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The management of the current pandemic COVID-19 has been challenging and complex The main and only successes have been achieved with non-pharmacological interventions (NPI) When tracking, monitoring, and early intervention at home have been delivered to citizens, the contagion can be controlled In the current pandemic, various methods have been applied to track the COVID-19 virus, such as Korea’s mobile phone tracking system We propose a method based on a wearable bracelet prototype able to detect biomedical parameters, which can be very useful to monitor the virus infection when the patient develops symptoms, such as a high temperature or low blood oxygenation In particular, the prototype bracelet can measure the blood oxygenation using an infrared optical sensor and measure the temperature of the patient The bracelet can record the identification number of other bracelet devices that came in proximity The bracelet is equipped with a built-in low power Bluetooth, aimed to send the recorded data to a smartphone or another device in order to connect them with proper geo-localization and to the web The identification number of the patient device can be used to trace the number of people and whom he has been in contact with, immediately by the sanitary authorities Moreover, the bracelet can be used for monitoring the patient’s health at home, avoiding hospital’s overcrowding The proposed system not only can effectively localize the trace path of patients positive to the COVID-19 virus or to other respiratory diseases, but also can provide an evolution of the patient symptoms and monitor people in-home quarantine The system is simple and could be an efficient tool to track any other future pandemics © 2021, Electromagnetics Academy All rights reserved

A better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

Mobile phone tracking poses challenges to individual privacy because a phone's sensor data and metadata logs can reveal behavioral, contextual, and psychological information about the individual who uses the phone. Here, I argue for a process-oriented approach to respecting individual privacy in the context of mobile phone tracking by treating informed consent as a process, not a mouse click. This process-oriented approach allows individuals to exercise their privacy preferences and requires the design of self-tracking systems that facilitate transparency, opt-in default settings, and individual control over personal data, especially with regard to: (1) what kinds of personal data are being collected and (2) how the data are being used and shared. In sum, I argue for the development of self-tracking systems that put individual user privacy and control at their core, while enabling people to harness their personal data for self-insight and behavior change. This approach to mobile phone privacy is a radical departure from current standard data practices and has implications for a wide range of stakeholders, including individual users, researchers, and corporations.

Most current accessibility equity studies ignore travel time uncertainties. This study investigates the travel time uncertainty impacts on the equity of individual accessibility. Travel time distributions of the road network and mobility data for a large number of individual samples across the entire study area are extracted using comprehensive big datasets of taxi trajectories and mobile phone tracking data. Two reliability-based individual accessibility measures are proposed to evaluate individual accessibility by explicitly considering individual’s on-time arrival probability concern for activity participations. The proposed measures are further applied to quantify travel time uncertainty impacts on the equity of individual accessibility to shopping services. Results of this study demonstrate the capabilities of using spatiotemporal big data to examine the equity of accessibility in a disaggregated individual level. The results also suggest that travel time uncertainties have negative impacts on accessibility of all people groups, but more serious impacts on disadvantaged people groups with a lower accessibility level.

The movements of individuals are fundamental to building and maintaining social connections. This pictorial presents Wanderlust, an experimental three-dimensional data visualization on the universal visitation pattern revealed from large-scale mobile phone tracking data. It explores ways of visualizing recurrent flows and the attractive places they implied. Inspired by the 19th-century art movement Impressionism, we develop a multi-layered effect, an impression, of mountains emerging from consolidated flows, to capture the essence of human journeys and urban spatial structure., IEEE VIS Arts Program (VISAP) 2021, pictorial, 14 pages

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.

Mobility restrictions during the COVID-19 pandemic ostensibly prevented the public from transmitting the disease in public places, but they also hampered outdoor recreation, despite the importance of blue-green spaces (e.g., parks and natural areas) for physical and mental health. We assess whether restrictions on human movement, particularly in blue-green spaces, affected the transmission of COVID-19. Our assessment uses a spatially resolved dataset of COVID-19 case numbers for 848 administrative units across 153 countries during the first year of the pandemic (February 2020 to February 2021). We measure mobility in blue-green spaces with planetary-scale aggregate and anonymized mobility flows derived from mobile phone tracking data. We then use machine learning forecast models and linear mixed-effects models to explore predictors of COVID-19 growth rates. After controlling for a number of environmental factors, we find no evidence that increased visits to blue-green space increase COVID-19 transmission. By contrast, increases in the total mobility and relaxation of other non-pharmaceutical interventions such as containment and closure policies predict greater transmission. Ultraviolet radiation stands out as the strongest environmental mitigant of COVID-19 spread, while temperature, humidity, wind speed, and ambient air pollution have little to no effect. Taken together, our analyses produce little evidence to support public health policies that restrict citizens from outdoor mobility in blue-green spaces, which corroborates experimental studies showing low risk of outdoor COVID-19 transmission. However, we acknowledge and discuss some of the challenges of big data approaches to ecological regression analyses such as this, and outline promising directions and opportunities for future research.

Background Thailand is among the top five countries with effective COVID-19 transmission control. This study examines how news of presence of COVID-19 in Thailand, as well as varying levels of government restriction on movement, affected human mobility in a rural Thai population along the border with Myanmar. Methods This study makes use of mobility data collected using a smartphone app. Between November 2019 and June 2020, four major events concerning information dissemination or government intervention give rise to five time intervals of analysis. Radius of gyration is used to analyze movement in each interval, and movement during government-imposed curfew. Human mobility network visualization is used to identify changes in travel patterns between main geographic locations of activity. Cross-border mobility analysis highlights potential for intervillage and intercountry disease transmission. Results Inter-village and cross-border movement was common in the pre-COVID-19 period. Radius of gyration and cross-border trips decreased following news of the first imported cases. During the government lockdown period, radius of gyration was reduced by more than 90% and cross-border movement was mostly limited to short-distance trips. Human mobility was nearly back to normal after relaxation of the lockdown. Conclusions This study provides insight into the impact of the government lockdown policy on an area with extremely low socio-economic status, poor healthcare resources, and highly active cross-border movement. The lockdown had a great impact on reducing individual mobility, including cross-border movement. The quick return to normal mobility after relaxation of the lockdown implies that close monitoring of disease should be continued to prevent a second wave.

Official statistics does not capture the whole volume of tourism, particularly in natural parks without physical boundaries to enter and count visitors. For this reason, the aim of this paper is to use and adjust data from mobile tracking to estimate the size of unmeasured tourism. Mobile phone tracking includes many type of visitors who does not have a touristic purpose such as commuting, visiting family, etc., specially in small areas as this one, so we need to be able to identify them in order to remove them from the total.

Background Italy's severe COVID-19 outbreak was addressed by a lockdown that gradually increased in space, time and intensity. The effectiveness of the lockdown has not been precisely assessed with respect to the intensity of mobility restriction and the time until the outbreak receded. Methods We used processed mobile phone tracking data to measure mobility restriction, and related those data to the number of new SARS-CoV-2 positive cases detected on a daily base in the three most affected Italian regions, Lombardy, Veneto and Emilia-Romagna, from February 1 through April 6, 2020, when two subsequent lockdowns with increasing intensity were implemented by the Italian government. Findings During the study period, mobility restriction was inversely related to the daily number of newly diagnosed SARS-CoV-2 positive cases only after the second, more effective lockdown, with a peak in the curve of diagnosed cases of infection occurring 14 to 18 days from lockdown in the three regions and 9 to 25 days in the included provinces. An effective reduction in transmission must have occurred nearly immediately after the tighter lockdown, given the lag time of around 10 days from asymptomatic infection to diagnosis. The period from lockdown to peak was shorter in the areas with the highest prevalence of the infection. This effect was seen within slightly more than one week in the most severely affected areas. Interpretation It appears that the less rigid lockdown led to an insufficient decrease in mobility to reverse an outbreak such as COVID-19. With a tighter lockdown, mobility decreased enough to bring down transmission promptly below the level needed to sustain the epidemic. Funding No funding sources have been used for this work.

The COVID-19 pandemic has catalysed numerous changes worldwide in healthcare systems and service delivery practices, many relying on biomedical technologies including digital health. The rapid development and widespread adoption of these changes has led to many being reported on extensively in grey literature and public media, but not yet in the conventional scientific peer reviewed literature. In particular, digital health contributions have received much attention but the main topics of reporting have been prominent public perception issues, with technical aspects being largely ignored. This perspective paper therefore responds to the need for a systematic contextualisation framework to describe digital health contributions to the current COVID-19 pandemic situation. The framework recommends four focus areas or “dimensions” for contextual settings: clinical processes, health system, stakeholder and technology. Two examples are used to motivate these dimensions: mobile phone tracking, and telehealth consultations. It is suggested that use of the framework in presenting digital health innovations more broadly and in context using these example cases, will convey a more informative understanding of the nature of such contributions now and in the post-COVID-19 period.

Many existing accessibility studies ignore human mobility due to the lack of large-scale human mobility data. This study investigates the impacts of human mobility on accessibility using massive mo...

In order to provide assessment of mobile phone user exposure to electromagnetic fields emitted in the close vicinity of a mobile device we have measured real life UMTS and LTE signals at mobile data turn on. The paper presents some preliminary results of mobile phone user exposure assessment at mobile data turn by making use of a non-standardized procedure that enables the calculus of the radiated signal energy and duty cycle. Our results sustain that higher user exposure is expected within the first minute after mobile data is switched on as higher duty cycles and more than a third of the total radiated energy is found in this time period, as compared to the total signal monitoring time. Authors expect increased exposure if other application services are launched within this time frame. Moreover, special attention should be paid to user exposure assessment in the case of active call services with mobile data turned on. Because SAR is currently the sole standardized metrics in dosimetry, the authors propose the use of the measured signal radiated energy as well as the calculated duty cycle as complementary indicators of user exposure to electromagnetic fields in order to provide a more comprehensive view on mobile phone radiation exposure.

Methods of statistical pattern recognition are powerful tools for analysis of statistical data on COVID-19 pandemic. In this chapter, we offer a new effective online algorithm for detection of change-points in tracking data (movement data, health rate data etc.). We developed a non-parametric test for evaluation of the statistical hypothesis that data in two adjacent time intervals have the same distribution. In the context of the mobile phone tracking this means that the coordinates of the tracked object does not deviated from the base point significantly. For estimation of the health rate it means the absence of significant deviations from the norm. The significance level for the test is less than 0.05. The test permits ties in samples. Also, we show that results of comparison of the test with well-known Kolmogorov–Smirnov test. These results show that the proposed test is more robust, sensitive and accurate than the alternative ones. In addition, the new method does not require high computational capacity.

Social distancing, a non-pharmaceutical tactic aimed at reducing the spread of COVID-19, can arise because individuals voluntarily distance from others to avoid contracting the disease. Alternatively, it can arise because of jurisdictional restrictions imposed by local authorities. We run reduced form models of social distancing as a function of county-level exogenous demographic variables and jurisdictional fixed effects for 49 states to assess the relative contributions of demographic and jurisdictional effects in explaining social distancing behavior. To allow for possible spatial aspects of a contagious disease, we also model the spillovers associated with demographic variables in surrounding counties as well as allow for disturbances that depend upon those in surrounding counties. We run our models weekly and examine the evolution of the estimated coefficients over time since the onset of the COVID-19 pandemic in the United States. These estimated coefficients express the revealed preferences of individuals who were able to and chose to stay at home to avoid the disease. Stay-at-home behavior measured using cell phone tracking data exhibits considerable cross-sectional variation, increasing over nine-fold from the end of January 2020 to the end of March 2020, and then decreasing by about 50% through mid-June 2020. Our estimation results show that demographic exogenous variables explain substantially more of this variation than predictions from jurisdictional fixed effects. Moreover, the explanations from demographic exogenous variables and jurisdictional fixed effects show an evolving correlation over the sample period, initially partially offsetting, and eventually reinforcing each other. Furthermore, the predicted social distance from demographic exogenous variables shows substantial spatial autoregressive dependence, indicating clustering in social distancing behavior. The increased variance of stay-at-home behavior coupled with the high level of spatial dependence can result in relatively intense hotspots and coldspots of social distance, which has implications for disease spread and mitigation.

Background We present a cellular phone-enhanced GPS tracking system (GPS mobile with CTG-001G receiver triangulation) suitable for urban carnivores, in tandem with appropriate home range analysis, as an additional tracking technology option in metropolitan settings. We conduct this proof-of-concept study working with the management of introduced invasive raccoons in Japan (and conditions implicit to this control program). Results Each tracking period averaged 17 days, and a tracking accuracy of Conclusions As a proof of concept, these prototype collars were successful in tracking raccoons in the difficult suburban environment, where this system (CTG) made fixes against FOMA antennae-augmented GPS and yielded data that could support home range analysis. We advocate further research and development to refine this system, with broad application as a tool to diversify wildlife tracking technology options in urbanized environments, where synanthropic and/or invasive species can cause a nuisance warranting effective management.

Smartphones are linked with individuals and are valuable and yet easily available sources for characterizing users' behavior and activities. User's location is among the characteristics of each individual that can be utilized in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical model that relates the received signal from the transmitters (model input) and the location of that user (the output of the model). Considering all the improvements in indoor positioning, there is still a gap between practical solutions and the optimal solution that provides near theoretical accuracy for positioning. This accuracy directly impacts the level of usability and reliability in corresponding LBSs. In this paper, we develop a smartphone app with the ability to be trained and detect users' location, accurately. We use Gaussian Process Regression (GPR) as a probabilistic method to find the parameters of a non-linear and non-convex indoor positioning model. We collect a dataset of received signals' strength (RSS) in several RPs by using a software which is prepared and installed on an Android smartphone. We also find the accurate 2σ confidence interval in the presented GPR method and evaluate the performance of the proposed method by measured data in a realistic scenario. The measurements confirm that our proposed method outperforms some conventional methods including KNN, SVR and PCA-SVR in terms of accuracy.

The study addresses some security challenges and opportunities brought by mobile phone tracking technologies, especially in the business of selling and buying used handsets. Cases, for instant, arise on the issue of stolen or missing phones. Some of the cases may involve the police, who make digital investigations to apprehend the culprits. However, as a result of some complainants' inability to provide details that will aid the police in making successful investigation, an innocent person or persons may be mistakenly apprehended as the culprit of a phone-theft related crime. The paper reviews literature on Anti-Theft and mobile tracking technologies, with the aim of providing guidance and awareness for phone owners and the potential buyers/sellers of used mobile phones who want to protect their handsets against any other GSM related issues.

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days&rsquo, worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

Do internal migration networks benefit or harm their home communities in case of a communicable disease? Looking at the spread of Covid in Italy and using pre-determined province-to-province migration, excess mortality and mobile phone tracking data, we document that provinces with a greater share of migrants in outbreak areas show greater compliance with self-isolation measures (information mechanism), but also a greater population inflow from outbreak areas (carrier mechanism). For a subset of localities, the net effect on mortality is negative. However, for the average locality, the effect is positive and large, suggesting that the role of migrants as information providers is trumped by their role as virus carriers. The effect is quantitatively important and could be incorporated in epidemiological models forecasting the spread of communicable diseases.

This paper studies the impact of working from home (WFH) on work relations and public health during the COVID-19 pandemic in Germany. Combining administrative data on SARS-CoV-2 infections and short-time work registrations, firm- and employee-level surveys and cell phone tracking data on mobility patterns, we find that WFH effectively shields employees from short-time work, firms from COVID-19 distress and substantially reduces infection risks. Counties with a higher share of teleworkable jobs experience fewer short-time work registrations and less SARS-CoV-2 cases. At the firm level, an exogenous increase in the take-up of WFH reduces the probability of filing for short-time work by up to 72 p.p. and the probability of being very negatively affected by the crisis by up to 75 p.p. Health benefits of WFH appeared mostly in the early stage of the pandemic and became smaller once tight confinement rules were implemented. This effect was driven by lower initial mobility levels in counties with more teleworkable jobs and a subsequent convergence in traffic levels once confinement was implemented. Our results imply that confinement and incentivizing WFH are substitutive policies to slow the spread of the coronavirus.

Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers’. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructure-free and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.

This study presents a wearable antenna for a global positioning system-global system for mobile communication (GPS-GSM)-based anti-theft tracking system to be embedded in high-cost clothing and luggage accessories (such as leather bags). To assess the feasibility of this solution, a prototype antenna was designed mimicking the shape of one of the Levi Strauss & Co.'s logos, and it was fabricated using a conductive non-woven fabric on a layer of leather. Furthermore, to cover both the GPS L1 and the GSM-1800 bands with a single radiating element, positive-intrinsic-negative (PIN) diodes were employed to reconfigure the operating frequency of the antenna. Experimental data demonstrating the feasibility of the proposed design strategy are presented and discussed.

Insecurity is one of the major challenges that the entire world is facing now, each country having their peculiar security issues. The crime rate in every part of the society these days has become a threatening issue such that vehicles are now used for co mmitting criminal activities mo re than before. The issue of vehicle theft has increased tremendously, mostly at gunpoint or car parks. In view of these, there is a need for adequate records of stolen, identified and recovered vehicles which are not readily available in our society and as such very important. The development of a vehicle theft alert and location identification system beco mes more necessary for vehicle o wners to ensure theft prevention and a speedy identification towards recovery efforts in situations where a vehicle is missing, stolen or driven by an unauthorized person. The theft alert function makes use of a GSM applicat ion developed and installed in a mobile phone device wh ich is embedded in the vehicle to communicate with the vehicle owner's mobile phone. The communicat ion is established via SMS (i.e. between the installed mobile phone device and that of the vehicle owner). The co mmunications established include; (i). Sending an SMS alert fro m installed mobile phone device to vehicle owner mobile phone when the car ignition is put on. (ii). Sending an SMS fro m the vehicle owner's mobile phone to start and stop the installed mobile phone device application. The location identificat ion function makes use of a web application developed to; (i). Determine the real time location of a vehicle by means of tracking using GPS. (ii). Broadcast missing or stolen vehicle informat ion to social media and security agency. The implementation of the installed mobile phone device application was done using JAVA because of its capabilit ies in programming mobile applications while PHP and MySQL was used for the web application functions. Integration testing of the system was carried out using simple percentage calculation for the performance evaluation. Fifty seven (57) vehicle owners were sampled and questionnaires were d istributed to them in order to ascertain the acceptability and wo rkab ility of the developed system. The result obtained shows the effectiveness of the system and hence it can be used to effectively monitor vehicle as it is been driven within or outside its jurisdiction. More so, the system can be used as database of missing, identified or recovered vehicles by various security agencies. Index Terms—Veh icle, Theft alert, Location identification, Tracking, GPS.

The development of ways to reduce malpractices in examination centers in all educational institutions is an important area of research. One of the major problems discovered aiding malpractices has been the creation of mobile phones, which however is a good communication device but needs to be curtailed during examinations. In order to have a 100% assurance that no student can cheat with his or her mobile phone a mobile detector system with a frequency jamming feature was developed using mobile cell phone detection techniques of measuring a cell phone's electromagnetic properties, determining an identifiable signature, measuring the RF spectrum and meeting the jamming requirements.

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available for navigation purposes, may consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a cloud-offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEON, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.

Human movements and interactions with cities are characterized by urban mobility networks. Many studies that address urban mobility are inspired by complex networks. The models of complex networks require a large amount of empirical data. However, current works relied on traditional survey data and were unable to take full advantage of the capabilities offered by complex networks; thus, the possibility of quantifying urban mobility networks by considering individual travel patterns has not yet been addressed. This study presents a data-driven approach for characterizing urban mobility networks based on massive-scale mobile phone tracking data. Individual travel motifs are first extracted using a graph-based approach. The global urban mobility network (G-UMN) and the motif-dependent urban mobility subnetworks (MD-UMNs) are then constructed. Next, network properties, including statistical measures and scaling relations between the basic measures, are proposed for characterizing mobility networks. We have conducted experiments focusing on Shenzhen, China. The results demonstrated that (1) the individual travel motifs are structurally and spatially heterogeneous, (2) the G-UMN exhibits a evolutionary hierarchical structure, and (3) the MD-UMNs show many behavioral differences in their spatial and topological properties, reflecting the impacts of the heterogeneity of the individual travel motifs. These results bridge the gap between complex network properties and urban mobility patterns and provide crucial implications and policies for data-informed urban planning.

International audience; With the rapid expansion of cities around the world, large number of movements are made daily as people commute from their homes to their destinations, including workplaces. From these movements, trends and patterns can be derived which in turn, can provide valuable insights for urban planning. This is particularly relevant in the ‘smart cities' context. However, such movement data are often difficult to gather and analyse without infringing on privacy rights, especially with the increasing concerns on privacy issues. This paper reports on the use of aggregated mobile phone tracking data together with train network data to analyse movement patterns in, out, and within La Défense (Paris' business district). The findings can assist city planners by providing a better understanding of people's travel patterns

Modeling individual-based urban mobility plays an important role in traffic management, urban planning, public health, public safety and many other fields. Compared with census and travel survey data, which are costly in collection and slow in update, the emergence of massively and automatically generated individual trajectory data, such as mobile phone tracking data and transit smart card data, offers new datasets to develop individual mobility models. However, these new types of human trajectory data suffer some inherent limitations for many research and application domains. First, these large-scale trajectory data often have certain types of sampling biases in the representation of entire population. For example, mobile phone data do not likely cover children and have little coverage in elder people. However, this portion of population is important in some studies, such as household-based travel demand modeling and epidemic modeling. Second, these large-scale individual trajectory data do not often come with individual sociodemographic attributes due to privacy or technical issues. Sociodemographic attributes however are critical in many studies such as household-based travel demand modeling, sociology studies, and epidemic modeling. Therefore, this study proposes a generalizable modeling framework for individual-based urban mobility with entire population and sociodemographic details, through integrating different types of data sources. To demonstrate the proposed modeling framework, we select several typical data sources, design a set of data fusion algorithms, and simulate the daily activities and trips of the entire population in Shenzhen, China. The simulation results show that the proposed data fusion approach can effectively help with sampling bias issues and reasonably fill up sociodemographic details for the large-scale trajectory data. The proposed individual-based urban mobility model can be useful in many studies that require inputting entire population with sociodemographic attributes. This study also gives an example of addressing an important topic in the “big data” era, that is to integrate the so called “big data” and traditional data in urban studies.

Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

Summary. This research contributes to both theory and application. This is an important supplement to the research on the quality and uncertainty of spatial big data and it will help mobile phone data play a more effective application value in future research. In recent years, the rapid development of data acquisition means has led to the rapid accumulation of spatial data and generated spatial big data with geographic location information. However, the uncertainty in spatial big data leads to the quality problem of spatial big data, which is one of the main bottlenecks restricting the effective utilization of spatial big data. As an important part of spatial big data, it is necessary to study the quality and uncertainty of mobile phone data. In view of this, we delve into the key geographical environment factors that affect the positioning error of mobile phone data, extract error evaluation indexes and construct the spatial distribution model of positioning error with machine learning algorithms. To explore the distribution law of mobile phone data positioning error under different geographical scenarios. In addition, considering the high accuracy of personal travel GPS positioning, this study takes personal travel GPS positioning data as the comparative object of mobile phone data for analysis.

Access to emergency numbers such as 911 or 112 is generally universally available to cell phone users. Nevertheless, emergencies may occur in areas without cell phone coverage, for example, in remote areas frequented by climbers or hikers. In this article, we describe the design, development, and evaluation of a global system for mobile communications (GSM) base station that can be placed in a vehicle (car, plane, helicopter, etc.) to locate a lost person with an active GSM phone. The system includes the resources to provide a good estimation of the location of the phone, allowing the rescue team to significantly narrow its search and to establish communication with the person where possible, as the system also provides GSM coverage.

This study analyzes metropolitan dynamics in a small country with an “island state” context of closed boundaries, using commuting data and mobile phone tracking data. We examine whether the Israeli context encourages the formation of a monocentric “metropolitan state,” characterized by increasing links between localities throughout the country and its principal metropolitan node (Tel Aviv)—rather than with secondary metropolitan areas—and by fuzzy, overlapping metropolitan boundaries. Commuting data from the 1995 and 2008 censuses show that metropolitan expansion processes in Israel are gradual. Mobile phone tracking data for 2013 reveal similar patterns, confirming the urban structure’s stability and the reliability of tracking data as a means of assessing metropolitan processes. The “island state” context supports growing monocentricity, but, when it comes to commuting and travel for other purposes, Israel is not yet a metropolitan state; metropolitan boundaries are not as fuzzy and rapidly changing as ...

Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone's sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the efficiency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.

The recent trend in transportation planning information is to reduce traffic survey costs and enhance accuracy by using and converging various sources of external data. In Korea, mobile phone data can help generate useful transportation planning information, thanks to the universal use of mobile phones, which are present in a number greater than that of the population. This paper addresses measures to derive trip generation information from mobile phone data and verifies the value of the system for practical use by correlation analysis with KTDB trip generation data. The results show that trip generation information produced by mobile phone data correlates with existing (KTDB) trip generation data.

Nowadays, smart phone has become a necessity as it offers more than just making a phone call. Smart phone combines the features of cell phone with other mobile devices such as personal digital assistant (PDA) and GPS navigation unit that propel the popularity of smart phones. In recent years, the interest in mobile communication has been increased. Previous research using mobile application has been successfully done in varies areas of study. Areas of study that have been done are health care, education, and traffic monitoring. Besides, mobile application has also been applied in agricultural sector for various purposes such as plant pest risk management. In this study, mobile application for data collection on Ganoderma disease of oil palm has been successfully developed. The application uses several devices in a smart phone such as GPS, Wifi/ GPRS connection and accelerometer devices. The application can be installed in the smart phone and users can use the application while working on-site. The data can be updated immediately through their smart phones to the service. Besides, the application provides offline map so the user can be productive even though their network connectivity is poor or nonexistent. The data can be synced when the users online again. This paper presents an application that allows users to download features from a sync-enabled ArcGIS Feature Service, view and edit the features even when the devices fail to connect with any network connectivity while collecting data on-site.

Smart phones or mobile phones enabled with global positioning system (GPS), different types of sensors, and communication technologies have become ubiquitous application development platform for Internet of Things (IoT) and new sensing technologies. Improving sensing area coverage, reducing overlap of sensing area, and energy consumption are important issues under mobile phone sensing. This paper presents human mobility-based mobile phone sensors sampling algorithm. Human mobility patterns and geographical constraints have an impact on performance of mobile phone sensing applications. The real-outdoor location traces of volunteers, collected using GPS-enabled mobile phones are used for performance analysis of proposed work. The proposed mobile phone sensor sampling algorithm considers velocity of human mobility as an important parameter for improving sensing area coverage and reduction of energy consumption. To an extent overlap between sensing area coverage is allowed to overcome, the reduction of sensor data samples caused by spatial regularities of human mobility. The performance is analyzed and evaluated by considering general regular sampling and proposed sampling method for mobile phone sensing activity. The results show that for normal human walking velocity ( $ 1.5 m/s) proposed mobile phone sensor sampling algorithm performs better in terms of sensing area coverage and reduction of battery energy consumption for mobile phone sensing activity.

As the proliferation of smart phone, almost every user has one`s own smart phone, and the user could get the global position and location based services using GPS system outdoors. But indoor positioning system using GPS does not work, and it could not detect global position using TDOA local positioning system. In this paper, a new indoor global positioning system for smart phone employing GPS receiver and electronic compass device is proposed with the TDOA local positioning system using acoustic signal, and the performance and the experimental result are described.

In modern days, gadgets are essential part of everyday lives and they enable to access a variety of ubiquitous services. Recently, the availability of these ubiquitous and mobile services has increased due to the different form of access provided by mobile devices, such as Bluetooth and Wi-Fi. In the same trend, the typologies of vulnerabilities exploiting these services and communication channels have boosted as well. Therefore, the presence of malware in mobile platform can seriously impact the end users privacy and security, reducing the end users trust in performing mobile access to its network services. In this, an authentication mechanism is based on screen brightness which prevents and increases security against side channel attacks.