scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

scholarly journals Measuring inequality in community resilience to natural disasters using large-scale mobility data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Boyeong Hong ◽  
Bartosz J. Bonczak ◽  
Arpit Gupta ◽  
Constantine E. Kontokosta
Keyword(s):  
Large Scale ◽  
Local Population ◽  
Community Resilience ◽  
Data Driven ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Response And Recovery ◽  
Mobility Behavior ◽  
Insight Into ◽  
Resilience Capacity

AbstractWhile conceptual definitions provide a foundation for the study of disasters and their impacts, the challenge for researchers and practitioners alike has been to develop objective and rigorous measures of resilience that are generalizable and scalable, taking into account spatiotemporal dynamics in the response and recovery of localized communities. In this paper, we analyze mobility patterns of more than 800,000 anonymized mobile devices in Houston, Texas, representing approximately 35% of the local population, in response to Hurricane Harvey in 2017. Using changes in mobility behavior before, during, and after the disaster, we empirically define community resilience capacity as a function of the magnitude of impact and time-to-recovery. Overall, we find clear socioeconomic and racial disparities in resilience capacity and evacuation patterns. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions that prioritize the equitable allocation of resources to vulnerable neighborhoods.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data

2019 ◽  
Vol 8 (3) ◽  
pp. 134 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Model Building ◽  
Fixed Time ◽  
Spatial Proximity ◽  
Mobility Data ◽  
Motion Activity ◽  
Tracking Motion ◽  
Processing Techniques ◽  
Vector Representations

The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people.

Passive Health Monitoring Using Large Scale Mobility Data

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yunke Zhang ◽  
Fengli Xu ◽  
Tong Li ◽  
Vassilis Kostakos ◽  
Pan Hui ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Fast Food ◽  
Large Scale ◽  
Demographic Data ◽  
High Mobility ◽  
Neighborhood Environment ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Health Related ◽  
Hospital Visits

In this paper, we investigate the feasibility of using mobility patterns and demographic data to predict hospital visits. We collect mobility traces from two thousand users for around two months. We extract 16 mobility features from these passively collected mobility traces and train an XGBoost model to predict users' hospital visits. We demonstrate that the designed mobility features can significantly improve prediction accuracy (p < 0.01, AUC = 0.79). We further analyze how these mobility features affect the prediction results and measure their importance by using Shapley additive explanation values. We discover that users with less mobility activity, less visit diversity, and few sports facilities, bountiful entertainment around their visited locations are more likely to visit hospitals. Moreover, we conduct predictions on the populations with different demographic features, which achieves meaningful and insightful results, i.e. maintaining a high mobility activity is crucial for older people's health, while fast food store more substantially affects younger people's health; visit patterns can indicate females' health, while the neighborhood environment is more indicative of males, etc. These results shed light on how to use and understand large scale mobility data in health monitoring and other health-related applications in practice.

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

2021 ◽  
pp. 036119812110312
Author(s):  
Biao Yin ◽  
Fabien Leurent
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Individual Activity ◽  
Data Set ◽  
Individual Mobility ◽  
Daily Mobility ◽  
Home Based ◽  
The Individual

Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

scholarly journals Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Satish V. Ukkusuri ◽  
P. Suresh C. Rao
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Recovery Process ◽  
Mobile Phone Data ◽  
Mobility Patterns ◽  
Social Forms ◽  
Recovery Processes ◽  
Mobility Data ◽  
New Perspective

Abstract Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

scholarly journals Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data

2018 ◽  
Author(s):  
Yingzi Wang ◽  
Xiao Zhou ◽  
Anastasios Noulas ◽  
Cecilia Mascolo ◽  
Xing Xie ◽  
...  
Keyword(s):  
Chronic Diseases ◽  
Population Health ◽  
Large Scale ◽  
Human Life ◽  
Human Mobility ◽  
Gaussian Mixture ◽  
Disease Rate ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Spatio Temporal

Chronic diseases like cancer and diabetes are major threats to human life. Understanding the distribution and progression of chronic diseases of a population is important in assisting the allocation of medical resources as well as the design of policies in preemptive healthcare. Traditional methods to obtain large scale indicators on population health, e.g., surveys and statistical analysis, can be costly and time-consuming and often lead to a coarse spatio-temporal picture. In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area. By viewing local human lifestyles we predict the evolution rate of several chronic diseases at the level of a city neighborhood. We apply the combination of a collaborative topic modeling (CTM) and a Gaussian mixture method (GMM) to tackle the data sparsity challenge and achieve robust predictions on health conditions simultaneously. Our method enables the analysis and prediction of disease rate evolution at fine spatio-temporal scales and demonstrates the potential of incorporating datasets from mobile web sources to improve population health monitoring. Evaluations using real-world check-in and chronic disease morbidity datasets in the city of London show that the proposed CTM+GMM model outperforms various baseline methods.

scholarly journals Exploitation of Aggregate Mobility Sensing Data for the Synthesis of Disaggregate Multimodal Tours in Megacities

2021 ◽  
Vol 2 ◽  
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Private Information ◽  
Large Scale ◽  
Service Providers ◽  
Telecommunication Service ◽  
Mobility Patterns ◽  
Transport Mode ◽  
Sensing Data ◽  
Trip Purpose ◽  
Mobility Data ◽  
Time Period

The unprecedented volume of urban sensing data has allowed the tracking of individuals at remarkably high resolution. As an example, Telecommunication Service Providers (TSPs) cannot provide their service unless they continuously collect information regarding the location of their customers. In conjunction with appropriate post-processing methodologies, these traces can be augmented with additional dimensions such as the activity of the user or the transport mode used for the completion of journeys. However, justified privacy concerns have led to the enforcement of legal regulations aiming to hinder, if not entirely forbid, the use of such private information even for purely scientific purposes. One of the most widely applied methods for the communication of mobility information without raising anonymity concerns is the aggregation of trips in origin–destination (OD) matrices. Previous work has showcased the possibility to exploit multi-period and purpose-segmented ODs for the synthesis of realistic disaggregate tours. The current study extends this framework by incorporating the multimodality dimension into the framework. In particular, the study evaluates the potential of synthesizing multimodal, diurnal tours for the case where the available ODs are also segmented by the transport mode. In addition, the study proves the scalability of the method by evaluating its performance on a set of time period-, trip purpose-, and transport mode-segmented, large-scale ODs describing the mobility patterns for millions of citizens of the megacity of Tokyo, Japan. The resulting modeled tours utilized over 96% of the inputted trips and recreated the observed mobility traces with an accuracy exceeding 80%. The high accuracy of the framework establishes the potential to utilize privacy-safe, aggregate urban mobility data for the synthesis of highly informative and contextual disaggregate mobility information. Implications are significant since the creation of such granular mobility information from widely available data sources like aggregate ODs can prove particularly useful for deep explanatory analysis or for advanced transport modeling purposes (e.g., agent-based, microsimulation modeling).

scholarly journals Human mobility data from the BBC Pandemic project

2021 ◽  
Author(s):  
Andrew JK Conlan ◽  
Petra Klepac ◽  
Adam J Kucharski ◽  
Stephen Kissler ◽  
Maria L Tang ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sets ◽  
Mobility Patterns ◽  
Administrative District ◽  
Data Set ◽  
Public Science ◽  
Mobility Data ◽  
The United Kingdom ◽  
Television Documentary ◽  
Gps Trajectories

AbstractWe present human mobility data for the United Kingdom collected from the “BBC Pandemic”, a public science project linked to the BBC Four television documentary of the same name. Mobile phone GPS trajectories submitted by users and collected over a 24 hour period were aggregated to construct anonymised origin-destination flux matrices at the local administrative district (LAD). We use these data to explore how mobility patterns change with age and employment status - unique stratifications that are not available from other publicly and privately held mobility data sets. We validate the consistency of the aggregated BBC mobility data set against census workflow data and illustrate how the systematic differences in mobility rates with age affect the risk and pattern of transmission between regions with 18-30 year old’s contributing the greatest risk of transmission to adjacent regions, but older 60-100 years playing the most important role in more remote low-density locations.

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