Two new volumes in big data, cities and regions, with a strong spatial focus. The first Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics’ is edited by Piyushimita (Vonu) Thakuriah, Nebiyou Tilahun, and Moira Zellner and published by Springer. This has some 30 key articles on a variety of methods including one by Greg Erhardt, Oliver Lock, Elsa Arcaute and myself called ‘A Big Data Mashing Tool for Measuring Transit System Performance’.
The second book Big Data for Regional Science edited by Laurie Schintler and Zhenhua Chen focuses the argument on regions as well in particular regional science, and contains some 28 chapters including a foreword by myself. There is a lot of food for thought here and the two books provide rather a good snapshot of the state of the art.
Written a paper on how we can compare and even integrate space syntax with spatial interaction. You can get it here by clicking on this link or on the image above. This is based on the notion that we need to develop a way of disentangling the underlying planar graph of the street network into components that when put together either lead to operations on the planar graph itself as in spatial interaction or on the dual of this graph between the streets which is the graph used in space syntax. The link is obvious when developed in this way but the ways of integration are somewhat convoluted. We develop these ideas for some simple hypothetical graphs and make comparisons of the various accessibilities associated with these graphs which appear both in space syntax and spatial interaction. We then develop a semi-real application using data on the nearest neighbour generic graph for Greater London and this reveals the problems of specifying this graph in the first place. Our comparisons with real data are disappointing for many obvious reasons but what I think this paper does is throw light on space syntax and on ideas about accessibility, suggesting that we need a sustained effort to develop the right sorts of underlying graphs from which space syntax can be developed. We are only at the beginning of the process and in space syntax, we have not really explored the properties of the underlying graphs in any depth hitherto. This establishes directions for further research. Click here for the paper.
Chen Zhong is leading CASA’s work in measuring variability and regularity in big data from automated capture of travel demand on subway systems in three world cities. This paper in PLOS One is our first on a comparison of tap in and tap out data in London, Singapore and Beijing where we show how variability is greater in London than Singapore, then Beijing. We argue that this is likely to be due to the much grater crowding on the London than the other systems but also that demand management is less at subway stations than in Singapore and Beijing. Thus the culture of these places and how people travel plays a part. You can get the PDF directly with the online version here
Here is what we say about this work from the paper’s abstract:
“To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable. “