首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions – up to thousands of times faster – on a standard desktop.  相似文献   

2.
ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.  相似文献   

3.
Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.  相似文献   

4.
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。  相似文献   

5.
A recent paper in this journal proposed a form of geographically weighted regression (GWR) that is termed parameter-specific distance metric geographically weighted regression (PSDM GWR). The central focus of the PSDM generalization of the GWR framework is that it allows the kernel function that weights nearby data to be specified with a distinct distance metric. As with the recent paper on Multiscale GWR (MGWR), the PSDM framework presents a form of GWR that also allows for parameter-specific bandwidths to be computed. As a result, a secondary focus of the PSDM GWR framework is to reduce the computational overhead associated with searching a massive parameter space to find a set of optimal parameter-specific bandwidths and parameter-specific distance metrics. In this comment, we discuss several concerns with the PSDM GWR framework in terms of model interpretability, complexity, and computational efficiency. We also recommend some best practices when using these models, suggest how to more holistically assess model variations, and set out an agenda to constructively focus future research endeavors.  相似文献   

6.
胡实  韩建  占车生  刘梁美子 《地理研究》2020,39(7):1680-1690
高时空分辨率降雨数据的获取对陆地水循环研究至关重要。遥感卫星反演降水产品虽然能有效再现降雨的空间格局,但存在空间分辨率较低的问题。以植被指数NDVI(Normalized Difference Vegetation Index)和海拔高度为自变量,通过构建太行山区GPM降水(Global Precipitation Measurement Mission)的时滞地理加权回归模型,得到了2014—2016年研究区1 km分辨率GPM降水数据。研究结果表明：利用植被指数和海拔高度构建的时滞地理加权回归模型能够有效地对太行山月尺度GPM降雨数据进行尺度下延,在提高GPM数据空间分辨率的同时保留了原始数据的观测精度。考虑NDVI的时滞性提高了地理加权回归模型的降尺度效果,相对于多元线性回归模型和不考虑NDVI时滞效应的地理加权回归模型,时滞地理加权回归模型的降尺度结果与站点实测数据的确定性系数更高,RMSE更低。冬季降雨与第二年春季植被NDVI的关系较为密切,虽然采用第二年春季的NDVI作为解释变量构建降尺度模型能有效地提高冬季降雨的降尺度效果,但基于植被指数和海拔高度构建的时滞地理加权回归模型更加适用于植被生长季GPM降雨数据的降尺度研究。  相似文献   

7.
The mountain permafrost distribution in the Abisko region in northern Sweden has been assessed using a combination of empirical and statistical analysis. The empirical data was obtained using the bottom temperature of snow cover (BTS) method, supported by continuous ground temperature measurements. The statistical analysis was based on 148 data points in total and used logistic regression to model the probability of permafrost occurrence. Further, Geographically Weighted Regression (GWR) was introduced as an exploratory tool for detecting non-stationarity in the relationships between permafrost and the independent variables models and showed to be a useful tool in the statistical analysis. As a result of the GWR analysis the region was divided into two subregions. The models show probabilities >0.8 for permafrost at elevations above 1300 m a.s.l. in the western part of the region. In the eastern part, the probabilities are likely to be influenced by the potential incoming shortwave summer radiation, indicating a probability >0.8 above 850 m a.s.l. on the north-east and east-facing slopes, above 1000 m on the west-facing slopes and above 1100 m a.s.l. on the south-facing slopes. Permafrost conditions throughout the region were found to be marginal and sensitive to current warming trends.  相似文献   

8.
Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R2 = 0.642) and geographically weighted regression (GWR) (Adjusted R2 = 0.797) models were worse than those of MGWR (Adjusted R2 = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R2 = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.  相似文献   

9.
The resolution achievable for chironomid identifications has increased in recent years because of significant improvements in taxonomic literature. However, high taxonomic resolution requires more training for analysts. Furthermore, with greater taxonomic resolution, misidentifications and the number of rare, poorly represented taxa in chironomid calibration datasets may increase. We assessed the effects of various levels of taxonomic resolution on the performance of chironomid-based temperature inference models (transfer functions) and temperature reconstruction. A calibration dataset consisting of chironomid assemblage and temperature data from 100 lakes was examined at four levels of taxonomic detail. The coarsest taxonomic resolution primarily represented identifications to genus or suprageneric level. At the highest level of taxonomic resolution, identification to genus level was possible for 37% of taxa, and identification below genus was possible for 60% of taxa. Transfer functions were obtained using Weighted Averaging (WA) and Weighted Averaging-Partial Least Squares (WA-PLS) regression. Cross-validated performance statistics, such as the root mean square error of prediction (RMSEP) and the coefficient of determination (r 2) between inferred and observed values improved considerably from the lowest taxonomic resolution level (WA: RMSEP 1.91°C, r 2 0.78; WA-PLS: RMSEP 1.59°C, r 2 0.86) to the highest taxonomic resolution level (WA: RMSEP 1.66°C, r 2 0.84; WA-PLS: RMSEP 1.41°C, r 2 0.89). Reconstructed July air temperatures during the Lateglacial period based on fossil chironomid assemblages from Hijkermeer (The Netherlands) were similar for all levels of taxonomic resolution, except the coarsest level. At the coarsest taxonomic level, reconstruction failed to infer one of the known Lateglacial cold episodes in the record. Also, the difference in reconstructed values based on lowest and highest taxonomic resolutions exceeded sample-specific estimated standard errors of prediction in several instances. Our results suggest that chironomid-based transfer functions at the highest taxonomic resolution outperform models based on lower-resolution calibration data. However, transfer functions of intermediate taxonomic resolution produced results very similar to models based on high-resolution taxonomic data. In studies that include analysts with different levels of expertise, inference models based on intermediate taxonomic resolution, therefore, might provide an alternative to transfer functions of maximum taxonomic detail in order to ensure taxonomic consistency between calibration datasets and down-core records produced by different analysts.  相似文献   

10.
In this article, we respond to ‘A comment on geographically weighted regression with parameter-specific distance metrics’ by Oshan et al. (2019), published in this journal, where several concerns on the parameter-specific distance metric geographically weighted regression (PSDM GWR) technique are raised. In doing so, we review the developmental timeline of the multiscale geographically weighed regression modelling framework with related and equivalent models, including flexible bandwidth GWR, conditional GWR and PSDM GWR. In our response, we have tried to answer all the concerns raised in terms of applicability, veracity, interpretability and computational efficiency of the PSDM GWR model.  相似文献   

11.
There is a strong connection between cultural and biological diversity at a global scale, especially in the linguistic domain, but less is known at regional scales. Indicators of such reciprocity are found in the linguistic expression of natural elements, and their representation in the landscape through toponymy. Here we evaluate the geographic distribution of phyto-toponyms, places named after a native local flora, in Galicia, NW Spain. We created the concept of toponymic species (topo-species) for groups of places named after a plant taxon. By using different regression models assuming global effects of the variables (Ordinary Least Squares, OLS) or non-stationarity (Geographically Weighted Regression, GWR), we explored the connection of topo-species richness and diversity with environmental (river density, altitude and natural habitats density) and social (total density of toponyms, population density) factors.Topo-species richness and diversity were significantly correlated with the studied factors. Total density of toponyms, river density, altitude and natural habitats density showed significant positive values in the models, while population density had little or no effects. GWR performed better for all variables, especially for Shannon diversity index. We conclude that place names of natural elements depict human’s interaction with the environment. They are stable, spatially-explicit elements that may be used as indicators of bio-cultural diversity. In addition, they represent an intangible cultural heritage that should also be preserved.  相似文献   

12.
基于MODIS传感器的植被指数产品(MOD13Q1)及50年气候数据,通过地理加权回归与普通最小二乘回归模型对比,对中国黄土高原地区NDVI与气候因子间的空间尺度依存性及非平稳性进行研究,以期准确建立二者间关系.结果表明:① 研究区域内,NDVI与气候因子间存在很强的空间尺度依存关系,相同空间尺度下,年均降水较年均温对NDVI影响的波动性更大;② 与普通最小二乘回归模型相比,地理加权回归模型能够更准确地展现二者间关系;③气候因子对该地区NDVI的影响差异明显,降水存在直接正向影响,而温度的影响则较复杂;④ NDVI与气候因子间沿东北--西南的分布格局体现出区域内不同植被--气候区差异特征.二者间的异质情况还反映出除气候外,人类活动,地形等其他因素对NDVI的影响.  相似文献   

13.
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   

14.
合肥市商业地价驱动因素的空间非平稳性分析   总被引：2，自引：1，他引：1  
王爱  陆林  包善驹 《地理科学》2017,37(10):1535-1545
基于微观因素的视角,对合肥市商业地价的空间格局、空间异质性进行空间插值和回归分析,研究发现： ① 商业地价空间格局呈现多中心的结构,峰值区集中分布在城市中心,低值区分布在二环线以外的地区;② GWR模型能清晰地反映出各因素对地价影响力的空间差异,容积率、交通站点、CBD和公园是研究区商业地价的关键驱动因素;③ 较之外在因素,内在因素对商业地价的边际作用效率较大,其中尤以容积率最为突出;④ 相较于欠发达地区,容积率在高度发达的区域对地价的正向效应更为显著,而交通站点和CBD对地价的作用正好相反。商业开发更愿意为小型公园支付更高的价格。  相似文献   

15.
The objective of this computational study was to investigate to which extent the availability and the way of use of historical maps may affect the quality of the calibration process of cellular automata (CA) urban models. The numerical experiments are based on a constrained CA applied to a case study. Since the model depends on a large number of parameters, we optimize the CA using cooperative coevolutionary particle swarms, which is an approach known for its ability to operate effectively in search spaces with a high number of dimensions. To cope with the relevant computational cost related to the high number of CA simulations required by our study, we use a parallelized CA model that takes advantage of the computing power of graphics processing units. The study has shown that the accuracy of simulations can be significantly influenced by both the number and position in time of the historical maps involved in the calibration.  相似文献   

16.
Revealing the drivers and scale effects of water pollutant discharges is an important issue in the study of the environmental consequences during urban agglomeration evolution. It is also a prerequisite for realizing collaborative water pollutant reduction and environmental governance in urban agglomerations. This paper takes 305 counties in the Yangtze River Delta (YRD) as an example and selects chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) as two distinctive pollutant indicators, using the Spatial Lag Model (SLM) and Spatial Error Model (SEM) to estimate the drivers of water pollutant discharges in 2011 and 2016. Then the Multiscale Geographically Weighted Regression (MGWR) model is constructed to diagnose the scale effect and spatial heterogeneity of the drivers. The findings show that the size of population, the level of urbanization, and the economic development level show global-level increase impacts on water pollutant discharges, while the level of industrialization, social fixed assets investment, foreign direct investment, and local fiscal decentralization are local-level impacts. The spatial heterogeneity of local drivers presents the following characteristics: Social fixed assets investment has a strong promoting effect on both COD and NH3-N discharges in the Hangzhou-Jiaxing-Huzhou region and the coastal area of the YRD; industrialization has a promoting effect on COD discharges in the Taihu Lake basin and Zhejiang province; foreign direct investment has a local inhibitory effect on NH3-N discharge, and the pollution halo effect is more prominent in the marginal areas of the YRD such as northern Jiangsu, northern Anhui, and southern Zhejiang; local fiscal decentralization has a noticeable inhibitory effect on COD discharge in the central areas of the YRD, reflecting the positive impacts on improved local environmental awareness and stronger constraints of multilevel environmental regulations in the urban agglomeration. Therefore, it is recommended to guide greener development to reduce the water pollutant discharge; to embed an environmental push-back mechanism in the fields of industrial production, capital investment, and financial income and expenditure; and to establish a high-quality development pattern of urban agglomerations systematically compatible with the carrying capacity of the water environment.  相似文献   

17.
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   

18.
Abstract

As interest in outdoor activities in remote areas is increasing, there is a strong need for improved avalanche forecasting at the regional scale. Due to important logistical and safety matters, avalanche terrain measurements (avalanche observations, snowpack profiles, and stability tests) are not always possible for practitioners/forecasters. An interesting alternative would be to analyze the snowpack without these challenges by using snow model outputs. The SNOWPACK model is currently used operationally for avalanche forecasting and research in the Swiss Alps. Thus, this paper presents a summary of analyses that have been conducted to assess the potential of using the SNOWPACK model driven with both in-situ and forecasted meteorological data in three different Canadian climate and geomorphological contexts. A comparison of meteorological data from in-situ and predicted datasets for two winters shows that the GEMLAM weather model is the most accurate for the three climatic contexts of this project, but also showed a bias proportional to precipitation intensity/rate. Snow simulations forced with GEMLAM are the closest to field measurements. Finally, predictions of persistent weak layers have been validated using the InfoEx platform from Avalanche Canada. Crust and surface hoar formation dates agree with the information reported in InfoEx.  相似文献   

19.
Diatoms were identified and enumerated from surface sediments of 25 sites in Double Haven, Hong Kong. The relationship between diatom species distribution and 14 environmental variables was examined using Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA). Water depth was identified as the most important environmental variable influencing the distribution of diatoms in Double Haven. Subsequently a Weighted Average (WA) calibration model was developed to infer water depth. The reliability of the model was evaluated by the error of prediction (RMSEboot= 3.479) and the correlation (r 2= 0.7342) between observed and diatom-inferred values. This predictive calibration model has the potential to infer past sea level change in Hong Kong and the adjacent coastal areas.  相似文献   

20.
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   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号