scholarly journals Tourism Event Analytics with Mobile Phone Data

2021 ◽  
Vol 2 (3) ◽  
pp. 1-22
Author(s):  
Yan Leng ◽  
Alejandro Noriega ◽  
Alex Pentland
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Added Value ◽  
Mobile Phone Data ◽  
Small Samples ◽  
Environment Policy ◽  
Analysis And Evaluation ◽  
Event Tourism ◽  
Negative Impacts ◽  
Cultural Performances

Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a low granularity. Anonymous call detail record (CDR) is a novel source of data with enormous potential in areas of high societal value: epidemics, poverty, and urban development. This study demonstrates the added value of CDR in event tourism, especially for the analysis and evaluation of marketing strategies, event operations, and the externalities at the local and national levels. To achieve this aim, we formalize 14 indicators in high spatial and temporal resolutions to measure both the positive and the negative impacts of the touristic events. We exemplify the use of these indicators in a tourism country, Andorra, on 22 high-impact events including sports competitions, cultural performances, and music festivals. We analyze these touristic events using the large-scale CDR data across 2 years. Our approach serves as a prescriptive and a diagnostic tool with mobile phone data and opens up future directions for tourism analytics.

scholarly journals A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weifeng Li ◽  
Xiaoyun Cheng ◽  
Zhengyu Duan ◽  
Dongyuan Yang ◽  
Gaohua Guo
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Pattern Mining ◽  
Interaction Analysis ◽  
Spatial Interaction ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Mobile Phone Data ◽  
Interaction Patterns ◽  
Critical Activity

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework.

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.

Understanding evacuation and impact of a metro collision on ridership using large-scale mobile phone data

2017 ◽  
Vol 11 (8) ◽  
pp. 511-520 ◽  
Author(s):  
Zhengyu Duan ◽  
Zengxiang Lei ◽  
Michael Zhang ◽  
Weifeng Li ◽  
Jia Fang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Mobile Phone Data

scholarly journals Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chao Yang ◽  
Yuliang Zhang ◽  
Xianyuan Zhan ◽  
Satish V. Ukkusuri ◽  
Yifan Chen
Keyword(s):  
Mobile Phone ◽  
Survey Data ◽  
Large Scale ◽  
Activity Patterns ◽  
Mobile Phone Data ◽  
Activity Data ◽  
Travel Survey ◽  
Activity Chain ◽  
Urban Activity ◽  
Activity Dynamics

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.

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.

Real-Time Large-Scale Map Matching Using Mobile Phone Data

2017 ◽  
Vol 11 (4) ◽  
pp. 1-38 ◽  
Author(s):  
Essam Algizawy ◽  
Tetsuji Ogawa ◽  
Ahmed El-Mahdy
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Map Matching

scholarly journals Comparing Sources of Mobility for Modelling the Epidemic Spread of Zika Virus in Colombia

2021 ◽  
Author(s):  
Daniela Perrotta ◽  
Enrique Frias-Martinez ◽  
Ana Pastore y Piontti ◽  
Qian Zhang ◽  
Miguel Luengo-Oroz ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Zika Virus ◽  
Social Impact ◽  
Initial Conditions ◽  
Human Mobility ◽  
National Level ◽  
Surveillance Data ◽  
Added Value ◽  
Mobile Phone Data ◽  
Mobility Patterns

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne disease. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson's r=0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility network in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed network, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data capture a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

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.

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