Books by Piyushimita (Vonu) Thakuriah
by Moira Zellner, Daniel Felsenstein, Richard Sinnott, Yingling Fan, David King, Francisco Pereira, Tae Hong Park, Joseph Y J Chow, Seth Spielman, Piyushimita (Vonu) Thakuriah, Frank Douma, Timothy Johnson, Nader Afzalan, Jonathan Peters, Li Yin, Mubassira Khan, Sungsoon Hwang, Steven French, Kristin Tufte, Yao-Li Wang, Lise Dirks, Camille Barchers, Gregory Erhardt, Phil Delaney, Yu-Luen Ma, Laiyun Wu, Luca Morandini, and Josep Maria Salanova Grau
Transformations in wireless connectivity and location-aware technologies hold the promise of brin... more Transformations in wireless connectivity and location-aware technologies hold the promise of bringing a sea-change in the way transportation information is generated and used in the future. Sensors in the transportation system, when integrated with those in other sectors (for example, energy, utility and health) have the potential to foster novel new ways of improving livability and sustainability.
The end-result of these developments has been somewhat contradictory. Although automation in the transportation environment has become increasingly widespread, the level of involvement and active participation by people, in terms of co-creation and contribution of information, has also increased. As a result, the following two major trends have been observed: (1) increases in Machine-to- Machine (M2M) communications; and (2) increases in the variety and volume of User-Generated Content.
In this transportation paradigm, the pervasive use of Information and Communication Technologies will serve as the foundation for mobility intelligence towards an “ubiquitous information-centered mobility environment”. However, many technical and operational questions, as well as social, management and legal challenges present themselves in the transformation to this vision. The book presents a non-technical review of research and initiatives and a discussion of such opportunities and challenges.
Papers by Piyushimita (Vonu) Thakuriah
Transport Policy, 2024
This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizi... more This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizing a nationally representative household survey spanning from January 2020 to March 2022. Using a multilevel Hurdle model with month and state random effects, vehicle ownership trends are separately modeled for car-owning and carless households within the same modeling framework, while accounting for endogeneity and unobserved heterogeneity. An increase in the total amount of economic stimulus funding received increased the odds of vehicle ownership, while high car prices, high levels of housing expenditures, living in multi-family dwelling, and being from minority and low-skilled worker families negatively affected car-ownership. Greater household
labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected carownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure,
living arrangements and employment-related characteristics. The analysis jointly considered how short-term
pandemic-related influences as well as long-term demographic and occupational factors differently affect carownership
for car-owning households and those without cars. Policy implications are drawn for consumer
protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for
public transportation which has continued to suffer from lower levels of use, and sustainability programs due to
higher volumes of older used cars.
Transport Findings, 2023
This paper examines changes in car-ownership levels before and after the COVID-19 pandemic in the... more This paper examines changes in car-ownership levels before and after the COVID-19 pandemic in the US. In contrast to the two years before the pandemic, the propensity of households to be carless decreased for all households considered, as well as for low- and middle-income, and minority households. There is also evidence of an increase in the average number of vehicles for low-income households. The results highlight the additional financial burden faced by households during the pandemic as a result of higher levels of car-ownership, and that the recovery of public transportation ridership may be negatively impacted with the rise in car-ownership among transit-using groups.
Environment and Planning B: Urban Analytics & City Science, 2023
This study analyzed physical distancing in people’s daily lives and its association with travel b... more This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic
images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s
physical distancing and travel behavior and transportation choices. The multidisciplinary research
approach to understand these associations accounted for a number of social, economic, and cultural
factors that potentially influenced their physical isolation levels. We found that participants spend a
significant amount of time physically separated from others, without anyone else within 2 meters.
The use of public transportation, automobiles, active travel, and an increase in trip frequency,
including trips to transportation facilities, reduced the extent of physical distancing, with public
transportation having the most significant impact. Higher incomes, strong social networks, and a
sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable
about climate change issues increased the likelihood of physical distancing. The paper addresses a
crucial gap in our understanding of how these factors intersect to create the dynamics of physical
distancing in non-emergency situations and highlights their planning and operational implications
while showcasing the use of unique person-based physical distancing measures derived from
autonomously collected image data.
Transport Findings, 2023
This paper examines changes in car-ownership levels before and after the COVID-19 pandemic in the... more This paper examines changes in car-ownership levels before and after the COVID-19 pandemic in the US. In contrast to the two years before the pandemic, the propensity of households to be carless decreased for all households considered, as well as for low-and middle-income, and minority households. There is also evidence of an increase in the average number of vehicles for lowincome households. The results highlight the additional financial burden faced by households during the pandemic as a result of higher levels of car-ownership, and that the recovery of public transportation ridership may be negatively impacted with the rise in car-ownership among transit-using groups.
Proceedings of the 11th International Conference on Travel Behaviour Research, Kyoto, August 2006, 2006
In this paper, the relationship between expenditures that households make on transportation and t... more In this paper, the relationship between expenditures that households make on transportation and their earnings levels are investigated. Mobility expenditures are proxied by a construct called the Annual Daily Transportation Expenditure Index (ADTEI) while the earnings levels of households are measured by Residual Income (RES_INC). The latter reflects incomes that potentially accrue due to current mobility and accessibility-related expenditures and excludes income from sources such as pensions and transfer payments that are generated by potentially non-travel factors. We consider a two-stage model in which annual daily transportation expenditures and residual incomes are considered to be endogenous, using a variety of demographic, community/spatial, economic, family and life-cycle conditions of the household as independent variables. The modeling approach is within the framework of the Extended Linear Expenditure System. For empirical implementation, we adopt a simultaneousequations framework, originally given by Nelson and Olsen (1978); this approach allows transportation expenditures and residual incomes to be jointly modeled and also allows us to take into consideration the fact that some households make zero expenditures on transportation over the period of a year.
Transportation, 2021
The amount of time we spend online has been increasing dramatically, influencing our daily travel... more The amount of time we spend online has been increasing dramatically, influencing our daily travel and activity patterns. However, empirical studies on changes in the extent to which the amount of time spent online are related to changes in our activity and travel patterns are scarce, mainly due to a lack of available longitudinal or quasi-longitudinal data. This paper explores how the relationships between the time spent using the Internet, and the time spent on non-mandatory maintenance and leisure activities, have evolved over a decade. Maintenance activities include out-of-home activities such as shopping, banking, and doctor visits, while leisure activities include entertainment activities, visiting friends, sporting activities, and so forth. Our approach uses two datasets from two major cross-sectional surveys in Scotland, i.e. the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and formed. The ...
The publication date is one day earlier then the EST date to provide the proceedings to attendees... more The publication date is one day earlier then the EST date to provide the proceedings to attendees in Australian on the first day of the conference It is our great pleasure to welcome you to the 1st Workshop on Understanding the City with Urban Informatics -- UCUI'15. This workshop aims to provide a multidisciplinary forum which brings together researchers in Big Data (BD), Information Retrieval (IR), Data Mining, and Urban Studies, to explore novel solutions to the numerous theoretical, practical and ethical challenges arising in this context. These include difficulties in collecting city data, creating data management infrastructures, and providing new effective and efficient information access techniques, to as many users as possible in the context of a smart city. Our call has attracted nine papers. The program committee, which is formed of 14 experienced researchers, accepted six papers for presentation in the workshop, i.e. three technical papers, and three position papers. These papers represent the ideas and opinions of the authors who are trying to stimulate debate. In addition, two keynotes helped us frame the problem, and create a common understanding of the challenges. Finally, as part of the workshop, we introduced our newly published datasets relating to the iMCD project of the University of Glasgow's Urban Big Data Centre and participants to our Urban Informatics Data Challenge presented their works.
Transportation Research Record, 1993
There is serious concern over the fact that travel surveys often overrepresent smaller households... more There is serious concern over the fact that travel surveys often overrepresent smaller households with higher incomes and better education levels and, in general, that nonresponse is nonrandom. However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, estimates of parameters are unbiased regardless of the nature of the respondents, and the issues of how response rates and nonresponse bias are ameliorated. The more important task then is the complete specification of the model, without leaving out variables that have some effect on the variable to be predicted. The theoretical basis for this reasoning is given along with an example of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but the manner in which these and other more nonstandard methods have been systematically put together to assess bias in estimates shows that careful model building, n...
The authors examine the relationships between environmental factors (neighborhood characteristics... more The authors examine the relationships between environmental factors (neighborhood characteristics and community-based transportation services) and the degree of perceived and functional independence in travel for a sample of persons with disabilities residing in urban and suburban U.S. locations with a wide variety of neighborhood conditions. Perceived ability is a self-reported measure on a Likert-type ordinal scale, while the functional ability scale reflects the “Mode of Transportation” and “Shopping” aspects of the Lawton-Brody Instrument Activities of Daily Living scale. Neighborhood characteristics examined are walkability, population density, percent owner-occupied housing and age and racial/ethnic diversity, while community-based transportation services considered are volunteer driver services, van-based demand response programs and taxi services that are operated either by community organizations, senior care centers or transit agencies. Using cluster analysis, the authors ...
Transformations in wireless connectivity and location-aware technologies hold the promise of brin... more Transformations in wireless connectivity and location-aware technologies hold the promise of bringing a sea-change in the way transportation information is generated and used in the future. Sensors in the transportation system, when integrated with those in other sectors (for example, energy, utility and health) have the potential to foster novel new ways of improving livability and sustainability. The end-result of these developments has been somewhat contradictory. Although automation in the transportation environment has become increasingly widespread, the level of involvement and active participation by people, in terms of co-creation and contribution of information, has also increased. As a result, the following two major trends have been observed: (1) increases in Machine-to- Machine (M2M) communications; and (2) increases in the variety and volume of User-Generated Content. In this transportation paradigm, the pervasive use of Information and Communication Technologies will s...
This presentation explores the emerging concept of ‘Big Data in Education’ and introduces novel t... more This presentation explores the emerging concept of ‘Big Data in Education’ and introduces novel technologies and approaches for addressing inequalities in access to participation and success in lifelong learning, to produce better life outcomes for urban citizens. It introduces the work of the new Urban Big Data Centre (UBDC) at the University of Glasgow, presenting a case study of its first data product – the integrated Multimedia City Data (iMCD) project. Educational engagement and predictive factors are presented for adult learners, and older adult learners, in a representative survey of 1500 households. This was followed up with mobility tracking data using GPS data and wearable camera images, as well as one year’s worth of contextual data from over one hundred web sources (social media, news, weather). The chapter introduces the complex dataset that can help stakeholders, academics, citizens and other external users examine active aging and citizen learning engagement in the mo...
Wprowadzenie. Ciąża dla większości kobiet to okres radosnego oczekiwania na narodziny dziecka, al... more Wprowadzenie. Ciąża dla większości kobiet to okres radosnego oczekiwania na narodziny dziecka, ale także czas przeżywania wielu negatywnych emocji. Jedną z nich, obserwowaną od dawna przez położników i psychologów, jest lęk przed porodem. Cel pracy. Ocena nasilenia lęku przed porodem u ciężarnych kobiet oraz ustalenie związku wybranych zmiennych niezależnych z jego występowaniem. Materiał i metody. Badaniem objęto 99 ciężarnych kobiet będących w 30. i powyżej 30. tygodnia ciąży. Lęk porodowy analizowano jako subiektywnie odczuwane przez kobiety natężenie lęku przed porodem. Za wskaźnik zmiennej przyjęto wynik według Kwestionariusza Lęku Porodowego (KLP-K) autorstwa L. Putyńskiego i M. Paciorka, który jest zbudowany z 9 pytań ocenianych na czteropunktowej skali. Im wyższy wynik, tym większe natężenie lęku porodowego. Wyniki. Przeprowadzone badania wykazały, że 65,66% ciężarnych kobiet charakteryzował niski/przeciętny poziom lęku porodowego, 18,18%-podwyższony, 10,10%-wysoki i 6,06%-bardzo wysoki. Podjęto także próbę sprawdzenia, czy poziom lęku porodowego u kobiet ciężarnych był zróżnicowany wybranymi zmiennymi niezależnymi, takimi jak: rodność, wiek, wykształcenie, miejsce zamieszkania i status materialny. W badaniu własnym nie wykazano różnic istotnych statystycznie w poziomie lęku porodowego ze względu na wybrane zmienne socjodemograficzne i położnicze (p > 0,05). Wnioski. Kobiety ciężarne w III trymestrze ciąży charakteryzował w większości niski/przeciętny poziom lęku przed porodem. Nasilenie lęku porodowego nie było związane z rodnością, wiekiem, wykształceniem, statusem materialnym i miejscem zamieszkania badanej próby kobiet.
As ride-hailing services become increasingly popular, being able to accurately predict demand for... more As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To ...
Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2017
Recently, the geolocalisation of tweets has become an important feature for a wide range of tasks... more Recently, the geolocalisation of tweets has become an important feature for a wide range of tasks in Information Retrieval and other domains, such as real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geo-tagged tweets available remains insu cient to reliably perform such tasks. us, predicting the location of non-geotagged tweets is an important yet challenging task, which can increase the sample of geo-tagged data and help to a wide range of tasks. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets weighted based on the credibility of its source (Twi er user). Using geo-tagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) signi cantly outperforms our baselines in terms of accuracy, and error distance, in both cities, with the cost of decrease in recall.
PLOS ONE, 2019
Recent increases in the use of and applications for wearable technology has opened up many new av... more Recent increases in the use of and applications for wearable technology has opened up many new avenues of research. In this paper, we consider the use of lifelogging and GPS data to extend fine-grained movement analysis for improving applications in health and safety. We first design a framework to solve the problem of indoor and outdoor movement detection from sensor readings associated with images captured by a lifelogging wearable device. Second we propose a set of measures related with hazard on the road network derived from the combination of GPS movement data, road network data and the sensor readings from a wearable device. Third, we identify the relationship between different sociodemographic groups and the patterns of indoor physical activity and sedentary behaviour routines as well as disturbance levels on different road settings.
Springer Geography, 2016
to be published by Springer, Consisting of papers presented in an NSF-funded Workshop on Big Data... more to be published by Springer, Consisting of papers presented in an NSF-funded Workshop on Big Data and Urban Informatics.
Springer Geography, 2016
This paper assesses non-traditional urban digital infomediaries who are pushing the agenda of urb... more This paper assesses non-traditional urban digital infomediaries who are pushing the agenda of urban Big Data and Open Data. Our analysis identified a mix of private, public, non-profit and informal infomediaries, ranging from very large organizations to independent developers. Using a mixed-methods approach, we identified four major groups of organizations within this dynamic and diverse sector: general-purpose ICT providers, urban information service providers, open and civic data infomediaries, and independent and open source developers. A total of nine types of organizations are identified within these four groups.
Built Environment, 2016
Sensing spatiotemporal patterns in urban areas: analytics and visualizations using the integrated... more Sensing spatiotemporal patterns in urban areas: analytics and visualizations using the integrated multimedia city data platform. Built Environment, 42(3), pp. 415-429.
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Books by Piyushimita (Vonu) Thakuriah
The end-result of these developments has been somewhat contradictory. Although automation in the transportation environment has become increasingly widespread, the level of involvement and active participation by people, in terms of co-creation and contribution of information, has also increased. As a result, the following two major trends have been observed: (1) increases in Machine-to- Machine (M2M) communications; and (2) increases in the variety and volume of User-Generated Content.
In this transportation paradigm, the pervasive use of Information and Communication Technologies will serve as the foundation for mobility intelligence towards an “ubiquitous information-centered mobility environment”. However, many technical and operational questions, as well as social, management and legal challenges present themselves in the transformation to this vision. The book presents a non-technical review of research and initiatives and a discussion of such opportunities and challenges.
Papers by Piyushimita (Vonu) Thakuriah
labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected carownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure,
living arrangements and employment-related characteristics. The analysis jointly considered how short-term
pandemic-related influences as well as long-term demographic and occupational factors differently affect carownership
for car-owning households and those without cars. Policy implications are drawn for consumer
protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for
public transportation which has continued to suffer from lower levels of use, and sustainability programs due to
higher volumes of older used cars.
images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s
physical distancing and travel behavior and transportation choices. The multidisciplinary research
approach to understand these associations accounted for a number of social, economic, and cultural
factors that potentially influenced their physical isolation levels. We found that participants spend a
significant amount of time physically separated from others, without anyone else within 2 meters.
The use of public transportation, automobiles, active travel, and an increase in trip frequency,
including trips to transportation facilities, reduced the extent of physical distancing, with public
transportation having the most significant impact. Higher incomes, strong social networks, and a
sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable
about climate change issues increased the likelihood of physical distancing. The paper addresses a
crucial gap in our understanding of how these factors intersect to create the dynamics of physical
distancing in non-emergency situations and highlights their planning and operational implications
while showcasing the use of unique person-based physical distancing measures derived from
autonomously collected image data.
The end-result of these developments has been somewhat contradictory. Although automation in the transportation environment has become increasingly widespread, the level of involvement and active participation by people, in terms of co-creation and contribution of information, has also increased. As a result, the following two major trends have been observed: (1) increases in Machine-to- Machine (M2M) communications; and (2) increases in the variety and volume of User-Generated Content.
In this transportation paradigm, the pervasive use of Information and Communication Technologies will serve as the foundation for mobility intelligence towards an “ubiquitous information-centered mobility environment”. However, many technical and operational questions, as well as social, management and legal challenges present themselves in the transformation to this vision. The book presents a non-technical review of research and initiatives and a discussion of such opportunities and challenges.
labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected carownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure,
living arrangements and employment-related characteristics. The analysis jointly considered how short-term
pandemic-related influences as well as long-term demographic and occupational factors differently affect carownership
for car-owning households and those without cars. Policy implications are drawn for consumer
protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for
public transportation which has continued to suffer from lower levels of use, and sustainability programs due to
higher volumes of older used cars.
images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s
physical distancing and travel behavior and transportation choices. The multidisciplinary research
approach to understand these associations accounted for a number of social, economic, and cultural
factors that potentially influenced their physical isolation levels. We found that participants spend a
significant amount of time physically separated from others, without anyone else within 2 meters.
The use of public transportation, automobiles, active travel, and an increase in trip frequency,
including trips to transportation facilities, reduced the extent of physical distancing, with public
transportation having the most significant impact. Higher incomes, strong social networks, and a
sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable
about climate change issues increased the likelihood of physical distancing. The paper addresses a
crucial gap in our understanding of how these factors intersect to create the dynamics of physical
distancing in non-emergency situations and highlights their planning and operational implications
while showcasing the use of unique person-based physical distancing measures derived from
autonomously collected image data.
Workshop Proceedings: All papers to be published in the online proceedings have now been revised and are available, in an early release, at http://urbanbigdata.uic.edu/proceedings/. A complete document version of the workshop proceedings, with an introduction, page numbers and a digital identifier is forthcoming.
Edited Book: Reviews for inclusion of selected papers in the book titled Seeing Cities through Big Data: Research Methods and Applications in Urban Informatics are ongoing. This applies only to papers whose authors indicated interest in being considered for the book.
Big Data has opened up several opportunities to obtain new insights on cities. We invite papers at the intersection of the urban social sciences and the data sciences to be presented in an NSF-sponsored workshop to be held on Aug 11-12, 2014, in the University of Illinois at Chicago, Chicago, Illinois. We hope that the workshop will generate discussions in this emerging area of research, with the goal of long-term community-building on the topic. Travel funds will be available for presenters.
We welcome papers that discuss research results as well as idea pieces of work in progress which highlight research needs and data limitations. Workshop papers will be published in an online workshop proceeding.
Selected papers will be published, after additional peer-review, in an edited book titled "Seeing Cities Through Big Data – Research, Methods and Applications in Urban Informatics" to be published with Springer.
The objective of the workshop is to bring together researchers with an interest in the use of Big Data for urban analysis. The focus will be on understanding of urban systems, and related examples of urban applications, methods and tools. We are seeking papers that clearly create or use such novel sources of information for urban and regional analysis. Urban and regional analysis spans a broad range of areas. A far from complete list of areas include transportation, environment, public health, land-use, housing, economic development, labor markets, criminal justice, population demographics, urban ecology, energy, community development and public participation.
We invite original research, including position papers, on theoretical developments and applications demonstrating the use of urban Big Data, and the next-generation of Big Data services, tools and technologies for urban informatics. We are interested in papers that use Big Data in one or more of the following five themes:
1) Theoretical developments and knowledge discovery in urban systems;
2) Planning and operational uses of urban Big Data;
3) Urban Big Data measurement, analysis and methodological questions;
4) Information management for urban informatics;
5) Institutional issues, organizations, networks and infomediaries in urban Big Data.
Travel funds of up to $700 will be available for a single presenter per paper, on a reimbursement basis. Student presenters will be able to compete for an additional limited pool of funds, for upto an additional $250 per student presenter.
Dates: Extended abstracts of 750-1000 words are due April 1, 2014. Full papers for accepted presenters will be due July 15, 2014.
For more information on the workshop, please visit http://urbanbigdata.uic.edu/workshop-2014/call-for-papers/
For additional information, please contact Prof. Nebiyou Tilahun at [email protected]
The University of Glasgow has received a share of the Big Data Phase 2 funding to create a unique facility designed to research complex and cross-cutting urban issues such as transport, employment, migration, housing, education and social exclusion and to bring a mix of expertise in the urban social sciences and the data sciences to address problems of dynamic resource management, social justice, lifelong learning and urban engagement.
The centre will develop technologies to create a linked urban Big Data infrastructure to support such research. The project will be led by the University of Glasgow, with the Universities of Edinburgh, Cambridge, Reading, Bristol and Illinois-Chicago as partners.
Dr. Piyushimita (Vonu) Thakuriah, Halcrow Chair of Transport and Professor, Urban Studies and Affiliated Professor, School of Engineering, University of Glasgow is the Principal Investigator for the project. She said: "This is a fantastic opportunity for researchers to analyse and mine data that has been created from a host of different public and private organisations and agencies across cities, as well as through citizen-science projects and urban infrastructure-based sensors. Complex issues like housing, transport, social exclusion and environment need to be looked at in a broader context to come up with robust planning and policy solutions and business innovations.
We are delighted that the ESRC has chosen Glasgow to build on the existing academic expertise that exists across a number of academic disciplines".
The Data Research Centres will make data, routinely collected by business and local government organisations, accessible to academics in order to undertake outstanding research in the social sciences in ways that safeguard individuals identities. That research will provide a sound evidence-base to inform policy development, implementation, evaluation and business innovations in cities. The UBDC will support open data technologies and initiatives to bring data in cities closer to citizens and communities.
This requires not just the development of a safe, secure and efficient system for linking, managing and analyzing such data, founded on secure technologies, but also trust between data owners, researchers and other interested parties including the public.
The new centres, together with the Administrative Data Research Network (ADRN), will make a significant contribution to ensuring the future sustainability of UK research competitiveness; supporting the UK in maximising its innovation potential and driving economic growth.
The UK Minister for Universities and Science David Willetts MP said: Data is a huge priority for government as it has the potential to transform public and private sector organisations, drive research and development, increase productivity and innovation, and enable market-changing products and services. The new data research centres will help the UK grasp these opportunities and get ahead in the global race.
Media enquiries:
[email protected]
01413303683
Notes for editors:
- The centres that are being established are: Urban Big Data Research Centre, at the University of Glasgow, Consumer Data Research Centre at Leeds and UCL and Smart Analytics Data Research Centre at Essex University. The ESRC funded UK Data Service will have a key role in supporting and co-ordinating these centres.
- The Economic and Social Research Council (ESRC) is the UK's largest organisation for funding research on economic and social issues. It supports independent, high quality research which has an impact on business, the public sector and the third sector. The ESRCs total budget for 2013/14 is 212 million. At any one time the ESRC supports over 4,000 researchers and postgraduate students in academic institutions and independent research institutes.
- In addition to the 14 million announced, additional funds totalling c.14 million are being provided to these centres from other ESRC capital sources. Collectively, they will benefit from a grants package totalling approximately 28 million.
- The ESRC's Big Data Network has been divided into three phases. In Phase 1 of the Big Data Network the ESRC invested in the development of the Administrative Data Research Network. Phase 2 is focusing primarily on business data and local government data. Phase 3 will focus on social media data and third sector data."
Welcome to the UBDC's latest e-newsletter. We've had a fantastic first half of the year. Our staff continue to present the UBDC's research and services all over the world, including the Big Data Society Conference in Edinburgh, the IASSIST Conference in Minneapolis, the AAG Annual Meeting in Chicago and more.
Recent activities also include seminars and masterclasses with visiting Professors Bob Stimson, Mark S. Fox, Harvey J. Miller, Chris Pettit and Jennifer Evans-Cowley on a range of urban topics. Visit the UBDC website to learn more.
Please read on to view new vacancies, read recent blog articles, and learn about our upcoming events and courses.