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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]

Early empirical studies in labour and urban economics addressing the role of commuting (on, e.g., wages and locational choice) have typically been confined to the use of survey data. Researchers are, however, increasingly getting access to large register databases with detailed information on where individuals live and work. A variety of methods have thus emerged to exploit the geocoded characteristic of the data to calculate commuting measures that go beyond simple Euclidean metrics. These methods involve new techniques that make use of geographic information system (GIS) routing software or application programming interfaces provided by third-party developers. This article provides (i) a brief survey of the small but emerging literature that uses geocoded register data to calculate different commuting measures, (ii) an example on how register-based commuting measures can be constructed along with descriptive evidence on how different commuting measures compare for different socio-economic groups using rich Swedish register data, (iii) a discussion of the pros and cons of different methods and measures, and (iv) a discussion of the potential of using mobile phone data to further improve registerbased commuting measures. [ABSTRACT FROM AUTHOR]

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas. [ABSTRACT FROM AUTHOR]

The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals’ recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility. [ABSTRACT FROM AUTHOR]

It is becoming more and more important to make analysis on activity regularity of population in urban commercial districts, which would be beneficial to commercial infrastructure distribution and construction. The paper presents an approach to mining hot time periods in urban commercial districts using POI information and mobile phone data. First, a data field-based self-adaptive DBS can algorithm ( DFB - saDBS can ) is proposed to make clusters for commercial districts. Second, boundary determination algorithm is presented based on convex hull. Then, a K- means algorithm with constraints ( cK - means ) is proposed to make extraction of hot time periods from flow of people. On the other hand, we also propose an approach to regional POI proportion analysis via urban POI data. Taking advantage of structure measurement indexes of landscape ecosystem, we analyze relationship between population mobility and facility distribution in commercial districts. Taking a downtown area of Wuhan city, China as case study, our approach is verified to be effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

The relevance of the monitoring of visitor flows (VF), namely the general or aggregate patterns of travellers’ movements in a given area is twofold. On the one hand, they are relevant for the spatial description of travel networks. On the other hand, VF patterns are challenging traditional organization of destination management (DM) and are becoming a strategic tool. VFs are useful for reshaping the DM organization’s governance model from a static-central model to a dynamic network. The aim of this research is to estimate SVF using the data movement recorded by a test carried out with an anonymised and highly aggregated mobile phone data set, provided by Swisscom—the major Swiss mobile company. This research sheds some light on the relevance of VF in the understanding and improving of DM organization governance. Furthermore, it provides evidence of the existence of SVF at different levels of geographical scale obtained by network analysis techniques. [ABSTRACT FROM AUTHOR]

Mobile phone data are a rich source to infer all kinds of mobility- related information. In this research, we present an approach where mobile phone data are used and analyzed for enriching the transport model of the region of Rotterdam. In this research Call Detail Records (CDR) are used from a mobile phone provider in the Netherlands that serves between 30 and 40 percent of Dutch mobile phones. Accessing these data provides travel information of about one-third of the Dutch population. No other data source is known that gives travel information at a national scale at this high level. The raw data of one month is processed into basic information which is subsequently translated into OD-information (Origin-Destination) based on several decision rules. This OD information is compared with the traditionally estimated a priori OD matrix of the Rotterdam transport model and the Dutch yearly national household travel survey. Based on the analysis and assignment results, an approach is developed to combine the mobile phone OD-information and an a priori OD matrix using the best of both worlds. Results show a better match of the assignment results of this matrix with the counts indicating a better quality of the matrix. [ABSTRACT FROM AUTHOR]

Recent advanced information and communication technologies can provide more accurate and comprehensive information. In particular, mobile phone data provide a new data source for urban structures and mobility. It is important for urban and transportation planners to be able to use data containing valuable location information that is not easily obtainable from traditional datasets, such as expensive household surveys, extensive traffic counts, or aggregated socioeconomic statistics. This study explored the potential of using mobile phone data to characterize and compare urban activity and mobility patterns from daily and hourly mobile phone records across 10 cities in Korea. We compared the internal and external mobility of phone users, and calculated urban attractiveness and home-based trip length frequency distribution for comparisons of different sized cities. The spatiotemporal evolution of urban activity within a day was examined, and the spatiotemporal extension for each city was calculated, showing the degree of spatial dispersion of residences and other activity locations. We also identified urban activity subcenters and hotspots based on density and time persistence criteria, as well as their compactness. Policy makers can expect to see more applications using mobile phone data, and this study helps demonstrate the potential of such data. We hope that spatiotemporal activity analysis can provide a foundation for future research that will help improve urban and transportation policies. [ABSTRACT FROM AUTHOR]

Although new available big data sources have revealed themselves to be extraordinarily useful for transport demand modelling, they have not come into widespread use due to the justifiable privacy concerns of data stewards. In this study, we step back and re-evaluate the way in which mobile phone telco data can be introduced for the task of transport and land-use policy evaluation, travel demand forecasting and transport infrastructure testing through large-scale transportation simulations. We investigated that question by deploying a multi-agent transport simulation driven primarily by hourly-aggregated telco Origin-Destination (OD) matrices. We address the principal four challenges: spatial and temporal disaggregation, mode imputation and route choice. For temporal disaggregation, we propose a convolution with an exponential kernel method. As for transport mode imputation, a supervised-learning framework is designed. The simulation results are compared against traffic count data and public transport smart card transactions, showing accurate patterns for private cars but overestimated public transport demand in the morning peak. Lastly, we set the future steps for the improvement of simulations driven by aggregated mobile phone data. [ABSTRACT FROM AUTHOR]

Understanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects. [ABSTRACT FROM PUBLISHER]

Understanding the features of travel activities is important in elaborating travel behaviors and segmenting travelers based on the similarity of activity patterns. This research applying mobile big data analytics suggests a novel method to classify travelers by considering the sequences of travel activity with individuals' trajectories. The result revealed five distinct travel types visiting city destinations and demonstrated dynamic travel flow among different mobility types. Recognizing that different types of travel patterns present important information in understanding destinations' roles (or functions), this study attempts to characterize the functionality dynamics of city destinations based on travel activity types. As a result, the findings of this research provide insights into the demand-driven construct (or flow-based) of destination planning, which is the foundation of smart destination design. In addition, important methodological and practical implications that could be useful for city destination planners/designers are suggested. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected travelling in general, and the leisure mobility and the spatial distribution of travellers in particular. In most parts of the world, both domestic and international travel has been replaced by restrictive policies and recommendations on mobility. A modal shift from public transport towards private cars and micro-mobility was also observed. This study seeks to trace the implications of the COVID-19 pandemic for leisure mobility. We use a unique Swedish database containing daily mobility patterns of pseudonymised mobile phone users, combined with a survey on vacation transport behaviour. By contrasting mobility patterns for selected holiday days during the unaffected summer of 2019 with corresponding dates in 2020 and 2021, we are able to model and detect the pandemic effects on tourism and recreational mobility. Moreover, by identifying the general mobility patterns, we analyse whether and how the transport mode has changed. Using data on the spatial distribution of recreational amenities, we identify locations that were favoured during the pandemic. In Sweden, even though the pandemic decreased in spread and severity during the summers, most travel restrictions were still enforced, international vacations uncommon, and larger vacation spots, such as amusement parks and cultural institutions, were closed down. Swedish vacation homes in remote or rural areas were quickly booked. This change in recreational behaviour, where less populated areas, open air and nature recreation were favoured over indoor or crowded urban cultural activities, was more substantial in 2021 than in 2020. This result shows how policies can effectively be developed, so that Swedes respond properly to recommendations and adjust their vacation plans. [ABSTRACT FROM AUTHOR]

Household size and its spatial distributions reflect not only the socioeconomic development in a city but also the rationality of urban resource allocation. Most existing studies rely heavily on census data to explore the potentially influential factors using methods such as macro-statistical analysis and socioeconomic analysis, of which the spatial resolutions and geographic scales are constrained. More importantly, the association between the household size distribution and the built environment is oversimplified or even neglected to some extent. In this work, we use massive mobile phone data combined with travel surveys of Beijing inhabitants' data (TSBI) to infer the household size and analyze the effect of spatial heterogeneity in a finer spatial resolution in Beijing, China. First, the machine learning method (i.e., support vector machine (SVM)) is applied to identify the household relationships of mobile users, and there are around 3.44 million households (families) with different sizes are obtained. Second, we analyze the spatial distribution patterns of household size and its association with built environmental indicators (e.g., public service density, public transportation density, etc.). The results exhibit a heterogeneous effect of the regional built environment on average household size (AHS). For instance, "commercial density" and "administrative density" show a negative impact on household size, while "public service density" and "public transportation density" show positive correlations with household size. As a complement to census data, mobile phone data can be used to obtain the household size in real-time. This paper provides quantified evidence for government departments to allocate facilities in a more targeted, balanced, and reasonable way according to the regional differences in household size, which would potentially support the sustainable urban development. • Using phone call characteristics between family members to infer family relationships • Family relationship was determined using SVM algorithm. • MGWR is applied to explore the effects of built environment factors on family size. • House prices and distance to city center have significant effects on household size. • Potential decision-making support is provided for the facility allocation for government. [ABSTRACT FROM AUTHOR]

In this study, we set out to understand how the changes in daily mobility of people during the first wave of the COVID-19 pandemic in spring 2020 influenced daytime spatial segregation. Rather than focusing on spatial separation, we approached this task from the perspective of daytime socio-spatial diversity – the degree to which people from socially different neighbourhoods share urban space during the day. By applying mobile phone data from Greater Stockholm, Sweden, the study examines weekly changes in 1) daytime social diversity across different types of neighbourhoods, and 2) population groups' exposure to diversity in their main daytime activity locations. Our findings show a decline in daytime diversity in neighbourhoods when the pandemic broke out in mid-March 2020. The decrease in diversity was marked in urban centres, and significantly different in neighbourhoods with different socio-economic and ethnic compositions. Moreover, the decrease in people's exposure to diversity in their daytime activity locations was even more profound and long-lasting. In particular, isolation from diversity increased more among residents of high-income majority neighbourhoods than of low-income minority neighbourhoods. We conclude that while some COVID-19-induced changes might have been temporary, the increased flexibility in where people work and live might ultimately reinforce both residential and daytime segregation. • People's daily mobility changes during the COVID-19 pandemic influenced daytime spatial segregation. • We studied weekly changes in daytime social diversity from the perspectives of both places and people in Greater Stockholm. • Daytime diversity in neighbourhoods dropped when the pandemic broke out in March 2020 but slowly recovered. • The increase in people's isolation from diversity was more drastic and long-lasting, and varied between population groups. • While some COVID-19-induced changes were temporary, the increased flexibility in where people work and live might have longer-term influence on segregation. [ABSTRACT FROM AUTHOR]

The travel mobility gap is among the indicators that can be used to evaluate the level of social and transport inequity. To achieve a large and representative sample for this investigation of the different impacts of the built environment on travel mobility of various income and migrant groups, we have utilized big data from mobile phones for over 10 million users in Shenzhen, China. Travel mobility was measured by non-commute travel frequency and activity space. Our descriptive analysis demonstrates lower-income groups and migrant workers have lower levels of travel mobility than higher-income groups and non-migrant workers. The results produced by our linear regression models also reveal a significant travel mobility gap between different income and migration groups. That gap appears to be positively impacted by job density and bus stop distance and negatively impacted by residential density and metro station distance. Our modeling results also demonstrate that the travel mobility gap is larger in the outer suburbs than in the city center and inner suburbs. Our research findings reveal that the built environment influences the travel mobility gap, which implies that marginalized groups experience some degree of social inequality and exclusion. Based on these findings, we provide policy recommendations that aim to reduce the travel mobility gap between the marginalized and reference groups. • Travel mobility gap between the marginalized and reference groups is examined. • A mobile phone dataset covering over 10 million users in Shenzhen is analyzed. • Lower income groups and migrant workers have lower levels of travel mobility. • The gap is larger in the outer suburbs than in the city center or inner suburbs. • The built environment influences the travel mobility gap. [ABSTRACT FROM AUTHOR]

Rising income segregation perpetuates socioeconomic inequalities, preventing us from developing more sustainable environments. In the literature, little is known about the spatiotemporal dynamics of income segregation under natural disaster. To address this gap, this paper aims to quantify how people experience income segregation in the context of a disaster through their mobility patterns. Specifically, we propose a generalized framework to construct a dynamic mobility network that describes the footprint of residents within their activity spaces, leveraging large-scale mobile phone data. The construction of this network is formulated as a nonlinear programming model and solved by the Lagrangian relaxation and numerical iterative method. A case study is presented to examine income segregation of residents in Harris County (Texas) in access to various critical facilities before, during, and after the winter storm Uri. Our results indicate that income segregation behaviors vary at different critical facilities, as facilities with smaller catchment areas are found to have higher degrees of segregation. We also highlight the disparities in access to critical facilities between low-income and high-income neighborhoods. This paper provides a new venue to better understand income segregation behavior under natural disaster through human mobility networks, which could inform equitable resource allocation in disaster management. • An optimal human mobility network is formulated as a nonlinear programming model. • Income segregation behavior varies at different critical facilities before, during, and after a disaster. • Facilities with smaller catchment areas are found to have higher degrees of segregation. • Mobility patterns in response to a natural disaster are heterogeneous across economic groups. • Significant disparities in access to critical facilities between low-income and high-income neighborhoods are revealed. [ABSTRACT FROM AUTHOR]

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution.

Pokémon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pokémon Go on people's mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pokémon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game's release in August 2016: in the following week, up to 13.8% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city. Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live. Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how 'the street' may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic. [ABSTRACT FROM AUTHOR]

The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. [ABSTRACT FROM AUTHOR]

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code. [ABSTRACT FROM PUBLISHER]

Our planet is gradually moving towards an urbanized world. Modern urban agglomerations tend to turn nowadays into advanced information hubs supporting a smart management of dynamic urban systems. The currently popular notion of ‘smart cities’ aims to provide a new perspective for sustainable and high-performance strategies of city stakeholders in our ‘ urban century ’. In this context, digital information technology provides a new tool for efficient and effective management and planning of urban space, inter alia in the field of transportation, environment, public facilities or advanced service provision to citizens. This paper aims to offer, first, a concise overview of the emerging opportunities of information and communication technology (ICT) for smart urban policy; digital technology in particular, appears to provide novel pathways for modern planning strategies in smart cities. Against this background, the paper sketches out the complex force field of global urbanisation phenomena and highlights the data and information needs for strategic planning of cities (using inter alia as a framework the so-called ‘ urban piazza ’ strategy framework). Secondly, various new decision support tools that are currently emerging and that offer a new promising scope for handling complex urban management issues (for instance, on accessibility, congestion, safety or sustainability) are briefly presented. And finally, the potential of such digital data systems for urban management and policy is concisely illustrated by means of some recent applications in the area of smartphone data systems. The paper concludes with a discussion of the challenges ahead for urban policy, inter alia by paying attention to institutional and governance aspects of ‘ big digital data ’ management in urban systems. [ABSTRACT FROM AUTHOR]