Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional prom...Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.展开更多
Urban eco-environmental management is a key focus in the modernization of national governance systems and governance capacity.Advanced information technology should be used to identify ecological problems in urban are...Urban eco-environmental management is a key focus in the modernization of national governance systems and governance capacity.Advanced information technology should be used to identify ecological problems in urban areas accurately while enhancing the environmental management capacity to promote the sustainable development of cities.This study centers on statistical data from PM_(2.5)air monitoring stations in Shenzhen,China,and supplemental data,such as population distribution data from China Unicom’s mobile phone signaling.Data cleaning and fusion are used to construct a spatial dataset of an eco-environmental problem:PM_(2.5)concentrations.The geostatistical analysis tool ArcGIS is used to identify the most suitable interpolation method for reflecting this eco-environmental problem based on multiple parameter adjustments and repeated testing.A hotspot distribution map of PM_(2.5)concentrations is generated,and correlation analysis is conducted on the population density and distribution patterns in these hotspot areas.This enables the quantitative analysis and exploration of the spatial characteristics and coupling relationships of PM_(2.5)concentrations.The results show a positive correlation between the PM_(2.5)concentration distribution and the points of interest,road network density,number of dead-end roads,and average building height in Shenzhen.No correlation is found between population and building densities and the PM_(2.5)concentration distribution,possibly due to the city’s effective environmental management and pollution control measures.These findings help advance the development of precise,scientific,legally compliant pollution control strategies and decision-making processes.Furthermore,they provide technical support for urban eco-environmental planning,management,and sustainable development.展开更多
Background:Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions.However,capturing fine-scale dynamics is challenging.-Methods:This study analyzed h...Background:Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions.However,capturing fine-scale dynamics is challenging.-Methods:This study analyzed high-resolution human mobility patterns in Shanghai,China,during the 2022 SARS-CoV-2 Omicron BA.2 outbreak using large-scale anonymized cellular signaling data.We investigated mobility shifts across five distinct epidemic phases(pre-outbreak,targeted interventions,citywide lockdown,targeted lifting,and reopening)stratified by age,sex,and travel purpose.A comprehensive evaluation of four gravity and four radiation spatial interaction models was conducted to assess their ability to explain the observed mobility patterns under varying demographic and behavioral conditions.Results:Population size and distance were found to be primary drivers of mobility,with notable variations across demographic groups and travel purposes.During the lockdown,mobility significantly decreased,particularly for social-related trips and the working-age population,while the effect of distance was substantially higher.Although mobility volumes recovered post-lockdown,a larger effect of distance persisted,implying long-lasting behavioral changes.Our comparative analysis showed that while several variants of gravity and radiation models captured overall patterns effectively,their performance was context-dependent,varying significantly across epidemic phases,population subgroups,and travel purposes.Conclusion:These findings highlight the importance of integrating different mobility models to capture the complex human mobility picture by different population groups during an epidemic outbreak.Overall,this study advances our understanding of behavioral adaptations during crises,enhancing preparedness and response planning.展开更多
The complexity and fragmentation of people’s activity space are challenging to planners.However,the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of re...The complexity and fragmentation of people’s activity space are challenging to planners.However,the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of residents in a single or several communities,or the spatiotemporal laws of activity space on a macro scale.The research on the spatial characteristics of residents’activity space still needs to be strengthened.The present study analyses the spatial patterns of residents’activity space based on mobile phone signaling data to fill the gap of previous studies that assessed residents’activity space across small geographic areas.First,according to the spatial scope and direction of an activity space and residents’activity coverage rate,spatial patterns can be divided into three types:compact,extended,and directional extension patterns.The CatBoost method is then used to statistically analyze the influencing variables of spatial patterns,and the order of importance of the following influencing factors is determined:the built environment is more influential than social and economic situations.This study aims to strengthen the understanding of residents’activity space at the spatial level and provide a basis for the optimization of communities with different spatial patterns.展开更多
This paper identifies the employment and housing locations of residents in Shanghai based on mobile phone signaling data, so as to obtain the employment density and commuting data and analyze the development of nine s...This paper identifies the employment and housing locations of residents in Shanghai based on mobile phone signaling data, so as to obtain the employment density and commuting data and analyze the development of nine suburban new towns from the perspective of jobs-housing spatial relationship. Firstly, the paper defines employment-intensive areas and gets the average employment density of each new town according to the employment density data. Then it marks out the scope of the employment influence through analyzing the sources of workers in each new town in accordance with the commuting data. Finally, it analyzes the jobs-housing balance of each new town using independence index, finding that suburban new towns in Shanghai have become main clusters of economic activities, while the scope of employment influence in each new town is still concentrated in its administrative area, with less attraction to residents in other areas. The independence index demonstrates a law that the suburban new town which is farther from the central city sees a higher degree of jobs-housing balance. Among them, new towns located in the outer suburbs with a low independence index indicate their special development situation, the reason of which is worth further study.展开更多
Urban greenspace has a profound impact on public health by purifying the air,blocking bacteria,and creating activity venues.Due to people's different position,the greenspace exposure to different age groups change...Urban greenspace has a profound impact on public health by purifying the air,blocking bacteria,and creating activity venues.Due to people's different position,the greenspace exposure to different age groups changes at various times.In this study,we combined NDVI(normalized difference vegetation index)and GVI(green view index)green indices with mobile signaling big data to evaluate the greenspace exposure of 3 age groups in Shanghai at different times.A dynamic assessment model for greenspace exposure has been adopted in this study.April 2021 and April 2022 were selected as the study periods,representing the non-lockdown period and the lockdown period,respectively.The results indicate that greenspace exposure changes slightly during the lockdown period.During lockdown,the NDVI exposure in the age groups of 31 to 50,51,and above was higher than that during non-lockdown.However,the NDVI exposure of people aged 0 to 30 during lockdown is lower than that during non-lockdown.The GVI exposure of people aged 51 and above is lower than that of the other age group.Whether it is under lockdown or not,from 8:00 to 17:00,the NDVI exposure showed a slightly higher value than at other hours.The value of GVI exposure fluctuates steadily during 6:00 to 24:00.This study enriches the evaluation dimensions of urban greenspace exposure.展开更多
基金supported by the Key Research Base of Philosophy and Social Sciences in Sichuan Province—Modern Design and Culture Research Center(Grant No.MD24C003)the National Natural Science Foundation of China(Grant No.52308084)+1 种基金China Postdoctoral Science Foundation(Grant No.2022M712877)Key R&D and Promotion Projects of Henan Province(Grant No.222102110125)。
文摘Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.
基金supported by the National Key Research and Development Program of China under the theme“Research on urban sustainable development evaluation data fusion management technology”[Grant No.2022YFC3802903]Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources[Grant No.KF-2022-07-013]。
文摘Urban eco-environmental management is a key focus in the modernization of national governance systems and governance capacity.Advanced information technology should be used to identify ecological problems in urban areas accurately while enhancing the environmental management capacity to promote the sustainable development of cities.This study centers on statistical data from PM_(2.5)air monitoring stations in Shenzhen,China,and supplemental data,such as population distribution data from China Unicom’s mobile phone signaling.Data cleaning and fusion are used to construct a spatial dataset of an eco-environmental problem:PM_(2.5)concentrations.The geostatistical analysis tool ArcGIS is used to identify the most suitable interpolation method for reflecting this eco-environmental problem based on multiple parameter adjustments and repeated testing.A hotspot distribution map of PM_(2.5)concentrations is generated,and correlation analysis is conducted on the population density and distribution patterns in these hotspot areas.This enables the quantitative analysis and exploration of the spatial characteristics and coupling relationships of PM_(2.5)concentrations.The results show a positive correlation between the PM_(2.5)concentration distribution and the points of interest,road network density,number of dead-end roads,and average building height in Shenzhen.No correlation is found between population and building densities and the PM_(2.5)concentration distribution,possibly due to the city’s effective environmental management and pollution control measures.These findings help advance the development of precise,scientific,legally compliant pollution control strategies and decision-making processes.Furthermore,they provide technical support for urban eco-environmental planning,management,and sustainable development.
基金supported by the National Natural Science Foundation of China(82304202 and 92369118)Shanghai Municipal Science and Technology Major Project(No.ZD2021CY001)+2 种基金Shanghai Rising-Star Program(No.22QA1402300)financial support from the Shanghai Municipal Science and Technology Major Project(No.ZD2021CY001)Key Program of the National Natural Science Foundation of China(No.82130093).
文摘Background:Understanding human mobility changes during epidemics is critical for predicting disease spread and planning interventions.However,capturing fine-scale dynamics is challenging.-Methods:This study analyzed high-resolution human mobility patterns in Shanghai,China,during the 2022 SARS-CoV-2 Omicron BA.2 outbreak using large-scale anonymized cellular signaling data.We investigated mobility shifts across five distinct epidemic phases(pre-outbreak,targeted interventions,citywide lockdown,targeted lifting,and reopening)stratified by age,sex,and travel purpose.A comprehensive evaluation of four gravity and four radiation spatial interaction models was conducted to assess their ability to explain the observed mobility patterns under varying demographic and behavioral conditions.Results:Population size and distance were found to be primary drivers of mobility,with notable variations across demographic groups and travel purposes.During the lockdown,mobility significantly decreased,particularly for social-related trips and the working-age population,while the effect of distance was substantially higher.Although mobility volumes recovered post-lockdown,a larger effect of distance persisted,implying long-lasting behavioral changes.Our comparative analysis showed that while several variants of gravity and radiation models captured overall patterns effectively,their performance was context-dependent,varying significantly across epidemic phases,population subgroups,and travel purposes.Conclusion:These findings highlight the importance of integrating different mobility models to capture the complex human mobility picture by different population groups during an epidemic outbreak.Overall,this study advances our understanding of behavioral adaptations during crises,enhancing preparedness and response planning.
基金This work was supported by the National Natural Science Foundation of China[grant numbers 51778125].
文摘The complexity and fragmentation of people’s activity space are challenging to planners.However,the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of residents in a single or several communities,or the spatiotemporal laws of activity space on a macro scale.The research on the spatial characteristics of residents’activity space still needs to be strengthened.The present study analyses the spatial patterns of residents’activity space based on mobile phone signaling data to fill the gap of previous studies that assessed residents’activity space across small geographic areas.First,according to the spatial scope and direction of an activity space and residents’activity coverage rate,spatial patterns can be divided into three types:compact,extended,and directional extension patterns.The CatBoost method is then used to statistically analyze the influencing variables of spatial patterns,and the order of importance of the following influencing factors is determined:the built environment is more influential than social and economic situations.This study aims to strengthen the understanding of residents’activity space at the spatial level and provide a basis for the optimization of communities with different spatial patterns.
文摘This paper identifies the employment and housing locations of residents in Shanghai based on mobile phone signaling data, so as to obtain the employment density and commuting data and analyze the development of nine suburban new towns from the perspective of jobs-housing spatial relationship. Firstly, the paper defines employment-intensive areas and gets the average employment density of each new town according to the employment density data. Then it marks out the scope of the employment influence through analyzing the sources of workers in each new town in accordance with the commuting data. Finally, it analyzes the jobs-housing balance of each new town using independence index, finding that suburban new towns in Shanghai have become main clusters of economic activities, while the scope of employment influence in each new town is still concentrated in its administrative area, with less attraction to residents in other areas. The independence index demonstrates a law that the suburban new town which is farther from the central city sees a higher degree of jobs-housing balance. Among them, new towns located in the outer suburbs with a low independence index indicate their special development situation, the reason of which is worth further study.
基金supported by the National Key R&D Program of China(2022YFC3802600 and 2022YFC3802603)China National Forestry and Grassland Administration,Forestry and Grassland Science and Technology Youth Talent Project(2024132024)
文摘Urban greenspace has a profound impact on public health by purifying the air,blocking bacteria,and creating activity venues.Due to people's different position,the greenspace exposure to different age groups changes at various times.In this study,we combined NDVI(normalized difference vegetation index)and GVI(green view index)green indices with mobile signaling big data to evaluate the greenspace exposure of 3 age groups in Shanghai at different times.A dynamic assessment model for greenspace exposure has been adopted in this study.April 2021 and April 2022 were selected as the study periods,representing the non-lockdown period and the lockdown period,respectively.The results indicate that greenspace exposure changes slightly during the lockdown period.During lockdown,the NDVI exposure in the age groups of 31 to 50,51,and above was higher than that during non-lockdown.However,the NDVI exposure of people aged 0 to 30 during lockdown is lower than that during non-lockdown.The GVI exposure of people aged 51 and above is lower than that of the other age group.Whether it is under lockdown or not,from 8:00 to 17:00,the NDVI exposure showed a slightly higher value than at other hours.The value of GVI exposure fluctuates steadily during 6:00 to 24:00.This study enriches the evaluation dimensions of urban greenspace exposure.
