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Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions
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作者 ZHU Xiao Xiang WANG Song Wang +3 位作者 LI Yan Fei ZHANG Ye Wu SU Xue Mei ZHAO Xiao Tao 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2024年第5期511-520,共10页
Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-tempor... Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation. 展开更多
关键词 Dengue fever Meteorological factor geographically and temporally weighted regression
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Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression:A case study in the Bohai Sea,China
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作者 Yongtian Shen Zhe Zeng +1 位作者 Dan Liu Pei Du 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2023年第12期77-89,共13页
Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically wei... Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically weighted regression(GWR)model to explore the spatial non-stationarity of near-miss collision risk,as detected by a vessel conflict ranking operator(VCRO)model from automatic identification system(AIS)data under the influence of sea fog in the Bohai Sea.Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data.The spatial distributions of near-miss collision risk,sea fog,and the parameters of GWR were mapped.The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea,in which near-miss collision risk in the fog season is significantly higher than that outside the fog season,especially in the northeast(the sea area near Yingkou Port and Bayuquan Port)and the southeast(the sea area near Yantai Port).GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season,with higher R-squared(0.890 in fog season,2018),than outside the fog season(0.723 in non-fog season,2018).GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally.Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk. 展开更多
关键词 NEAR-MISS sea fog geographically weighted regression automatic identification system(AIS)
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Exploration of the spatial pattern of urban residential land use with geographically weighted regression technique: a case study of Nanjing,China 被引量:1
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作者 胡明星 吴江 朱选 《Journal of Southeast University(English Edition)》 EI CAS 2015年第1期149-156,共8页
As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS (geo... As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS (geographic information system ) technique combined with the related theories and models. Taking the city of Nanjing as an example, a spatial database of urban land use and other environmental and socio-economic data is constructed. A multiple linear regression model is developed to determine the statistically significant factors affecting the residential land use distributions. To explain the spatial variations of urban land use patterns, the geographically weighted regression (GWR) is employed to establish spatial associations between these significant factors and the distribution of urban residential land use. The results demonstrate that the GWR can provide an effective approach to the exploration of the urban land use spatial patterns and also provide useful spatial information for planning residential development and other types of urban land use. 展开更多
关键词 urban residential land use GIS geographic information system) multiple linear regression geographically weighted regression
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Spatial Downscaling of the Tropical Rainfall Measuring Mission Precipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China 被引量:6
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作者 LI Yungang ZHANG Yueyuan +2 位作者 HE Daming LUO Xian JI Xuan 《Chinese Geographical Science》 SCIE CSCD 2019年第3期446-462,共17页
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their ... Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation. 展开更多
关键词 PRECIPITATION Tropical Rainfall Measuring Mission(TRMM) 3B43 geographically weighted regression Kriging(GWRK) SPATIAL DOWNSCALING the Lancang River Basin China
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Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K) 被引量:7
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作者 Samad EMAMGHOLIZADEH Shahin SHAHSAVANI Mohamad Amin ESLAMI 《Chinese Geographical Science》 SCIE CSCD 2017年第5期747-759,共13页
Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of thi... Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients. 展开更多
关键词 precision agriculture soil characteristics INTERPOLATION artificial neural networks geographically weighted regression COKRIGING
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Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China 被引量:7
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作者 WEI Zongcai ZHEN Feng +3 位作者 MO Haitong WEI Shuqing PENG Danli ZHANG Yuling 《Chinese Geographical Science》 SCIE CSCD 2021年第1期54-69,共16页
Mobile information and communication technologies(MICTs) have fully penetrated everyday life in smart societies;this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel beha... Mobile information and communication technologies(MICTs) have fully penetrated everyday life in smart societies;this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China;furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest(POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments. 展开更多
关键词 sharing bicycles travel behaviours smart societies geographically weighted regression analysis Guangzhou China
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Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model 被引量:13
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作者 YANG Jun BAO Yajun +2 位作者 ZHANG Yuqing LI Xueming GE Quansheng 《Chinese Geographical Science》 SCIE CSCD 2018年第3期505-515,共11页
This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source ... This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas. 展开更多
关键词 geographically weighted regression model accessibility house price Dalian City
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Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model 被引量:2
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作者 ZHANG Haitao GUO Long +3 位作者 CHEN Jiaying FU Peihong GU Jianli LIAO Guangyu 《Chinese Geographical Science》 SCIE CSCD 2014年第2期191-204,共14页
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199... This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors. 展开更多
关键词 spatial lag model spatial error model geographically weighted regression model global spatial autocorrelation local spatial aurocorrelation
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Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China 被引量:5
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作者 LIU Yang LI Lan-hai +2 位作者 CHEN Xi YANG Jin-Ming HAO Jian-Sheng 《Journal of Mountain Science》 SCIE CSCD 2018年第1期33-45,共13页
Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect ... Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution. 展开更多
关键词 Snow depth Spatial distribution regression kriging geographically weighted regression kriging
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Selecting suitable sites for mountain ginseng(Panax ginseng)cultivation by using geographically weighted logistic regression 被引量:1
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作者 HAN Hee JANG Kwang-min CHUNG Joo-sang 《Journal of Mountain Science》 SCIE CSCD 2017年第3期492-500,共9页
With the well-being trends to pursue a healthy life, mountain ginseng(Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for ide... With the well-being trends to pursue a healthy life, mountain ginseng(Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for identifying suitable sites for mountain ginseng cultivation in the country. Forest vegetation data were collected from 46 sites and the spatial distribution of all sites was analyzed using GIS data for topographic position, landform, solar radiation, and topographic wetness. The physical and chemical properties of the soil samples, including moisture content, p H, organic matter, total nitrogen, exchangeable cations, available phosphorous, and soil texture, were analyzed. The cultivation suitability at each site was assessed based on the environmental conditions using logistic regression(LR) and geographically weighted logistic regression(GWLR) and the results of both methods were compared. The results show that the areas with northern aspect and higher levels of solar radiation, moisture content, total nitrogen, and sand ratio are more likely to be identified as suitable sites for ginseng cultivation. In contrast to the LR, the spatial modeling with the GWLR results in an increase in the model fitness and indicates that a significant portion of spatialautocorrelation in the data decreases. A higher value of the area under the receiver operating characteristic(ROC) curve presents a better prediction accuracy of site suitability by the GWLR. The geographically weighted coefficient estimates of the model are nonstationary, and reveal that different site suitability is associated with the geographical location of the forest stands. The GWLR increases the accuracy of selecting suitable sites by considering the geographical variations in the characteristics of the cultivation sites. 展开更多
关键词 Panax ginseng Site suitability Logistic regression geographically weighted logistic regression geographic Information System South Korea
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Association between Macroscopic-factors and Identified HIV/AIDS Cases among Injecting Drug Users: An Analysis Using Geographically Weighted Regression Model 被引量:1
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作者 XING Jian Nan GUO Wei +5 位作者 QIAN Sha Sha DING Zheng Wei CHEN Fang Fang PENG Zhi Hang QIN Qian Qian WANG Lu 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2014年第4期311-318,共8页
Drug use (DU), particularly injecting drug use (IDU) has been the main route of transmission and spread of Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDSJ among injecting drug use... Drug use (DU), particularly injecting drug use (IDU) has been the main route of transmission and spread of Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDSJ among injecting drug users (IDUs)[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics. 展开更多
关键词 AIDS HIV An Analysis Using geographically weighted regression Model
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GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression 被引量:1
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作者 Felix Ndidi Nkeki Animam Beecroft Osirike 《Journal of Geographic Information System》 2013年第6期531-542,共12页
Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness ... Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region. 展开更多
关键词 LOCAL STATISTICS Global STATISTICS geographically weighted regression CHOLERA Ordinary Least SQUARE
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Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error
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作者 Ida Mariati Hutabarat Asep Saefuddin +1 位作者 Anik Djuraidah I Wayan Mangku 《Open Journal of Statistics》 2013年第6期417-421,共5页
Geographically weighted regression models with the measurement error are a modeling method that combines the global regression models with the measurement error and the weighted regression model. The assumptions used ... Geographically weighted regression models with the measurement error are a modeling method that combines the global regression models with the measurement error and the weighted regression model. The assumptions used in this model are a normally distributed error with that the expectation value is zero and the variance is constant. The purpose of this study is to estimate the parameters of the model and find the properties of these estimators. Estimation is done by using the Weighted Least Squares (WLS) which gives different weighting to each location. The variance of the measurement error is known. Estimators obtained are . The properties of the estimator are unbiased and have a minimum variance. 展开更多
关键词 geographICAL weighted regression Measurement Error INSTRUMENTAL Variable weighted Least SQUARES
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Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams
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作者 Ken R. Sheehan Stuart A. Welsh 《Journal of Geographic Information System》 2021年第2期194-209,共16页
Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of da... Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R<sup>2 </sup>values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time. 展开更多
关键词 Stream Habitat Modeling geographically weighted regression Spatial Scale Habitat Interpolation geographic Information System
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Geographically Weighted Regression and Secondary Variables for Mapping of Meteorological Data
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作者 Ismail Bulent Gundogdu 《Journal of Geodesy and Geomatics Engineering》 2015年第2期63-72,共10页
GA (geostatistical analyst) is an indispensable tool to analyze various and plenty of data in GIS (geographic information system). Spatial distribution is the most effective factor for predicting of meteorological... GA (geostatistical analyst) is an indispensable tool to analyze various and plenty of data in GIS (geographic information system). Spatial distribution is the most effective factor for predicting of meteorological maps at the point of performance or reliability of the model. Generally, classical interpolation methods may not be sufficient to produce accurate maps. GA is more considerable in this state. Secondary variables affect the precious of prediction models especially meteorological data mapping. In this study 245 meteorological data stations have been evaluated to produce precipitation model maps in Turkey. Long term (25 years) mean annual and monthly precipitation data from Turkish State Meteorological Service and elevation, slope and aspect values from DEM (Digital Elevation Model) were registered. OK (Ordinary Kriging), OCK (Ordinary Co-Kriging) and GWR (Geographically Weighted Regression) have been used as a method to compare the models. With the study if there are effects of secondary variables to precipitation models have been illustrated on the prediction maps. Besides comparing statistical values, regional effects of secondary variables have been determined and illustrated on the maps numerically. As a result to define precipitation distribution spatially R2 values between measured and predicted values have been calculated 0.55 for Kriging, 0.67 for OCK and 0.86 for GWR. Cross validation indicated that GWR interpolation yields the smallest prediction error with elevation, slope and aspect. Spatial distribution of meteorological stations is also other important factor for similar studies. 展开更多
关键词 Geostatistical analyst precipitation map ordinary Co-Kriging geographically weighted regression meteorological data.
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Exploring the drivers of urban expansion in a medium-class urban agglomeration in India using the remote sensing techniques and geographically weighted models
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作者 Tirthankar Basu Arijit Das Paulo Pereira 《Geography and Sustainability》 CSCD 2023年第2期150-160,共11页
Rapid urbanization urges the immediate attention of policymakers to ensure sustainable city development.Under-standing the urban growth drivers is essential to address effective strategies for urbanization-related cha... Rapid urbanization urges the immediate attention of policymakers to ensure sustainable city development.Under-standing the urban growth drivers is essential to address effective strategies for urbanization-related challenges.This work aims to study Raiganj’s urban development and the factors associated with this expansion.This study employed global logistic regression(LR)and geographical weighted logistic regression(GWLR)to explore the role of different factors.The results showed that the role of the central business district(covariate>-1),commercial market(covariate>-3),and police station(covariate>-4)were significant to the development of new built-up areas.In the second period,major roads(covariate>-2)and new infrastructures(covariate>-4)became more relevant,particularly in the eastern and southern areas.GWLR was more accurate in assessing the different fac-tors’impact than LR.The results obtained are essential to understanding urban expansion in India’s medium-class cities,which is critical to effective policies for sustainable urbanization. 展开更多
关键词 DRIVERS geographically weighted logistic regression (GWLR) Logistic regression LULC Urban growth
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Covid-19 in West &East Africa, a Geographical Weighted Regression Exploration with http://mygeoffice.org/
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作者 Joao Negreiros Samia Loucif +1 位作者 Mohammed Amin Kuhail Ahmed Seffah 《Journal of Geoscience and Environment Protection》 2021年第9期20-33,共14页
Understanding the dynamics that affect the spread of Covid-19 is critical for the development of government measures to stop and reverse this nowadays disease propagation. Like in any epidemiological study, it is esse... Understanding the dynamics that affect the spread of Covid-19 is critical for the development of government measures to stop and reverse this nowadays disease propagation. Like in any epidemiological study, it is essential to analyze the spatial data to account for the inherent spatial heterogeneity within the data (spatial autocorrelation). This paper uses Geographically Weighted Regression (GWR) to identify the factors that influence the outbreak of Covid-19 in Western and Eastern countries of Africa. The analyses include traditional linear regression (including descriptive statistics, hierarchical clustering and correlations were not forgotten either) to reveal the importance of eight risk factors (population density, median age, aged over 65 years, GDP per capita, cardiovascular death rates, diabetes prevalence</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> female and male smokers) regarding Covid-19 dissemination. It is believed that this is the first attempt to explore possible causes associated with the spread of the Covid-19 pandemic in these disadvantage countries, where some intriguing clues are presented for further research such as the positive relationship between the financial purchase power of nations and the total number of infected people or the smoker’s gender impact on Covid-19. 展开更多
关键词 Covid-19 STATISTICS Spatial Analysis geographical weighted regression myGeoffice©
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Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method 被引量:1
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作者 Wei Guo Jinke Liu +5 位作者 Xuesheng Zhao Wei Hou Yunxuan Zhao Yongxing Li Wenbin Sun Deqin Fan 《International Journal of Digital Earth》 SCIE EI 2023年第1期2704-2723,共20页
The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological... The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects. 展开更多
关键词 Nighttime light Population density geographically weighted regression Population spatialization Driving force analysis
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Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018 被引量:4
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作者 WANG Ying CHEN Xiaohong +2 位作者 SUN Pingjun LIU Hang HE Jiaxin 《Chinese Geographical Science》 SCIE CSCD 2021年第3期429-443,共15页
To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and ge... To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China. 展开更多
关键词 urban-rural coordination relationship spatial-temporal evolution coupling coordination degree model geographically and temporally weighted regression(GTWR) Northeast China
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Urban Expansion and Spatiotemporal Relationships with Driving Factors Revealed by Geographically Weighted Logistic Regression 被引量:3
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作者 DONG Guanglong XU Erqi ZHANG Hongqi 《Journal of Resources and Ecology》 CSCD 2017年第3期277-286,共10页
Urbanization improves our lives but also threatens human health and sustainable development. Revealing the spatiotemporal pattern of urban expansion and spatiotemporal relationships with driving forces, especially in ... Urbanization improves our lives but also threatens human health and sustainable development. Revealing the spatiotemporal pattern of urban expansion and spatiotemporal relationships with driving forces, especially in terms of the ubiquitous and fast growing small city, is a crucial prerequisite to solving these problems and realizing sustainable development. Kunshan, China was used as a case study here. Eleven variables from four aspects covering physical, socioeconomic, accessibility and neighborhood were selected, and logistic regression and geographically weighted logistic regression modeling were employed to explore spatiotemporal relationships from 1991-2014. Results reveal that urban expansion in Kunshan shows an accelerating tendency with annual expansion from 2000-2014 four times higher than for 1991-2000. More importantly, the annual expansion rate of Kunshan of 28.42% in 2000-2014 is higher than that of a large city. Urban expansion and related factors have spatiotemporal varying relationships. From a global perspective, the closer to a city, town, main road and the higher the GDP, the more likely a region will undergo urbanization. Interestingly, the effect of population on urban expansion is decreasing, especially in developed areas, and the effect of distance to lake is enhanced. From a local perspective, the magnitude and even the sign of the coefficients vary across the study area. However, the range of the coefficient of GWLR is around that of the corresponding variable in LR, and the sign of most variables in GWLR is consistent with that of corresponding variables in LR. GWLR surpasses LR with the same explanatory variables in revealing regional differences and improving model reliability. Based on these findings, more attention should be given to small cities in China. Promoting the connotation of city culture and public services to realize New-type Urbanization and regional diversity policy in order to manage urban expansion scientifically are also recommended. 展开更多
关键词 urban expansion spatiotemporal variation spatial heterogeneity geographically weighted logistic regression China
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