BEGIN seminar – Dr David McArthur

Date: 18 February 2020
Time: 15:00-16:00
Venue: School VI

Understanding and promoting cycling using crowdsourced data

Cycling is increasingly seen as a way of dealing with a variety of social problems: from dealing with the climate emergency to improving public health. Unfortunately, in the UK cycling is not a common mode choice. Governments have made money available to encourage more people to get on their bikes. A large portion of this money has been spent on new infrastructure. However, it hasn’t always been easy to evaluate whether this is effective due to a lack of appropriate data. In recent years, the proliferation of smartphones and activity-tracking apps such as Strava has made new, detailed mobility data available to researchers. In this talk, I will present some of the ways we have been making use of this data at the Urban Big Data Centre (UBDC).

Biography

David McArthur is Senior Lecturer in Transport Studies and Associate Director of the Urban Big Data Centre at the University of Glasgow. An economist by training, his current work looks at how new and emerging forms of data can be used to better understand cities; with a particular focus on the promotion of walking and cycling as modes of transport.

 

 

BEGIN seminar – Vanessa Brum-Bastos

Date: 21 January 20120
Time: 15:00-16:00
Venue: School VI

Movement analytics: using geospatial temporal data to understand behavior

Movement analytics has been boosted in the recent years by the ubiquitous availability and quality of spatio-temporal data on people and wildlife. Movement ecology and human mobility are the two main application areas of movement analytics, the first one aims to understand wildlife behavior for conservation purposes mostly, whilst the second one looks at human movement to improve transportation and urban planning, particularly in the context of smart cities. Location-based services and GPS trackers are constantly creating massive data-sets on individuals’ locations at specific timestamps. These data-sets can be analyzed to extract movement patterns, which in conjunction with contextual data can lead to a further understanding of behavior.  In this seminar, Dr Brum-Bastos will present her work on the influence of the weather on human movement in Scotland – UK, bicycling ridership patterns in San Francisco – US and the impact of e-scooters in Tempe – US.

Biography

Vanessa Brum-Bastos is a Research Fellow at the School of Geography and Sustainable Development at the University of St Andrews. Currently, she works with Dr Urska Demsar on the project Uncovering the Mechanisms of Migratory Bird Navigation with Big Data Analytics funded by the Leverhulme Trust. Dr Bastos research focuses on the development and implementation of Context-Aware Movement Analysis (CAMA) to further understand behavior from movement data. More specifically, she is interested in combining movement data with environmental and socio-economic variables to understand how different factors can influence human mobility and wildlife behavior. This knowledge is critical for planning equalitarian sustainable transportation systems, as well as designing biodiversity conservation plans.

 

 

BEGIN seminar – Theoni Photopoulou

Date: 17 December 2019
Time: 15:00-16:00
Venue: School VI

Using time series models to make biological inferences from animal tracking data

Animal location data can be thought of as time series of individual animal behaviour. This type of animal movement data can be effectively analysed using statistical models for time series to help understand what the animal is “doing”. One class of time series models that has become popular for analysing animal tracking data are hidden Markov models (HMMs). These models fall under the umbrella of state-space models, where we assume that the time series of observations is generated by an underlying “hidden” time series of system states, and that the observations and underlying states are linked in some way. The dependence structure between the observations and underlying, unobserved states allows us to make inferences about the unobserved time series, from the observed one. I will show examples of what we can learn about animal movement data using HMMs, based on two case studies from marine systems: acoustic detections of great white sharks and satellite locations of Weddell seals.

Bio

Theoni Photopoulou is a Newton International Fellow in the School of Biology. She has a background in marine ecology and statistics and splits her time between the Scottish Oceans Institute and the Centre for Research into Ecological and Environmental Modelling.