In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to q...In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.展开更多
Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of ...Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.展开更多
Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application ...Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.展开更多
COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.D...COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.展开更多
Energy crisis and climate change have become two seriously concerned issues universally. As a feasible solution, Global Energy Interconnection(GEI) has been highly praised and positively responded by the international...Energy crisis and climate change have become two seriously concerned issues universally. As a feasible solution, Global Energy Interconnection(GEI) has been highly praised and positively responded by the international community once proposed by China. From strategic conception to implementation, GEI development has entered a new phase of joint action now. Gathering and building a global grid database is a prerequisite for conducting research on GEI. Based on the requirement of global grid data management and application, combining with big data and geographic information technology, this paper studies the global grid data acquisition and analysis process, sorts out and designs the global grid database structure supporting GEI research, and builds a global grid database system.展开更多
Urban vitality is a complex and multifaceted concept that is pivotal to the livability and sustainability of cities.Recent studies have measured urban vitality and its relationship with the built environment from the ...Urban vitality is a complex and multifaceted concept that is pivotal to the livability and sustainability of cities.Recent studies have measured urban vitality and its relationship with the built environment from the perspectives of rationality and efficiency.However,in the context of new urbanization with Chinese characteristics which emphasizes people-oriented values,more emphases need to be placed on the subjective feelings of residents in studies of urban vitality.This paper focuses on Hefei,a representative second-tier city in central China,to explore the relationship between urban vitality and the built environment by utilizing multi-source big data,spatial autocorrelation,and geographic detector model.Urban vitality is measured in the two dimensions of population intensity and emotion intensity.The built environment is measured based on Maslow's theory of needs,encompassing the five dimensions of accessibility,convenience,safety,socialization,and aesthetics.Taking Hefei as a case,the paper proposes 18 built environment factors that may influence urban vitality and identifies 14 factors that significantly influence the urban vitality of emerging cities in China.The built environment factors with the most significant impact on urban vitality are POI accessibility on weekdays and public transport on weekends.In addition,the interaction effects between any two built environment factors are higher than that of a single factor.The results effectively reveal the influencing mechanisms of urban vitality and can help urban planners and policymakers to develop more targeted strategies to enhance urban vitality by optimizing the built environment.展开更多
In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,thi...In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.展开更多
Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetl...Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.展开更多
【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报...【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报完整的学术论文,对文章信息、样本基本信息、研究分析方法、因变量和自变量信息等内容进行系统梳理,在此基础上对论文结果进行量化荟萃分析。【结果】自然环境、建成环境、社会环境及主观感知环境均与移动型体力活动存在一致的显著相关关系,关联程度因体力活动类型而异。自然环境中,归一化植被指数、绿化空间密度等自上而下的绿化水平与各类体力活动的正相关性最强;建成环境中,道路密度也与移动型体力活动存在一致的显著正相关关系,而便利设施的供给、人行道宽度仅对步行活动有积极的促进作用;除骑行活动外,居住用地密度与步行、跑步及一般体力活动都有显著的正相关关系。【结论】大批量、多尺度、高精度的体力活动自发地理信息有助于研究者客观掌握城市街区体力活动的分布,比较不同建成环境在多种时空尺度下的体力活动访问模式及使用效能,进而构建街区环境特征与体力活动适宜性的关联性模型;基于荟萃分析的发现为城市规划者和政策制定者优化和新建体力活动干预设施提供了使用效能预测的经验模型,有助于更科学合理地建设促进健康行为的人居环境。展开更多
为协调生活、生产和生态空间的用地矛盾,解决数据驱动法在识别城市“三生空间”方面存在的判别不准确和数据覆盖范围不够等问题,提出了一种能够精准识别“三生空间”功能的方法。基于数据驱动法,结合POI(point of interest)、AOI(area o...为协调生活、生产和生态空间的用地矛盾,解决数据驱动法在识别城市“三生空间”方面存在的判别不准确和数据覆盖范围不够等问题,提出了一种能够精准识别“三生空间”功能的方法。基于数据驱动法,结合POI(point of interest)、AOI(area of interest)和遥感等多源异构数据的多特征信息,分析在功能评价体系和分类模型中将不同数据源作为特征因子时的识别精度与尺度效应。以高原城市昆明市五华区建成范围为实验对象,研究结果表明:基于多源地理数据的识别准确率达到92%和94%。多源数据的多特征信息能够明显提升城市功能区的识别精度,为城市功能区精准识别提供了新的方法,能够在更小的尺度上为国土空间规划提供数据与方法支撑。展开更多
基金Beijing Municipal Social Science Foundation(22GLC062)Research on service function renewal of Beijing subway station living circle driven by multiple big data.Beijing Municipal Education Commission Social Science Project(KM202010009002)Young YuYou Talents Training Plan of North China University of Technology.
文摘In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.
基金Under the auspices of Natural Science Foundation of China(No.41971166)。
文摘Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.
基金Under the auspices of National Natural Science Foundation of China (No.40871188)Knowledge Innovation Programs of Chinese Academy of Sciences (No.INFO-115-C01-SDB4-05)
文摘Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.
基金This research was supported by the UBC APFNet Grant(Project ID:2022sp2 CAN).
文摘COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.
文摘Energy crisis and climate change have become two seriously concerned issues universally. As a feasible solution, Global Energy Interconnection(GEI) has been highly praised and positively responded by the international community once proposed by China. From strategic conception to implementation, GEI development has entered a new phase of joint action now. Gathering and building a global grid database is a prerequisite for conducting research on GEI. Based on the requirement of global grid data management and application, combining with big data and geographic information technology, this paper studies the global grid data acquisition and analysis process, sorts out and designs the global grid database structure supporting GEI research, and builds a global grid database system.
基金funded by the National Natural Science Foundation of China(projects of No.52008143 and No.52478050)the Fundamental Research Funds of the Central Universities of China。
文摘Urban vitality is a complex and multifaceted concept that is pivotal to the livability and sustainability of cities.Recent studies have measured urban vitality and its relationship with the built environment from the perspectives of rationality and efficiency.However,in the context of new urbanization with Chinese characteristics which emphasizes people-oriented values,more emphases need to be placed on the subjective feelings of residents in studies of urban vitality.This paper focuses on Hefei,a representative second-tier city in central China,to explore the relationship between urban vitality and the built environment by utilizing multi-source big data,spatial autocorrelation,and geographic detector model.Urban vitality is measured in the two dimensions of population intensity and emotion intensity.The built environment is measured based on Maslow's theory of needs,encompassing the five dimensions of accessibility,convenience,safety,socialization,and aesthetics.Taking Hefei as a case,the paper proposes 18 built environment factors that may influence urban vitality and identifies 14 factors that significantly influence the urban vitality of emerging cities in China.The built environment factors with the most significant impact on urban vitality are POI accessibility on weekdays and public transport on weekends.In addition,the interaction effects between any two built environment factors are higher than that of a single factor.The results effectively reveal the influencing mechanisms of urban vitality and can help urban planners and policymakers to develop more targeted strategies to enhance urban vitality by optimizing the built environment.
基金supported by the National Natural Science Foundation of China under Grant Nos.72004144and 71971007the Fundamental Research Funds for the Beijing Municipal Colleges and Universities in Capital University of Economics and Business under Grant No.XRZ2020026。
文摘In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.
基金supported by the Natural Science Foundation of Jilin Province,China[YDZJ202301ZYTS218]the National Natural Science Foundation of China[42301430,42222103,42171379,U2243230,and 42101379]+1 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017277 and 2021227]the Professional Association of the Alliance of International Science Organizations[ANSO-PA-2020-14].
文摘Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.
文摘【目的】掌握体力活动与建成环境特征的关联对主动干预公众健康具有重大意义。【方法】为系统地验证建成环境的移动型体力活动使用效能,根据自发地理信息、体力活动、环境特征等关键词从Web of Science等数据库筛选出31篇描述统计汇报完整的学术论文,对文章信息、样本基本信息、研究分析方法、因变量和自变量信息等内容进行系统梳理,在此基础上对论文结果进行量化荟萃分析。【结果】自然环境、建成环境、社会环境及主观感知环境均与移动型体力活动存在一致的显著相关关系,关联程度因体力活动类型而异。自然环境中,归一化植被指数、绿化空间密度等自上而下的绿化水平与各类体力活动的正相关性最强;建成环境中,道路密度也与移动型体力活动存在一致的显著正相关关系,而便利设施的供给、人行道宽度仅对步行活动有积极的促进作用;除骑行活动外,居住用地密度与步行、跑步及一般体力活动都有显著的正相关关系。【结论】大批量、多尺度、高精度的体力活动自发地理信息有助于研究者客观掌握城市街区体力活动的分布,比较不同建成环境在多种时空尺度下的体力活动访问模式及使用效能,进而构建街区环境特征与体力活动适宜性的关联性模型;基于荟萃分析的发现为城市规划者和政策制定者优化和新建体力活动干预设施提供了使用效能预测的经验模型,有助于更科学合理地建设促进健康行为的人居环境。
文摘为协调生活、生产和生态空间的用地矛盾,解决数据驱动法在识别城市“三生空间”方面存在的判别不准确和数据覆盖范围不够等问题,提出了一种能够精准识别“三生空间”功能的方法。基于数据驱动法,结合POI(point of interest)、AOI(area of interest)和遥感等多源异构数据的多特征信息,分析在功能评价体系和分类模型中将不同数据源作为特征因子时的识别精度与尺度效应。以高原城市昆明市五华区建成范围为实验对象,研究结果表明:基于多源地理数据的识别准确率达到92%和94%。多源数据的多特征信息能够明显提升城市功能区的识别精度,为城市功能区精准识别提供了新的方法,能够在更小的尺度上为国土空间规划提供数据与方法支撑。
