What would it be like to live in a city administered using the business model of Amazon (or Apple, IKEA, Uber,…)? A new book playfully combines speculative fiction and analysis of 38 different business models when applied to running cities of the future. How to Run a City Like Amazon, and Other Fables, edited by Mark Graham, Rob Kitchin, Shannon Mattern and Joe Shaw, is available in paperback and PDF from Meatspace Press.
My contribution to the book, Cities Need Mass Transit, shows how a highly personalized transportation system envisioned by Tesla and Elon Musk cannot possibly scale to be an effective urban mobility solution.
It took some time to be posted, but you can now enjoy a video of my February 2019 College of Arts and Sciences Science Sundays lecture – “Mobility Matters: Why Sustainable Transportation is Essential for Our Future.” I will still take questions too!
New publication: Miller, H.J., Jaegal, Y. and Raubal, M. (2019) “Measuring the geometric and semantic similarity of space-time prisms using temporal signatures,” Annals of the American Association of Geographers, 109, 730-753.
Well-established techniques exist for measuring the similarity of space–time paths. These measures support clustering and aggregation of space–time paths as well as moving objects database queries based on similar movement patterns or semantics. Little attention has been paid, however, to the analogous problem of measuring space–time prism (STP) similarity, despite comparable applications. This article presents and evaluates a method for measuring STP similarity through dimensionality reduction that leverages their inherent temporal ordering. The technique sweeps an STP along the time axis and derives one-dimensional temporal signatures based on a measured STP property that captures its geometry or semantics. These temporal signatures can be visualized directly as curves. We can also apply existing space–time path similarity measures to these signatures. To demonstrate the feasibility of this approach, we perform two sets of experiments measuring geometric and semantic similarity among STPs and assess the information within these curves using visualization, Fréchet distances, and clustering techniques. Results suggest that the temporal signature curves capture meaningful similarities and differences among STPs.