Using mobile phone data for quantifying large-scale household-level disaster recovery

Recognizing and classifying users' routine behavior patterns from sensor data has been a hot topic in pervasive computing. Its objective is to automatically discover recurrent routine patterns in a user's daily life by leveraging the multimodal data generated from wearable sensors such as mobile phones. This kind of knowledge can be utilized in many ways such as identifying similar users in terms of their behaviors, providing behavior contexts to enable advanced human-centered applications, etc. While numerous works have been done in this area, most of them rely on densely sampled mobile data collected from specially-programmed sensors that can “follow” people throughout the day. In this paper, we study how to achieve the same objective when the mobile data presented is much sparser, such as traditional mobile phone data where a user's location is reported only when he makes a call. Although a single user's mobile data is far from sufficient to reveal his characteristic behavior, we show that when exploiting a large number of users' mobile data in a principled collaborative way which facilitate similar users' data to complement each other, representative routine patterns can be revealed and each user can be characterized properly. Experiments on synthetic and real mobile phone data set demonstrate the effectiveness of our methods, and also show our model's ability in predicting human activity using the patterns learned.

This study uses mobile phone data to understand mobility patterns in a country, with limited mobility data, in order to give advice about decisions on how to design the national and regional road network. Our method consists of three parts: (1) filtering mobile phone traces to derive mobility patterns, (2) building an adapted formulation of the gravity-based trip distribution model, which considers telecommunication intensity (i.e., aggregate number of calls and text messages) and travel time as input to forecast the influence of road improvements on country-wide mobility, and (3) optimizing the road network investment based on the adapted trip distribution model by using a local search algorithm. The method was applied to the case study country of Senegal. The mobile phone data was transformed to support informed decisions on road network development in that country given different objectives, namely accessibility and equity. We believe that the methodology is valuable and reproducible to other countries where traditional mobility data is scarce but mobile phone data is available to transport planners.

Evidence and future potential of mobile phone data for disease disaster management

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.

Advanced demand data collection technologies for multi modal strategic modelling

Mobility data to aid assessment of human responses to extreme environmental conditions

As cities continuously expand and with the emergence of mega-city regions, the urban functional zones (UFZs) have spread beyond their original administrative boundaries. An accurate and updated delineation of the UFZs is crucial for assessing the functional integration between cities within a mega-city region. Mobility data provides a chance to depict the UFZs from actual human activities at a finer spatial scale. Existing studies mostly adopted network-based approaches relying on the topological relationship but ignoring spatial factors, causing the lack of sensitivity in detecting the cross-cities integration of the functional region. This research proposed a novel regionalisation algorithm that redraws non-overlap boundaries of urban functional zones based on the commuting origin-destination matrix, representing the spatial interactions within cities and cross-cities. In particular, functional zones are drawn by searching for the best partition with the best goodness of fitting in the hierarchical spatial interaction model. The algorithm was applied to a case study of a mega-city region, Shenzhen-Dongguan-Huizhou (SDH) area in China using mobile phone signalling data. By adopting two different settings, this model evaluated the current status and predict the future trend of urban integration respectively. The results show the current boundary of UFZs in the SDH area almost coincides with administrative boundaries. Meanwhile, the results of long-term predictions might be utilised by policymakers to give more attention to the areas near the Dongguan-Huizhou boundary to promote industry cooperation and avoid mismatches between the functional and administrative regions.

The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

BackgroundAs global mobile phone adoption increases, mobile phone data has been increasingly used to measure movement patterns of populations at risk of malaria. However, the representativeness of mobile phone data for populations at risk of malaria has not been assessed. This study aimed to assess this representativeness using prospectively collected data on mobile phone ownership and use from malaria patients in Lao PDR.MethodsA prospective observational study was conducted from 2017 to 2021. 6320 patients with confirmed malaria in 107 health facilities in the five southernmost provinces of Lao PDR were surveyed regarding their demographics, mobile phone ownership and use. Data on the demographics of mobile phone owners and users in the general population of Lao PDR were obtained from the 2017 Lao Social Indicator Survey II, which was a nationally representative survey sample. Descriptive analysis was performed, and logistic regression with weights on aggregate data was used to compare the demographic distribution of mobile phone ownership and use in malaria patients with that in the general population.ResultsMost patients with malaria (76%) did not own or use a mobile phone. From 2017 to 2021, mobile phone usage in the general population consistently ranged between 53 and 67%, whereas among malaria patients, usage remained significantly lower, fluctuating between 20 and 28%. At the district level, log malaria incidence rate (API) was weakly negatively correlated with the proportion of mobile owners (R2 = 0.3, p = 0.005). Mobile phone ownership and usage among malaria patients were significantly lower than in the general population (p-value < 0.001). This trend was consistent across all provinces, suggesting a widespread issue rather than isolated cases. Both male and female malaria patients showed reduced mobile phone access compared to their peers in the general population. Furthermore, this disparity persisted across all age groups, indicating that regardless of age or gender, malaria patients faced barriers to mobile phone ownership and usage. This could have implications for communication and access to health resources, highlighting a critical area for public health interventions.ConclusionMobility data from anonymized and aggregated call data records (CDR) from the general population may not sufficiently represent the population at risk of malaria to accurately model disease transmission. Yet mobile phone data is commonly used to model malaria transmission in endemic countries. Before doing so, it is critical to quantify mobile usage among the population at risk of malaria. Where this is low, either movement estimates derived from mobile phone data need to be adjusted to increase model accuracy, or another method should be used to measure the mobility of populations with malaria.

In the recent decade, a new concept, urban community life circle (CLC), has been introduced and widely applied to Chinese community planning and public service facilities configuration alongside people-oriented urbanization. How to delineate the CLC has become a core task of urban CLC planning. The traditional way to determine the CLC using administrative boundaries does not fully consider the needs of residents. Recent research on urban CLC delineation is usually based on residential behavior survey using sample surveys or GPS data. However, it is difficult to generalize the sample surveys or GPS surveys for one specific community to that for others, because of the extremely high cost. Due to the ubiquity of the location-based service (LBS) data, i.e., the mobile phone data and points of interest (POI) data, they can serve as a fine-grained and continuous proxy for conducting human daily activity research with easy accessibility and low cost. Mobile phone data can represent the daily travel activities of residents, and POI data can comprehensively describe the physical conditions. In this paper, we propose a method from both the social and physical perspectives to delineate the CLC based on mobile phone and POI data, named DMP for short. The proposed DMP method is applied to Wuhan. We decipher the CLC’s boundary and residents’ travel activity patterns and demonstrate that (1) the CLC is not a regular circle but a non-homogeneous corridor space extending along streets; and (2) adjacent CLCs are found to share some daily facilities. Based on these findings, we propose that CLC planning should be data-based and people-oriented in general. In addition, sufficient space in the overlapping region of the CLCs should be preserved for future planning of public service facilities configuration, given that adjacent CLCs share some daily facilities.

Today, 95% of the global population has 2G mobile phone coverage and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data has the potential to revolutionize how we tackle humanitarian problems, such as the many suffered by refugees all over the world. While promising, mobile phone data and the new computational approaches bring both opportunities and challenges. Mobile phone traces contain detailed information regarding people's whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals is of paramount importance. Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data is to be successfully applied in humanitarian contexts.

ABSTRACTNovel 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.

With the urban traffic planning and management development, it is a highly considerable issue to analyze and estimate the original-destination data in the city. Traditional method to acquire the OD information usually uses household survey, which is inefficient and expensive. In this paper, the new methodology proposed that using mobile phone data to analyze the mechanism of trip generation, trip attraction and the OD information. The mobile phone data acquisition is introduced. A pilot study is implemented on Beijing by using the new method. And, much important traffic information can be extracted from the mobile phone data. We use the K-means clustering algorithm to divide the traffic zone. The attribution of traffic zone is identified using the mobile phone data. Then the OD distribution and the commuting travel are analyzed. At last, an experiment is done to verify availability of the mobile phone data, that analyzing the “Traffic tide phenomenon” in Beijing. The results of the experiments in this paper show a great correspondence to the actual situation. The validated results reveal the mobile phone data has tremendous potential on OD analysis.

The increasing availability of data in the urban context (e.g., mobile phone, smart card and social media data) allows us to study urban dynamics at much finer temporal resolutions (e.g., diurnal urban dynamics). Mobile phone data, for instance, are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics. While previous studies often use call detail record (CDR) data, this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage. We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data. Specifically, urban areas’ diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data. Urban areas are then classified based on the obtained signatures. The classification provides insights into city planning and development. Using the proposed framework, a case study was implemented in the city of Wuhu, China to understand its urban dynamics. The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone (TAZ) level. This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu. This article concludes with discussions on several common challenges associated with using network-driven mobile phone data, which should be addressed in future studies.

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.