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1.
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.  相似文献   

2.
This study evaluates the influences of air pollution in China using a recently proposed model—multi‐scale geographically weighted regression (MGWR). First, we review previous research on the determinants of air quality. Then, we explain the MGWR model, together with two global models: ordinary least squares (OLS) and OLS containing a spatial lag variable (OLSL) and a commonly used local model: geographically weighted regression (GWR). To detect and account for any variation of the spatial autocorrelation of air pollution over space, we construct two extra local models which we call GWR with lagged dependent variable (GWRL) and MGWR with lagged dependent variable (MGWRL) by including the lagged form of the dependent variable in the GWR model and the MGWR model, respectively. The performances of these six models are comprehensively examined and the MGWR and MGWRL models outperform the two global models as well as the GWR and GWRL models. MGWRL is the most accurate model in terms of replicating the observed air quality index (AQI) values and removing residual dependency. The superiority of the MGWR framework over the GWR framework is demonstrated—GWR can only produce a single optimized bandwidth, while MGWR provides covariate‐specific optimized bandwidths which indicate the different spatial scales that different processes operate.  相似文献   

3.
This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well (R 2 = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires.  相似文献   

4.
This paper describes the results of a geo-statistical analysis carried out at the provincial level in Southern Europe to model wildfire occurrence from socio-economic and demographic indicators together with land cover and agricultural statistics. We applied a classical ordinary least squares (OLS) linear regression together with a geographically weighted regression (GWR) to explain long-term wild-fire occurrence patterns (mean annual density of >1 ha fires). The explanatory power of the OLS model increased from 52% to 78% as a result of the non-constant relationships between fire occurrence and the underlying explanatory variables throughout the Mediterranean Basin. The global model we developed (i.e., OLS regression) was not sufficient to fully describe the underlying causal factors in wildfire occurrence modeling. Indeed, local approaches (i.e., GWR) can complement the global model in overcoming the problem of non-stationarity or missing variables. Our results confirm the importance of agrarian activities, land abandonment, and development processes as underlying factors of fire occurrence. The identification of regions with spatially varying relationships can contribute to the better understanding of the fire problem, especially over large geographic areas, while at the same time recognizing its local character. This can be very important for fire management and policy.  相似文献   

5.
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.  相似文献   

6.
由美国国防气象卫星搭载的可见光成像线性扫描业务系统(DMSP/OLS)和国家极轨卫星搭载的可见光近红外成像辐射仪(NPP/VIIRS)获取的夜间灯光影像是监测人类社会经济活动和自然现象(如林火、油气燃烧等)的主要数据源。然而,现有的夜间灯光数据存在缺乏星上的辐射定标、像元饱和、时间尺度不连续、多源夜间灯光影像辐射不一致等问题。基于此,本文以中巴经济走廊区域为研究区,提出了一种基于线性拟合提取不变目标区域的方法,实现了DMSP/OLS影像间、DMSP/OLS与NPP/VIIRS两种数据间的相互校正。然后对中巴经济走廊的校正结果在不同空间尺度上选用区域灰度总量、标准化差异指数以及标准化差异指数和作为评价指标进行检验。结果表明:两种校正模型的拟合优度均在0.78以上,校正后的DMSP/OLS影像灰度总量与GDP和人口数据的相关性显著提高(GDP:R~2=0.7689;人口:R~2=0.9033),且标准化差异指数明显降低;NPP/VIIRS影像经过与DMSP/OLS互校正后在辐射亮度、时空分布上与DMSP/OLS更加一致,空间细节信息更加突出,从而增强了多源夜间灯光影像的一致性,更加适合用于长时间序列社会经济要素发展趋势的分析。  相似文献   

7.
互联网记录了人们的日常生活,对带有位置信息的搜索引擎数据进行分析和挖掘可以获得隐藏于其中的地理信息。本文通过分析中国各省流感月度发病数与相关关键词百度搜索指数之间的相关性,选取相关性较高关键词的百度指数作为解释变量,发病数作为因变量,在采用主成分分析法消除变量共线性后,分别使用普通最小二乘回归（OLS）、地理加权回归（GWR）及时空地理加权回归（GTWR）构建流感发病数的空间分布模型。模型的拟合度能够从OLS的0.737、GWR的0.915提高到GTWR的0.959,赤池信息准则（AIC）也表明,GTWR模型明显优于OLS与GWR模型。验证结果显示,GTWR模型能准确识别流感高发地区,将该方法与搜索引擎数据结合能较好地模拟流感空间分布,为空间流行病学的研究提供预测模型和统计解释。  相似文献   

8.
Abstract

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density–median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping.  相似文献   

9.
遥感技术具备实时快速、时空连续、广覆盖尺度等独特优势,在全球气候恶化大背景下,利用遥感干旱监测方法相比于传统地面监测手段,能够提供实时、准确、稳定的旱情信息,辅助科学决策。目前常用遥感旱情监测方法大多依赖全域性数学模型建模,假定了旱情模式的空间平稳特性,因而难以准确反映旱情模式的局部差异特征。本文提出利用地理加权回归模型GWR (Geographically Weighted Regression),考虑旱情模式的空间非平稳特性,综合多种遥感地面旱情监测指数,以实现传统全域旱情监测模型的局部优化。以美国大陆为研究区,监测2002年—2011年共10年的旱情状态。研究表明,GWR模型能够提供空间变化的局部最佳估计模型参数,监测结果更加吻合标准美国旱情监测USDM (U.S Drought Monitor)验证数据,且与地面实测值的最高相关系数R达到0.8552,均方根误差RMSE达到0.972,显著优于其他遥感旱情监测模型。GWR模型具备空间非平稳探测优势,实现了旱情模式的局部精细探测,能够显著提升遥感旱情监测精度,具备较好的应用前景。  相似文献   

10.
The principal rationale for applying geographically weighted regression (GWR) techniques is to investigate the potential spatial non-stationarity of the relationship between the dependent and independent variables—i.e., that the same stimulus would provoke different responses in different locations. The calibration of GWR employs a geographically weighted local least squares regression approach. To obtain meaningful inference, it assumes that the regression residual follows a normal or asymptotically normal distribution. In many classical econometric analyses, the assumption of normality is often readily relaxed, although it has been observed that such relaxation might lead to unreliable inference of the estimated coefficients' statistical significance. No studies, however, have examined the behavior of residual non-normality and its consequences for the modeled relationships in GWR. This study attempts to address this issue for the first time by examining a set of tobacco-outlet-density and demographic variables (i.e., percent African American residents, percent Hispanic residents, and median household income) at the census tract level in New Jersey in a GWR analysis. The regression residual using the raw data is apparently non-normal. When GWR is estimated using the raw data, we find that there is no significant spatial variation of the coefficients between tobacco outlet density and percentage of African American and Hispanics. After transforming the dependent variable and making the residual asymptotically normal, all coefficients exhibit significant variation across space. This finding suggests that relaxation of the normality assumption could potentially conceal the spatial non-stationarity of the modeled relationships in GWR. The empirical evidence of the current study implies that researchers should verify the normality assumption prior to applying GWR techniques in analyses of spatial non-stationarity.  相似文献   

11.
Local regression methods such as geographically weighted regression (GWR) can provide specific information about individual locations (or places) in spatial analysis that is useful for mapping nonstationary covariate relationships. However, the distance-based weighting schemes used in GWR are only adaptable for spatial objects that are point or area features. In particular, spatial object-pairs pose a challenge for local analysis because they have a linear dimensionality rather than a point dimensionality. This paper proposes using an alternative local regression model – quantile regression (QR) – for investigating the stationarity of regression parameters with respect to these linear features as well as facilitating the visualization of the results. An empirical example of a gravity model analysis of trade patterns within Europe is used to illustrate the utility of the proposed method.  相似文献   

12.
Geographically weighted regression (GWR) is a popular technique to deal with spatially varying relationships between a response variable and predictors. Problems, however, have been pointed out (see Wheeler and Tiefelsdorf in J Geogr Syst 7(2):161–187, 2005), which appear to be related to locally poor designs, with severe impact on the estimation of coefficients. Different remedies have been proposed. We propose two regularization methods. The first one is generalized ridge regression, which can also be seen as an empirical Bayes method. We show that it can be implemented using ordinary GWR software with an appropriate choice of the weights. The second one augments the local sample as needed while running GWR. We illustrate both methods along with ordinary GWR on an example of housing prices in the city of Bilbao (Spain) and using simulations.  相似文献   

13.
 Industry is the most important sector in the Chinese economy. To identify the spatial interaction between the level of regional industrialisation and various factors, this paper takes Jiangsu province of China as a case study. To unravel the existence of spatial nonstationarity, geographically weighted regression (GWR) is employed in this article. Conventional regression analysis can only produce `average' and `global' parameter estimates rather than `local' parameter estimates which vary over space in some spatial systems. Geographically weighted regression (GWR), on the other hand, is a relatively simple, but useful new technique for the analysis of spatial nonstationarity. Using the GWR technique to study regional industrialisation in Jiangsu province, it is found that there is a significant difference between the ordinary linear regression (OLR) and GWR models. The relationships between the level of regional industrialisation and various factors show considerable spatial variability. Received: 4 April 2001 / Accepted: 17 November 2001  相似文献   

14.
Spatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a S patial A nomaly P oints and R egions D etection method using multi‐constrained graphs and local density ( SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm.  相似文献   

15.
Socio‐demographic data are typically collected at various levels of aggregation, leading to the modifiable areal unit problem. Spatial non‐stationarity of statistical associations between variables further influences the demographic analyses. This study investigates the implications of these two phenomena within the context of migration‐environment associations. Global and local statistical models are fit across increasing levels of aggregation using household level survey data from rural South Africa. We raise the issue of operational scale sensitivity, which describes how the explanatory power of certain variables depends on the aggregation level. We find that as units of analysis (households) are aggregated, some variables become non‐significant in the global models, while others are less sensitive to aggregation. Local model results show that aggregation reduces spatial variation in migration‐related local associations but also affects variables differently. Spatial non‐stationarity appears to be the driving force behind this phenomenon as the results from the global model mask this relationship. Operational scale sensitivity appears related to the underlying spatial autocorrelation of the non‐aggregated variables but also to the way a variable is constructed. Understanding operational scale sensitivity can help to refine the process of selecting variables related to the scale of analysis and better understand the effects of spatial non‐stationarity on statistical relationships.  相似文献   

16.
Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.This study was supported by grant number 1 R1 CA95982-01, Geographic-Based Research in Cancer Control and Epidermiology, from the National Cancer Institute. The author thank the anonymous reviewers and the editor for their helpful comments.  相似文献   

17.
Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005.  相似文献   

18.
The Role of External Variables and GIS Databases in Geostatistical Analysis   总被引：3，自引：0，他引：3  
Although many geostatistical studies only study a measured attribute in relation to its spatial coordinates, this paper argues that other layers in the GIS database can be of additional use for spatial prediction purposes. They may enter the prediction equations as predictors in a regression model, or as correlated measurements. In an example we will show how this is done for predicting PCB138, a sediment pollution variable, over the North Sea floor. Issues of exploratory data analysis, required sample size, sample configuration, local versus global neighbourhoods, non‐stationarity, non‐linear transformations, change of support and conditional simulation will be discussed in the light of this example.  相似文献   

19.
Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R2 = 0.74, %RMSE = 23.1%), followed by stand age (R2 = 0.62, %RMSE = 35.6%), and stem density (R2 = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.  相似文献   

20.
Geographically Weighted Regression (GWR) is a method of spatial statistical analysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis, it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an "embarrassing parallel" problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.  相似文献   

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