Big Data Techniques to Improve Learning Access and Citizen Engagement for Adults in Urban Environments

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 modern urban city, and thus support the development of the learning city. It concludes with a call for a more three-dimensional view of citizen-learners’ daily activity and mobility, such as satellite, mobile phone and active travel application data, alongside administrative data linkage to further explore lifelong learning participation and success. Policy implications are provided for addressing inequalities, and interventions proposed for how cities might promote equal and inclusive adult learning engagement in the face of continued austerity cuts and falling adult learner numbers

Development and usability analysis of a mixed reality GPS navigator application for the microsoft hololens

The present work aims to perform a comparative usability analysis between two Human- Computer Interaction systems (HCI) for global geolocation (GPS) navigators. The intent is to compare the conventional use of a navigation application on a mobile device, such as a smartphone attached to the dashboard of a vehicle, to an implementation in Mixed Reality (MR) powered by the Head Mounted Display (HMD) Microsoft HoloLens. By connecting the MR device to a local network routed by an ordinary cellular phone, which is connected to a mobile data network, it is possible to ubiquitously acquire the phone’s geolocation data, its magnetometer deviation and a route graph of a navigation Application Programming Interface (API) from its current location to a destination entered by the user. Thus, a series of three-dimensional holograms are created at runtime, geolocated and placed around the user, guiding him through a path indicated on the floor, pertinent to the streets around him that lead to the desired destination. Apart from that, arrows are projected on the way at each crucial point of the path, where some maneuver must be performed, e.g., turning right or taking an exit at a roundabout. In a user experiment, performance and usability were assessed. Results show that users deemed the MR solution to offer a higher visibility both to the oncoming traffic and the suggested route, when compared to the conventional interface, being less attention demanding. EEG readings for most participants also exposed a significantly more demanding focus level for the handheld device. Additionally, an easiness to learn and use was indicated for our system, being almost on par with the already known and highly used application tested.O presente trabalho visa realizar uma análise comparativa de usabilidade entre dois sistemas de interação humano-computador para navegadores de geolocalização global (GPS). Foi almejado comparar o uso convencional do sistema, através de um dispositivo móvel tal qual um smartphone afixado ao painel de um veículo, com uma nova implementação em Realidade Mista potencializada pelo HMD Microsoft HoloLens. Conectando o dispositivo de realidade mista (MR) a uma rede local roteada por um aparelho celular convencional, este conectado a uma rede de dados móvel, foi possível receber ubiquamente os dados de sua geolocalização, de seu magnetômetro e um grafo de rota de uma API de navegação de alta disponibilidade partindo do presente local até um destino inserido pelo usuário. Com isso, é criada em tempo de execução uma série de hologramas tridimensionais geolocalizados ao redor do usuário, guiando-o através de um caminho indicado em seu chão, pertinente às ruas a sua volta que o levarão ao destino desejado. Também são projetadas flechas em seu caminho em cada ponto crucial de seu trajeto, onde deve-se realizar alguma manobra, e.g., dobrar à direita ou tomar uma saída de uma rotatória. Em um experimento realizado com usuários reais, seu desempenho e usabilidade foram aferidos. Resultados mostram que os usuários estimaram que a solução em MR oferecia uma visibilidade maior tanto ao tráfego passante quanto à rota sugerida, em comparação à interface convencional, requerindo menos atenção. Leituras de eletroencefalografia (EEG) na maioria dos participantes indicaram uma demanda significativamente maior de atenção focada no uso do dispositivo móvel. Uma grande facilidade de aprendizado e de uso também foi apontada para nosso sistema, estando quase a par da aplicação móvel altamente conhecida e usada

Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain.

BACKGROUND: Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level. METHODS: We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling. RESULTS: We found high correlations between GSV counts of cyclists ('GSV-cyclists') and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = -0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes. CONCLUSIONS: GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world's population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments

Conducting Stated Choice Experiments within an Immersive Virtual Reality Environment: An Application to the Discrete Choice of Automated versus Normal Taxi

\ua9 2024 Elsevier BV. All rights reserved. This paper describes the methodology set up to measure consumers\u27 preferences in a choice between a fully automated and normal taxi, using a Stated Choice (SC) experiment embedded in an immersive Virtual Reality (VR) environment. VR represents an important tool to allow respondents to "live" their choice with the potential to reduce the typical problem of lack of realism in SC experiments. This paper describes the work done to build the VR-based SC experiment, and discusses challenges and potentialities. The study is applied to the choice of taxi in the city centre of Newcastle upon Tyne in the UK

How useful is GSV as an environmental observation tool? An analysis of the evidence so far.

Researchers in many disciplines have turned to Google Street View to replace pedestrian- or carbased in-person observation of streetscapes. It is most prevalent within the research literature on the relationship between neighborhood environments and public health but has been used as diverse as disaster recovery, ecology and wildlife habitat, and urban design. Evaluations of the tool have found that the results of GSV-based observation are similar to the results from in-person observation although the similarity depends on the type of characteristic being observed. Larger, permanent and discrete features showed more consistency between the two methods and smaller, transient and judgmental features were less consistent. There are some difficulties in using GSV for research purposes including, 1) the fixed point of view, 2) the processing, 3) the quality, and 4) the fixed point in time of the images. These issues have had little discussion by researchers using GSV but could bias their results in some circumstances and therefore should be addressed by researchers using GSV

Massachusetts Urban Bicycle Preparedness

Since 2007, Boston has made tremendous strides in shedding its designation by Bicycling Magazine as one of the “Worst Biking Cities” (Zezima, 2009, p. A12) by designating over 92 miles of bike lanes throughout the city and introducing bicycle initiatives like Boston Bikes, the Hubway bicycle sharing program. These efforts have resulted in a dramatic rise in the number of cyclists in Greater Boston and a decrease in accidents involving bicycles ((Pedroso, Angriman, Bellows & Taylor, 2016). While the quantitative research has been primarily positive, a 2017 survey initiated LivableStreets and the Longwood Area Cyclists of commuters in the Longwood area of Boston reveal fear and anxiety over the claustrophobic riding conditions with motor vehicles and urban infrastructure in desperate need of updating to improve cyclists’ safety (McFarland, 2017). While the Massachusetts Department of Transportation has introduced a public outreach campaign that encourages motorists to be cognizant of the threat they represent to cyclists and pedestrians through the “Scan the Streets for Wheels and Feet” campaign, there hasn’t been a serious push to empower cyclists to prepare themselves for riding in aggressive, high-density urban areas and to advocate for improvements to transportation infrastructure that will promote their safety. This paper addresses the development of an online course that addresses urban bicycle commuter preparedness and how it can be an effective resource for both novice and seasoned commuters to connect and further advance a bicycle culture in Greater Boston. It also explores a potential partnership with a bicycle sharing program such as Hubway to encourage their patrons to participate in the course

Web-based PPGIS application for participatory spatial planning in context of bikeability

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe spatial planning processes are complex and require public participation to get insights about important problems and development of the neighborhood from the communities during final decision-making. The traditional participatory methods offer limited two-way communication just inform the public rather than to obtain suggestions from them and few public can participate due to time & location restrictions. Due to low public empowerment, they do not know how their participation can influence the spatial planning and decision-making process. This study tries to design and develop the web-based Public Participation GIS application with the integration of the internet, public participation, and GIS technologies to increase public participation during spatial planning and decision-making to overcome the limitations of traditional participatory methods. The web-based PPGIS application development is based on open-source technologies and allows the participants to visualize spatial data layer, perform spatial analyses and contribute to increasing and improving the bikeability of the city. The user study experiment is conducted to evaluate the usability and usefulness of the application. The evaluation results show that the web-based PPGIS application is easy to use with a System Usability Scale (SUS) score of 84.6 and an effective approach to increase public engagement and give suggestions on the spatial planning process and decision making