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
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
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
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
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
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
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
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
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
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
With the rapid expansion of urban areas,intercity highways have become crucial for daily transportation.Traffic administrators and planners increasingly rely on evaluating high way traffic volume.This paper aims to in...With the rapid expansion of urban areas,intercity highways have become crucial for daily transportation.Traffic administrators and planners increasingly rely on evaluating high way traffic volume.This paper aims to investigate the relationship between various factors and intercity traffic volume,with a specific focus on exploring the advancing and lagging effects of weather conditions on traffic volume in the districts of urban agglomerations.Using multiple data sources in the Guangdong-Hong Kong-Macao Greater Bay Area,includ ing weather factors(i.e.,rain,temperature,wind,and visibility),traffic factors(i.e.,total traffic volume and travel time),and other factors(i.e.,node degree,hub cities,and time of day),a mixed geographically weighted regression(MGWR)model is applied to examine the spatial heterogeneity of these factors.The results show that intercity traffic volume is influenced by weather,traffic,and other factors.Additionally,the advancing and lagging effects of different weather factors exhibit spatial heterogeneity across districts.Moreover,the weather lagging effect has a more significant impact than the advancing effect on intercity traffic volume.These findings provide valuable insights into the impact of weather on intercity travel volume and offer precise traffic guidance for intercity travelers.展开更多
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
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 uti...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 R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=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(R^(2)=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.展开更多
The surface air temperature lapse rate(SATLR)plays a key role in the hydrological,glacial and ecological modeling,the regional downscaling,and the reconstruction of high-resolution surface air temperature.However,how ...The surface air temperature lapse rate(SATLR)plays a key role in the hydrological,glacial and ecological modeling,the regional downscaling,and the reconstruction of high-resolution surface air temperature.However,how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for researchers.The geographically weighted regression(GWR)model was applied in this paper to estimate the SATLR in China’s mainland,and then the assessment and validation for the GWR model were made.The spatial pattern of regression residuals which was identified by Moran’s Index indicated that the GWR model was broadly reasonable for the estimation of SATLR.The small mean absolute error(MAE)in all months indicated that the GWR model had a strong predictive ability for the surface air temperature.The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation.Therefore,the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition.展开更多
This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province,China,owing to the influences of spatial attri...This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province,China,owing to the influences of spatial attributes on the irrigation efficiency.The sample set of this study comprised 165 agricultural test sites.A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables,including the actual irrigation area,the percentage of farmland using water-saving irrigation,the type of irrigation area,the net water consumption per mu,the water intake method,the terrain slope,and the soil field capacity.Results revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang Province.The geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression model.The obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang,and offer a new approach on a regional scale.展开更多
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
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
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
Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like pop...Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.展开更多
Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explo...Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.展开更多
基金The National Natural Science Foundation of China(No.51378099)
文摘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.
基金Foundation item:Under the auspices of Shahrood University of Technology,Iran(No.348517)
文摘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.
基金Under the auspices of National Natural Science Foundation of China(No.41801150,41571146,41801144)Natural Science Foundation of Guangdong Province(No.2018A030310392)+2 种基金Guangdong Planning Project of Philosophy and Social Science(No.GD17YGL01)Science and Technology Program of Guangzhou(No.201906010033)GDAS’(Guangdong Academy of Sciences)Project of Science and Technology Development(No.2020GDASYL-20200104007)。
文摘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.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘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.
基金Under the auspices of National Natural Science Foundation of China(No.41471140,41771178)Liaoning Province Outstanding Youth Program(No.LJQ2015058)
文摘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.
基金supported by Projects of International Cooperation and Exchanges NSFC (grant: 41361140361)the Special fund project of Chinese Academy of Sciences (grant: Y371164001)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2, KZZD-EW12-3)
文摘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.
基金supported by the National Scientific Research Mega-Project under the 12th Five-Year Plan of China(2012ZX10001001)
文摘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.
文摘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.
文摘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.
文摘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.
基金This study was partially funded by the National Natural Science Foundation of China through grant 52072130the Guangdong Basic and Applied Basic Research Foundation through grant 2021A1515010409.
文摘With the rapid expansion of urban areas,intercity highways have become crucial for daily transportation.Traffic administrators and planners increasingly rely on evaluating high way traffic volume.This paper aims to investigate the relationship between various factors and intercity traffic volume,with a specific focus on exploring the advancing and lagging effects of weather conditions on traffic volume in the districts of urban agglomerations.Using multiple data sources in the Guangdong-Hong Kong-Macao Greater Bay Area,includ ing weather factors(i.e.,rain,temperature,wind,and visibility),traffic factors(i.e.,total traffic volume and travel time),and other factors(i.e.,node degree,hub cities,and time of day),a mixed geographically weighted regression(MGWR)model is applied to examine the spatial heterogeneity of these factors.The results show that intercity traffic volume is influenced by weather,traffic,and other factors.Additionally,the advancing and lagging effects of different weather factors exhibit spatial heterogeneity across districts.Moreover,the weather lagging effect has a more significant impact than the advancing effect on intercity traffic volume.These findings provide valuable insights into the impact of weather on intercity travel volume and offer precise traffic guidance for intercity travelers.
基金supported by National Science and Technology Infrastructure Platform National Population and Health Science Data Sharing Service Platform Public Health Science Data Center[NCMI-ZB01N-201905]。
文摘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.
文摘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 R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=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(R^(2)=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.
基金The National Key R&D Program,No.2018YFA0605603National Natural Science Foundation of China,No.41575003。
文摘The surface air temperature lapse rate(SATLR)plays a key role in the hydrological,glacial and ecological modeling,the regional downscaling,and the reconstruction of high-resolution surface air temperature.However,how to accurately estimate the SATLR in the regions with complex terrain and climatic condition has been a great challenge for researchers.The geographically weighted regression(GWR)model was applied in this paper to estimate the SATLR in China’s mainland,and then the assessment and validation for the GWR model were made.The spatial pattern of regression residuals which was identified by Moran’s Index indicated that the GWR model was broadly reasonable for the estimation of SATLR.The small mean absolute error(MAE)in all months indicated that the GWR model had a strong predictive ability for the surface air temperature.The comparison with previous studies for the seasonal mean SATLR further evidenced the accuracy of the estimation.Therefore,the GWR method has potential application for estimating the SATLR in a large region with complex terrain and climatic condition.
基金This study was supported by the National Key R&D Program of China(Nos.2016YFC0401005 and 2016YFA0601703)the National Natural Science Foundation of China(Grant Nos.42075191,92047203 and 91847301)Nanjing Hydraulic Research Institute Fund(No.Y520009).We thank Chinese Academy of Meteorological Sciences for providing monitoring data of the study area.
文摘This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province,China,owing to the influences of spatial attributes on the irrigation efficiency.The sample set of this study comprised 165 agricultural test sites.A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables,including the actual irrigation area,the percentage of farmland using water-saving irrigation,the type of irrigation area,the net water consumption per mu,the water intake method,the terrain slope,and the soil field capacity.Results revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang Province.The geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression model.The obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang,and offer a new approach on a regional scale.
基金R&D Program for Forestry Technology funded by Korea Forest Service(Project No.S121012L100100)the framework of international cooperation program funded by National Research Foundation of Korea(2013K2A2A4000649,FY2013)
文摘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.
文摘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.
文摘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.
基金funded by The Third Comprehensive Scientific Investigation in Xinjiang(Grant No.2021xjkk1001)Program of National Social Science Foundation of China(Grant No.22BJL061)+1 种基金Major Project of Xinjiang Social Science Foundation(Grant No.21AZD008)The National Natural Science Foundation of China(Grant No.41461035).
文摘Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.
基金Jiangxi Provincial Innovative Training Project“Post-earthquake Landslide Risk Evaluation Study under Spatial Scale Modelling”(Project No.:S202311318050)。
文摘Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.
