1,882 research outputs found
Flooding through the lens of mobile phone activity
Natural disasters affect hundreds of millions of people worldwide every year.
Emergency response efforts depend upon the availability of timely information,
such as information concerning the movements of affected populations. The
analysis of aggregated and anonymized Call Detail Records (CDR) captured from
the mobile phone infrastructure provides new possibilities to characterize
human behavior during critical events. In this work, we investigate the
viability of using CDR data combined with other sources of information to
characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map
has been reconstructed using Landsat-7 images to identify the floods. Within
this frame, the underlying communication activity signals in the CDR data have
been analyzed and compared against rainfall levels extracted from data of the
NASA-TRMM project. The variations in the number of active phones connected to
each cell tower reveal abnormal activity patterns in the most affected
locations during and after the floods that could be used as signatures of the
floods - both in terms of infrastructure impact assessment and population
information awareness. The representativeness of the analysis has been assessed
using census data and civil protection records. While a more extensive
validation is required, these early results suggest high potential in using
cell tower activity information to improve early warning and emergency
management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC)
201
Mobile phone indicators and their relation to the socioeconomic organisation of cities
Thanks to the use of geolocated big data in computational social science
research, the spatial and temporal heterogeneity of human activities are
increasingly being revealed. Paired with smaller and more traditional data,
this opens new ways of understanding how people act and move, and how these
movements crystallise into the structural patterns observed by censuses. In
this article we explore the convergence of mobile phone data with more
classical socioeconomic data from census in French cities. We extract mobile
phone indicators from six months worth of Call Detail Records (CDR) data, while
census and administrative data are used to characterize the socioeconomic
organisation of French cities. We address various definitions of cities and
investigate how they impact the relation between mobile phone indicators, such
as the number of calls or the entropy of visited cell towers, and measures of
economic organisation based on census data, such as the level of deprivation,
inequality and segregation. Our findings show that some mobile phone indicators
relate significantly with different socioeconomic organisation of cities.
However, we show that found relations are sensitive to the way cities are
defined and delineated. In several cases, differing city definitions
delineations can change the significance or even the signs of found
correlations. In general, cities delineated in a restricted way (central cores
only) exhibit traces of human activity which are less related to their
socioeconomic organisation than cities delineated as metropolitan areas and
dispersed urban regions.Comment: 19 pages, 9 figures, 2 table
Revisiting Urban Dynamics through Social Urban Data
The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?
To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.
In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.
To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.
After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.
A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.
The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities
Revisiting Urban Dynamics through Social Urban Data:
The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?
To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.
In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.
To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.
After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.
A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.
The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities
Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network
Epidemiologic studies have established associations between various air
pollutants and adverse health outcomes for adults and children. Due to high
costs of monitoring air pollutant concentrations for subjects enrolled in a
study, statisticians predict exposure concentrations from spatial models that
are developed using concentrations monitored at a few sites. In the absence of
detailed information on when and where subjects move during the study window,
researchers typically assume that the subjects spend their entire day at home,
school or work. This assumption can potentially lead to large exposure
assignment bias. In this study, we aim to determine the distribution of the
exposure assignment bias for an air pollutant (ozone) when subjects are assumed
to be static as compared to accounting for individual mobility. To achieve this
goal, we use cell-phone mobility data on approximately 400,000 users in the
state of Connecticut during a week in July, 2016, in conjunction with an ozone
pollution model, and compare individual ozone exposure assuming static versus
mobile scenarios. Our results show that exposure models not taking mobility
into account often provide poor estimates of individuals commuting into and out
of urban areas: the average 8-hour maximum difference between these estimates
can exceed 80 parts per billion (ppb). However, for most of the population, the
difference in exposure assignment between the two models is small, thereby
validating many current epidemiologic studies focusing on exposure to ozone
Using mobility data as proxy for measuring urban vitality
In this paper, we propose a computational approach to Jane Jacobs\u27 concept of diversity and vitality, analyzing new forms of spatial data to obtain quantitative measurements of urban qualities frequently employed to evaluate places. We use smart card data collected from public transport to calculate a diversity value for each research unit. Diversity is composed of three dynamic attributes: intensity, variability, and consistency, each measuring different temporal variations of mobility flows. We then apply a regression model to establish the relationship between diversity and vitality, using Twitter data as a proxy for human activity in urban space. Final results (also validated using data sourced from OpenStreetMap) unveil which are the most vibrant areas in London
Analysis of Lisbon visitors’ internet access behavior: behavior analysis through the identification of clusters
Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing IntelligenceThis master's thesis focuses on clustering the internet access behavior of urban visitors in the Lisbon
urban area. To promote smart city development, the study aims to provide insights into visitors'
behaviors while accessing the internet in Lisbon, enabling improved decision-making processes for city
management, and enhancing the overall online and offline experience for visitors. The over-tourism
phenomenon has put a strain on infrastructure, public transportation, and cultural heritage sites.
Therefore, innovative methods are needed for effective smart city management, particularly in urban
mobility. The increasing availability of Wi-Fi networks during travel has generated valuable data that
can be used to develop groundbreaking approaches to understanding visitors’ behaviors and mobility
patterns in urban areas. This knowledge enables the analysis and clustering of urban visitors' behavior,
contributing to improved decision-making processes in smart city management
Detecting country of residence from social media data : a comparison of methods
Identifying users' place of residence is an important step in many social media analysis workflows. Various techniques for detecting home locations from social media data have been proposed, but their reliability has rarely been validated using ground truth data. In this article, we compared commonly used spatial and Spatio-temporal methods to determine social media users' country of residence. We applied diverse methods to a global data set of publicly shared geo-located Instagram posts from visitors to the Kruger National Park in South Africa. We evaluated the performance of each method using both individual-level expert assessment for a sample of users and aggregate-level official visitor statistics. Based on the individual-level assessment, a simple Spatio-temporal approach was the best-performed for detecting the country of residence. Results show why aggregate-level official statistics are not the best indicators for evaluating method performance. We also show how social media usage, such as the number of countries visited and posting activity over time, affect the performance of methods. In addition to a methodological contribution, this work contributes to the discussion about spatial and temporal biases in mobile big data.Peer reviewe
Emerging technologies to measure neighborhood conditions in public health: Implications for interventions and next steps
Adverse neighborhood conditions play an important role beyond individual characteristics. There is increasing interest in identifying specific characteristics of the social and built environments adversely affecting health outcomes. Most research has assessed aspects of such exposures via self-reported instruments or census data. Potential threats in the local environment may be subject to short-term changes that can only be measured with more nimble technology. The advent of new technologies may offer new opportunities to obtain geospatial data about neighborhoods that may circumvent the limitations of traditional data sources. This overview describes the utility, validity and reliability of selected emerging technologies to measure neighborhood conditions for public health applications. It also describes next steps for future research and opportunities for interventions. The paper presents an overview of the literature on measurement of the built and social environment in public health (Google Street View, webcams, crowdsourcing, remote sensing, social media, unmanned aerial vehicles, and lifespace) and location-based interventions. Emerging technologies such as Google Street View, social media, drones, webcams, and crowdsourcing may serve as effective and inexpensive tools to measure the ever-changing environment. Georeferenced social media responses may help identify where to target intervention activities, but also to passively evaluate their effectiveness. Future studies should measure exposure across key time points during the life-course as part of the exposome paradigm and integrate various types of data sources to measure environmental contexts. By harnessing these technologies, public health research can not only monitor populations and the environment, but intervene using novel strategies to improve the public health
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