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We provide a comprehensive review of the literature on inferring activity-travel patterns (ATP) using multi-source big data; the increasing number of publications over time on this subject, demonstrates the importance of big data in this task. Our aims are to identify the advantages and research gaps in ATP inference and to promote further developments in this field. We clarify the fundamental concepts (i.e. ATP and its components), commonly used data sources, and inference processes employed in ATP inference studies. Emphasis is placed on two prominent big data sources: mobile phone data and smart card data. We outline the various approaches involved in the inference process, and we highlight existing shortcomings in data sources, ATP inference methodologies, and result validation. Based on the review, it is evident that future research should address several limitations in ATP inference. Firstly, it is necessary to improve the comprehensive understanding of ATP and understand the interrelationships among its different components. Secondly, it is necessary to integrate different data sources and leverage their respective strengths to gain deeper insights into activity-travel behaviour. Lastly, further investigation into emerging technologies such as artificial intelligence in ATP inference is warranted to improve inference accuracy. The findings of this study could provide valuable insights for policy makers, enabling them to gain a deeper understanding of activity-travel choice behaviour and develop more effective policies related to transportation system. [ABSTRACT FROM AUTHOR]

This paper aims to map daily mobility to work and services within the suburban zone of Prague and to explain temporal changes in the structuration of the suburban zone during the day. This involves the identification of local centres outside Prague's administrative boundaries, defining their catchment areas, and exploring the temporal variability of daily mobility. Using mobile phone data, the paper identifies the main flows of commuting to work and mobility for services or leisure activities. The focus on two distinct timeframes presented in two synthetic maps compares the mobility patterns of these activities. Empirical findings identify 53 local centres of inter‐suburban mobility and delimitate their commuting areas. Noticeable variation in mobility patterns throughout the day was observed. Beyond empirical insights relevant to Prague and similar Central and Eastern European cities, the paper contributes to theoretical knowledge by introducing a hierarchical classification of mobility relationships within metropolitan areas. [ABSTRACT FROM AUTHOR]

Time series of traffic flows, extracted from mobile phone origin–destination data, are employed for monitoring people crowding and mobility in areas subject to flooding risk. By applying a vector autoregressive model with exogenous covariates combined with dynamic harmonic regression to such time series, we detected the presence of many extreme events in the residuals, which exhibit heavy-tailed distribution. For this reason, we propose a time series clustering procedure based on tail dependence which is suitable for data characterized by a spatial dimension, since objects' geographical proximity is taken into account. The final aim is to obtain clusters of areas characterized by the common tendency to the manifestation of extreme events, that in this case study are represented by extremely high incoming traffic flows. The proposed method is applied to the Mandolossa, a strongly urbanized area located on the western outskirts of Brescia (northern Italy) which is subject to frequent flooding. [ABSTRACT FROM AUTHOR]

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population, infrastructure, and socioeconomic activities. Scaling laws are basic but effective rules for evaluating a city's internal growth logic and assessing its efficiency by investigating whether urban indicators scale with population. To date, only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few meters. Using multiple urban-sensed and human-sensed data, this study proposes a thorough framework for quantifying the scaling laws in a city. To begin, urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking data. To demonstrate the spatiotemporal variability of urban mobility, various network-based mobility measures are proposed. Following that, three different features of urban mobility laws are exposed, explaining spatial agglomeration, spatial hierarchical structures, and the temporal growth process. The scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed data. Applying this framework to the case study of Shenzhen, China revealed (a) spatial travel heterogeneity, hierarchical spatial structures, and mobility growth, and (b) not only a robust sub-linear relationship between infrastructure volume and population, but also a sub-linear relationship for socioeconomic activity. The identified scaling laws, both in terms of mobility measures and urban indicators, provide a multi-faceted portrait of the spatio-temporal variations of urban settings, allowing us to better understand intra-urban developments and, consequently, provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future. [ABSTRACT FROM AUTHOR]

Metro transit is the core of urban transportation, and the mobility analysis of metro ridership can contribute to enhance the overall service level of the metro transit. Researchers studying metro ridership are focused on the spatiotemporal distribution characteristics of the ridership in the underground system of metro station by metro smart card data. However, limited by lack of travel mobility chain of ridership integrity, their activity patterns cannot be used to identify the heterogeneity of metro ridership's origin and transfer travel mode. In our research, we applied full spatiotemporal coverage of mobile phone data to identify the complete travel mobility of metro ridership in the perspective of ground and underground transit. First, the mobility of the boarding and alighting stations was extracted and the order of the transfer station was then extracted. Second, relying on the ridership flow identification method, the aboveground origin and destination of the ridership outside the metro system were extracted, and their transferred traffic mode was identified. The empirical results have shown that our proposed framework can accurately analyze the mobility patterns of metro ridership in an aboveground area and underground station. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population, infrastructure, and socioeconomic activities. Scaling laws are basic but effective rules for evaluating a city’s internal growth logic and assessing its efficiency by investigating whether urban indicators scale with population. To date, only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few meters. Using multiple urban-sensed and human-sensed data, this study proposes a thorough framework for quantifying the scaling laws in a city. To begin, urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking data. To demonstrate the spatiotemporal variability of urban mobility, various network-based mobility measures are proposed. Following that, three different features of urban mobility laws are exposed, explaining spatial agglomeration, spatial hierarchical structures, and the temporal growth process. The scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed data. Applying this framework to the case study of Shenzhen, China revealed (a) spatial travel heterogeneity, hierarchical spatial structures, and mobility growth, and (b) not only a robust sub-linear relationship between infrastructure volume and population, but also a sub-linear relationship for socioeconomic activity. The identified scaling laws, both in terms of mobility measures and urban indicators, provide a multi-faceted portrait of the spatio-temporal variations of urban settings, allowing us to better understand intra-urban developments and, consequently, provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future.

Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change's effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities. [ABSTRACT FROM AUTHOR]

Natural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor‐intensive and of limited geographic scope. In this article, we develop an approach to understanding facility‐level inaccessibility across a broad geographic area based on location‐based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017. [ABSTRACT FROM AUTHOR]

Both an offender's home area and their daily activity area can impact the spatial distribution of crime. However, existing studies are generally limited to the influence of the offender's home area and its immediate surrounding areas, while ignoring other activity spaces. Recent studies have reported that the routine activities of an offender are similar to those of the residents living in the same vicinity. Based on this finding, our study proposed a flow-based method to measure how offenders are distributed in space according to the spatial mobility of the residents. The study area consists of 2643 communities in ZG City in southeast China; resident flows between every two communities were calculated based on mobile phone data. Offenders' activity locations were inferred from the mobility flows of residents living in the same community. The estimated count of offenders in each community included both the offenders living there and offenders visiting there. Negative binomial regression models were constructed to test the explanatory power of this estimated offender count. Results showed that the flow-based offender count outperformed the home-based offender count. It also outperformed a spatial-lagged count that considers offenders from the immediate neighboring communities. This approach improved the estimation of the spatial distribution of offenders, which is helpful for crime analysis and police practice. [ABSTRACT FROM AUTHOR]

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM 2.5 exposure by combining residents' mobility traces and PM 2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM 2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users' mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users' stay locations and high-resolution PM 2.5 concentration, we quantify individual PM 2.5 exposure and find that the average PM 2.5 exposure of residences in Shanghai is 60.37 ug·h·m − 3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM 2.5 exposure in Shanghai. Our PM 2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development. [ABSTRACT FROM AUTHOR]

Abstract The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows.

Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using largescale digital behavioral observations as a natural laboratory, here we analyze how lockdown interventions lead to the reorganization of socioeconomic segregation patterns simultaneously in communication and mobility networks in Sierra Leone. We find that while segregation in mobility clearly increased during lockdown, the social communication network reorganized into a less segregated configuration as compared to reference periods. Moreover, due to differences in adaption capacities, the effects of lockdown policies varied across socioeconomic groups, leading to different or even opposite segregation patterns between the lower and higher socioeconomic classes. Such secondary effects of interventions need to be considered for better and more equitable policies. [ABSTRACT FROM AUTHOR]

The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows. [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Destination, as a key concept in tourism geography, has largely determined the scale at which tourist activity space was modeled and studied. Existing studies usually focused on investigating tourists' activities and movements either at the intradestination (e.g., within a city) or interdestination scale. Although useful in numerous research contexts, these models based on fixed spatial scales are incapable of portraying the complex spatial structure of tourist activity spaces, which sometimes exhibit hierarchical structures, and could span across different spatial scales. In this study, we propose a new representation of tourist activity space to bridge these gaps. The representation takes tourists' accommodation locations as key reference points. At the macroscale, the sequence of accommodation locations forms the backbone of tourist activity space, denoted as itinerary type. At the microscale, we introduce the concept of territory to describe how individuals organize activities around these overnight "base camps" (i.e., accommodation locations). We apply this representation over a large-scale mobile phone data set of international travelers visiting South Korea to demonstrate its capability. Results show that four generic itinerary types capture the activity space structure of 89 percent of the tourists. The interrelationships of territories and their topological structures further categorize activity spaces into subtypes, leading to a new method of tourist classification based on their spatiotemporal activity patterns. We believe the proposed representation could enrich new perspectives and debates on how tourist activities can be studied. The representation can also be extended as a generic framework to delineate complex forms of human activity space. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionUrban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies.MethodsWe developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city.ResultsOur analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city.DiscussionThe study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change’s effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities.

This data descriptor presents two main datasets and a set of auxiliary files. The mobility dataset presents a long-term study of human mobility in the Rio de Janeiro Metropolitan Area (RJMA) performed in the entire year of 2014 based on mobile phone data. The socioeconomic dataset presents selected socioeconomic variables of the Brazilian 2010 census. A set of auxiliary files is included to present georeferenced information and geographic features (shapefiles) and data used to validate the mobility estimates. The human mobility estimation was carried out using a methodology that allows direct integration with census data, based on an approximation of the geographic boundaries of census units by an aggregation of Voronoi polygons of the mobile phone antennas. The study area is the Brazilian local area 21, which includes the entire RJMA and four other municipalities. The mobility dataset is divided into two files: one is an estimation of the origin-destination (OD) matrix per day, and the other is a visitors’ dataset where the number of visitors of each location is estimated in four shifts each day. The socioeconomic dataset presents information of 55 variables for each location, which have been used in different studies and present the longest human mobility dataset available for public use.

The rational evaluation of pollution control measures is crucial for advancing water pollution management, ensuring water quality, and creating high-standard urban environments. Currently the evaluation heavily relies on water quality indicators with little regard to the sense of gain of the people. In this paper, we proposed an objective indicator PVH (Park Visiting Hours) to represent the behavioral response, as a materialized sense of humans, to water quality. We demonstrated the application of PVH in four typical waterscape parks in Beijing with multi-source data, and the results showed that: (1) The Mann-Kendall Test and an independent correlation test utilizing Sina Weibo data (Chinese Twitter) indicated that PVH was significantly negatively correlated with the Satellite image based Sensory Pollution Index (SPI), as corroborated the competence of PVH to appraise the sense of gain from water quality improvement. (2) Multivariate regressions showed that an increase in SPI of 0.1 corresponds to an average 8.8 min reduction in PVH. (3) The relationship between PVH and SPI was consistent across various visitor demographics, and whether visitors were in groups or alone. This study introduced PVH as a novel but concise indicator to represent water quality-related behavior of park visitors and would inspire the use of big multisource data for measuring human perceptions of ecosystem services.

ABSTRACTMobility functional areas are tools based on human mobility that can be useful for spatial and transport planning in delicate situations such as the COVID-19 pandemic. In this work, we aim to map functional areas in Spain from four days corresponding to different phases of the disease. For that goal, mobile phone data provided by Spanish Statistical National Institute (INE) has been used due to its value and potential to provide constantly updated information of mobility at almost-real time. The methodology consists of a network analysis over an origin-destination matrix to obtain modularity values for 3214 population cells provided by the INE. These values were then used to cluster the cells into functional areas. The results show how different confinement and mobility restriction policies influence the amount, size and shape of the functional areas, and therefore, they affect access to services or jobs.

Mobile phones have achieved a high rate of penetration and gained great interest in the field of travel behavior studies. However, mobile phone data exploitation for national travel models has only been sporadically studied thus far. This work focuses on one of the most extensive cellular surveys of its kind carried out thus far in the world, which was performed for two years between 2018 and 2019 with the participation of the two largest cellular providers in Israel, as well as leading GPS companies. The large-scale cell phone survey covered half the population using cellphones aged 8+ in Israel and uncovered local and national trip patterns, revealing the structure of nationwide travel demand. The methodology consists of the following steps: (1) plausibility and quality checks for the data of the mobile operators and the GPS data providers; (2) algorithm development for trip detection, home/work location detection, location and time accuracy, and expansion factors; (3) accuracy test of origin–destination matrices at different resolutions, revisions of algorithms, and reproduction of data; and (4) validation of results by comparison to reliable external data sources. The results are characterized by high accuracy and representativeness of demand and indicate a strong correlation between the cellular survey and other reliable sources. [ABSTRACT FROM AUTHOR]

Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented '720' Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, 'reverse bathtub', 'ever-increasing' and 'ever-decreasing' patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people's gender or age. In view of the common association between travel behaviors and travelers' socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population's mobility and routine locations after the pandemic's peak. We use clustering algorithms to identify citizens' stops and routine locations, at an antenna level, during the following months after the pandemic's first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ. [ABSTRACT FROM AUTHOR]

A fundamental question of any new relationship is, will it last? Transient relationships, recently defined by the authors, are an ideal type of social tie to explore this question: these relationships are characterized by distinguishable starting and ending temporal points, linking the question of tie longevity to relationship finite lifetime. In this study, we use mobile phone data sets from the UK and Italy to analyse the weekly allocation of time invested in maintaining transient relationships. We find that more relationships are created during weekdays, with a greater proportion of them receiving more contact during these days of the week in the long term. The smaller group of relationships that receive more phone calls during the weekend tend to remain active for more time. We uncover a sorting process by which some ties are moved from weekdays to weekends and vice versa, mostly in the first half of the relationship. This process also carries more information about the ultimate lifetime of a tie than the part of the week when the relationship started, which suggests an early evaluation period that leads to a decision on how to allocate time to different types of transient ties.

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.

Urban commuting characteristics have important implications for both the spatial planning and governance of cities. However, the traditional methods of surveying the characteristics of commuting are very time- and labour-intensive, with the results susceptible to subjective influences. In this work, taking the central city of Nanning as the research object, the commuting space of the population was constructed on the grid-block-subdistrict scale, and the distribution characteristics of the commuting space were systematically analysed. In addition, the influencing factors of the commuting volume were explored by combining multi-source and spatiotemporal data with a geodetector. From our analysis, it was demonstrated that the population density in the central city of Nanning showed a spatial distribution pattern of "decaying distance from the city centre", with a weak agglomeration effect of large-scale commuters at the grid scale and a larger east-west than north-south commuter scale. At the block scale, large-scale commuters were more concentrated, and the commuting distances were shorter in areas with large commuter populations. At the subdistrict scale, the internal commuting population was also more than the cross-subdistrict commuting population, with more cross-subdistrict commuting flows and an uneven distribution of the flow sizes, with most commuters concentrating on two or three subdistricts for commuting. Various important factors that affect the size of the commuting population should be controlled, including the permanent population, residential distribution, medical facilities, recreational facilities, food services and workplace distribution; the interactions between the permanent population, the residential distribution and the house price factors have the strongest impact values. Our work provides valuable insights for the understanding of commuting patterns in cities and can be used as a scientific basis for urban spatial decision-making. [ABSTRACT FROM AUTHOR]

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates. [ABSTRACT FROM AUTHOR]

Identifying urban employment centers (UECs) is crucial for understanding urban spatial structures. Conventional identification methods use census-type aggregated data. The increasing pervasiveness of mobile phone data provides new possibilities to analyze UECs at finer spatial resolution but requires novel identification methods. This study proposes a new approach, the Locally Decaying Model (LDM), to fit the employment density locally and identify UECs based on statistically significant local peaks. We compared the proposed LDM with conventional methods by Monte-Carlo simulation and real data experiments on employment distributions generated from Shanghai mobile phone data. The Monte-Carlo simulation showed that the LDM performed significantly better. The Shanghai case study demonstrated greater stability of the LDM in identifying UEC numbers and locations on aggregated data than the conventional methods. Furthermore, UECs extracted by LDM from the employment density raster were more consistent with the existing local plan. This research contributed a new subcenter identification method that can be applied to recently available urban big data and a comprehensive analytical framework for evaluating such methods. [ABSTRACT FROM AUTHOR]

Objectives: Individual location prediction is significant in applications such as precise prevention, control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. Therefore, based on the framework of graph convolution network and long short-term memory (LSTM) model, this paper proposes an individual location prediction algorithm considering the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. Methods: First, we construct a user trajectory similarity algorithm, and select screen users with high similarity. Second,we use the graph convolution model to extract user trajectory features with high similarity. Finally, we use the LSTM model framework to extract historical trajectory features and integrate similar user trajectory features, so as to achieve individual location prediction. Results and Conclusions: Based on the data of more than 80 000 users in a city for 4 consecutive working days, the results show that the accuracy of the proposed algorithm decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day. Compared to the previous models, our proposed algorithm has an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has rapidly altered the habits and lifestyles of every citizen worldwide and will have effects in the medium to long terms. The need for ‘social distancing’ has negatively affected urban areas and encouraged a rescheduling of working methods. Knowledge-intensive activities have been massively undertaken on a large scale in remote working, mainly carried out at home. Within this context, the paper explores the changes in working modalities towards remote working by focusing on the Lombardy region in north-west Italy. Specifically, it explores the renewed suitability of the municipalities outside the regional capital city of Milan with regard to remote workers during the pandemic and compares it with the period before the pandemic, and also it analyses which determinants play a role. The results show that municipalities closer to Milan with a strong broadband connection, a high concentration of knowledge workers and foreign immigrants are more suitable for hosting remote workers.

The paper investigates if and how mobile phone data can help to describe the complexity of urban phenomena, highlighting the challenges faced by researchers integrating mobile phone data into their activities. Two perspectives are offered: the first is a reflection on the features of these data collected anonymously by mobile phone users, as a condition for understanding its potentialities and limits for the analysis, visualization, and interpretation of people’s presence and movements in urban spaces (research on mobile phone data). The second perspective focuses on the uses of mobile phone data in spatial research, starting from the outcomes described in this special issue and highlighting the potentialities and limits of these data in facing several research questions in urban studies (research with mobile phone data).

While the impacts of the Covid-19 outbreak on urban areas have been deeply investigated, the effects of the virus on sparsely populated and marginal areas are still poorly explored. In Italy, those “inner” areas are often characterized by processes of marginalization due to aging and loss of population, low occupational rate and income, a progressive deprivation of local know-how, and the shrinking of essential services. Yet, a reverse migration from urban centers to rural and peripheral areas has been reported worldwide among the main effects of the Covid-19 pandmic, being in some cases an opportunity to slow down and even reverse the process of marginalization. By combining mobile phone and socio-spatial data, this paper aims to analyze the space-time variability of human presence before and during the Covid-19 lockdown in the Apennine area of the e province of Piacenza, a representative case of Italy's inner areas, to read if and how the pandemic has contributed to modify the rhythms and trends of those territories. Two dynamics have been investigated: remote and near-home tourism. In addition to provide a picture of the changes that occurred in these marginal contexts, the outcomes have shown the great potentiality of mobile phone data, along with some limits that may prejudice their usability, particularly for territorial research in low-density areas.

Mobile phone tracking data collected by telecommunication companies allow recording and reconstructing the practices of mobilities and the presence of users with significant spatial-temporal detail. If properly managed, analysed, and possibly combined with other sources of information, mobile phone data can represent an interesting opportunity for urban research and mobility studies as they shed light on complex socio-territorial dynamics difficult to infer from conventional data analysis. At the same time, reports of numerous experiments using these sources reveal some of the challenges that researchers face in accurately capturing the behaviours of individuals through digital data and translating them into useful research knowledge. Referring both to the direct experience of managing and analysing mobile phone data within the Department of Architecture and Urban Studies of the Politecnico di Milano and to the relevant literature, the paper proposes an overview of the potentialities and limitations of telephone data for urban research and their usability in different territorial contexts characterised by varying socio-spatial and demographic conditions. Besides positioning themselves within and enriching an already lively debate, the issues discussed here will be useful in reading the contributions of the special section that this paper introduces.

This paper aims to present the presence and mobility data provided by TIM, highlighting the acquisition methodology, the levels of spatial and temporal disaggregation, as well as the additional information related to age groups, gender, and classification of behaviours, which are directly supplied by TIM. The construction of a baseline based on mobile phone data for the comparison of temporal trends in the presence of people is also discussed. At the same time, the supporting data obtained from traditional sources or ad hoc surveys will be presented to show how they can facilitate the interpretation of telephone data, its validation, and its use. Finally, a reference on the operational tools used for their processing and visualization will highlight the need to integrate skills, methodologies, and tools for the maximum exploitation of this wealth of information.

Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women's walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: (1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and a more dispersed distribution of scores. (2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. (3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety. [ABSTRACT FROM AUTHOR]

The relationship between urban mobility, social networks, and socioeconomic status is complex and difficult to apprehend, notably due to the lack of data. Here we use mobile phone data to analyze the socioeconomic structure of spatial and social interaction in the Chilean urban system. Based on the concept of spatial and social events, we develop a methodology to assess the level of spatial and social interactions between locations according to their socioeconomic status. We demonstrate that people with the same socioeconomic status preferentially interact with locations and people with a similar socioeconomic status. We also show that this proximity varies similarly for both spatial and social interactions during the course of the week. Finally, we highlight that these preferential interactions appear to hold when considering city–city interactions. [ABSTRACT FROM AUTHOR]

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project. [ABSTRACT FROM AUTHOR]

Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study is that we have considered influx population instead of staying-population, as the data represent congestion. This enables us to apply our analysis method to all meshes because the influx population may always represent the congestion of specific areas, which include the residential areas as well. In this study, we report the correlation between the influx population in downtown areas and business districts in Tokyo during the pandemic considering the effective reproduction number and associated time delay. Moreover, we validate our method and the influx population data by confirming the consistency of the results with those of the previous research and epidemiological studies. As a result, it is confirmed that the social risk with regard to the spread of COVID-19 infection when people travel to downtown areas and business districts is high, and the risk when people visit only residential areas is low. [ABSTRACT FROM AUTHOR]