Assessing the impact of major historical events on urban landscapes via local entropy measures

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dc.contributor.authorMazzamurro, Matteo
dc.contributor.authorGuo, Weisi
dc.date.accessioned2021-10-27T14:17:21Z
dc.date.available2021-10-27T14:17:21Z
dc.date.issued2021-10-15
dc.description.abstractIn this paper we show how Shannon entropy, an intuitive and versatile measure of uniformity of a probability distribution, can be adapted to quantify the heterogeneity of land use and population density in and around human settlements. Using a raster data set of estimates of historical population density and land use, we show that local entropy measures capture salient aspects of the evolution of urban systems. Through the case studies of the UK, India, and Italy we reconnect the temporal evolution of the measures to some of the main socioeconomic and political changes and epidemic events these countries went through during the last three centuries. We argue that the diffusion of technological innovations is more apparently correlated to changes in the measures than epidemic events in themselves. We discuss the potential significance and limitations of this finding in understanding changes in urban systems in the context of the ongoing COVID-19 pandemic.en_UK
dc.identifier.citationMazzamurro M, Guo W. (2021) Assessing the impact of major historical events on urban landscapes via local entropy measures. In: 2021 IEEE international Smart Cities Conference (ISC2), 7-10 September 2021, Virtual Eventen_UK
dc.identifier.isbn978-1-6654-4920-5
dc.identifier.issn2687-8860
dc.identifier.urihttps://doi.org/10.1109/ISC253183.2021.9562899
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17212
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdata analysisen_UK
dc.subjectresilienceen_UK
dc.subjectland useen_UK
dc.subjectentropyen_UK
dc.titleAssessing the impact of major historical events on urban landscapes via local entropy measuresen_UK
dc.typeConference paperen_UK

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