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1.
基于MODIS数据的青藏高原气温与增温效应估算 总被引：12，自引：2，他引：10
利用2001-2007 年MODIS地表温度数据、137 个气象观测台站数据和ASTERGDEM数据, 采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR), 研究了高原月均地表温度与气温的相关关系, 最终选择精度较高的GWR分析方法, 建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(Adjusted R2) 都达到了0.91 以上(0.91~0.95), 标准误差(RMSE) 介于1.16~1.58℃;约70%以上的台站各月残差介于-1.5~1.5℃之间, 80%以上的台站的残差介于-2~2℃之间。根据该模型, 估算了青藏高原气温, 并在此基础上, 将高原及周边地区7 月份月均气温转换到4500 m和5000 m海拔高度上, 对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1) 利用GWR方法, 结合地面台站的观测数据和MODIS Ts、DEM等, 对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2~3℃), 精度可以提高到1.58℃;(2) 高原夏半年海拔5000 m左右的高山区气温能达到0℃以上, 尤其是7 月份, 海拔4000~5500 m的高山区的气温仍能达到10℃左右, 为山地森林的发育提供了温度条件, 使高原成为北半球林线分布最高的地方;(3) 高原的增温效应非常突出, 初步估算, 在相同的海拔高度上高原内部气温要比外围地区高6~10℃。 相似文献
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Yelena Ogneva-Himmelberger Hamil Pearsall Rahul Rakshit 《Applied geography (Sevenoaks, England)》2009,29(4):478-487
Several studies indicate that there is a positive relationship between green vegetation land cover and wealthy socio-economic conditions in urban areas. The purpose of this research is to test for and explore spatial variation in the relationship between socio-economic and green vegetation land cover across urban, suburban, and rural areas, using geographically weighted regression (GWR). The analysis was conducted at the census block group level for Massachusetts, using Census 2000 data and impervious surface data at 1-m resolution. To explore regional variations in the relationship, four scenarios were generated by regressing each of the following socio-economic variables – median household income, percentage of poverty, percentage of minority population, and median home value – against two environmental variables – percent of impervious surface and population density. GWR results show that there is a considerable spatial variation in the character and the strength of the relationship for each model. There are two main conclusions in this study. First, the impervious surface is generally a strong predictor of the level of wealth as measured by four variables included in the analysis, at the scale of census block group; however, the strength of the relationship varies geographically. Second, GWR, not ordinary least squares technique, should be used for regional scale spatial analysis because it is able to account for local effects and shows geographical variation in the strength of the relationship. 相似文献
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MODIS-based estimation of air temperature of the Tibetan Plateau 总被引：1，自引：0，他引：1
The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau. 相似文献
4.
Land price plays an important role in guiding land resource allocation for urban planning and development, particularly in big cities of fast developing countries where infrastructures and populations change frequently. Therefore, detecting spatially implicit information in the spatial pattern of relationships between land price and related impact factors is critical. Geographically weighted regression (GWR) analysis was conducted in this study for the purpose in Wuhan, China, by using a 10-year panel data set of residential land price. Based on twelve factors in three aspects (land attributes, location factors and neighborhood attributes), an evaluation index system of resident land price was established. The spatial distributions of estimated coefficients and pseudo t-values of three major explanatory variables (floor area ratio, distance to nearest center business district (CBD) and distance to nearest lake), obtained from GWR analysis, indicated that their relationships of the impact factors with land price are spatially non-stationary. The positive impact of floor area ratio on land price is more significant in highly developed areas than in less developed areas. Conversely, the negative impact of distance to nearest CBD on land price is larger in highly developed areas than in less developed areas. Moreover, wealthier dwellers may be willing to pay a higher price for a good lake view (especially views of small lakes), but infrastructure barriers (near some large lakes) cause negative effect. The outputs of this study, which provide detailed information on the relationships between land price and impact factors in local areas, are promising for urban planners to scientifically evaluate land price and make area-specific strategies. 相似文献
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都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比 总被引：2，自引：2，他引：0
以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。 相似文献
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Application of geographically weighted regression model in the estimation of surface air temperature lapse rate 总被引：1，自引：0，他引：1
The surface air temperature lapse rate(SATLR)plays a key role in the hydrological,glacial and ecological modeling,the regional downscaling,and the reconstruction of high-resolution surface air temperature.However,how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for re-searchers.The geographically weighted regression(GWR)model was applied in this paper to estimate the SATLR in China's mainland,and then the assessment and validation for the GWR model were made.The spatial pattern of regression residuals which was identified by Moran's Index indicated that the GWR model was broadly reasonable for the estimation of SATLR.The small mean absolute error(MAE)in all months indicated that the GWR model had a strong predictive ability for the surface air temperature.The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation.Therefore,the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition. 相似文献
7.
Nazarin Ezzaty Mohd Najib Kasturi Devi Kanniah 《Singapore journal of tropical geography》2019,40(2):272-290
This study aims to map forest cover in Peninsular Malaysia using satellite images as deforestation is of concern in the recent decades, and is an important environmental issue for the future too. The Carnegie Landsat Analysis System‐Lite (CLASlite) program was used in this study to detect forest cover in Peninsular Malaysia using Landsat satellite data. The results of the study show that CLASlite algorithm misclassified some oil palm, rubber and urban areas as forest vegetation. A reliable forest cover map was produced by first combining Landsat and ALOS PALSAR images to identify oil palm, rubber and urban areas, and then subsequently removing them. The HH and HV polarization data of ALOS PALSAR (threshold method) could detect oil palm plantations with 85.26 per cent of overall accuracy. For urban area detection, Enhance Build up Index (EBBI) using spectral bands from Landsat provided higher overall accuracy of 94 per cent. These methods produced a forest cover reading of 5 914 421 ha with an overall classification accuracy of 94.5 per cent. The forest cover (including rubber areas) detected in this study is 0.38 per cent higher than the percentage of 2010 forest cover detected by the Forestry Department of Peninsular Malaysia. The technique described in this paper presents an alternative and viable approach for updating forest cover maps in Malaysia. 相似文献
8.
多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用 总被引：3，自引：2，他引：1
对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。 相似文献
9.
美国俄亥俄州土壤有机碳密度空间分布(英文) 总被引：2，自引：1，他引：1
Historical database of National Soil Survey Center containing 1424 geo-referenced soil profiles was used in this study for estimating the organic carbon(SOC) for the soils of Ohio,USA.Specific objective of the study was to estimate the spatial distribution of SOC density(C stock per unit area) to 1.0-m depth for soils of Ohio using geographically weighted regression(GWR),and compare the results with that obtained from multiple linear regression(MLR).About 80% of the analytical data were used for calibration and 20% for validation.A total of 20 variables including terrain attributes,climate data,bedrock geology,and land use data were used for mapping the SOC density.Results showed that the GWR provided better estimations with the lowest(3.81 kg m 2) root mean square error(RMSE) than MLR approach.Total estimated SOC pool for soils in Ohio ranged from 727 to 742 Tg.This study demonstrates that,the local spatial statistical technique,the GWR can perform better in capturing the spatial distribution of SOC across the study region as compared to other global spatial statistical techniques such as MLR.Thus,GWR enhances the accuracy for mapping SOC density. 相似文献
10.
Bo Huang Bo Wu Michael Barry 《International journal of geographical information science》2013,27(3):383-401
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. 相似文献
11.
Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R2 for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected. 相似文献
12.
良好的城市生态环境有益于居民的健康福祉。至今鲜有研究阐明绿化环境对中国城市居民健康的心理—社会—行为机制。本文运用广州调查问卷数据、遥感影像数据和百度街景数据,提取多种社区绿化指标,并运用多层线性回归模型和中介效应分析技术,阐明社区绿化环境影响居民自评健康的路径和机制,定量测度社区绿化环境的健康效应在不同社会群体间的差异。研究发现：① 社区绿化水平与居民的自评健康水平存在显著关联;② 社区绿化通过缓解心理压力提升居民的自评健康水平;③ 社区绿化的健康效应在不同收入群体和不同性别群体之间存在明显差异,表现为社区绿化水平与健身时长和心理压力的关系,中低收入群体强于高收入群体,女性群体强于男性群体。本文以期丰富健康地理学视角下的绿化环境与公共健康的实证研究,并为健康社区建设和人居环境提升提供科学依据。 相似文献
13.
Binbin Lu Martin Charlton Paul Harris A. Stewart Fotheringham 《International journal of geographical information science》2014,28(4):660-681
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set. 相似文献
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近30年北京市ISP-LST空间特征及其变化 总被引：1，自引：0，他引：1
本文聚焦长时序地表的不透水与温度特征,利用Landsat影像数据,获取1991—2015年北京市的不透水地表盖度（Impervious Surface Percentage, ISP）与地表温度（Land Surface Temperature, LST）数据,构建不透水地表盖度-地表温度（ISP-LST）二维空间。结合标准差椭圆法,对ISP-LST空间密度分布的聚集特性进行分析,定量化表述各时期的特征与变化。研究发现：① ISP-LST二维空间特征表现为三种类型：弱相关、非完全正相关和显著正相关。② ISP-LST标准差椭圆的方向性和离散性均值为11.26和2.87,空间聚集性良好。随时间推移,高温现象受不透水地表的影响过程趋于复杂化。③ ISP-LST聚集区是城市热环境的重要表征,其在各功能区年际增长率为：功能扩展区（2.97%）>核心功能区（1.75%）>发展新区（1.63%）>生态涵养区（0.18%）。聚集区在东南方向增长明显,研究时段内累计增长14.77%。④ ISP-LST聚集区的斑块密度及形状复杂度的景观格局变化不大,但斑块连接性随时间推移有所降低。本文研究结果可为缓解城市热岛效应、制定生态环境调控政策提供相应参考。 相似文献
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Rubén Alejandro Villar-Navascués Alfredo Pérez-Morales 《The Professional geographer》2018,70(3):513-525
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. 相似文献
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长春市城市形态及风环境对地表温度的影响 总被引：2，自引：1，他引：2
随着城市化进程的加快,城市热岛效应越来越受到关注。然而,很少有研究分析城市形态和城市风环境对地表温度（LST）的影响。利用建筑和遥感等多源数据,基于GIS空间方法,结合迎风面指数（FAI）和地表温度,研究长春市城区迎风面指数时空差异,探索城市形态对城市地表温度影响。结果表明：① 迎风面指数呈现从中心城区向外扩散的空间趋势,高密度以及高建筑对风的阻碍程度大。朝阳区北部迎风面指数最大,最大值达到15.1,各个区边缘区迎风面指数最小,最小值为0.01。② 研究区昼夜地表温度温差大,范围分别是18.15 ℃~31.73 ℃、4.27 ℃~18.43 ℃。空间分布上与迎风面指数存在相同特征,城市中心城区温度高,以同心圆方式逐渐向外扩散。受城市建筑形态、人为热源等因素影响,与夜间相比,白天高温地区范围更大。③ 迎风面指数与地表温度在一定程度上相关,昼夜相关系数分别为0.371和0.355。建筑在垂直方向和水平方向上空间形态不同,对地表温度影响存在差异。结合城市风环境状况开展城市形态信息的定量研究,对于城市气候学家、城市规划师探寻城市潜在通风廊道、改善城市通风环境具有重要理论价值和现实意义。 相似文献
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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(Radj2)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的Radj2<... 相似文献
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中国基础教育公共服务均等化空间格局及其影响因素 总被引：2，自引：0，他引：2
基于教育POI设施数据,利用ArcGIS比较统计地图（Cartogram）、核密度分析和探索性空间数据分析（ESDA）等方法对基础教育公共服务均等化空间格局进行研究,并采用地理加权回归（GWR）方法对其格局形成的影响因素进行探究。结果表明：东部和中部地区基础教育公共服务资源数量约占65%,基础教育公共服务的高值区域与城市群所在区域较为吻合,低值区域主要分布于胡焕庸线以西,东西部基础教育资源绝对数量差异明显。学前教育服务人均数量的低值区主要分布在西部,中部也存在部分低值塌陷区,相较于中小学,学前教育公共服务的非均等化问题突出。小学和中学教育公共服务人均数量的高高集聚（H-H）和低低集聚（L-L）区域在东中西地区均有分布,综合而言,地区基础教育公共服务人均数量均等化优于绝对数量的均等化状况。常住人口、第三产业比例、建成区面积是基础教育公共服务均等化空间格局分异的主要影响因素。 相似文献