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.

scholarly journals Interactive effects between travel behaviour and COVID-19: a questionnaire study

2021 ◽  
Author(s):  
Jinbao Zhang ◽  
Jaeyoung Lee
Keyword(s):  
Early Stage ◽  
Travel Behaviour ◽  
Interactive Effects ◽  
Factors Affecting ◽  
Individual Level ◽  
Passenger Volume ◽  
Spatial Autoregressive ◽  
Railway Passenger ◽  
The Individual ◽  
And Control

Abstract This study has two main objectives: (i) to analyse the effect of travel characteristics on the spreading of disease, and (ii) to determine the effect of COVID-19 on travel behaviour at the individual level. First, the study analyses the effect of passenger volume and the proportions of different modes of travel on the spread of COVID-19 in the early stage. The developed spatial autoregressive model shows that total passenger volume and proportions of air and railway passenger volumes are positively associated with the cumulative confirmed cases. Second, a questionnaire is analysed to determine changes in travel behaviour after COVID-19. The results indicate that the number of total trips considerably decreased. Public transport usage decreased by 20.5%, while private car usage increased by 6.4%. Then the factors affecting the changes in travel behaviour are analysed by logit models. The findings reveal significant factors, including gender, occupation and travel restriction. It is expected that the findings from this study would be helpful for management and control of traffic during a pandemic.

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 Conceptualizing Competences in E-Services Adoption and Assimilation in SMEs

2010 ◽  
pp. 225-238
Author(s):  
Ada Scupola
Keyword(s):  
Medium Size ◽  
Web Based ◽  
Management Level ◽  
Individual Level ◽  
System A ◽  
Conceptual Skills ◽  
Reservation System ◽  
The Individual ◽  
And Control ◽  
Business To Business

This article investigates the competences deemed necessary both at top managerial and individual levels for the successful adoption and assimilation of business-to-business e-services in small and medium size enterprises. To this end, an in-depth case study of a business-to-business e-service system, a Web-based travel reservation system, was conducted. The results show that three main competences, namely vision, value and control, are important at top management level for the primary adoption of e-services. For secondary adoption and assimilation, three categories of competences were identified as being important either to have or to develop at the individual level, namely technical, interpersonal and conceptual skills.

scholarly journals Ego-zones: non-symmetric dependencies reveal network groups with large and dense overlaps

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Milos Kudelka ◽  
Eliska Ochodkova ◽  
Sarka Zehnalova ◽  
Jakub Plesnik
Keyword(s):  
Network Structure ◽  
Real World ◽  
Ground Truth ◽  
Structural Similarity ◽  
Global Network ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
First Case ◽  
New Perspective ◽  
The Individual

Abstract The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks. Uncovering such groups and the relationships between them is, therefore, necessary for understanding these structures. Groups can either be found by detection algorithms based solely on structural analysis or identified on the basis of more in-depth knowledge of the processes taking place in networks. In the first case, these are mainly algorithms detecting non-overlapping communities or communities with small overlaps. The latter case is about identifying ground-truth communities, also on the basis of characteristics other than only network structure. Recent research into ground-truth communities shows that in real-world networks, there are nested communities or communities with large and dense overlaps which we are not yet able to detect satisfactorily only on the basis of structural network properties.In our approach, we present a new perspective on the problem of group detection using only the structural properties of networks. Its main contribution is pointing out the existence of large and dense overlaps of detected groups. We use the non-symmetric structural similarity between pairs of nodes, which we refer to as dependency, to detect groups that we call zones. Unlike other approaches, we are able, thanks to non-symmetry, accurately to describe the prominent nodes in the zones which are responsible for large zone overlaps and the reasons why overlaps occur. The individual zones that are detected provide new information associated in particular with the non-symmetric relationships within the group and the roles that individual nodes play in the zone. From the perspective of global network structure, because of the non-symmetric node-to-node relationships, we explore new properties of real-world networks that describe the differences between various types of networks.

scholarly journals Regional Living Conditions and the Prevalence, Awareness, Treatment, Control of Hypertension at the Individual Level in Russia

2021 ◽  
Author(s):  
Sergey Alekseevich Maksimov ◽  
Yulia A. Balanova ◽  
Svetlana A. Shalnova ◽  
Galina A. Muromtseva ◽  
Anna V. Kapustina ◽  
...  
Keyword(s):  
Positive Impact ◽  
Principal Component ◽  
Living Conditions ◽  
Federal State ◽  
Cross Sectional ◽  
Individual Level ◽  
Final Sample ◽  
The Individual ◽  
And Control ◽  
Regions Of Russia

Abstract Background. The objective of this study was to analyze the influence of the living characteristics of large regions on the possible presence, awareness, management and control of hypertension at the individual level in the Russian population.Methods. Regional characteristics were obtained from the official website of the Federal State Statistics Service of Russia. Principal component analysis was used to reduce the dimensionality of data; it allowed defining 5 integral regional indices: Socio-Geographical, Demographic, Industrial, Mixed, Economic. Presence, awareness, management and control of hypertension were assessed according to the data of the cross-sectional stage of ESSE-RF study that was conducted in 2013-2014. The final sample included 19,791 patients from 12 regions of Russia. Generalized estimation equations were used to determine associations between regional indices and presence, awareness, management and control of hypertension at the individual level taking into consideration nested data structures (individuals in regions).Results. The Socio-Geographic Index demonstrated the positive impact on hypertension among male (OR = 1.18; 95% CI: 1.05-1.32), undereducated individuals (OR = 1.14; 95% CI: 1.02-1.27) and elderly people (OR = 1.16; 95% CI: 1.02-1.32). Awareness of hypertension is positively associated with Demographic (OR = 1.13; 95% CI: 1.02-1.25) and Industrial Indices (OR = 1.15; 95% CI: 1.01-1.33). Worsening of social environment (Socio-Geographic Index) reduces adherence to management (OR = 0.76; 95% CI: 0.64-0.90) and control of hypertension (OR = 0.79; 95% CI: 0.69-0.90). Impact of regional living conditions on the prevalence of hypertension is not high, compared to the individual predictors, but for the awareness, treatment and control of hypertension, this influence is important.Conclusions: The study performed allowed assessing the influence of living characteristics of the population in the large regions of Russia on the prevalence of hypertension and on the awareness, management and control of this disease. The data obtained provide new knowledge not only in terms of epidemiology of cardiovascular diseases in Russia but also in general, that is, in the context of studying the influence of living conditions on the health of population.

scholarly journals Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case

2021 ◽  
Vol 13 (24) ◽  
pp. 13713
Author(s):  
Xuesong Gao ◽  
Hui Wang ◽  
Lun Liu
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Future Research ◽  
Aggregate Level ◽  
Individual Level ◽  
Privacy Leakage ◽  
Mobility Data ◽  
Level Data ◽  
Data Source ◽  
Movement Trace

People’s movement trace harvested from mobile phone signals has become an important new data source for studying human behavior and related socioeconomic topics in social science. With growing concern about privacy leakage of big data, mobile phone data holders now tend to provide aggregate-level mobility data instead of individual-level data. However, most algorithms for measuring mobility are based on individual-level data—how the existing mobility algorithms can be properly transformed to apply on aggregate-level data remains undiscussed. This paper explores the transformation of individual data-based mobility metrics to fit with grid-aggregate data. Fifteen candidate metrics measuring five indicators of mobility are proposed and the most suitable one for each indicator is selected. Future research about aggregate-level mobility data may refer to our analysis to assist in the selection of suitable mobility metrics.

scholarly journals Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data

2020 ◽  
Vol 9 (11) ◽  
pp. 666
Author(s):  
Chengming Li ◽  
Jiaxi Hu ◽  
Zhaoxin Dai ◽  
Zixian Fan ◽  
Zheng Wu
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Smart Cities ◽  
Sampling Rate ◽  
Individual Characteristics ◽  
Mobile Phone Data ◽  
Mobility Pattern ◽  
Individual Mobility ◽  
Short Period ◽  
The Individual

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers’ behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities.

scholarly journals Research on Behavior Cause of Limited Space Accident Based on “2-4” Model

2020 ◽  
Vol 198 ◽  
pp. 04010
Author(s):  
Fengjie Yu ◽  
Lijing Zhang ◽  
Gang Tao
Keyword(s):  
Prevention And Control ◽  
Organizational Level ◽  
Behavioral Safety ◽  
Individual Level ◽  
Limited Space ◽  
Habitual Behavior ◽  
The Individual ◽  
Safety Knowledge ◽  
And Control ◽  
Space Operation

In order to prevent accidental casualties in the course of limited space operation, the behavioral safety “2-4” model is used to study the behavioral causes of typical limited space operation accidents. First, the causes of one-time and habitual behavior are studied from the individual level, that is, unsafe action, physical state and safety knowledge, consciousness; then, the research of operational behavior and guiding behavior is studied from the organizational level. Finally, the prevention and control suggestions are put forward to reduce the occurrence of such accidents.

Gravitational community detection by predicting diameter

2021 ◽  
pp. 2150145
Author(s):  
Himansu Sekhar Pattanayak ◽  
Harsh K. Verma ◽  
Amrit Lal Sangal
Keyword(s):  
Community Detection ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Local Information ◽  
Ground Truth Data ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Np Hard Problem ◽  
Community Detection Algorithm ◽  
The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

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