Opportunities at the Mathematics/Future Cities Interface

AbstractWe make the case for mathematicians and statisticians to staketheir claim in the fast-moving and high-impact research eld that isbecoming known as Future Cities. After assessing the Future Citiesarena, we provide some illustrative challenges where mathematicalscientists can make an impact. More than half of the world’s population lives in a city, and this proportionis estimated to reach 60% by 2030 and 70% by 2050 [World Health Organiza-tion, Urban Population Growth, July, 2014]. See Figure 1 for a graphic show-ing our current \megacities."Thanks to the proliferation of smart devices andinterconnected services, cities are gushing with data, much of which relatesto human behavior. City life generates data streams around on-line socialmedia, telecommunication, geo-location, crime, health, transport, air qual-ity, energy, utilities, weather, CCTV, wi- usage, retail footfall and satelliteimaging. Viewing urban centres as \Living Labs" is a powerful new conceptthat is inspiring novel research leading to improved wellbeing and economicgrowth. We argue here that mathematicians can make an impact at the

[1]  D. W. Chambers Agents. , 2002, The Journal of the American College of Dentists.

[2]  Michael Batty,et al.  Agents, Cells, and Cities: New Representational Models for Simulating Multiscale Urban Dynamics , 2005 .

[3]  G. Linden,et al.  People Who Read This Article Also Read... , 2008, IEEE Spectrum.

[4]  V. Dmitriev,et al.  Mathematical models of urban growth , 2011 .

[5]  Peter Grindrod,et al.  Bistability through Triadic Closure , 2012, Internet Math..

[6]  Georgios K. Ouzounis,et al.  Smart cities of the future , 2012, The European Physical Journal Special Topics.

[7]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[8]  M. Batty The New Science of Cities , 2013 .

[9]  Peter Grindrod,et al.  Aperiodic dynamics in a deterministic model of attitude formation in social groups , 2013, ArXiv.

[10]  M. Batty,et al.  City boundaries and the universality of scaling laws , 2013 .

[11]  Tze Meng Low,et al.  Exploiting Symmetry in Tensors for High Performance , 2013, ArXiv.

[12]  Vincent A. Traag,et al.  Dynamical Models Explaining Social Balance and Evolution of Cooperation , 2012, PloS one.

[13]  Nikos D. Sidiropoulos,et al.  Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization , 2013, ArXiv.

[14]  Luciano da Fontoura Costa,et al.  Journal of Complex Networks , 2013 .

[15]  Peter Grindrod,et al.  A dynamical systems view of network centrality , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Zbigniew Smoreda,et al.  The scaling of human interactions with city size , 2012, Journal of The Royal Society Interface.

[17]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[18]  Ralph C. Smith,et al.  Uncertainty Quantification: Theory, Implementation, and Applications , 2013 .

[19]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[20]  M. Batty,et al.  Constructing cities, deconstructing scaling laws , 2013, Journal of The Royal Society Interface.