Urban Analytics (RGS-IBG 2016) - Session Details
This session will take place at the Royal Geographical Society Annual International Conference 2016 (RGS 2016). London - Tuesday 30 August to Friday 2 September 2016. The original call for papers is here.
A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:
- New methodologies for tackling large, complex or dirty data sets;
- Case studies involving analysis of novel or unusual data sources;
- Policy analysis, predictive analytics, other applications of data;
- Intensive modelling or simulation applied to urban areas or processes;
- Individual-level and agent-based models (ABM) of geographical systems;
- Validating and calibrating models with novel data sources;
- Ethics of data collected en masse and their use in simulation and analytics.
1. Overcoming noise and volatility in urban datasets by applying learning algorithms
Author: Gideon D.P.A. Aschwanden, Melbourne School of Design, The University of Melbourne
The two case studies presented show the application of the SOM (self- organizing map) method to infer aerosol exposure of pedestrians from urban indicators (volatile data) and to detect changes in neighborhood configurations from longitudinal urban transportation data (noisy data). Concentrations of particles and other aerosols publish today are average measurements from stations on rooftops that don’t represent pedestrian exposure. The reason lies in the high temporal and spatial volatility of ground level concentrations. The SOM method, an unsupervised learning algorithm, is applied to find associations between 8 aerosol concentrations and 67 urban indicators covering typology, morphology, land use and street design. The method showed clear correlations between the indicators and the aerosol concentrations and enabled us to infer ground level concentrations in places where no measurements were conducted earlier.
The second case applies the same method to transportation and geographic indicators. In this case the methods multi dimensional sorting ability is used to identify similar places. Places with similar transport and functional patterns are called neighborhoods. The method uses 55 dimension on distance, time traveled, purpose of the trip and characteristics of origin and destination to sort 9747 statistical areas in the metropolitan area in a virtual space and tracks their changes over the course of 3 years. The result highlights that neighborhoods borders are not aligned with municipality borders and are shifting even in this short period of observation. These examples highlight how new algorithm can overcome analytical problems in understanding the urban fabric. This empirical understanding allows anticipating outcomes of planning and design decisions more closely.
2. The Growth and Composition of Shopping Centres in Greater London South of the Thames during the 19th Century.
Author: Michael P Collins
The hierarchy of London’s shopping centres underpins the Greater London Authority’s proposed strategic retail planning policies, notwithstanding the reservations expressed by the Layfield Panel of Inquiry in 1973. Recent urban morphological research has noted the ‘persistence’ of these centres. Historically there was a well defined hierarchy of shopping centres in south London providing a wide range of retail facilities, professional and commercial services. The number of centres increased from 33 in 1824 to 93 in 1901 reflecting the growth of population, rising income levels, changing living conditions and consumer demands and preferences, improved transportation, and the ‘revolution’ in retailing. New forms of retailing (multiples, department stores, co-operatives) and new forms of large buildings emerged. Store location presented difficulties for new market entrants and often led to ‘clustering’.
Stepwise regression analysis has been used to examine the relationship between resident population and the size and composition of these shopping centres in 1824, 1872 and 1901.These results present cross-sections in time of south London’s changing spatial structure and evolving hierarchy of retail activity. Some of the shopping centres identified in 1824 are now designated as either metropolitan, major and district centres in the current London Plan prepared by the Greater London Authority. They are the designated main locations for commercial activity outside the Central Area Zone. Together with local neighbourhoods they constitute the main focal points for most Londoners’ sense of place and local identity.
3. Simulating Urban Flows to Estimate the Disease Burden of Air Pollution
Author: Nick Malleson, School of Geography, University of Leeds
An understanding of how people move around cities is vital for building up reliable estimates of the population at risk for phenomena that vary in space and time. For example, to reliably assess the disease burden of air pollution on a population it is important to understand when and where the activities of individuals intersect with pollution hotspots. However, modelling dynamic populations is extremely difficult as most well-established data sources contain sparse information about non-residential activities. Fortunately, emerging data sources such as those arising through the use of social media or loyalty cards hold the promise of providing more reliable information about non-residential daily activities. The challenge, therefore, is to create a high-resolution model of urban flows that is able to take advantage of good quality residential and non-residential data sources.
This research proposes an original approach: to use agent-based modelling as the tool to bring together disparate data sources and create an accurate, high-resolution model of individual-level daily urban flows. The model will draw on advances from disciplines such as meteorology to dynamically calibrate the agent-based model as new data streams become available. Ultimately the results of the simulation will be married to estimates of pollutant concentrations to estimate the disease burden of some pollutants. Preliminary results illustrate that the model holds promise as a reliable tool for modelling the ambient urban population.
4. Improving analysis of passively collected data through probabilistic analytics
- Roger Beecham, giCentre, City University London
- Cagatay Turkay, giCentre, City University London
The increasing and unprecedented volume of digital socially produced data is rapidly changing the way that knowledge is being derived. The consequences of these new data for understanding social behaviour are significant: “[the social sciences] can finally have access to masses of data that are of the same order of magnitude as that of their older sisters, the natural sciences” (Latour, 2007). A particularly rich source of such data comes from transportation. We previously studied over 20 million journeys made through the London bikeshare scheme during a two-year period. Attending to individual users’ journeys, we characterised distinct travel behaviours and, paying attention to the spatiotemporal context behind behaviours, speculated about users’ motivations and preferences. The task of formally explaining behaviours and preferences is more problematic. As with most passively collected data, the bikeshare dataset is not necessarily collected for the purpose of studying behaviour: very little is known about the population of London bikeshare cyclists, their personal circumstances and cycling history. Whilst this fact might make formal explanation of behaviour appear intractable, we describe a set of analytical measures that augment the bikeshare dataset with important context behind individual users. Using this data- driven context, we make probabilistic claims about observed behaviour, as well as outline opportunities and limitations of this approach.
5. Analysis of Urban Risks in a Complex Adaptive System
Author: Yukyung Oh, King’s College London
High population densities and diverse economic activities in urban areas have created a range of environmental impact including air pollution, soil contamination, losses of biodiversity and health problems (Alberti et al. 2003; Grimm et al. 2008). Urban area is a complex adaptive system wherein multiple components are dynamically and continuously interconnected with the impacts from external stressors (Eidelson, 1997), encompassing wide and diverse ranges of social and natural patterns within a civil society. In addition, the system maintenance basically requires an external stimulus and the capacity to self-organise and to adapt to radical changes (Emison, 1996; Levin, 2003). The concept of such a complex adaptive system has limits to be explained with a sole methodology. In other words, either quantitative or qualitative is not enough for understanding analytics of urban risks and disaster. For instance, urban open spaces provide diverse ecosystem service functions (e.g. carbon sequestration, alleviation of air pollution, and recreational activities). Such analysis of urban ecosystem services functions can be conducted, figuring out connectivity and features of open spaces in urban areas. Obtained spatial data via open street map or authorised data agencies is usually handled in a geographical software (e.g. QGIS or ArcGIS). In addition, semi-constructed interview method or such more objective indirect interview method as analytical hierarchical process should be followed so as to effectively and strategically manage open spaces. Such a combined methodology would allow urban policy makers to consider management of open spaces in connection with the function of ecosystem services in the framework of complex adaptive.
- Nick Malleson, School of Geography, University of Leeds
- Alex Singleton, School of Environmental Sciences, University of Liverpool
- Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)
- Ed Manley, Centre for Advanced Spatial Analysis (CASA), UCL.
- Alison Hepenstall, School of Geography, University of Leeds