Mobile Phone Data Research Articles

Predicting People’s Concentration and Movements in a Smart City

With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments.

Quantifying Individual PM2.5 Exposure with Human Mobility Inferred from Mobile Phone Data

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM2.5 exposure by combining residents’ mobility traces and PM2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM2.5 concentration, we quantify individual PM2.5 exposure and find that the average PM2.5 exposure of residences in Shanghai is 60.37 ug·h·m−3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM2.5 exposure in Shanghai. Our PM2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development.

Mobile Phone Technology for Preventing HIV and Related Youth Health Problems, Sexual Health, Mental Health, and Substance Use Problems in Southwest Uganda (Youth Health SMS): Protocol for a Pilot Randomized Controlled Trial.

East and Southern Africa have the highest HIV incidence and prevalence in the world, with adolescents and young adults being at the greatest risk. Despite effective combination prevention tools, including the recently available pre-exposure prophylaxis (PrEP), HIV incidence among adolescents and young adults in Uganda remains high, and PrEP use remains low. Mental health and substance use (behavioral health) play a role in sexual behavior and decision-making, contributing to an increase in the risk for acquiring HIV. Interventions that target multiple HIV risk factors, including sexual and mental health and problematic substance use, are crucial to ending the HIV epidemic. Yet few interventions addressing HIV related health disparities and comorbidities among adolescents and young adults in East and Southern Africa currently exist. This study aims to evaluate the acceptability and feasibility of Kirabo, an SMS text message intervention informed by the information, motivation, and behavior model and to be disseminated through secondary schools. The study will gather preliminary estimates of Kirabo's effectiveness in increasing HIV testing and linking users to mental health counselors. We identified Mobile 4 Reproductive Health for adaptation using the assessment, decision, administration, production, topical experts, integration, training, testing (ADAPT-ITT) framework. Mobile 4 Reproductive Health is an evidence-based automated 2-way SMS text messaging and interactive voice response platform that offers sexual and reproductive health information and links users to HIV clinics in East Africa. Through ADAPT-ITT we refined our approach and created Kirabo, an SMS text message-based intervention for linking adolescents and young adults to health services, including HIV testing and mental health counseling. We will conduct a 2-arm randomized controlled trial in Masaka, Uganda. Adolescents (N=200) will be recruited from local schools. Baseline sociodemographic characteristics, HIV test history, and behavioral health symptoms will be assessed. We will evaluate acceptability and feasibility using surveys, interviews, and mobile phone data. The preliminary efficacy of Kirabo in increasing HIV testing and linking users to mental health counselors will be evaluated immediately after the intervention and at the 3-month follow-up. We will also assess the intervention's impact on self-efficacy in testing for HIV, adopting PrEP, and contacting a mental health counselor. Intervention adaptation began in 2019. A pretest was conducted in 2021. The randomized controlled trial, including usability and feasibility assessments and effectiveness measurements, commenced in August 2023. Kirabo is a tool that assists in the efforts to end the HIV epidemic by targeting the health disparities and comorbidities among adolescents in Uganda. The intervention includes local HIV clinic information, PrEP information, and behavioral health screening, with referrals as needed. Increasing access to prevention strategies and mitigating factors that make adolescents and young adults susceptible to HIV acquisition can contribute to global efforts to end the HIV epidemic. ClinicalTrials.gov NCT05130151; https://clinicaltrials.gov/study/NCT05130151. DERR1-10.2196/49352.

Using mobile phone data to map evacuation and displacement: a case study of the central Italy earthquake

Population displacement is one of the most common consequences of disasters, and it can profoundly affect communities and territories. However, gaining an accurate measure of the size of displacement in the days and weeks following a major disaster can be extremely difficult. This study uses aggregated Call Detail Records as an inexpensive and efficient technique to measure post-disaster displacement in four Italian regions affected by repeated earthquakes in 2016–2017. By comparing post-disaster mobile phone count with a forecast computed before the earthquake hit, we can compute an index of change in the presence of mobile phones (MPE). This measure, obtained thanks to advanced analytical techniques, provides a reliable indication of the effect of the earthquake in terms of immediate and medium-term displacement. We test this measure against census data and in combination with other datasets. Looking into available data on economic activities and requests for financial support to rebuild damaged buildings, we can explain MPE and identify significant factors affecting population displacement. It is possible to apply this innovative methodology to other disaster scenarios and use it by policymakers who want to understand the determinants of population displacement.

Linking physical violence to women’s mobility in Chile

Despite increased global attention on violence against women, understanding the factors that lead to women becoming victims remains a critical challenge. Notably, the impact of domestic violence on women’s mobility—a critical determinant of their social and economic independence—has remained largely unexplored. This study bridges this gap, employing police records to quantify physical and psychological domestic violence, while leveraging mobile phone data to proxy women’s mobility. Our analyses reveal a negative correlation between physical violence and female mobility, an association that withstands robustness checks, including controls for economic independence variables like education, employment, and occupational segregation, bootstrapping of the data set, and applying a generalized propensity score matching identification strategy. The study emphasizes the potential causal role of physical violence on decreased female mobility, asserting the value of interdisciplinary research in exploring such multifaceted social phenomena to open avenues for preventive measures. The implications of this research extend into the realm of public policy and intervention development, offering new strategies to combat and ultimately eradicate domestic violence against women, thereby contributing to wider efforts toward gender equity.

Mapping cultural ecosystem services in mountain forests using mobile phone data

The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex “Sudety Zachodnie” using mobile phone data. The study identified the sites with the highest (hot spot) and lowest (cold spot) use. Habitat, stand, demographic, topographic and spatial factors affecting the distribution of activity were also analyzed. Two approaches were applied in our research: global and local Moran’s coefficients, and a machine learning technique, Boosted Regression Trees. The results show that 11,503,320 visits to forest areas were recorded in the “Sudety Zachodnie” in 2019. The most popular season for activities was winter, and the least popular was spring. Using global and local Moran’s I coefficients, three small hot clusters of activity and one large cold cluster were identified. Locations with high values with similar neighbours (hot-spots) were most often visited forest areas, averaging almost 200,000 visits over 2019. Significantly fewer visits were recorded in cold-spots, the average number of visits to these areas was about 4,500. The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation. Results of Boosted Regression Trees modeling of visits in forest, using tree stand habitat and spatial factors accurately explained 76% of randomly selected input data. The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography. The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data. It also allows the identification of factors that may influence the distribution of visits in forests. Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.

Understanding the impact of COVID-19 on consumer mobility and recovery from a distance perspective: a mobile phone data application

研究目的在我们的研究中, 为了调查COVID-19大流行期间的旅游出行（例如：酒店访问）, 我们根据保护动机理论制定了三个目标：（1）研究在COVID-19大流行前后美国的旅行距离的变化, （2）在大流行期间识别不同地区的不同旅行模式; 以及（3）探讨影响旅游出行的威胁和应对因素。研究方法我们利用了两个主要数据源。首先, 来自SafeGraph的智能手机数据提供了63,610家美国酒店的酒店特定变量（例如位置和访客计数）以及旅行距离数据。其次, 代表与旅行距离相关的各种因素的州级数据来自COVID-19数据中心和美国人口普查局。我们分析了州级和地区级的旅行距离随时间的变化, 并调查了与这些变化相关的临床、政策和人口因素。研究发现我们的研究结果揭示了不同阶段的实际旅行动态和地区内的差异, 以及在公共卫生风险中塑造旅行模式的健康相关政策和其他外部因素的作用。研究创新我们的研究是第一个利用智能手机数据以及与COVID-19和人口统计数据相关的州级数据, 经验性地研究了旅行距离到酒店作为目的地的变化。我们的研究结果表明, 旅游企业和利益相关者可以通过考虑威胁评估和应对机制来主动调整他们的策略, 这两者都受到健康相关政策等外部因素的影响。

Mobile phone data reveals spatiotemporal recreational patterns in conservation areas during the COVID pandemic

Understanding visitation patterns is crucial in developing effective conservation strategies for protected areas, as it serves as an indicator for operating an ecosystem management plan that balances biodiversity and ecosystem services intertwined with public health and social benefits. However, limited data availability during the COVID-19 pandemic has hindered the comprehensive understanding of temporal changes in realized cultural ecosystem services, particularly in recreational activities within these areas. Our study utilized GPS data from mobile phones to quantify visitor characteristics and their contribution to recreational ecosystem services in protected areas at a national scale during the COVID-19 pandemic. We estimated the pandemic's relative impact on visitor patterns at 98 visitor centers in national parks and Ramsar sites in Japan. The total number of visitors and travel distance in various sizes of protected areas decreased after the outbreak of COVID-19. The number of visitors in the protected areas displayed a quick recovery despite the increasing positive COVID-19 cases during the following summer. Post-pandemic, visitors showed a preference for less densely populated protected areas closer to their home range. Our findings partly suggest that protecting a diverse range of conservation areas along the urban gradient could be an effective strategy for maintaining the resilience of recreational services during a prolonged pandemic.

Did Liberal Lockdown Policies Change Spatial Behaviour in Sweden? Mapping Daily Mobilities in Stockholm Using Mobile Phone Data During COVID-19

Sweden had the most liberal lockdown policies in Europe during the Covid-19 pandemic. Relying on individual responsibility and behavioural nudges, their effectiveness was questioned from the perspective of others who responded with legal restrictions on behaviour. In this study, using mobile phone data, we therefore examine daily spatial mobilities in Stockholm to understand how they changed during the pandemic from their pre-pandemic baseline given this background. The analysis demonstrates: that mobilities did indeed change but with some variations according to (a) the residential social composition of places and (b) their locations within the city; that the changes were long lasting; and that the average fall in spatial mobility across the whole was not caused by everybody moving less but instead by more people joining the group of those who stayed close to home. It showed, furthermore, that there were seasonal differences in spatial behaviour as well as those associated with major religious or national festivals. The analysis indicates the value of mobile phone data for spatially fine-grained mobility research but also shows its weaknesses, namely the lack of personal information on important covariates such as age, gender, and education.

Working and residential segregation of migrants in Longgang City, China: A mobile phone data-based analysis

Despite being a long-established research topic, most urban segregation studies have neither explored the socio-spatial disparities beyond the context of housing, nor systematically examined both residential and non-residential segregation patterns at a very fine spatial scale. Recently, mobile phone data has been increasingly used as a data source to investigate experiences of segregation. While being celebrated for its high spatiotemporal resolution, mobile phone data analysis is often limited by the lack of users' socioeconomic information. To fill these gaps and by taking a small Chinese county city, Longgang, as the case of study, we measure migrants' residential and working segregation experiences through mobile phone data at a 100-metre scale. Results showed that both working and residential populations show a high level of segregation using the population diversity and location quotient indicators, but the segregation experienced by working population is less than that by residential population. Migrants are highly segregated with local citizens, but less segregated in some areas like the industrial zones surrounding the traditional downtown area. This study helps us better understand migrants' working and residential segregation of migrants in subordinate Chinese cities, and inform the planning and policy-making process to promote urban integration.

New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China

New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China

Unveiling the pandemic's impact on visits to Madrid’s parks: insights from mobile phone data analysis

ABSTRACT Changes in human mobility due to the COVID-19 pandemic have particularly impacted urban parks, altering their use patterns. The use of Big Data sources enables the quantification and tracking of changes, although few studies delve into their spatial representation and the socio-demographic characterisation of park users. In this research, we use anonymised cellular network-based data with associated user profile information to quantify and map the changes operated in trips to Madrid’s urban parks in a week of reference before and after the pandemic. Our results show a general decrease in trips to urban parks, especially by males in all age ranges. We also observe a marked decrease in trips by the high-income population. Finally, we have nicely presented some of these results in a composition of several maps that provide visual insight into the main changes.

A standardised differential privacy framework for epidemiological modeling with mobile phone data.

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.

Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area

In this paper, we present a robust spatiotemporal statistical methodology that is capable of accurately forecasting traffic in the flood-prone area of the Mandolossa in the Province of Brescia (Italy). An innovative combination of two sources of mobile phone data is proposed to obtain an extremely accurate representation of the flows of people passing by the streets directly linked to the risky area. Three types of flows have been considered: outflows (from the flood-prone area to the neighborhood), inflows (from the neighborhood to the flood-prone area), and internal flows (within the flood-prone area). The three flows are assumed to be dependent on each other and are modeled using a vector autoregressive approach. We found evidence of both weekly and daily seasonal components in the time series. To capture the seasonality, a dynamic harmonic regression component has been included, where the optimal number of Fourier bases in the periodic functions has been chosen according to a criterion based on the Akaike Information Criteria. On the other side, the set of autoregressive parameters has been defined in such a way as to represent the time period necessary for the mobile phone company to observe, process, and release the data. The forecasting ability of the model has been assessed using blocked k-folds cross-validation along with the mean absolute percentage error and the hit rate. Though the model performs better for non-summer days, we found that it satisfactorily forecasts both the number and the level of people moving.

Nowcasting tourist nights spent using innovative human mobility data.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan

Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15–59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases.

Population mobility change for controlling the transmission of COVID-19: mobile phone data analysis in nine cities of China

ABSTRACT Mobility restriction measures were the main tools to control the spread of COVID-19, but the extent to which the mobility has decreased remained unsure. We investigated the change in local population mobility and its correlation with COVID-19 infections, using 1185 billion aggregated mobile phone data records in nine main cities in China from 10 January to 24 February 2020. The mobility fell by as much as 79.57% compared to the normal days in 2020 and by 58.13% compared to the same lunar period in 2019. The daily incidence of COVID-19 was significantly correlated with local daily mobility (R2 = 0.77, P < 0.001). The instantaneous reproduction number R(t) declined by 3% when mobility was reduced by 10% in the GLM analysis (P < 0.05). Our study indicated that the decreased mobility level, driven by a mixture effect of holiday and public health interventions, could substantially reduce the transmission of COVID-19 to a low level. Our study could provide evidence of mobility restriction to control local transmission for other places facing COVID-19 outbreaks or potential next waves.

The rhythms of transient relationships: allocating time between weekdays and weekends.

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

Multi-day activity pattern recognition based on semantic embeddings of activity chains

Understanding residents' multi-day activity patterns is crucial for urban transportation planning. However, traditional travel surveys have limitations in observing the recurring regularity of residents' activities over multiple days, resulting in limited insights into long-term activity patterns. This study aims to provide a general and practical analytic framework for multi-day activity pattern recognition by fusing mobile phone data, travel survey data, and built environment data. First, a rule-based model is proposed to infer home/work activities, while a machine learning model is proposed to infer non-home/work activities of mobile phone users. Then, their multi-day activity chains are reconstructed with sequences of activity units which are labeled by inferred activity types. To measure the differences and similarities among these activity chains, this study develops a word embedding model to represent activity units and a sentence embedding model to represent activity chains in numerical vector space. Finally, regular multi-day activity patterns of mobile phone users can be recognized based on the clustering of vectorized activity chains. A case study in Shanghai is conducted to validate the proposed analytic framework for understanding the multi-day activity patterns of residents. The results indicate the effective recognition of 39 representative patterns of 7-day activity chains in Shanghai, reflecting the various lifestyles of residents. In summary, the main contribution of this study is to transform the problem of multi-day activity pattern recognition into similarity measurement of sentence vectors of activity chains by employing techniques from the field of natural language processing.

Capturing the Dynamics of Homelessness Through Ethnography and Mobile Technology: Protocol for the Development and Testing of a Smartphone Technology-Supported Intervention.

US military veterans who have experienced homelessness often have high rates of housing transition. Disruptions caused by these transitions likely exacerbate this population's health problems and interfere with access to care and treatment engagement. Individuals experiencing homelessness increasingly use smartphones, contributing to improved access to medical and social services. Few studies have used smartphones as a data collection tool to systematically collect information about the daily life events that precede and contribute to housing transitions, in-the-moment emotions, behaviors, geographic movements, and perceived social support. The study aims to develop and test a smartphone app to collect longitudinal data from veterans experiencing homelessness (VEH) and to evaluate the feasibility and acceptability of using the app in a population that is unstably housed or homeless. This study's design had 3 phases. Phase 1 used ethnographic methods to capture detailed data on day-to-day lived experiences of up to 30 VEH on topics such as housing stability, health, and health behaviors. Phase 2 involved focus groups and usability testing to develop and refine mobile phone data collection methods. Phase 3 piloted the smartphone mobile data collection with 30 VEH. We included mobile ethnography, real-time surveys through an app, and the collection of GPS data in phase 3. The project was launched in June 2020, and at this point, some data collection and analysis for phases 1 and 2 are complete. This project is currently in progress. This multiphase study will provide rich data on the context and immediate events leading to housing transitions among VEH. This study will ensure the development of a smartphone app that will match the actual needs of VEH by involving them in the design process from the beginning. Finally, this study will offer important insights into how best to develop a smartphone app that can help intervene among VEH to reduce housing transitions. DERR1-10.2196/53022.