Collective Human Mobility Patterns: A Case Study Using Data Usage Detail Records

2017 ◽  
Author(s):  
Qian Li ◽  
Hao Jiang ◽  
Yuan Li ◽  
Xian Zhou ◽  
Yanqiu Chen ◽  
...  
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Data Usage ◽  
Using Data

scholarly journals Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

2017 ◽  
Vol 3 (2) ◽  
pp. 208-219 ◽  
Author(s):  
Shan Jiang ◽  
Joseph Ferreira ◽  
Marta C. Gonzalez
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Mobility Patterns

scholarly journals Exploring the Roles of Local Mobility Patterns, Socioeconomic Conditions, and Lockdown Policies in Shaping the Patterns of COVID-19 Spread

2021 ◽  
Vol 13 (5) ◽  
pp. 112
Author(s):  
Mauricio Herrera ◽  
Alex Godoy-Faúndez
Keyword(s):  
Latin American ◽  
Low Income ◽  
Large Scale ◽  
Human Mobility ◽  
Epidemiological Data ◽  
High Income ◽  
Metropolitan Region ◽  
Mobility Patterns ◽  
Large Cities

The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. This pandemic has also highlighted the critical importance of generating rigorous evidence for decision-making, and actionable insights from data, considering further the intricate web of causes and drivers behind observed patterns of contagion diffusion. Using mobility, socioeconomic, and epidemiological data recorded throughout the pandemic development in the Santiago Metropolitan Region, we seek to understand the observed patterns of contagion. We characterize human mobility patterns during the pandemic through different mobility indices and correlate such patterns with the observed contagion diffusion, providing data-driven models for insights, analysis, and inferences. Through these models, we examine some effects of the late application of mobility restrictions in high-income urban regions that were affected by high contagion rates at the beginning of the pandemic. Using augmented synthesis control methods, we study the consequences of the early lifting of mobility restrictions in low-income sectors connected by public transport to high-risk and high-income communes. The Santiago Metropolitan Region is one of the largest Latin American metropolises with features that are common to large cities. Therefore, it can be used as a relevant case study to unravel complex patterns of the spread of COVID-19.

Uncovering stable and occasional human mobility patterns: A case study of the Beijing subway

2018 ◽  
Vol 492 ◽  
pp. 28-38 ◽  
Author(s):  
Nuo Yong ◽  
Shunjiang Ni ◽  
Shifei Shen ◽  
Peng Chen ◽  
Xuewei Ji
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Beijing Subway

scholarly journals Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

2020 ◽  
Vol 17 (10) ◽  
pp. 3437 ◽  
Author(s):  
Murat Simsek ◽  
Burak Kantarci
Keyword(s):  
Artificial Intelligence ◽  
Worst Case Analysis ◽  
Assessment Centers ◽  
Mobility Patterns ◽  
Worst Case ◽  
Mobile Crowdsensing ◽  
Using Data ◽  
Effective Assessment ◽  
The City

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

scholarly journals Characterizing Urban Mobility Patterns: A Case Study of Mexico City

2021 ◽  
pp. 153-170
Author(s):  
Pierre Melikov ◽  
Jeremy A. Kho ◽  
Vincent Fighiera ◽  
Fahad Alhasoun ◽  
Jorge Audiffred ◽  
...  
Keyword(s):  
Mexico City ◽  
Human Mobility ◽  
Data Sets ◽  
Mobility Patterns ◽  
Distribution Models ◽  
The World ◽  
The Right ◽  
Different Parts

AbstractSeamless access to destinations of value such as workplaces, schools, parks or hospitals, influences the quality of life of people all over the world. The first step to planning and improving proximity to services is to estimate the number of trips being made from different parts of a city. A challenge has been representative data available for that purpose. Relying on expensive and infrequently collected travel surveys for modeling trip distributions to facilities has slowed down the decision-making process. The growing abundance of data already collected, if analyzed with the right methods, can help us with planning and understanding cities. In this chapter, we examine human mobility patterns extracted from data passively collected. We present results on the use of points of interest (POIs) registered on Google Places to approximate trip attraction in a city. We compare the result of trip distribution models that utilize only POIs with those utilizing conventional data sets, based on surveys. We show that an extended radiation model provides very good estimates when compared with the official origin–destination matrices from the latest census in Mexico City.

scholarly journals EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON

2018 ◽  
Vol XLII-4/W11 ◽  
pp. 51-58 ◽  
Author(s):  
P. Sulis ◽  
E. Manley
Keyword(s):  
Spatial Data ◽  
Human Mobility ◽  
Temporal Trends ◽  
Temporal Variations ◽  
Quantitative Information ◽  
Data Sets ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Spatial Differences ◽  
Using Data

<p><strong>Abstract.</strong> The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods.</p>

scholarly journals Exploring Urban Dynamics from Bluetooth Tracking Data: A Case Study of Austin, Texas

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Yihong Yuan ◽  
David Mills
Keyword(s):  
Real Time ◽  
Social Life ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Land Use Patterns ◽  
Urban Dynamics ◽  
Individual Level ◽  
Human Aspects ◽  
The Individual

<p><strong>Abstract.</strong> In recent decades, the growing availability of location-aware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns. However, previous studies on Bluetooth technologies have mainly focused on applying Bluetooth data to analyzing traffic and optimizing transportation networks or deploying new Bluetooth devices in civil engineering. The use of such datasets in understanding urban dynamics and real-time land use patterns is rather limited. This study develops an extendable workflow to explore urban dynamics from Bluetooth data based on a case study in Austin, Texas. We identified similar mobility patterns in different areas of Austin during various study periods, including the Memorial Day long weekend in 2016 and a national musical festival (South by Southwest). Our main goal is to prove the efficacy of this specific workflow and methodology to understand urban dynamics based on real-time Bluetooth data. The hypothesis is that Bluetooth data is sensitive to the daily patterns of human interactions and movements on the individual level, therefore it can capture detailed dynamic patterns. The proposed research also validates new concepts such as “human sensing” and “social sensing” in the field of geography and spatial sciences, which introduces new opportunities to monitor the human aspects of social life.</p>

scholarly journals An entropy-based approach to the study of human mobility and behavior in private homes

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243503
Author(s):  
Yan Wang ◽  
Ali Yalcin ◽  
Carla VandeWeerd
Keyword(s):  
Human Mobility ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Sensor Data ◽  
Movement Trajectory ◽  
Mobility Patterns ◽  
Outdoor Environments ◽  
Floor Plans ◽  
Using Data ◽  
Private Homes

Understanding human mobility in outdoor environments is critical for many applications including traffic modeling, urban planning, and epidemic modeling. Using data collected from mobile devices, researchers have studied human mobility in outdoor environments and found that human mobility is highly regular and predictable. In this study, we focus on human mobility in private homes. Understanding this type of human mobility is essential as smart-homes and their assistive applications become ubiquitous. We model the movement of a resident using ambient motion sensor data and construct a chronological symbol sequence that represents the resident’s movement trajectory. Entropy rate is used to quantify the regularity of the resident’s mobility patterns, and an upper bound of predictability is estimated. However, the presence of visitors and malfunctioning sensors result in data that is not representative of the resident’s mobility patterns. We apply a change-point detection algorithm based on penalized contrast function to detect these changes, and to identify the time periods when the data do not completely reflect the resident’s activities. Experimental results using the data collected from 10 private homes over periods of 178 to 713 days show that human mobility at home is also highly predictable in the range of 70% independent of variations in floor plans and individual daily routines.

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