The Leeds Institute for Data Analytics is please to announce the next seminar in the series showcasing data analytics.

We are pleased to welcome Professor Vernez-Moudon, Professor of Architecture, Landscape Architecture, and Urban Design and Planning; Adjunct Professor of Epidemiology and Civil and Environmental Engineering at the University of Washington, Seattle, where she also directs the Urban Form Lab (UFL).

We also welcome Professor Susan Grant-Muller, Professor of Technologies & Informatics and Dr. Gillian Harrison, Research Fellow in the Institute for Transport Studies, from the University of Leeds.

Schedule

15:30 – 15:50 – Susan Grant-Muller: Health Equity Outcomes Arising from Transport Scheme Innovation, Utilizing New Generation Mobility Data

15:50 – 16:40 – Anne Vernez-Moudon: Built Environment and Health in the Age of Big Data

16:40 – 17:00 – Gillian Harrison: Using system dynamics modelling to identify suppressors and supporters of electric vehicle uptake

Following the seminar there is a networking reception with drinks and nibbles in the LIDA Staff Room on level 11.

All are welcome and we look forward to seeing you there.

Abstracts

Anne Vernez-Moudon

Built Environment and Health in the Age of Big Data

Evidence is mounting that the built environment of where people live, work and play has an influence on their health. As a result, public health people are reaching out to city and transport planners in an effort to make healthier cities. However, how exposure to the built environment affects health still needs clarification. Past research focused on how the home built environment was associated with health. Yet the residential context is only a small part of daily environment hosting people’s activities. There is a need to examine associations between health behaviors and built environment in a precise spatio-temporal framework. This need is beginning to be addressed by rapid advances in sensor technology, which, combined with the ubiquitous use of smartphones loaded with Apps, are now providing not only “big” but also “detailed” data on people’s location, mobility and activity patterns. Sensors can also capture environmental (air breathed, ambient noise, etc.) and health-related conditions (physical activity, heart rate, stress, etc.).

Parallel advances in geographic information systems (GIS) bring detailed data on the built and transportation environment. So far, few “big data” researchers have used these data, opting instead to employ “black box” short cuts to characterize both mobility behaviors (e.g., to detect travel modes) and built environment characteristics (e.g., to measure walkability). This presentation reviews practical approaches to attach built environment data to the large numbers of GPS points included in big data. The aim is to capture spatially continuous, time-based measures of exposure to built environment attributes along multi-day GPS traces. The presentation also introduces ways to model the built environment in GIS in order to streamline environmental data capture along GPS traces.

Anne Vernez Moudon is Professor of Architecture, Landscape Architecture, and Urban Design and Planning; Adjunct Professor of Epidemiology and Civil and Environmental Engineering at the University of Washington, Seattle, where she also directs the Urban Form Lab (UFL).

Gillian Harrison

Using system dynamics modelling to identify suppressors and supporters of electric vehicle uptake

The EC JRC Powertrain Technology Market Agent Model (PTTMAM) is an extensive system dynamics based model of the EU automobile market, encompassing four conceptual market agent groups (users, manufacturers, infrastructure, authorities), the 28 member states of the EU and both current and future powertrains of the light duty vehicle market up to 2050.

Electric powertrain vehicles may be on the brink of commercialisation; however it is still early days in the understanding of how the market will develop and what competition to expect between the new technologies as we move from the fossil fuel dominated ICEV towards numerous technological options, each offering slightly differing utilities to the user. In this presentation we explore variables related to market conditions and policy interventions, sensitivities to infrastructural assumptions and consider in detail the dynamics between different electric vehicle types. Although model-based studies may be limited in their application towards market forecasting, due to future uncertainties, the strength of such research lies within the power to compare similar scenarios and vary individual assumptions, allowing the researcher to identify and isolate triggers of change, in order to glean light on previously unconsidered system feedbacks that may suppress or support technology transition. Traditionally, SD modelling relies heavily on historical data sets and standard formulations, but with new and emerging forms of data, more accurate feedback relationships may be developed.