Guest Editorial: Modelling Urban Behaviour

Abstract

This part special issue comprises three papers which originally formed the basis of presentations given at the 21st GIS Research UK (GISRUK) conference, held in Liverpool in April 2013. Each of the articles presents research which considers how individuals operate in, and react to, urban environments. More specifically, the papers focus on approaches to modelling the presence of people in urban environments at different times of day (Smith et al.) and the formation of spatial knowledge and decision-making (Manley, and Panagiotis et al.). An understanding of where people travel and what conditions their movement through urban environments is crucial in multiple domains, such as planning the development of transportation and place-specific services, providing information which helps individuals navigate their way through urban space, and mitigating risks of natural or human-induced disasters. The first paper, by Smith et al., considers a core theme in contemporary quantitative population research – how can we move from a detailed understanding of just residential (or night time) geographies, as represented by traditional census outputs, to one which encompasses the multiple activity spaces within which people operate. The paper utilises a spatio-temporal gridded population model constructed using the SurfaceBuilder247 software, making use of estimated retail activity and the retail workforce. The derived grids provide estimates of the total population for 200m cells for any specified time point and these are then used to assess exposure to flood risks. The analysis focuses on an area around the city of Southampton and it shows marked geographical variations in the population across the day, and thus in the population exposed to flood risks. Such approaches have considerable potential for better managing natural hazards. More generally, the use of multiple sources of information to derive spatially-detailed estimates of the presence and movements of people in the way represented by this study offers a powerful new framework for understanding how Appl. Spatial Analysis (2016) 9:141–143 DOI 10.1007/s12061-016-9195-1