Following people through time : an analysis of individual residential mobility biographies

Maarten van Ham’s contribution to this research was partly made possible through the financial support of the EU Marie Curie programme under the European Union's Seventh Framework Programme (FP/2007-2013) / Career Integration Grant n. PCIG10-GA-2011-303728 (CIG Grant NBHCHOICE, Neighbourhood choice, neighbourhood sorting, and neighbourhood effects).The life course framework guides us towards investigating how dynamic life course careers affect residential mobility decision-making and behaviour throughout long periods of individual lifetimes. However, most longitudinal studies linking mobility decision-making to subsequent moving behaviour focus only on year-to-year transitions. This study moves beyond this snapshot approach by analysing the long-term sequencing of moving desires and mobility behaviour within individual lives. Using novel techniques to visualise the desire–mobility sequences of British Household Panel Survey respondents, the study demonstrates that revealing the meanings and significance of particular transitions in moving desires and mobility behaviour requires these transitions to be arranged into mobility biographies. The results highlight the oft-neglected importance of residential stability over the life course, uncovering groups of individuals persistently unable to act in accordance with their moving desires.PostprintPeer reviewe

CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.Comment: AAAI-21 Camera Read

Health, ethics and environment: A qualitative study of vegetarian motivations

This qualitative study explored the motivations of vegetarians by means of online ethnographic research with participants in an international message board. The researcher participated in discussions on the board, gathered responses to questions from 33 participants, and conducted follow-up e-mail interviews with eighteen of these participants. Respondents were predominantly from the US, Canada and the UK. Seventy per cent were female, and ages ranged from 14 to 53, with a median of 26 years. Data were analysed using a thematic approach. While this research found that health and the ethical treatment of animals were the main motivators for participants’ vegetarianism, participants reported a range of commitments to environmental concerns, although in only one case was environmentalism a primary motivator for becoming a vegetarian. The data indicates that vegetarians may follow a trajectory, in which initial motivations are augmented over time by other reasons for sustaining or further restricting their diet

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065