异质性,整体上,从广州北到南,GWR模型中NDVI聚集密度与气温的回归系数由负值逐渐增加到正值,表明NDVI聚集密度对气温有着从负到正的影响;下垫面以不透水面为主的区域,GWR模型拟合度较低,以植被为主要下垫面的区域,GWR模型拟合度较高。     

东北地区旅游经济影响因素时空特征研究

从中观层面以东北地区41个市域为分析单元,选择东北振兴战略实施以来2004、2009和2015年截面数据,采用ESDA法分析旅游经济发展的空间关联特征,运用OLS和GWR模型分析旅游经济和旅游产业因子、消费因子、投资因子之间的关系,以此挖掘旅游经济影响因素的时空结构信息。结果表明：① 旅游经济发展呈显著空间正相关,相关性逐渐增大;② OLS回归结果表明,旅游产业因子对旅游经济发展的影响强度最大,在旅游产业发展中始终起基础性决定作用,其次为消费因子和投资因子,后两者差别不大;③ GWR回归结果显示,模型3 a拟合优度均比OLS有所提高,回归系数均为正值,但分布规律不同;旅游产业因子回归系数高值区经历了西南部-中南部-东北部转移,向外围圈层递减;消费因子回归系数高值区由东北部向东南部转移,向外围逐渐递减;投资因子回归系数高值区则由东北部-中南部-东北部转移,向外围逐渐递减。     

河南省人口城镇化特征及影响因素的空间异质性

采用变异系数、空间自相关和地理加权回归(GWR)等方法,分析了欠发达农区河南省1991年、2000年和2010年县域城镇化的时空动态变化,并对影响因素的空间异质性进行了分析。结果表明:河南省的城镇化初始呈低层次均衡,局部高值县区日趋集聚,总体差异程度减弱,城镇化率显著提高的县区主要分布在京广线以西,县区城镇化呈全局弱正自相关。分别采取OLS和GWR模型对社会经济发展水平、区位条件和产业结构等方面的8个影响因子进行分析,OLS和GWR分析显示第一和第二产业产值比重对所有县区城镇化为负效应,人口密度对所有县区为正效应,而GWR分析结果为变参数,因子的系数有正有负,表明各因素的影响效应存在显著的空间差异。     

基于地理加权回归的漫湾库区景观破碎化及影响因子分析

应用地理加权回归模型分析漫湾库区景观破碎化指数——有效筛网大小与相关因子之间的空间关系。选取的解释变量分别是距道路的距离、距乡村的距离、距河流的距离、坡度。结果表明：大坝修建后4种解释变量与有效筛网大小呈现较显著的正相关性。与线性回归模型相比,地理加权回归模型的拟合效果显著提高。1974~1988年,有效筛网大小对各影响因子最敏感的区域面积呈现显著的时空变化这为确定水电站建设及其他因素对景观破碎化影响的大小,并进一步改善库区景观破碎化的现状提供了依据。     

GWR模型在土壤重金属高光谱预测中的应用

目前土壤重金属高光谱反演模型大多忽视了重金属与光谱变量间相关关系的空间异质性,这与实际情况不相吻合,而地理权重回归（GWR）模型能有效地揭示变量间关系的空间异质性。本文以福州市土壤重金属Cd、Cu、Pb、Cr、Zn、Ni为对象,构建土壤重金属预测的GWR高光谱模型,并将预测结果与普通最小二乘法回归（OLS）结果进行比较分析,探讨GWR模型在土壤重金属高光谱预测中的适用性及局限性。结果表明：① GWR模型在土壤重金属高光谱预测中适用与否取决于重金属对光谱变量影响的空间异质性程度：对于Cr、Cu、Zn、Pb等对光谱变量影响空间异质性大的元素,其GWR预测精度较OLS提高明显,表现为GWR模型的调节R²较OLS模型有了明显提高,分别为OLS模型的2.69倍、2.01倍、1.87倍和1.53倍;而AIC值以及残差平方和较OLS模型却明显降低,AIC值减少量均大于3个单位,残差平方和则仅分别为OLS模型的25.33%、30.09%、47.22%和86.84%;对于Cd和Ni等对光谱变量影响空间异质性小的元素,相较于OLS模型,GWR模型的调节R²分别提高了0.015和0.007,残差平方和分别减少了5.97%和4.18%,但AIC值却分别增加了2.737和2.762,GWR预测效果改善不明显;② 光谱变换可以有效增强土壤重金属的光谱特征,其中以光谱的倒数变换效果最好,而且该变换及其微分形式可以很好地提高模型的预测效果;③ GWR模型的应用前提是变量间关系的空间非平稳性,适合在与土壤光谱变量间关系具有显著空间异质性的重金属高光谱预测中推广。     

地理加权回归在区域土地利用格局模拟中的应用——以常州市孟河镇为例

以江苏省常州市新北区孟河镇为研究区,在土地利用格局模拟的回归建模中考虑驱动因子对土地利用格局影响的空间不稳定性,实现基于地理加权的回归分析模型,并与基于全局最小二乘法(OLS)的Logistic回归模型进行比较。研究结果表明,运用地理加权回归(GWR)的建模方法,不但可以获得更好的拟合优度和更高的拟合准确率,而且可以获得各驱动因子对土地利用格局影响的空间分异特征。同时,研究结果也可以为孟河镇及其类似地区的土地利用规划决策提供科学依据。     

基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究

基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression,GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression,PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression,M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression,MLR)为基准比较3种GWR模型的精度。研究表明：(1)安徽省土壤pH具有空间异质性,且集聚特征明显。(2) 3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(R_adj²)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的R_adj²     

基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究

基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression, GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression, PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression, M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression, MLR)为基准比较3种GWR模型的精度。研究表明：(1)安徽省土壤pH具有空间异质性,且集聚特征明显。(2) 3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(R_adj²)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的R_adj^2<...     

建成环境对城市活力影响的时空异质性研究：基于大数据的分析

城市地理与城乡规划一直关注建成环境对城市活力的影响,但鲜有研究揭示该影响的时空异质性。城市活力表现为居民在实体空间上的分布及其活动,并呈现时间动态变化特征。通过采集广州市中心城区新浪微博签到数据以及建成环境大数据,在1 km×1 km方格网、2 h时间段的时空单元上可视化城市活力的时空动态变化特征,基于时空地理加权回归模型 (GTWR) 揭示区位、功能混合度与密度对城市活力影响的时空异质性,并对比工作日与双休日的差异。研究发现：① 广州中心城区城市活力呈现东西带状的“多节点”空间格局,在24 h内经历“分散-集聚-进一步集聚-分散”的时空动态变化。② 区位、功能混合度与密度对城市活力的边际效应表现出空间和时间双重维度的不稳定性。③ 由于居民活动的时空约束不同,城市活力时空特征及其建成环境影响在工作日与双休日存在差异。     

Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices

By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.     

Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R², lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.     

Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.     

Factors Affecting Domestic Water Consumption on the Spanish Mediterranean Coastline

During the last decades on the Spanish Mediterranean coastline there has been a great development of low-density urban areas, as well as a change in the sociodemographic structures, especially in the municipalities that have developed a residential tourism model. Likewise, urban and tourist development have stressed the balance between the availability of water resources and urban water demands, generating situations of scarcity that might be aggravated by climate change. This study identifies the determinants of water consumption on the Spanish Mediterranean coastline, focusing on the variables related to urban land uses and socioeconomic and sociodemographic variables at the municipal level using an ordinary least square (OLS) and a geographically weighted regression (GWR) model. The GWR model results substantially improved the results of the OLS model, explaining 88.27 percent of the variance in domestic water consumption and solving the spatial autocorrelation problem of some independent variables. The most influential variables include the percentage of second homes or the percentage of residential properties with swimming pools at the municipal level. These characteristics must be considered to develop demand management policies and an updated hydrological planning to ensure urban supply in a future with less available water resources.     

中国省域犯罪率影响因素的空间非平稳性分析

收入差距和流动人口是影响犯罪率的两个重要因素, 以往研究基于OLS模型, 在假设地域空间为均质的前提下分析其对犯罪率的影响, 但现实世界的空间单元往往难以满足“均质”的假设, 多数表现为“空间异质”。以OLS计量空间异质会造成计量结果出现偏差, 同时无法了解不同空间单元的不同影响。而地理加权回归模型通过将空间结构嵌入线性回归模型中, 很好的解决了空间异质的计量问题。利用地理加权回归模型研究2008 年中国大陆省域单元犯罪率的影响因素, 结果表明:① 犯罪率的影响因素表现出空间非平稳性, 流动人口与犯罪率显著相关, 但各个省份相关程度并不相同, 影响关系随空间位置变化而变化;② 地理加权回归模型的计量精度和拟合度比OLS模型有大幅提高     

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.