Passive mobile data for studying seasonal tourism mobilities: an application in a Mediterranean Coastal destination

The article uses passive mobile data to analyse the complex mobilities that occur in a coastal region characterised by seasonal patterns of tourism activity. A large volume of data generated by mobile phone users has been selected and processed to subsequently display the information in the form of visualisations that are useful for transport and tourism research, policy, and practice. More specifically, the analysis consisted of four steps: (1) a dataset containing records for four days—two on summer days and two in winter—was selected, (2) these were aggregated spatially, temporally, and differentiating trips by local residents, national tourists, and international tourists, (3) origindestination matrices were built, and (4) graph-based visualisations were created to provide evidence on the nature of the mobilities affecting the study area. The results of our work provide new evidence of how the analysis of passive mobile data can be useful to study the effects of tourism seasonality in local mobility patterns

Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework

© 2017 by the authors. This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data

Characterizing and Removing Oscillations in Mobile Phone Location Data

IEEE WoWMoM 2019, 20th IEEE International symposium on a World of Wireless, Mobile and Multimedia Networks, Washington, ETATS-UNIS, 10-/06/2019 - 12/06/2019International audienceHuman mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories

Uncovering population dynamics using mobile phone data : the case of Helsinki Metropolitan Area

Understanding the whereabouts of people in time and space is necessary for unraveling how our societies function. Regardless, our understanding of human presence is predominantly based on static residential population data, which is often outdated and excludes certain population groups, such as commuters or tourists. In the light of development towards 24-hour societies and the needs for promoting sustainable and equitable urban planning, reliable data of population dynamics are needed. To this end, ubiquitous mobile phones provide an attractive source for estimating the spatiotemporal digital footprints of people. In this study, I set out to investigate 1) the feasibility of three different aggregated network-based mobile phone data – the number of voice calls, data transmission and general network connection attempts – as a proxy for human presence, 2) how does the population distribution vary in Helsinki Metropolitan Area over the course of a regular weekday and 3) the role of temporally-sensitive population data when analysing dynamic accessibility to grocery stores and transport hubs. To my best knowledge, this is the first attempt when mobile phone data is used to reveal population dynamics for scientific purposes in Finland. Mobile phone data collected by the mobile network operator Elisa in 2017–2018 and ancillary data about land cover, buildings and a time use survey were used to estimate the 24-hour population distribution of the Helsinki Metropolitan Area. The mobile phone data were allocated to statistical 250 m x 250 m grid cells using an advanced dasymetric interpolation method and validated against population register data from Statistics Finland. The resulting 24-hour population was used to map the pulse of the city and to introduce the first fully dynamic accessibility model in the study area. The results show that data use is a good proxy for people and outperforms voice calls or overall network connection attempts. During daytime, the static population overestimates the population in residential areas and underestimates the population in work and service areas. In general, the 24-hour population reveals the pulse of a city, which is highlighted especially in the inner city of Helsinki, where the relative share of population of the study area increases by 50 % from the share at night-time to its peak at noon. The results of the case study suggest that integrating dynamic population data to location-based accessibility analysis provides more realistic results compared to static population data, but the significance of dynamic population data depends on the study context and research questions. In summary, aggregated network-driven mobile phone data is a feasible alternative for dynamic population modelling, however, different mobile phone data types vary in representativeness, which should be taken into account when using mobile phone data in research. To this end, critical evaluation of data and transparent data description are essential. Overall, understanding 24-hour societies and supporting sustainable urban planning necessitates dynamic population data, but advancements in data policy and availability are needed to harvest these possibilities. The results of this study also provide new empirical insights of the population dynamics in the study area, which can be used to advance planning and decision making.Ymmärrys väestön alueellisen jakautumisen ajallisesta vaihtelusta on keskeistä yhteiskuntamme toiminnan ymmärtämiseksi. Tästä huolimatta ymmärrys ihmisten läsnäolosta on vähäistä ja perustuu pääasiassa staattisiin asuinpaikkakohtaisiin väestötietoihin, jotka ovat usein vanhentuneita ja saattavat johtaa eräiden väestöryhmien, kuten työmatkalaisten tai turistien, sivuuttamiseen. Kehityksen kohti ympärivuorokautista yhteiskuntaa ja kestävän ja tasa-arvoisen kaupunkisuunnittelun edistämisen tarpeiden valossa tarvitaan luotettavia tietoja väestön dynamiikasta. Tässä tutkimuksessa tarkastelin 1) kolmen eri verkkopohjaisen matkapuhelinaineiston – puheluiden, tiedonsiirtoyhteyksien ja verkkoyhteyksien muodostusyritysten lukumäärän – soveltuvuutta ihmisen läsnäolon kuvaajana, 2) miten väestöjakauma vaihtelee pääkaupunkiseudulla säännöllisen arkipäivän aikana ja 3) temporaalisten väestötietojen käytön roolia saavutettavuusmallinnuksessa tarkasteltaessa ruokakauppojen ja liikenteen solmukohtien saavutettavuutta joukkoliikenteellä. Parhaan tietämykseni mukaan tämä on ensimmäinen kerta, kun matkapuhelinaineistoja käytetään väestön dynamiikan tarkasteluun tieteellisiin tarkoituksiin Suomessa. Matkapuhelinoperaattori Elisan keräämiä matkapuhelinaineistoja (2017–2018) sekä aineistoja maankäytöstä, rakennuksista ja ajankäyttötutkimuksen tuloksia käytettiin pääkaupunkiseudun 24 tunnin väestöjakauman arvioimiseen. Matkapuhelimen tiedot allokoitiin 250 m x 250 m tilastoruutuihin käyttäen edistynyttä dasymetristä interpolointimenetelmää ja validoitiin Tilastokeskuksen väestörekisteritietoja käyttäen. Tuloksena saatua 24 tunnin väestöaineistoa käytettiin kaupungin pulssin analysointiin ja ensimmäisen täysin dynaamisen saavutettavuusmallin toteuttamiseen tutkimusalueella. Tutkimuksen tulokset osoittavat, että matkapuhelinten tiedonsiirto on hyvä kuvaaja ihmisten sijainnille ja parempi kuin puhelut tai verkkoyhteyksien muodostusyritykset. Päivän aikana staattinen väestöaineisto yliarvioi väestöä erityisesti asuinalueilla samalla aliarvioiden väestöä alueilla, joilla on työpaikka- tai palvelukeskittymiä. Yleisesti katsottuna 24 tunnin väestö paljastaa kaupungin pulssin, mikä korostuu erityisesti Helsingin keskustassa, jossa tutkimusalueen väestön suhteellinen osuus kasvaa 50 %:lla yöstä sen huippuun keskipäivällä. Tapaustutkimuksen tulokset havainnollistavat kuinka dynaamisen väestötietojen integroiminen sijaintipohjaiseen saavutettavuustarkasteluun tarjoaa realistisempia tuloksia verrattuna staattiseen väestöaineistoon, mutta dynaamisten väestötietojen integroimisen merkitys riippuu tutkimuksen kontekstista ja tutkimuskysymyksistä. Yhteenvetona voidaan todeta, että aggregoitu verkkopohjainen matkapuhelinaineisto on hyvä vaihtoehto dynaamisen väestön mallintamiseen, mutta soveltuvuus vaihtelee aineistojen välillä, mikä on tärkeä huomioida käytettäessä matkapuhelinaineistoja tutkimuksessa. Tätä vasten aineiston kriittinen tarkastelu ja läpinäkyvä aineiston dokumentointi on olennaista. Kaiken kaikkiaan 24 tunnin yhteiskuntien ymmärtäminen ja kestävän kaupunkisuunnittelun tukeminen edellyttävät dynaamisia väestötietoja, mutta tietopolitiikan ja aineistojen saatavuuden edistäminen on välttämätöntä tämän toteutumiseksi. Tämä työ tarjoaa myös uutta empiiristä tietoa väestön dynamiikasta pääkaupunkiseudulla, jota voidaan käyttää suunnittelun ja päätöksenteon tukena

TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data

International audienceCall detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of proceessing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows

Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data

Through seven publications this dissertation shows how anonymized mobile phone data can contribute to the social good and provide insights into human behaviour on a large scale. The size of the datasets analysed ranges from 500 million to 300 billion phone records, covering millions of people. The key contributions are two-fold: 1. Big Data for Social Good: Through prediction algorithms the results show how mobile phone data can be useful to predict important socio-economic indicators, such as income, illiteracy and poverty in developing countries. Such knowledge can be used to identify where vulnerable groups in society are, reduce economic shocks and is a critical component for monitoring poverty rates over time. Further, the dissertation demonstrates how mobile phone data can be used to better understand human behaviour during large shocks in society, exemplified by an analysis of data from the terror attack in Norway and a natural disaster on the south-coast in Bangladesh. This work leads to an increased understanding of how information spreads, and how millions of people move around. The intention is to identify displaced people faster, cheaper and more accurately than existing survey-based methods. 2. Big Data for efficient marketing: Finally, the dissertation offers an insight into how anonymised mobile phone data can be used to map out large social networks, covering millions of people, to understand how products spread inside these networks. Results show that by including social patterns and machine learning techniques in a large-scale marketing experiment in Asia, the adoption rate is increased by 13 times compared to the approach used by experienced marketers. A data-driven and scientific approach to marketing, through more tailored campaigns, contributes to less irrelevant offers for the customers, and better cost efficiency for the companies.Comment: 166 pages, PHD thesi