Exploring Temporal Activity Patterns of Urban Areas Using Aggregated Network-driven Mobile Phone Data:A Case Study of Wuhu,China

The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.

Deriving Operational Origin-Destination Matrices From Large Scale Mobile Phone Data

A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.

Exploring relations between city regions based on mobile phone data

City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations between regions of Shanghai based on mobile phone data is explored. By formulating the regions as nodes in a network and the commuting between each pair of regions as link weights, the distribution of nodes degree, and spatial structures of communities in this relation network are studied. Statistics show that regions locate in urban centers and traffic hubs have significantly larger degrees. Moreover, two kinds of spatial structures of communities are found. In most communities, nodes are spatially neighboring. However, in the communities that cover traffic hubs, nodes often locate along corridors.

Understanding the spatiotemporal patterns of nighttime urban vibrancy in central Shanghai inferred from mobile phone data

In recent years,major cities around the world such as New York in USA,Melbourne in Australia,and Shanghai in China,have planned to boost their nighttime urban vibrancy levels to spur the economy and achieve cultural diversity.The study of nighttime urban vibrancy from the perspective of spatiotemporal characteristics is increasingly being recognized as part of the essential work in the field of urban planning and geography.This research used mobile phone signaling records to measure urban vibrancy in central Shanghai and revealed its spatiotemporal patterns during nighttime.Specifically,this research explored the changes of urban vibrancy within a day,studied the distribution of urban vibrancy during the nighttime,and visually presented the spatiotemporal changes of nighttime urban vibrancy in central Shanghai.Moreover,on the basis of the behavior pattern of each mobile user,we classified nighttime urban vibrancy into three different types:nighttime working vibrancy,nighttime leisure vibrancy,and nighttime floating vibrancy.We then tried to determine how land use affected nighttime leisure vibrancy.The results showed that urban vibrancy in central Shanghai exhibits a periodic pattern over one-day period.A high-level nighttime urban vibrancy belt is present within central Shanghai.Business offices,hotels,entertainment and recreational districts,wholesale markets,and express services contribute most to the vibrancy at nighttime.In addition,the correlation analysis shows that public and commercial facilities generate high levels of nighttime leisure vibrancy than residential facilities.The mixed land use of public and commercial facilities and residential facilities within 500 m is more critical than the mixed use of a single land lot.The research can be a basis for supporting land use planning and providing evidence for policy-making to improve the level of nighttime urban vibrancy in cities.

Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City,China

High-resolution,dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management.Mobile phone big data have real-time collection,wide coverage,and high resolution advantages and can thus be used to characterize human activities and population distributions at fne spatiotemporal scales.Based on six days of mobile phone user-location signal(MPLS)data,we assessed the dynamic spatiotemporal distribution of the population of Xining City,Qinghai Province,China.The results show that strong temporal regularity exists in the daily activities of local residents.The spatiotemporal distribution of the local population showed a signifcant downtown-suburban attenuation pattern.Factors such as land use types,holidays,and seasons signifcantly afect the spatiotemporal patterns of the local population.By combining other spatiotemporal trajectory data,high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Population distribution modelling can benefit many different domains,for example,transportation,urban planning,ecology or emergency management.Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises.Mobile phone data represents relatively reliable and time accurate information on real-time population distribution,movement and behaviour.In this study,we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area(City of Brno,Czech Republic).Analysis is based on the dataset describing the estimated human presence(EHP)with two values-visitors and transiting persons.The temporal change of data is first analysed and further processed using two methodological approaches.First,the dasymetric method is used where the building geometry and technical attributes served as a target layer.Second,the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP.Resulting spatio-temporal patterns are compared to the census data.Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP.Potential use of proposed methodology within selected emergency situations is further discussed.

Monitoring travel patterns in German city regions with the help of mobile phone network data

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning.Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes,a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access.Concepts need to serve the evaluation of policy objectives,for example in regional or local area plans.In this study,we,therefore,extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns.To accomplish this,we link mobile phone records with urban classifications and transport network data,using both visual and computational approaches to mine the data.The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany,testing hypotheses of transit-oriented regional development,as well as testing for congestion risks in the transport network.The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

Spatial patterns of residents’ daily activity space and its influencing factors based on the CatBoost model: A case study of Nanjing, China

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.

Development of Suburban New Towns in Shanghai: Jobs-Housing Spatial Relationship Analysis

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.