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Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data-ideally to be made free and publicly available-and offer lay descriptions of some of the difficulties in generating such data products. [ABSTRACT FROM AUTHOR]

The increasing availability and adoption of shared vehicles as an alternative to personally owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home–work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of the increased traveled kilometers. We consider the city of Singapore as a case study and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of the increasing total traveled kilometers of less than 2%. [ABSTRACT FROM AUTHOR]

The development of a trip distribution model requires a massive data collection task, such as expensive travel surveys to identify trip makers’ origin and destination zones. Intra-zonal trips are usually ignored in the development of trip distribution models because of the difficulty associated with measuring travel costs. However, ignoring intra-zonal trips leads to incomplete model estimation especially when the zone size is large, and thus, the number of intra-zonal trips is substantial. This is especially important, with the growing interest around the world on creating more walkable and bikeable cities where a high share of those trips is intra-zonal. In this paper, we use mobile phone data to derive country-wide mobility trends. A set of doubly constrained trip distribution models that integrates intra-zonal trips was estimated for 123-district-level traffic analysis zones. We present two approaches to estimate the intra-zonal travel costs measured in terms of trip distance. Our analysis reveals that the average intra-zonal trip distances obtained from the two approaches provide different levels of sensitivity to the distance-decay effect. Our result also shows that model estimation in the absence of intra-zonal trips produces biased estimates. [ABSTRACT FROM AUTHOR]

Distance decay is a primary characteristic of spatial interaction in human movements, and it has been incorporated into many spatial interaction models. Existing approaches mainly rely on travel survey datasets to fit the frictional coefficient of distance decay. However, limited sample size and spatiotemporal resolution make the determination of the spatial interaction characteristic from a comprehensive view difficult. Recently, this situation has been reversed due to emerging large human trajectory datasets, which have stimulated a body of literatures to re-examine the traditional issue of distance decay. However, these studies only focused on distance decay from a global perspective and neglected the spatial non-stationarity of spatial interaction. This study aims to reveal the spatial heterogeneity of distance decay of human movements extracted from massive mobile phone location data from Shenzhen, China. The power law function is utilized to fit the distance decay coefficients for inflow and outflow of each spatial analysis unit. Then, geographically weighted regression is employed to quantify the relationship between distance decay coefficients and land use distribution and between distance decay coefficients and traffic facilities. Results show that considerable spatial non-stationarity appears in the distance decay of spatial interaction, and the regression coefficients indicate the spatial variations of the influence of land use and traffic facilities on distance decay across urban space. These findings provide an in-depth insight into the distance decay characteristics of human movements in a more microcosmic space. [ABSTRACT FROM AUTHOR]

Given the abundant quantities of big spatiotemporal geographic data that are available, interactions among spatial entities can now be extracted from various perspectives. This research investigates the spatial interactions within the metropolis of Beijing quantitatively. Two methods of quantifying the interactions are proposed. These interactions can be calculated from either individual trajectories extracted from mobile phone records or the co-occurrence of the toponyms of administrative units mentioned in online news items. By fitting these two types of data with a gravity model and comparing the results, we determine that the distance decay effect exists in both data sets, and this effect is more obvious in the interactions computed from the human trajectories. The spatial interactions and connections quantified from the two data sources display greater numbers of mutual patterns in the central urban areas, whereas more diversity is observed in the suburban areas. We conclude that the choice of assumptions as to which data can adequately represent spatial interactions significantly affects the results; therefore, rigorous examination of specific problems is needed to redefine the problems in a more specific way. [ABSTRACT FROM AUTHOR]

Introduction: There have been many studies conducted on the effects of mobile phones radiations on people's health due to increasing number of mobile phones users. The present study aimed to investigate the effects of electromagnetic waves generated from 3G and 4G mobile phone radiations on student's reaction time and short-term memory. Material and Methods: This was a cross-sectional and descriptive-analytic study. A sample of 85 medical students from Shiraz University of Medical Sciences in the age range of 18-22 years was selected. After 10- min exposure to 3G and 4G mobile waves without any prognoses if mobile phone was on or off, response time and short-term memory tests were taken at once. The groups then left laboratory for about 2 h to take a rest, and they came back to laboratory to carry out the second mode of testing after two h (mobile phones on or off related to previous test). Both tests were performed in the afternoon to make students almost identical in terms of daily fatigue conditions. The data were analyzed in SPSS software (version 19) using t-test technique. The difference was statistically considered significant (P<0.05). Results: The results revealed that the reaction time and average short-term memory following the exposure to electromagnetic waves emitted from mobile 3G and 4G mobile phones increased and decreased, respectively. However, this difference was only significant in the reaction time. The electromagnetic waves generated by the 3G and 4G mobile phones led to slower response time among students under emission, compared to the control group. Conclusion: According to our findings, it can be concluded that the frequency of electromagnetic waves increased the response to stimulus time. [ABSTRACT FROM AUTHOR]

Abstract Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM 2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM 2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM 2.5 concentration and travel time spent on the road. By combining the estimated PM 2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM 2.5 per hour than those with high wealth level when staying at home, but exposed to less PM 2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM 2.5 , especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments. Highlights • A data analysis framework is proposed to estimate commuter's exposure to ambient PM 2.5 using mobile phone data. • Urban scale dynamic stay exposure is estimated based on the travel demand. • Travel exposure of commuters is estimated by combining the travel time and the PM 2.5 concentration. • Environmental justice in PM 2.5 exposure is investigated by comparing the exposure with housing price. [ABSTRACT FROM AUTHOR]

Highlights • We process two months mobile network trajectories from the Greater Paris region. • The transport mode is inferred from all trajectories, using few labeled data. • Total road and rail OD flows are estimated over time at different resolutions. • The estimates are validated against survey and travel cards flows. Abstract Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on multimodal transport networks. In this work, we propose the first methodology to infer dynamic Origin-Destination flows by transport modes using mobile network data e.g., Call Detail Records. For this study, we pre-process 360 million trajectories for more than 2 million devices from the Greater Paris as our case study region. The model combines mobile network geolocation with transport network geospatial data, travel survey, census and travel card data. The transport modes of mobile network trajectories are identified through a two-steps semi-supervised learning algorithm. The later involves clustering of mobile network areas and Bayesian inference to generate transport probabilities for trajectories. After attributing the mode with highest probability to each trajectory, we construct Origin-Destination matrices by transport mode. Flows are up-scaled to the total population using state-of-the-art expansion factors. The model generates time variant road and rail passenger flows for the complete region. From our results, we observe different mobility patterns for road and rail modes and between Paris and its suburbs. The resulting transport flows are extensively validated against the travel survey and the travel card data for different spatial scales. [ABSTRACT FROM AUTHOR]

Traditional approaches to trip generation modelling rely on household travel surveys which are expensive and prone to reporting errors. On the other hand, mobile phone data, where spatio-temporal trajectories of millions of users are passively recorded has recently emerged as a promising input for transport analyses. However, such data has primarily been used for the development of human mobility models, extraction of statistics on human mobility behaviour, and origin-destination matrix estimation as opposed to the development of econometric models of travel demand. This is primarily due to the exclusion of user demographics from mobile phone data made available for research (owing to privacy reasons). In this study, we address this limitation by proposing a hybrid trip generation model framework where demographic groups are treated as latent or unobserved. The proposed model first predicts the demographic group membership probabilities of individuals based on their phone usage characteristics and then uses these probabilities as weights inside a latent class model for trip generation, with different classes representing different socio-demographic groups. The model is calibrated using the call log data of a sub-sample of users with known demographics and trip rates extracted from their GSM mobility data. The performance of the hybrid model is compared with that of a traditional trip generation model which uses observed demographic variables to validate the proposed methodology. This comparative analysis shows that the model fit and the prediction results of the hybrid model are close to those of the traditional model. The research thus serves as a proof-of-concept that the mobile phone data can be successfully used to develop econometric models of transport planning by having additional information for a subset of the users. [ABSTRACT FROM AUTHOR]

This article studies the approaches to identifying labor commuting, its relation to permanent migration, and development stages of research of this phenomenon. Studies of labor commuting started in the 1950s and 1960s both in the Soviet Union and abroad. Survey questionnaires and transportation statistics used to be the main source of information about this phenomenon. Domestic studies of labor commuting lagged behind foreign studies for a long time due to the small amount of information, while special research has been conducted since the 1960s–1970s in the United States and Europe. In recent years, this gap has been decreasing primarily due to the use of Big Data in Russian studies. This includes data from mobile service providers on movements of mobile users, which is one of the most credible and detailed sources of information about labor commuting today. [ABSTRACT FROM AUTHOR]

Abstract Activity-based models appeared as an answer to the limitations of the traditional trip-based and tour-based four-stage models. The fundamental assumption of activity-based models is that travel demand is originated from people performing their daily activities. This is why they include a consistent representation of time, of the persons and households, time-dependent routing, and microsimulation of travel demand and traffic. In spite of their potential to simulate traffic demand management policies, their practical application is still limited. One of the main reasons is that these models require a huge amount of very detailed input data hard to get with surveys. However, the pervasive use of mobile devices has brought a valuable new source of data. The work presented here has a twofold objective: first, to demonstrate the capability of mobile phone records to feed activity-based transport models, and, second, to assert the advantages of using activity-based models to estimate the effects of traffic demand management policies. Activity diaries for the metropolitan area of Barcelona are reconstructed from mobile phone records. This information is then employed as input for building a transport MATSim model of the city. The model calibration and validation process proves the quality of the activity diaries obtained. The possible impacts of a cordon toll policy applied to two different areas of the city and at different times of the day are then studied. Our results show the way in which the modal share is modified in each of the considered scenarios. The possibility of evaluating the effects of the policy at both aggregated and traveller level, together with the ability of the model to capture policy impacts beyond the cordon toll area confirm the advantages of activity-based models for the evaluation of traffic demand management policies. [ABSTRACT FROM AUTHOR]

Emerging forms of data offer new opportunities for developing a deeper understanding of poorly understood social and spatial processes. This is no more important than in developing countries, where large-scale data collection and processing has been relatively limited. In this paper, we explore how two new datasets can be used to enhance our understanding of human activity and communication interactions in Dakar, Senegal. Starting from a premise of little contextual knowledge about the setting in which we are working, we explore how much these data, combined with novel quantitative methods, are able to inform us about the urban environment in question. Fine-grained infrastructural data are combined with k-means clustering to produce an 11-class land use classification, distinguishing dense and sparse, single and mixed use regions. Using these classifications and over 1.5 billion mobile phone call records, patterns of activity and interaction within and between land use types are analysed. These analyses reveal strong activity associated with high density commercial, governmental and administrative regions. These regions are also identified as relatively strong 'attractors' of communication, and wider patterns show higher interactions between areas with similar land use characteristics. Analyses of dynamic activity and interaction patterns highlight the movement of people from workplace zones to residential areas. Importantly, these analyses reflect the expected patterns of urban activity, providing some validation of the data, methods, and the empirical approach. The paper concludes in addressing the strengths and potential of these approaches, while recognising current limitations and areas for further work. [ABSTRACT FROM AUTHOR]

Highlights • This research attentioned the park users' actual activities, both spatially and temporally. • We used million anonymized mobile positioning data to understand their real green access. • We found there are warning signs that market mechanisms may worsen the uneven development. Abstract The benefits that urban green resources bring to humanity have received increasing attention, with the evidence from recent studies into public service provision—specifically regarding access to green space—being rather mixed. Despite a growing literature base, there is no consensus among scholars on how to measure green space access properly. The traditional GIS-based approach is criticized for inappropriately specifying geographic units and threshold distances, and for ignoring people's self-movement. This research proposes a novel approach, emphasizing the actual park users' activities, both spatially and temporally. We took advantage of the large dataset available from mobile phones to analyze billions of anonymized data samples in order to characterize the behavioral patterns of millions of people who accessed green space in an experimental procedure. We chose Shanghai as the case study because residential segregation had occurred—expected to be accompanied by issues of environmental justice. The results found that social equity could be achieved, even where territorial inequity was manifest in a high-population-density context that is undergoing rapid urban growth and transition. Shanghai's vulnerable groups are not found to be unequally treated at present, but there are warning signs that market mechanisms may worsen the uneven development. Therefore, the local municipalities are required to re-think how to provide green infrastructure to different social groups, responding to the inequality and uneven development that capital can bring. [ABSTRACT FROM AUTHOR]

Abstract Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy. Highlights • A novel approach for credit scoring using mobile phone data and social networks. • Implies enhanced financial inclusion in the context of positive credit information. • Incorporating mobile phone data increases statistical performance. • The best model in terms of profit includes only calling behavior features. • Individual calling behavior is most predictive of creditworthiness. [ABSTRACT FROM AUTHOR]

Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data can inform, support or even become an integral part of statistical production processes. In this article, we focus on arguably the most important problem hindering the application of mobile phone data in official statistics: detecting home locations. We argue that current efforts to detect home locations suffer from a blind deployment of criteria to define a place of residence and from limited validation possibilities. We support our argument by analysing the performance of five home detection algorithms (HDAs) that have been applied to a large, French, Call Detailed Record (CDR) data set (~18 million users, five months). Our results show that criteria choice in HDAs influences the detection of home locations for up to about 40% of users, that HDAs perform poorly when compared with a validation data set (resulting in 358-gap), and that their performance is sensitive to the time period and the duration of observation. Based on our findings and experiences, we offer several recommendations for official statistics. If adopted, our recommendations would help ensure more reliable use of mobile phone data vis-à-vis official statistics. [ABSTRACT FROM AUTHOR]

Recently, some studies have shown that human movement patterns are strongly associated with regional socioeconomic indicators such as per capita income and poverty rate. These studies, however, are limited in numbers and they have not reached a consensus on what indicators or how effectively they can possibly be used to reflect the socioeconomic characteristics of the underlying populations. In this study, we propose an analytical framework — by coupling large scale mobile phone and urban socioeconomic datasets — to better understand human mobility patterns and their relationships with travelers' socioeconomic status (SES). Six mobility indicators, which include radius of gyration, number of activity locations, activity entropy, travel diversity, k-radius of gyration, and unicity, are derived to quantify important aspects of mobile phone users' mobility characteristics. A data fusion approach is proposed to approximate, at an aggregate level, the SES of mobile phone users. Using Singapore and Boston as case studies, we compare the statistical properties of the six mobility indicators in the two cities and analyze how they vary across socioeconomic classes. The results provide a multifaceted view of the relationships between mobility and SES. Specifically, it is found that phone user groups that are generally richer tend to travel shorter in Singapore but longer in Boston. One of the potential reasons, as suggested by our analysis, is that the rich neighborhoods in the two cities are respectively central and peripheral. For three other mobility indicators that reflect the diversity of individual travel and activity patterns (i.e., number of activity locations, activity entropy, and travel diversity), we find that for both cities, phone users across different socioeconomic classes exhibit very similar characteristics. This indicates that wealth level, at least in Singapore and Boston, is not a factor that restricts how people travel around in the city. In sum, our comparative analysis suggests that the relationship between mobility and SES could vary among cities, and such relationship is influenced by the spatial arrangement of housing, employment opportunities, and human activities. Highlights • An analytical framework that integrates large scale mobile phone and urban socioeconomic datasets. • A data fusion approach to approximating socioeconomic status (SES) of mobile phone users. • Six mobility indicators for quantifying important aspects of phone users' mobility characteristics. • A comparative study of Singapore and Boston. • Results show that the relationship between mobility and SES could vary among cities. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to apply a synthetic approach to the delimitation of metropolitan areas, which combines traditional commuting data from the population census with alternative approaches. The presented delimitation, which was originally realized in response to a request from Prague’s planning authority, is based on three methodological pillars: the use of economic and social aspects of metropolization; suburbanization; and daily mobility within the Prague Metropolitan Area. Integrated systems of centers calculated from population census data are complemented with the use of mobile phone data. There was a surprising level of similarity in the spatial patterns gained from the two methods. Zones of residential suburbanization and time spent in the core city provided a complex perspective on the daily urban system within the Prague Metropolitan Area. A synthetic map based on the four methods is provided, accompanied by five analytical maps on a smaller scale. [ABSTRACT FROM AUTHOR]

Background: Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.Methods: We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.Results: Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.Conclusions: The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment. [ABSTRACT FROM AUTHOR]

Integration of the mobile phone data in the production of official statistics was one of the main goals of the ESSnet Big Data project. In this regard, we developed an R package to compute population estimates following a methodology inspired from the ecological sampling techniques. This methodology uses a Bayesian approach that is computationally intensive but allows a straightforward code parallelization. Since some functions of our package are very demanding from a computational point of view, we implemented them in C++ and integrated with the rest of the package using Rcpp. More, the C++ code is also parallelized, and we chose RcppParallel package for this purpose. The estimation procedure combines mobile phone data sets with another data source which can be a population register and produces estimates for each territorial division of a geographical area and along a sequence of time instants for which we have data from Mobile Network Operators. One of the hypotheses of the underlying mathematical model was the independence of the estimates between different cells that allowed us to use a parallel procedure to perform computations for each cell. Besides population estimates, it provides a set of accuracy indicators. pestim was developed with an eye on portability and it can be used on both Windows and Unix-like operating systems. We tested our package and showed that it has a good scalability that is an essential characteristic for very large data sets. [ABSTRACT FROM AUTHOR]

Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area.

Emerging forms of mobile phone data generated from the use of mobile phone applications have the potential to advance scientific research across a range of disciplines. However, there are risks regarding uncertainties in the socio-demographic representativeness of these data, which may introduce bias and mislead policy recommendations. This paper addresses the issue directly by developing a novel approach to assessing socio-demographic representativeness, demonstrating this with two large independent mobile phone application datasets, Huq and Tamoco, each with three years data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. We advance methods for detecting home location by including high-resolution land use data in the process and test representativeness across multiple dimensions. Our findings offer greater confidence in using mobile phone app data for research and planning. Both datasets show good representativeness compared to the known population distribution. Indeed, they achieve better population coverage than the 'gold standard' random sample survey which is the alternative source of data on population mobility in this region. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future. • Data from the use of mobile phone apps offer new potential for social scientists. • This potential is limited by questions of bias and data representativeness. • Applying a novel home detection approach we improve home location estimates. • We show a very high level of representativeness across two independent datasets. • These findings provide a foundation for the use of app data in social research. [ABSTRACT FROM AUTHOR]

• A new classification scheme for HSR stations was adopted using large-scale time series of passenger flow. • The interrelation between passenger flow patterns at HSR stations and the impacting factors was quantitatively analyzed. • The GWMNL model outperformed the MNL model in the case study of Jiangsu Province. • Effects of influential factors were discussed considering spatial heterogeneity. High-speed rail (HSR) stations play a vital role in the HSR system. HSR stations not only facilitate the accessibility of interregional transportation but also stimulate population movements across various cities in China. HSR stations in different cities can vary greatly, and an efficient HSR system requires an in-depth understanding of the interrelations between the related influential factors and spatiotemporal passenger flow patterns of different HSR stations. This study adopts a new scheme for clustering HSR stations based on passengers' arrival and departure time series using mobile phone data in Jiangsu, China. To this end, 71 HSR stations are clustered into 3 classes and the spatiotemporal characteristics of passenger flow at different stations are compared. Finally, a geographically weighted multinomial logit model (GWMNL) is built to explore the influence of the built environment, socioeconomic indicators, and HSR station attributes on the classification results of HSR stations related to the time-varying characteristics of passenger flow. The model results show that the number of entertainment POIs, population, population density, area, GDP and building area are significantly associated with the classification results of HSR stations. Additionally, for HSR stations under the same classification result, these variables also have different effects on them in the geographical dimension. According to these findings, quantitative analysis of the linkages between the passenger flow patterns at different HSR stations and the impacting factors would offer implications for planners and policymakers in HSR station planning and associated urban development. [ABSTRACT FROM AUTHOR]

• Urban vibrancy at business district level was revealed. • Base station-level mobile phone data were used. • Both individual and interactive influences of factors were quantified. • Land area and industrial agglomeration effect were most critical factors. • Urban planning should fully consider the interaction of multiple factors. Since Jane Jacob proposed the concept of urban vibrancy in 1961, it has been a research focus in multi-disciplines. This study portrays the business district vibrancy from the perspectives of measurement, spatial patterns, and influencing factors in Guangzhou, China with mobile phone data and the optimal parameters-based geographical detector (OPGD) model. The results reveal that distance decay was observed in the spatial distribution of business district vibrancy. The model for the interactive influence of factors has greater explanatory power than the model for the individual influence of each factor. Except for the distance from the city center, other factors including the number of neighbors, metro accessibility, POI density, area, road density, floor area ratio, and land use mixture were positively correlated with business district vibrancy. Among factors, the land area and the number of neighbors were the two most critical factors jointly explaining 97.3% of the variance of the spatial distribution of business district vibrancy. It emphasizes that the land area and industrial agglomeration effect are the most important aspect of business districts. Finally, this study suggests that urban planners should fully consider the interaction of multiple factors when developing plans to cultivate the vibrancy of business districts. [Display omitted] [ABSTRACT FROM AUTHOR]

The activity space of residents is an important mediator for the exploration of the relationship between residents' activities and the city. In this study, one-month mobile phone data are used to study the activity space of residents from three aspects, namely the radius of the activity space, the frequency of activity, and the diversity of activities. Combined with census data and point-of-interest data, a random forest model is established, and partial dependency plots are used to explore the non-linear effects of the built environment and socio-demographic attributes on the activity space. After analyzing mobile phone data from the central area of Shanghai, China, over one month, it is found that the facility density, accessibility, location, housing conditions, and marital status are the most important factors affecting the activity space of residents. Different from previous studies, it is found that the facility density and accessibility have a diminishing marginal effect on the activity space, and both the built environment and socio-demographic attributes have threshold effects on the activity space. The location relative to the Huangpu River is also found to influence the frequency of activity. Moreover, there are obvious differences between the activity spaces of home-owning residents and tenants. Among tenants, a too-low or too-high monthly rent can lead to a small radius of activity space and a low diversity of activities. Married residents often have a large radius of the activity space, a high frequency of activity, and a high diversity of activities. Some factors are also found to affect the influences of other factors on the activity space, and can even change the direction of correlation. The findings of this study can provide references for urban and transportation planning. • Mobile phone data are fused with POI and census data to study activity space. • Random forest and partial dependency plot are used to study non-linear effects of influencing factors on activity space. • Both built environment and socio-demographics have threshold effects on activity space. • Some factors are found to affect the influences of other factors on activity space. [ABSTRACT FROM AUTHOR]

Leisure mobility forms an important part of people's spatial activity and mobility spectrum. This study aims to analyse the inequality dimensions of spatial mobility of individuals who seek to move to recreational and leisure destinations (often 'green' and 'blue') on designated days. The study traces – through the use of spatially dependent multilevel models – the mobility patterns of people from the greater Stockholm area, using individual pseudonymised mobile phone data and other publicly accessible data. We find significant socio-demographic inequalities in the observed residents' spatial leisure choices, where less affluent groups display especially low variation in mobility when comparing between weekdays, weekends, vacation season and work-periods. [ABSTRACT FROM AUTHOR]

Social area analysis has been applied to analyze how urban space is spatially arranged. Related studies have divided urban residents into different groups whose socioeconomic status are similar. However, few studies have attempted to explore the mobility of these social groups, especially for developing countries where census data with travel information are not readily available. This study explores whether people in the same social area have similar work mobility and whether location matters from an aggregated neighborhood perspective. A case study is conducted in Shenzhen, China with fine-grained census and mobile phone data. The neighborhood committee based census and cell tower based mobile phone data are integrated into 500 m grid for analysis. Results show that different social areas have significantly different work mobility, and the within-group heterogeneity is also prominent. Three location factors are examined and it is found that there exist variations in the influence of spatial factors on work mobility for different social areas. Findings of this study will supplement current knowledge about social areas, location and mobility in urban China at the mesoscale. In addition, the methodology provides a feasible framework of integrating city-scale socioeconomic information and mobility data from different sources for future urban studies. • Similar social groups have different mobility at the aggregated level. • The difference is related to interactions of socio-economic status and location. • Variations in the influence of location on mobility for different social group • A framework of integrating census and mobile phone data [ABSTRACT FROM AUTHOR]

Mobile phone data provides information, such as the home (origin) and current locations of people. The data can be used to analyze the impact of new high-speed railway (HSR) openings. This study examined the population observed in stations and cities of the Honam HSR line in Korea, based on mobile phone data recorded one year before and after its opening. We analyzed the volume of the population observed at each railway station, density of the distance between home and station, and activity hotspots in a city. The results show that the number of people and travel distance increased after the opening of the HSR. The distance to access railway stations increased, as the HSR saves travel time. Moreover, the activity hotspots in a city increased after the opening of the HSR, as more people gathered near the station area. The findings show that the mobility measures enhanced after the opening of the HSR for regional travel and local activities. These measures can help transit agencies and planners in providing better intercity travel.

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.

Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commuting activity space, and recreational activity space—using mobile phone data. The proposed method identifies the range of three types of residents’ activity space at multiple geographic scales and analyzing the relationship between the built environment and activity space. The research takes Zhuhai City as its case study and discovers the spatial patterns for three activity space types. The proposed method enables us to achieve a better understanding of the human activities of different kinds, as well as their relationships with the built environment.

Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators.

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users' one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space. [ABSTRACT FROM AUTHOR]

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events. [ABSTRACT FROM AUTHOR]

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Several studies have pointed to the difficulties of obtaining good data on train ridership. There are at least two challenges regarding these data. First, train operators consider such data confidential business information, especially in high resolution. Second, the data that actually are available vary in quality and coverage. This paper studies mobile phone data as an alternative measure to obtain data about train ridership. Handset counts were obtained from one telecom operator for selected mobile phone base stations and compared with timetable data and APC. The selected base stations are located so that it is likely that a large share of the mobile phone traffic is generated by train passengers. The number of units connected to a base station is found to correspond relatively well with the trains that pass close to the base stations. A ratio between the handset count and APC data appear as promising in utilizing handset count to calculate train ridership, with ratios around one in the rush hours. We discuss preliminary results as well as methodological and technical challenges. To make sure that we do not violate privacy concerns, the data used in the study have been approved by personal privacy representatives. [ABSTRACT FROM AUTHOR]

To mark the centenary of the 1918 influenza pandemic, the broadcasting network BBC have put together a 75-min documentary called ‘Contagion! The BBC Four Pandemic’. Central to the documentary is a nationwide citizen science experiment, during which volunteers in the United Kingdom could download and use a custom mobile phone app called BBC Pandemic , and contribute their movement and contact data for a day. As the ‘maths team’, we were asked to use the data from the app to build and run a model of how a pandemic would spread in the UK. The headline results are presented in the TV programme. Here, we document in detail how the model works, and how we shaped it according the incredibly rich data coming from the BBC Pandemic app. We have barely scratched the depth of the volunteer data available from the app. The work presented in this article had the sole purpose of generating a single detailed simulation of a pandemic influenza-like outbreak in the UK. When the BBC Pandemic app has completed its collection period, the vast dataset will be made available to the scientific community (expected early 2019). It will take much more time and input from a broad range of researchers to fully exploit all that this dataset has to offer. But here at least we were able to harness some of the power of the BBC Pandemic data to contribute something which we hope will capture the interest and engagement of a broad audience. [ABSTRACT FROM AUTHOR]

Mining human behaviors has always been an important subarea of Data Mining.While it provides empirical evidences to psychological/behavioral studies, it also builds the foundation of various big-data systems, which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily commute behaviors of mobile subscribers and further conduct routine mining on them. In this article, we propose to model mobile subscribers' daily commute behaviors by three levels: location trajectory, oneday pattern, and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain two aspects of information: (1) How people's typical commute patterns are. (2) How much their commute behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learnmodel parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human commute behaviors and yields higher accuracy results in trajectory prediction task. [ABSTRACT FROM AUTHOR]