混合地理加权回归模型算法研究

以迭代算法为基础，推导出混合地理加权回归模型的常系数（全局参数）和变系数（局域参数）的计算方法，并以上海市住宅小区楼盘销售平均价格为例进行验证。结果表明，混合地理加权回归模型的计算量略大于地理加权回归模型，但对样本数据的拟合更好，局域参数估计更稳健。     

基于地理探测器的济南市历城区土地利用变化及其驱动力分析

以济南市历城区为研究区域,分别研究2009～2014年期间与2014～2019年期间研究区内土地利用变化特点,借助土地利用结构表、单一土地利用动态度与土地利用转移矩阵等方法描述研究区土地利用各地类的变化特点,通过地理探测器对研究区内建设用地变化内在驱动机制进行分析,揭示自然、社会经济等因素合力作用下对建设用地变化的影响...     

SuperMap GIS地理加权回归模型研究与实现

地理加权回归分析是对普通线性回归模型的扩展,将空间数据的地理位置嵌入线性回归参数之中,以此来研究空间关系的空间异质性或空间非平稳性,属于局部空间分析模型.通过地理加权回归分析可以确定两种或两种以上变量间相互依赖的定量关系,局部区域的参数估计可以得到地理空间存在的不同空间关系,核函数的选取规则和带宽参数的验证方法也是本文研究的内容.     

贪心算法的地理加权回归特征变量选择方法

针对建立地理加权回归(GWR)模型时,无法直接应用普通线性回归(OLR)常用的特征变量选择方法,且计算过程较复杂的问题,该文基于贪心算法原理,通过引入Akaike信息法则,设计了适用于GWR的特征变量选择方法:逐个引入或删除特征变量,判断该变量对模型置信水平影响程度,根据评价准则决定该变量的取舍,最终实现模型外没有关系强的变量、模型内没有关系弱的变量。实验结果表明,比较基于OLR的逐步回归、向前引入法和向后删除法3种方法选择变量建立模型,向前引入法优于向后剔除法,两者都优于基于OLR的逐步回归法,更适用于GWR分析。     

一种协同时空地理加权回归PM2.5浓度估算方法

针对PM2.5浓度估算中时空特征考虑不足和样本量较少的问题,该文将协同训练和时空地理加权回归相结合,提出了协同时空地理加权回归。采用两个不同参数的时空地理加权回归模型作为回归器,利用一个回归器训练另一个回归器的未标注样本,选择最优结果作为标注样本加入标注样本,通过不断学习扩大标注样本量提升模型的回归性能。以京津冀地区2015年3-7月的PM2.5浓度数据为实验数据,利用气溶胶光学厚度产品、温度、风速和相对湿度进行建模,采用不同核函数的时空地理加权回归作为对比方法进行实验。结果显示,协同时空地理加权回归性能比基于Gauss核函数时空地理加权回归提升了10%,比基于bi-square核函数时空地理加权回归提升了6.25%,证明该文方法能够提升时空样本数量不足时的PM2.5浓度估算精度。     

地理加权回归模型的多重共线性诊断方法

地理加权回归是常用的空间分析方法,已广泛应用于各个领域,但利用此方法进行回归分析前,往往忽略了对设计矩阵进行局部多重共线性的诊断,从而导致对模型的估计不准确。因此,本文在引入了全局模型的多重共线性诊断方法的基础上,对这些方法进行了改进,改进后构建了加权方差膨胀因子法和加权条件指标方法——分解比法,用于诊断地理加权回归模型设计矩阵的多重共线性问题。实验结果表明,多重共线性不存在于全局模型,而可能存在于局部模型中,构建的两种方法能够有效地诊断地理加权回归模型的多重共线性问题,且加权条件指标方法——分解比法比加权方差膨胀因子法在诊断多重共线性问题上更有优势。     

基于地理探测器的四川地区冰川面积变化影响因素探究

结合GIS和RS技术,应用地理探测器方法分析四川区域2007—2017年冰川面积变化空间分异特征及其影响因素,选取坡度、坡向、海拔等7个影响因子,通过权重叠加作出该区域冰川脆弱性示意图。结果表明：(1)10年间东南向冰川占比减少,北向冰川占比增加;东向冰川减退面积较大,西向冰川面积整体持平。(2)期间,影响四川地区冰川面积变化区域空间差异的主要因素是冰川规模,小型冰川（面积小于或等于1 km2）的面积变化主要受夏季温度和降水变化影响;而中型冰川（面积大于1 km2）不仅受夏季温度和降水变化影响,还受地形因子的影响。(3)四川冰川脆弱性示意图显示区域内冰川均呈不同程度的脆弱性,总体上为轻-强度脆弱。     

稳健度量选权迭代的地理加权回归研究

针对地理加权回归参数估计采用最小二乘方法,最小二乘估计易受离群值影响,导致地理加权回归模型并不稳健的问题,该文提出基于稳健度量选权迭代的地理加权回归分析方法,核心思想是通过标准化残差构造权重函数,通过迭代加权降低离群值对回归模型参数估计的影响。利用模拟数据与真实数据进行试验,分别与GWR、RGWR进行对比分析,以MSE、MAE为指标进行性能评价。模拟数据试验中,RMIWGWR模型比RGWR模型的MSE、MAE指标分别提升9.29%和8.34%;真实数据试验中,RMIWGWR模型比RGWR模型的MSE、MAE指标分别提升63.88%和38.45%。试验表明:该方法可改善粗差存在环境下地理加权回归模型参数估计精度,提升模型拟合效果。     

一种基于主成分分析的时空地理加权回归方法

针对时空地理加权回归模型(GTWR)进行预测时,输入变量较多导致计算复杂度高,而输入变量较少引起预测精度降低这一问题,提出了一种基于主成分分析的时空地理加权回归方法(PCA-GTWR)。该方法采用非线性主成分分析方法,先对影响PM2.5浓度的若干相关变量降维处理得到几个综合指标,并将其作为GTWR模型的输入变量进行预测。为验证该方法的有效性,采用北京市2014-04—2017-03的PM2.5数据,利用Pearson相关系数法选取与PM2.5浓度具有较高相关性的影响因素作为常规的GTWR模型的输入变量,在变量个数相同的前提下,与本文方法进行对比。结果表明应用非线性主成分分析方法对相关变量进行预处理后,有效地解决了变量之间的共线性,保留了原始影响因素主要信息,提高了运算效率,且该方法的MAE、RMSE、AIC均低于常规的GTWR模型,拟合优度GF最高达到88.11%。     

基于IGGⅢ的地理加权回归模型研究

针对离群值存在时地理加权回归模型拟合效果较差的问题,本文提出了基于IGGⅢ的地理加权回归方法。核心是采用IGGⅢ方案中的权函数计算权重矩阵,将权因子用于地理加权回归参数估计模型。利用模拟数据和真实数据与GWR、ACV-GWR进行对比试验,以MSE、MAE和R²作为指标对结果进行评价。模拟试验结果显示,IGGⅢ-GWR比GWR性能分别提升了51.14%、23.77%、28.4%,比ACV-GWR分别提升了49.96%、22.57%、27.1%;真实试验结果显示,IGGⅢ-GWR比GWR性能分别提升了12.65%、7.44%、0.37%,比ACV-GWR分别提升了11.85%、6.96%、0.34%。试验结果表明,基于IGGⅢ的地理加权回归可提高模型的抗差能力,拟合效果更好。     

Exploring relation of land surface temperature with selected variables using geographically weighted regression and ordinary least square methods in Manipur State,India

In the present study, relationship between Land surface temperature and selected indices, vegetation index (VARI), built-up index (BUI) and elevation (DEM) is investigated. Ordinary least square method and geographically weighted regression are used to analyse the spatial correlation between the indices with surface temperature. Subsequently, temporal trends (2001–2015) in surface temperature and vegetation are explored after every two years of interval. LANDSAT image and ASTER DEM are used to extract LST and additional indices. The selected variables (Built-up, vegetation and topography) explain 69% of the variation in surface temperature. The OLS and GWR revealed that topography and vegetation are the significant factor of LST in Manipur State. Topography being a constant parameter, its effect is constant over time. The changing scenario of vegetation is significantly contributing to LST. The surface temperature over a period of 15 years show increasing trend and is negatively and strongly correlated to vegetation cover.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.     

基于RS的平顶山市土地利用动态变化研究

平顶山市作为典型的资源型城市(以煤炭、耕地发展起来),是人地关系作用最强烈的区域之一,也是土地利用发生急剧变化且具有特色的区域。为此,基于平顶山市城区1994年TM和2002年ETM+两个时段的遥感图像,获取不同时期的土地利用信息,求取土地利用转移矩阵,分析土地利用的时空变化特征和变化规律,并从土地利用面积变化、动态度、耗减度、开发度等方面对土地利用变化进行分析。研究发现:煤炭开采、城市发展对平顶山市土地利用变化具有重要的影响;8年间,该地区耕地、林地大幅减少,城市用地、工矿用地、荒草地大幅增加;减少的耕地主要转化为荒草地、工矿用地、城市用地,减少的林地主要转化为荒草地;受北部矿区条件的影响,市区发展主要朝东、西、南三个方向。     

基于CLUE-S模型的南京市土地利用变化模拟

本文以南京市为研究区,以南京市1986年的土地利用现状图为基础,分析研究区概况并根据数据的可获取性,选取13类土地利用变化驱动因素,利用逻辑斯蒂回归分析求解土地利用变化驱动因素作用系数矩阵。在此基础上运行CLUE-S模型,对南京市1996年的土地利用空间格局进行模拟。将模拟结果与南京市1996年土地利用现状图与进行对比,结果较为理想,模拟正确率达88.57%,KAPPA指数0.86。这说明CLUE-S模型具有成功模拟区域土地利用时空动态变化的能力,对土地利用预测、规划具有重要的指导作用。     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

地理国情地表覆盖的城市建设用地扩张分析——以兰州新区为例

针对传统的土地利用变化研究方法无法实现大规模在建中的建设用地动态监测的问题,该文提出了一种新的动态监测方法。结合多时相遥感影像、规划数据和外业调查数据,能够快速、低成本地提取城市建设用地。选取合适的扩展指数构建综合扩展程度指数模型,便于对建设用地变化时序特征做出评价。定量分析建设用地变化时序特征与规划用地的对比情况,明确了城市建设用地的建设进度和符合度。结果表明,城市建设用地处于高速扩展阶段,建设现状和规划情况基本相符;该方法是对地理国情监测工作的一种新的探索。     

Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities

Urban heat island (UHI) effect is among the most typical characteristics of urban climate. The analysis of surface UHI (SUHI) mechanisms has received the most extensive attention in the world. Here, we quantify the diurnal and seasonal SUHI intensity (SUHII) in global 419 major cities during the period 2003-2013. A geographically weighted regression (GWR) was established to assess the relationships between SUHII and several driving factors, and it further was compared to the ordinary least square (OLS) and stepwise multiple linear regression (SMLR) models. We show that GWR model has higher determination coefficient (R²) than OLS and SMLR models (Time: summer daytime, summer night, winter daytime and winter nighttime; GWR: 0.805, 0.458, 0.699 and 0.582; OLS: 0.732, 0.347, 0.473 and 0.320; SMLR: 0.732, 0.341, 0.468 and 0.316), indicating the spatially non-stationarity in the relationships. During the day, both vegetation activity and tree cover fraction have stronger cooling effect on SUHI in the summer of Asia. At night, there are stronger albedo effects on SUHI in the summer of Eastern Asia and Western North America and in the winter of Eastern Asia. Furthermore, temperature has stronger effect on daytime SUHI in Africa, Europe and South America in summer, and precipitation has stronger effect on nighttime SUHI in Africa and Europe in summer. Our results emphasize the spatial variation of the relationships between SUHII and relevant driving factors across global major cities, further indicating that the spatially non-stationary effect of driving factors on SUHII need to be considered in the future.