scholarly journals Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Adrian M. Tompkins ◽  
Nicky McCreesh
Keyword(s):  
Mobile Phone ◽  
Malaria Transmission ◽  
Gravity Model ◽  
Mobile Phone Data ◽  
Agent Based ◽  
Migration Model ◽  
Location Data ◽  
Home Location ◽  
One Year ◽  
Overnight Stays

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agentbased model can approximately reproduce the patterns of migration involving overnight stays.

scholarly journals It Is All About Location: Smartphones and Tracking the Spread of COVID-19

2020 ◽  
Vol 6 (3) ◽  
pp. 205630512094825
Author(s):  
Jordan Frith ◽  
Michael Saker
Keyword(s):  
Mobile Phone ◽  
Contact Tracing ◽  
Mobile Phone Data ◽  
Locative Media ◽  
Location Data ◽  
Growing Body ◽  
Anonymized Data ◽  
Media Research ◽  
Data Visualizations

Mobile phone location data have become tied to understandings of and responses to the COVID-19 pandemic. Data visualizations have used mobile phone data to inform people about how mobility practices may be linked to the spread of the virus, and governments have explored contact tracing that relies upon mobile phone data. This article examines how these uses of location data implicate three particular issues that have been present in the growing body of locative media research: (1) anonymized data are often not anonymous, (2) location data are not always representative and can exacerbate inequality, and (3) location data are a key part of the extension of the surveillance state.

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

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

scholarly journals Analysis of Mobile Phone Data to Compare Mobility Flows and Hotspots Before and After the Opening of High-Speed Railway: Case Study of Honam KTX in Korea

2020 ◽  
Vol 10 (14) ◽  
pp. 5009
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ji Young Song ◽  
Jun Lee
Keyword(s):  
Mobile Phone ◽  
High Speed ◽  
Mobile Phone Data ◽  
High Speed Railway ◽  
Railway Stations ◽  
Intercity Travel ◽  
Before And After ◽  
Transit Agencies ◽  
One Year ◽  
The Impact

Mobile phone data provides information, such as the home (origin) and current locations of people. The data can be used to analyze the impact of new high-speed railway (HSR) openings. This study examined the population observed in stations and cities of the Honam HSR line in Korea, based on mobile phone data recorded one year before and after its opening. We analyzed the volume of the population observed at each railway station, density of the distance between home and station, and activity hotspots in a city. The results show that the number of people and travel distance increased after the opening of the HSR. The distance to access railway stations increased, as the HSR saves travel time. Moreover, the activity hotspots in a city increased after the opening of the HSR, as more people gathered near the station area. The findings show that the mobility measures enhanced after the opening of the HSR for regional travel and local activities. These measures can help transit agencies and planners in providing better intercity travel.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

scholarly journals Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luca Pappalardo ◽  
Leo Ferres ◽  
Manuel Sacasa ◽  
Ciro Cattuto ◽  
Loreto Bravo
Keyword(s):  
Mobile Phone ◽  
Ground Truth ◽  
Mobile Phone Data ◽  
Data Generation ◽  
Individual Level ◽  
Detection Algorithms ◽  
Home Location ◽  
Residence Location ◽  
The Individual ◽  
And Control

AbstractInferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.

Modelling the spatio-temporal dynamic of traffic flows with gravity models and mobile phone data

2021 ◽  
pp. 99-104
Author(s):  
Maurizio Carpita ◽  
Rodolfo Metulini
Keyword(s):  
Mobile Phone ◽  
Traffic Flow ◽  
Gravity Model ◽  
Mobile Phone Data ◽  
Traffic Flows ◽  
Explanatory Variables ◽  
Prone Area ◽  
Spatio Temporal ◽  
Flood Prone Area ◽  
Using Data

The analysis of origin-destination traffic flows may be useful in many contexts of application (e.g., urban planning, tourism economics) and have been commonly studied through the gravity model, which states that flows are proportional to ''masses" of both origin and destination, and inversely proportional to distance between them. Using data on the flow of mobile phone SIM among different aree di censimento, recorded hourly basis for several months and provided by FasterNet in the context of MoSoRe project, in this work we characterize and model the dynamic of such flows over the time in the strongly urbanized and flood-prone area of the Mandolossa (western outskirts of Brescia, northern Italy), with the aim of predicting the traffic flow during flood episodes. Whereas a traditional ”static” mass explanatory variable is represented by residential population (Pop), or by gross domestic product (GDP), here we propose to use a most accurate set of explanatory variables in order to better account for the dynamic over the time. First, we employ a time-varying mass variable represented by the number of city-users by area and by time period, which has been estimated from mobile phone data (provided by TIM) using functional data approach and already adopted to derive crowding maps for flood exposure. Secondly, we include in the model a proper set of factors such as areal and time dummies, and a novel set of indices related to (e.g.) the number and the type of streets, the number of offices, restaurants or cinemas, which may be retrieved from OpenStreetMap. The joint use of these two novel sets of explanatory variables should allow us to obtain a better linear fitting of the gravity model and a better traffic flow prediction for the flood risk evaluation.

scholarly journals An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Vol 8 (7) ◽  
pp. 313 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

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

Improving emergency evacuation planning with mobile phone location data

2019 ◽  
Vol 47 (6) ◽  
pp. 964-980 ◽  
Author(s):  
Ling Yin ◽  
Jie Chen ◽  
Hao Zhang ◽  
Zhile Yang ◽  
Qiao Wan ◽  
...  
Keyword(s):  
Emergency Management ◽  
Mobile Phone ◽  
Population Distribution ◽  
Computation Time ◽  
Experimental Result ◽  
Evacuation Planning ◽  
Agent Based ◽  
Knowledge Database ◽  
Location Data ◽  
Evacuation Plans

Timely responses to emergencies are critical for urban disaster and emergency management, particularly in densely populated mega-cities. Researchers and personnel involved in urban emergency management nowadays rely on computers to carry out complex evacuation planning. Agent-based modeling, which supports the representation of interactions among individuals and between individuals and their environments, has become a major approach to simulating evacuations wherein spatial–temporal dynamics and individual conditions need attention, such as congestion in urban areas. However, the development of optimal evacuation plans based upon agent-based evacuation simulations can be very time-consuming. In this study, to shorten the computation time to provide a timely response in an efficient way, we develop a knowledge database to store evacuation plans for typical population distributions generated by mobile phone location data. Subsequently, we use the prepared knowledge database (offline) to accelerate real-time (online) processes in searching for near-optimal evacuation plans. Our experimental result demonstrates that the evacuation plans generated with a knowledge database always outperform those that are generated without a knowledge database. Specifically, the knowledge database can reduce the computation time by an average of 96.76%, with an average fitness value improvement of 21.86%. This result confirms the effectiveness of our proposed approach in improving agent-based evacuation planning. With the rapid development of human sensor data collection and analysis, the estimation of a more accurate population distribution will become easier in future. Thus, we believe that the proposed approach of developing a knowledge database based on population distribution patterns will provide a more feasible alternative solution for evacuation planning in the practice of urban emergency management.

scholarly journals Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

2021 ◽  
Author(s):  
Tanjona Ramiadantsoa ◽  
C. Jessica E. Metcalf ◽  
Antso Hasina Raherinandrasana ◽  
Santatra Randrianarisoa ◽  
Benjamin L. Rice ◽  
...  
Keyword(s):  
Public Health ◽  
Mobile Phone ◽  
Gravity Model ◽  
Human Mobility ◽  
Health Planning ◽  
Data Availability ◽  
Disease Spread ◽  
Mobile Phone Data ◽  
Migration Flow ◽  
Mobility Data

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

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