scholarly journals Modelling urban vibrancy with mobile phone and OpenStreetMap data

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252015
Author(s):  
Federico Botta ◽  
Mario Gutiérrez-Roig
Keyword(s):  
Quantitative Analysis ◽  
Mobile Phone ◽  
Social Interactions ◽  
Age Groups ◽  
Spatial Models ◽  
Strong Relationship ◽  
Urban Environments ◽  
Mobile Phone Data ◽  
Third Places ◽  
Urban Places

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people’s location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy.

scholarly journals Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study

2019 ◽  
Vol 2673 (7) ◽  
pp. 74-88 ◽  
Author(s):  
Loïc Bonnetain ◽  
Angelo Furno ◽  
Jean Krug ◽  
Nour-Eddin El Faouzi
Keyword(s):  
Mobile Phone ◽  
Cellular Network ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Environments ◽  
Network Control ◽  
Mobile Phone Data ◽  
Transport Systems ◽  
Spatiotemporal Scales ◽  
Temporal Dimensions

Mobile phone data collected by network operators can provide fundamental insights into individual and aggregate mobility of people, at unprecedented spatiotemporal scales. However, traditional call detail records (CDR) have fundamental issues because of low accuracy in both spatial and temporal dimensions, which limits their applicability for detailed studies on mobility, especially in urban scenarios. This paper focuses on a new generation of mobile phone passive data, individual cellular network signaling data, characterized by higher spatiotemporal resolutions than traditional CDR. A framework based on unsupervised hidden Markov model is designed for map-matching such data on a multimodal transportation network, aimed at accurately inferring the complex multimodal travel itineraries and popular paths people follow in their urban daily mobility. This information, especially if computed at large spatiotemporal scales, can represent a solid basis for studying actual and dynamic travel demand, to properly dimension multimodal transport systems and even perform anomaly detection and adaptive network control. The approach is evaluated in a case study based on real cellular traces collected by a major French operator in the city of Lyon, and a validation study at both microscopic and macroscopic levels proposed. The results show that this approach can properly handle sparse and noisy cell phone trajectories in complex urban environments. Moreover, the results are promising concerning popular paths detection and reconstruction of origin–destination matrices.

Urban communications and social interactions through the lens of mobile phone data

2017 ◽  
Vol 1 ◽  
pp. 70-81 ◽  
Author(s):  
Sabrina Gaito ◽  
Christian Quadri ◽  
Gian Paolo Rossi ◽  
Matteo Zignani
Keyword(s):  
Mobile Phone ◽  
Social Interactions ◽  
Mobile Phone Data

scholarly journals Understanding and Analyzing Social Network Structure Among University Students

2021 ◽  
Author(s):  
Md. Sharif Hossen ◽  
Md. Aminul Islam
Keyword(s):  
University Students ◽  
Mobile Phone ◽  
Strong Relationship ◽  
Mobile Phone Data ◽  
Human Beings ◽  
Human Communication ◽  
Multiple Sensors ◽  
Social Network Structure ◽  
Rich Data ◽  
Public Health Epidemiology

Abstract Mobile phone arguably is one of the most reached and used technology in human history. Technology has become ubiquitous in the life of human beings. Equipped with multiple sensors and devices, smartphones can record each and every action, psychological and environmental states of users, making it a goldmine of rich data about and insight into the dynamics of human communication, human behavior, relationships, and social interaction. As a source of data for empirical research, this device has gotten much attention from scholars in various disciplines like sociology, social psychology, urban studies, communication and media studies, public health, epidemiology, and computer science. This research tries to understand the structure of social networks of university students by investigating their communication patterns using self-reported mobile phone data. We collected behavioral data for one month using a Call Log Analytics mobile phone app. The data contained information about respondents’ contacts, date and time of call, duration of the call, call type (e.g., incoming, outgoing, missed), and frequency of the call. We used UCINET to analyze the data. In this investigation, we can find those students who are connected to most of the classmates and maintain a strong relationship and perform a task successfully using the values of eigenvector, closeness, and betweenness centrality, respectively. Moreover, this study also helps us to find out the pattern of the students using contact duration, incoming and outgoing calls.

How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data

2020 ◽  
Vol 24 (6) ◽  
pp. 1504-1525
Author(s):  
Chaogui Kang ◽  
Li Shi ◽  
Fahui Wang ◽  
Yu Liu
Keyword(s):  
Mobile Phone ◽  
Matrix Factorization ◽  
Social Groups ◽  
Mobile Phone Data ◽  
Urban Places

scholarly journals Assessing the interplay between human mobility and mosquito borne diseases in urban environments

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emanuele Massaro ◽  
Daniel Kondor ◽  
Carlo Ratti
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Urban Environments ◽  
System Level ◽  
Mobile Phone Data ◽  
Transmission Model ◽  
Mobility Patterns ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
City State

AbstractUrbanization drives the epidemiology of infectious diseases to many threats and new challenges. In this research, we study the interplay between human mobility and dengue outbreaks in the complex urban environment of the city-state of Singapore. We integrate both stylized and mobile phone data-driven mobility patterns in an agent-based transmission model in which humans and mosquitoes are represented as agents that go through the epidemic states of dengue. We monitor with numerical simulations the system-level response to the epidemic by comparing our results with the observed cases reported during the 2013 and 2014 outbreaks. Our results show that human mobility is a major factor in the spread of vector-borne diseases such as dengue even on the short scale corresponding to intra-city distances. We finally discuss the advantages and the limits of mobile phone data and potential alternatives for assessing valuable mobility patterns for modeling vector-borne diseases outbreaks in cities.

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

scholarly journals Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamid Khataee ◽  
Istvan Scheuring ◽  
Andras Czirok ◽  
Zoltan Neufeld
Keyword(s):  
Mobile Phone ◽  
Basic Reproductive Number ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Expansion Phase ◽  
Social Distancing ◽  
Exponential Expansion ◽  
Total Death ◽  
Mobility Reduction ◽  
Epidemiological Parameters

AbstractA 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.

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