Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orien...Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.展开更多
Cropland abandonment has been a widespread phenomenon in mountainous areas due to the increasing number of natural disasters and the massive migration of rural labor in the process of rapid urbanization.Land transfer ...Cropland abandonment has been a widespread phenomenon in mountainous areas due to the increasing number of natural disasters and the massive migration of rural labor in the process of rapid urbanization.Land transfer is a crucial prerequisite for ensuring food security and fostering rural revitalization.How to promote land transfer in mountainous areas remains a challenging but important task.Nevertheless,there is a dearth of research examining land transfers among farm households that specifically address mountainous regions,and the influence of grassroots governance and geographic location has not been thoroughly elucidated within this particular context.Based on 895household samples collected in Dabie Mountainous Area in China,this study employs binary and ordinal logistic regression models to provide a more comprehensive analysis on land transfers among rural households and the determinants,including the decision to transfer land,the existence of land transfer rents,the channel of land transfer,the duration of the transfer,the pre-transfer cultivation situation,and the level of satisfaction with the land transfer rent.The findings reveal that grassroots governance,geographic location,livelihood capital,and demographic factors significantly influence land transfers among rural households.Specifically,villagers'public participation positively affects land transfer participation(β=0.235,p<0.05),while the operation of village rules and regulations negatively impacts it(β=-0.296,p<0.05).Village cadre satisfaction positively influences both land transfer rent(β=0.274,p<0.05)and rent satisfaction(β=0.303,p<0.05).Improved civil relations in the village correlate with lower land transfer rent(β=-0.511,p<0.05),while a better social atmosphere is associated with higher satisfaction with transfer rent(β=0.575,p<0.01).Households at higher elevations tend to prefer government-mediated land transfers with longer durations.The distances to the township and county centers have contrasting effects on land transfer rent,but their impacts on participation in land transfer,choice of transfer channel,and duration are consistent.The study also found that different types of livelihood capital,as well as the demographic characteristics of households,significantly affect various aspects of land transfer.These empirical findings can inform policymaking to promote more efficient land transfers in mountainous region.展开更多
AIM:To determine the differences in levels of systemic C-reactive protein(CRP)in patients with geographic atrophy(GA)and sex-based differences in CRP levels.METHODS:Blood samples from patients with GA and controls wer...AIM:To determine the differences in levels of systemic C-reactive protein(CRP)in patients with geographic atrophy(GA)and sex-based differences in CRP levels.METHODS:Blood samples from patients with GA and controls were collected in a prospective age-related macular degeneration(AMD)registry from August 2014 to June 2021.AMD was confirmed using multimodal imaging and the Beckman and Consensus of Atrophy Meeting criteria for GA.High-sensitivity serum CRP levels were measured using an automated nephelometer.A non-parametric(rank-based)linear regression model was fit with an interaction between sex and GA.RESULTS:There were 97 GA patients and 139 controls,with females comprising 55%and 66%of each cohort,respectively.There is no difference in CRP between cases and controls,with a median(interquartile range)of 1.2(0.6-2.6)mg/L in GA patients versus 1.3(0.8–2.9)mg/L in controls(P=0.52).Although females had higher CRP levels compared to males in both the GA and control groups,this difference did not reach statistical significance after adjustment for multiple comparisons.CONCLUSION:There is no significant difference in systemic CRP levels between GA cases and controls.展开更多
This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)syste...This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.展开更多
Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ...Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ogy)with social and human(e.g.,education,demography,sociology)sciences.The spatialisation of information from different sciences allows us to understand distribution patterns and connections between different realities.Thus,geographical knowledge is essential for an integrated and consistent understanding of our world.The Sus tainable Development Goals(SDGs)established by the United Nations(UN)in 2015 were essential to unifying the world towards a common goal.To achieve these,17 goals and 169 targets were created,and knowledge from multiple sciences is needed to support them.It is a huge challenge,and different knowledge branches are needed to connect.Geography and geographical knowledge have this capacity and support all 17 goals and 169 targets.Although this is a reality,as it will be explained in this editorial,SDG’s achievement for some is becoming utopic and unrealistic due to our world’s differences.It is time to think about the post-2030 SDGs,in which geography and geographic knowledge will be essential unequivocally.展开更多
The Yangtze River Economic Belt is the main rice producing area in China.The rice industry chain is the agricultural pillar industry chain of this economic belt and it is the key to ensuring national food security and...The Yangtze River Economic Belt is the main rice producing area in China.The rice industry chain is the agricultural pillar industry chain of this economic belt and it is the key to ensuring national food security and promoting comprehensive rural revitalization.This study discusses the entire rice industry chain in the Yangtze River Economic Belt from the national rice production functional zones,agricultural product quality and safety,national famous and excellent new agricultural products,national specialty agricultural products,"China's good grain and oil"products,and national advantageous characteristic industrial clusters.Then,it discusses the geographical indications of rice and its products in this economic belt from geographical indication products,geographical indication trademarks,agricultural geographical indications,geographical indication standards,geographical indication special indications,national geographical indication product protection demonstration zones,and Chinese geographical indication products protected by the European Union.In addition,it analyzes the five main problems between geographical indications and public brands,such as the limited use of geographical indication specific signs and the imperfect intellectual property protection system for geographical indications.Finally,it proposes eight strategies,including promoting the high-quality development of the entire rice industry chain,creating a geographical indication regional public brand for rice and its products,and implementing geographical indication protection projects.展开更多
Dear Editor,In recent decades,vector-transmitted emerging and re-emerging diseases pose public health issues around the world,in which emerging tick-borne viruses(TBVs)have played a major role since they are widely di...Dear Editor,In recent decades,vector-transmitted emerging and re-emerging diseases pose public health issues around the world,in which emerging tick-borne viruses(TBVs)have played a major role since they are widely distributed.TBVs have a wide range of hosts,including humans,livestock and rodents,with some of them able to cause severe diseases in human and domestic animals,such as Jingmen tick virus(JMTV)(Qin et al.,2014),tick-borne encephalitis virus(TBEV)(Xing et al.,2017),and Alongshan virus(ALSV)(Wang et al.,2019).Of the merging TBVs,JMTV is a novel pathogen that was first identified in Rhipicephalus microplus collected from the Jingmen city of Hubei province,China in 2010(Qin et al.,2014).展开更多
Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling...Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.展开更多
Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study propos...Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.展开更多
OBJECTIVE:To evaluate the quality of Moyao(Myrrh)in the identification of the geographical origin and processing of the products.METHODS:Raw Moyao(Myrrh)and two kinds of Moyao(Myrrh)processed with vinegar from three c...OBJECTIVE:To evaluate the quality of Moyao(Myrrh)in the identification of the geographical origin and processing of the products.METHODS:Raw Moyao(Myrrh)and two kinds of Moyao(Myrrh)processed with vinegar from three countries were identified using near-infrared(NIR)spectroscopy combined with chemometric techniques.Principal component analysis(PCA)was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories.A classical chemometric algorithm(PLS-DA)and two machine learning algorithms[K-nearest neighbor(KNN)and support vector machine]were used to conduct a classification analysis of the near-infrared spectra of the Moyao(Myrrh)samples,and their discriminative performance was evaluated.RESULTS:Based on the accuracy,precision,recall rate,and F1 value in each model,the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results.In all of the chemometric analyses,the NIR spectrum of Moyao(Myrrh)preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins,and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best.The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.CONCLUSIONS:NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao(Myrrh)and can also provide a reference for evaluations of its quality and the clinical use.展开更多
Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection...Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.展开更多
Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local conten...Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.展开更多
In anurans,advertisement calls(ACs)are an essential form of intraspecific communication.This study evaluates geographical variation in the ACs of Leptobrachella ventripunctata in the Guizhou Plateau,southwestern China...In anurans,advertisement calls(ACs)are an essential form of intraspecific communication.This study evaluates geographical variation in the ACs of Leptobrachella ventripunctata in the Guizhou Plateau,southwestern China,and explores correlations between call characteristics,body size,and environmental factors.ACs are simple calls of L.ventripunctata,and apparent differences were observed in the ACs among different geographical populations of L.ventripunctata.The Call duration(CD)revealed a significant positive correlation with altitude and a significant negative correlation with temperature and humidity.Moreover,the Dominant frequency(DF)exhibited a significant negative correlation with altitude and the habitat closure degree and a significant positive correlation with temperature.These variations in ACs between different geographical populations of L.ventripunctata may critically impact the adaptive evolution of species,and the calls may also be relevant for environmental selection.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
This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysi...This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.展开更多
Nowadays,an increasing number of crises worldwide,triggered by climate extremes,natural and human-made hazards,the coronavirus pandemic,and more,pose a high pressure on crisis,emergency,and disaster management.Spatial...Nowadays,an increasing number of crises worldwide,triggered by climate extremes,natural and human-made hazards,the coronavirus pandemic,and more,pose a high pressure on crisis,emergency,and disaster management.Spatial data and Volunteered Geographic Information(VGI)are key issues in the successful and immediate response to crises.This paper aims to explore the use of VGI in crisis management,including emergency and disaster management,based on a scoping review of existing literature in English for five years(2016-2020).Specifically,the research intends to answer Scoping Review Questions(SRQ)regarding the use of VGI in crisis,emergency,and disaster management,and the verified cases’spatial distribution,the VGI sources utilized(e.g.OpenStreetMap-OSM,Crowdsourcing,Twitter),the types of hazards(e.g.natural and human-made hazards,pandemic),the specific tasks in crisis,emergency or disaster management and VGI use in the management of actual crisis events,e.g.COVID-19 pandemic,Hurricane Katrina,etc.Eligible papers on VGI use in crisis,emergency,and disaster management are geolocated based on first-author affiliation,and as a result,a spatial bibliography is provided.Thus,the term Spatial Scoping Review is introduced.Scoping Review Questions are answered,and the results are analyzed and discussed.Finally,implementing the“VGICED Atlas”,a web atlas,permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps.展开更多
Augmented Reality(AR)offers new opportunities for Citizen Science(CS)projects regarding data visualization,data collection,and training of participants.Since limited research on the usage of AR in CS projects exists,a...Augmented Reality(AR)offers new opportunities for Citizen Science(CS)projects regarding data visualization,data collection,and training of participants.Since limited research on the usage of AR in CS projects exists,an online survey is conducted in this study by reaching out to CS project managers to determine the extent of its current use.The survey can identify areas where CS project managers themselves see the greatest potential for AR in their projects and reasons that exist against the use of AR.A total of 53 CS project managers participated in the survey and shared their opinions and concerns.Of all participating CS projects,only three are currently using AR.However,27 CS projects indicated that AR could be beneficial for their project.Especially projects with a geographic focus,in which participants are involved in the process of collecting spatial data,expressed this opinion.Particularly in the areas“data visualization”and“attraction/motivation of participants”the projects identified potential for AR.Arguments against the use of AR named by 23 CS projects include remote study areas,financial considerations,and the lack of a practical use case.This study shows initial trends regarding the use of AR in CS projects and highlights specific use cases for the application of AR.展开更多
Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried ...Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.展开更多
In Mali,access to healthcare is a major concern.It has been a national priority since the Alma-Ata Declaration in 1978.Since then,major efforts have been made by the State and its partners to achieve this objective.Th...In Mali,access to healthcare is a major concern.It has been a national priority since the Alma-Ata Declaration in 1978.Since then,major efforts have been made by the State and its partners to achieve this objective.These efforts appear insufficient in the Bougouni health district,wheremore than half the population is still far frombasic health services.Given this situation,it is essential to assess geographical accessibility to healthcare to identify the localities that have been left behind,hence the purpose of this research in the Bougouni District.Three types of data were used:the size of the population,the location of community health centers and the road network.Two consecutive stages summarize the approach used,the improved two-stage floating attraction area method(E2SFCA)was used to measure the spatial dimension of accessibility to health services.It was supplemented by spatial autocorrelation analyses,particularly the local Moran index,to detect clusters of localities with low or high spatial accessibility values.The results reveal strong spatial disparities in access to primary healthcare within and between communes and at the same time call into question the effectiveness of the policy of geographical coverage of the population in terms of basic healthcare.They also highlight the demographic pressure on existing health services and suggest ways of significantly improving geographical access to health services.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42293270)。
文摘Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.42371315,41901213)the Humanities and Social Sciences General Research Program of the Ministry of Education(Grant No.23YJC790141)。
文摘Cropland abandonment has been a widespread phenomenon in mountainous areas due to the increasing number of natural disasters and the massive migration of rural labor in the process of rapid urbanization.Land transfer is a crucial prerequisite for ensuring food security and fostering rural revitalization.How to promote land transfer in mountainous areas remains a challenging but important task.Nevertheless,there is a dearth of research examining land transfers among farm households that specifically address mountainous regions,and the influence of grassroots governance and geographic location has not been thoroughly elucidated within this particular context.Based on 895household samples collected in Dabie Mountainous Area in China,this study employs binary and ordinal logistic regression models to provide a more comprehensive analysis on land transfers among rural households and the determinants,including the decision to transfer land,the existence of land transfer rents,the channel of land transfer,the duration of the transfer,the pre-transfer cultivation situation,and the level of satisfaction with the land transfer rent.The findings reveal that grassroots governance,geographic location,livelihood capital,and demographic factors significantly influence land transfers among rural households.Specifically,villagers'public participation positively affects land transfer participation(β=0.235,p<0.05),while the operation of village rules and regulations negatively impacts it(β=-0.296,p<0.05).Village cadre satisfaction positively influences both land transfer rent(β=0.274,p<0.05)and rent satisfaction(β=0.303,p<0.05).Improved civil relations in the village correlate with lower land transfer rent(β=-0.511,p<0.05),while a better social atmosphere is associated with higher satisfaction with transfer rent(β=0.575,p<0.01).Households at higher elevations tend to prefer government-mediated land transfers with longer durations.The distances to the township and county centers have contrasting effects on land transfer rent,but their impacts on participation in land transfer,choice of transfer channel,and duration are consistent.The study also found that different types of livelihood capital,as well as the demographic characteristics of households,significantly affect various aspects of land transfer.These empirical findings can inform policymaking to promote more efficient land transfers in mountainous region.
基金Supported by the Greenwald Family Research Fund,a Research to Prevent Blindness grant to the Department of Ophthalmology,University of Colorado,the Frederic C.Hamilton Macular Degeneration Center,Sue Anschutz-Rodgers Eye Center Research Fund,NIH/NCATS Colorado CTSA(No.UL1 TR002535)in part by the National Eye Institute of the National Institutes of Health[No.R01EY032456(AML)].
文摘AIM:To determine the differences in levels of systemic C-reactive protein(CRP)in patients with geographic atrophy(GA)and sex-based differences in CRP levels.METHODS:Blood samples from patients with GA and controls were collected in a prospective age-related macular degeneration(AMD)registry from August 2014 to June 2021.AMD was confirmed using multimodal imaging and the Beckman and Consensus of Atrophy Meeting criteria for GA.High-sensitivity serum CRP levels were measured using an automated nephelometer.A non-parametric(rank-based)linear regression model was fit with an interaction between sex and GA.RESULTS:There were 97 GA patients and 139 controls,with females comprising 55%and 66%of each cohort,respectively.There is no difference in CRP between cases and controls,with a median(interquartile range)of 1.2(0.6-2.6)mg/L in GA patients versus 1.3(0.8–2.9)mg/L in controls(P=0.52).Although females had higher CRP levels compared to males in both the GA and control groups,this difference did not reach statistical significance after adjustment for multiple comparisons.CONCLUSION:There is no significant difference in systemic CRP levels between GA cases and controls.
文摘This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.
文摘Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ogy)with social and human(e.g.,education,demography,sociology)sciences.The spatialisation of information from different sciences allows us to understand distribution patterns and connections between different realities.Thus,geographical knowledge is essential for an integrated and consistent understanding of our world.The Sus tainable Development Goals(SDGs)established by the United Nations(UN)in 2015 were essential to unifying the world towards a common goal.To achieve these,17 goals and 169 targets were created,and knowledge from multiple sciences is needed to support them.It is a huge challenge,and different knowledge branches are needed to connect.Geography and geographical knowledge have this capacity and support all 17 goals and 169 targets.Although this is a reality,as it will be explained in this editorial,SDG’s achievement for some is becoming utopic and unrealistic due to our world’s differences.It is time to think about the post-2030 SDGs,in which geography and geographic knowledge will be essential unequivocally.
基金Supported by Social Science Foundation of Hubei Province (HBSKJJ20243227),Doctoral Initiation Project of Hubei University of Science and Technology (BK201819).
文摘The Yangtze River Economic Belt is the main rice producing area in China.The rice industry chain is the agricultural pillar industry chain of this economic belt and it is the key to ensuring national food security and promoting comprehensive rural revitalization.This study discusses the entire rice industry chain in the Yangtze River Economic Belt from the national rice production functional zones,agricultural product quality and safety,national famous and excellent new agricultural products,national specialty agricultural products,"China's good grain and oil"products,and national advantageous characteristic industrial clusters.Then,it discusses the geographical indications of rice and its products in this economic belt from geographical indication products,geographical indication trademarks,agricultural geographical indications,geographical indication standards,geographical indication special indications,national geographical indication product protection demonstration zones,and Chinese geographical indication products protected by the European Union.In addition,it analyzes the five main problems between geographical indications and public brands,such as the limited use of geographical indication specific signs and the imperfect intellectual property protection system for geographical indications.Finally,it proposes eight strategies,including promoting the high-quality development of the entire rice industry chain,creating a geographical indication regional public brand for rice and its products,and implementing geographical indication protection projects.
基金supported by Wildlife Borne Infectious Diseases Monitoring Project of the State Forestry and Grassland Administration of China(2020076060)the National Key Research and Development Program of China to Changchun Tu(32130104).
文摘Dear Editor,In recent decades,vector-transmitted emerging and re-emerging diseases pose public health issues around the world,in which emerging tick-borne viruses(TBVs)have played a major role since they are widely distributed.TBVs have a wide range of hosts,including humans,livestock and rodents,with some of them able to cause severe diseases in human and domestic animals,such as Jingmen tick virus(JMTV)(Qin et al.,2014),tick-borne encephalitis virus(TBEV)(Xing et al.,2017),and Alongshan virus(ALSV)(Wang et al.,2019).Of the merging TBVs,JMTV is a novel pathogen that was first identified in Rhipicephalus microplus collected from the Jingmen city of Hubei province,China in 2010(Qin et al.,2014).
基金National Natural Science Foundation of China,No.41971219,No.41571168Natural Science Foundation of Hunan Province,No.2020JJ4372+1 种基金Key Project of Philosophy and Social Science Foundation of Hunan Province,No.18ZDB015The Graduate Science and Innovation Project of Hunan Province,No.CX20230719。
文摘Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.
基金the National Key Research and Development Program of China(Grant No.2022YFF1302405)the Yunnan Province Key Research and Development Program(Grant No.202203AC100005)+1 种基金the National Natural Science Foundation of China(Grant No.42061005,42067033)Applied Basic Research Programs of Yunnan Province(Grant No.202101AT070110,202001BB050073).
文摘Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.
基金Jiangxi Provincial Administration of Traditional Chinese Medicine Key Research Laboratory on the Fundamentals of Chinese Medicine Evidence(Gan TCM Science and Education Word[2022]No.8-4)Jiangxi University of Chinese Medicine Science and Technology Innovation Team Development Program:Traditional Chinese Medicine Constitution-State Identification Health Management Research Team(No.CXTD22016)。
文摘OBJECTIVE:To evaluate the quality of Moyao(Myrrh)in the identification of the geographical origin and processing of the products.METHODS:Raw Moyao(Myrrh)and two kinds of Moyao(Myrrh)processed with vinegar from three countries were identified using near-infrared(NIR)spectroscopy combined with chemometric techniques.Principal component analysis(PCA)was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories.A classical chemometric algorithm(PLS-DA)and two machine learning algorithms[K-nearest neighbor(KNN)and support vector machine]were used to conduct a classification analysis of the near-infrared spectra of the Moyao(Myrrh)samples,and their discriminative performance was evaluated.RESULTS:Based on the accuracy,precision,recall rate,and F1 value in each model,the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results.In all of the chemometric analyses,the NIR spectrum of Moyao(Myrrh)preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins,and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best.The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.CONCLUSIONS:NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao(Myrrh)and can also provide a reference for evaluations of its quality and the clinical use.
基金the Natural Science Foundation of Inner Mongolia,China(2023JQ01)the National Key R&D Program of China(2019YFA0607103)+2 种基金the Central Government Guides Local Science and Technology Development Fund Projects(2022ZY0224)the Open Project Program of Ministry of Education Key Laboratory of Ecology and Resources Use of the Mongolian Plateau,Hohhot,Inner Mongolia,China(KF2023003)Major Science and Technology Project of Inner Mongolia Autonomous Region:Monitoring,Assessment and Early Warning Technology Research of Biodiversity in Inner Mongolia(2021ZD0011)for financial support.
文摘Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.
基金This work was supported by the National Key R&D Program of China(No.2022YFB3102904)the National Natural Science Foundation of China(No.62172435,U23A20305)Key Research and Development Project of Henan Province(No.221111321200).
文摘Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.
基金supported by the National Natural Science Foundation of China(32060307 and 31860610)Guizhou Provincial Science and Technology Planning Project[[2021]500].
文摘In anurans,advertisement calls(ACs)are an essential form of intraspecific communication.This study evaluates geographical variation in the ACs of Leptobrachella ventripunctata in the Guizhou Plateau,southwestern China,and explores correlations between call characteristics,body size,and environmental factors.ACs are simple calls of L.ventripunctata,and apparent differences were observed in the ACs among different geographical populations of L.ventripunctata.The Call duration(CD)revealed a significant positive correlation with altitude and a significant negative correlation with temperature and humidity.Moreover,the Dominant frequency(DF)exhibited a significant negative correlation with altitude and the habitat closure degree and a significant positive correlation with temperature.These variations in ACs between different geographical populations of L.ventripunctata may critically impact the adaptive evolution of species,and the calls may also be relevant for environmental selection.
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This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.
基金the interdisciplinary project“BigWa-Civil protection and security research in social and technological change”,funded by the NRW Ministry of Innovation,Science and Research and the MIWF-Funding Program FH-STRUKTUR 2016/08[Grant Number 322-8.03.04.02].
文摘Nowadays,an increasing number of crises worldwide,triggered by climate extremes,natural and human-made hazards,the coronavirus pandemic,and more,pose a high pressure on crisis,emergency,and disaster management.Spatial data and Volunteered Geographic Information(VGI)are key issues in the successful and immediate response to crises.This paper aims to explore the use of VGI in crisis management,including emergency and disaster management,based on a scoping review of existing literature in English for five years(2016-2020).Specifically,the research intends to answer Scoping Review Questions(SRQ)regarding the use of VGI in crisis,emergency,and disaster management,and the verified cases’spatial distribution,the VGI sources utilized(e.g.OpenStreetMap-OSM,Crowdsourcing,Twitter),the types of hazards(e.g.natural and human-made hazards,pandemic),the specific tasks in crisis,emergency or disaster management and VGI use in the management of actual crisis events,e.g.COVID-19 pandemic,Hurricane Katrina,etc.Eligible papers on VGI use in crisis,emergency,and disaster management are geolocated based on first-author affiliation,and as a result,a spatial bibliography is provided.Thus,the term Spatial Scoping Review is introduced.Scoping Review Questions are answered,and the results are analyzed and discussed.Finally,implementing the“VGICED Atlas”,a web atlas,permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps.
基金support by the Open Access Publication Funds of Technische Universitat Braunschweig.
文摘Augmented Reality(AR)offers new opportunities for Citizen Science(CS)projects regarding data visualization,data collection,and training of participants.Since limited research on the usage of AR in CS projects exists,an online survey is conducted in this study by reaching out to CS project managers to determine the extent of its current use.The survey can identify areas where CS project managers themselves see the greatest potential for AR in their projects and reasons that exist against the use of AR.A total of 53 CS project managers participated in the survey and shared their opinions and concerns.Of all participating CS projects,only three are currently using AR.However,27 CS projects indicated that AR could be beneficial for their project.Especially projects with a geographic focus,in which participants are involved in the process of collecting spatial data,expressed this opinion.Particularly in the areas“data visualization”and“attraction/motivation of participants”the projects identified potential for AR.Arguments against the use of AR named by 23 CS projects include remote study areas,financial considerations,and the lack of a practical use case.This study shows initial trends regarding the use of AR in CS projects and highlights specific use cases for the application of AR.
基金supported by the National Natural Science Foundation of China[grant numbers 42101449,42090012 and 61825103]the Natural Science Foundation of Hubei Province,China[grant numbers 2022CFB773 and 2020CFA001]+2 种基金the Key Research and Development Program of Hubei Province,China[grant number 2022BAA048]the Chutian Scholar Program of Hubei Provincethe Yellow Crane Talent Scheme.
文摘Road traffic crashes are becoming thorny issues being faced worldwide.Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out.However,the impact of built environment on traffic crash spatiotemporal trends has not received much attention.Moreover,the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected.To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes,this paper proposed a method of“ST-GWLR”for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression(GWLR)modeling methods.Firstly,the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model,hotspot analysis,and Mann-Kendall trend test.Then,the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression(GLR)model,to improve the accuracy of the model estimation.GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors,to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes.The performance of the GWLR models and GLR models was examined and compared sufficiently.The results showed that the proposed ST-GWLR,which captured spatial non-stationarity,performed better than the classic GLR combined with spatiotemporal analysis,and improved the prediction accuracy of the models by 14.9%,13.9%,and 15.1%,respectively.There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors.The findings of this paper have positive implications for traffic safety management and urban built environment planning.
文摘In Mali,access to healthcare is a major concern.It has been a national priority since the Alma-Ata Declaration in 1978.Since then,major efforts have been made by the State and its partners to achieve this objective.These efforts appear insufficient in the Bougouni health district,wheremore than half the population is still far frombasic health services.Given this situation,it is essential to assess geographical accessibility to healthcare to identify the localities that have been left behind,hence the purpose of this research in the Bougouni District.Three types of data were used:the size of the population,the location of community health centers and the road network.Two consecutive stages summarize the approach used,the improved two-stage floating attraction area method(E2SFCA)was used to measure the spatial dimension of accessibility to health services.It was supplemented by spatial autocorrelation analyses,particularly the local Moran index,to detect clusters of localities with low or high spatial accessibility values.The results reveal strong spatial disparities in access to primary healthcare within and between communes and at the same time call into question the effectiveness of the policy of geographical coverage of the population in terms of basic healthcare.They also highlight the demographic pressure on existing health services and suggest ways of significantly improving geographical access to health services.
