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dc.contributor.authorKhakpour, Mehdi
dc.contributor.authorRød, Jan Ketil
dc.date.accessioned2017-08-15T13:00:59Z
dc.date.available2017-08-15T13:00:59Z
dc.date.created2015-08-20T14:31:32Z
dc.date.issued2015
dc.identifier.citationEnvironment and Planning, B: Planning and Design. 2015, 43 (2), 297-319.nb_NO
dc.identifier.issn0265-8135
dc.identifier.urihttp://hdl.handle.net/11250/2450794
dc.description.abstractWe develop a cellular automaton (CA) model to produce spatiotemporal population maps that estimate population distributions in an urban area during a random working day. The resulting population maps are at 50 m and 5 minutes spatiotemporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. The maps indicate that some areas of the city, which are sparsely populated during the night, can be densely populated during the day. The developed CA model assumes that the population transition trends follow dynamics and propagation patterns similar to a contagious disease. Thus, our model designed to change the states of each grid cell (stable or dynamic) in a way that is similar to changes in the condition of individuals who are exposed to an infectious disease (susceptible or infected). In addition, the modeling space is informed by several geographic features, such as the transport routes, land-use categories, and population attraction points. The model is geosimulated for the city of Trondheim in Norway, where the synthetic day population could be validated using an estimated day-population map based on the registered workplace addresses and employee statistics. The generated maps can be used to estimate a value for the population-at-risk in the wake of a major disaster that occurs in an urban area at any time of a day. In addition to assessing exposure to hazards, the resulting maps also reveal movement patterns, transition trends, peak hours, and activity levels. Possible applications range from public safety, disaster management, transport modeling, and urban growth studies to strategic energy distribution planning.nb_NO
dc.language.isoengnb_NO
dc.publisherSAGE Publicationsnb_NO
dc.titleAn attraction-based cellular automaton model for generating spatiotemporal population maps in urban areasnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber297-319nb_NO
dc.source.volume43nb_NO
dc.source.journalEnvironment and Planning, B: Planning and Designnb_NO
dc.source.issue2nb_NO
dc.identifier.doi10.1177/0265813515604262
dc.identifier.cristin1259067
dc.relation.projectNorges forskningsråd: 235490nb_NO
cristin.unitcode194,67,10,0
cristin.unitnameGeografisk institutt
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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