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Modeling Building Use and Population Distribution Opportunity Using Open Geosocial Data in Urban Areas. GeoComputation 2019

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conference contribution
posted on 01.12.2019, 23:27 by Kevin Sparks, Kelly Sims, Gautam Thakur, Marie Urban, Robert Stewart
Knowing where people are and knowing how the land in urban areas is being used is critical for assessing human mobility patterns, monitoring population health, planning for transportation services, and responding to natural disasters and emergency situations. While geophysical phenomena like land cover are more easily observed through remote sensing, social phenomena like how a city block or building is being used by the population is generally unobservable through standard remote sensing practices. Current methods and land use data by their nature are also difficult to scale to national or global levels. While land use data are often available in large cities, there are abundant data gaps globally. Furthermore, land use data can often appear relatively coarse in its spatial and semantic resolution (i.e. 1km grid cells rather than building level, and "commercial" rather than "coffee shop"). In particular, there is a need to harvest scalable data and develop methods to create global land use maps. To this end, in this work we combine building footprints and Points of Interest (POI) to estimate land use at the building level, including its mixed-use intensity. Next, we propose a method for defining semantic temporal zones referring to Morning, Day, Evening, and Night times unique to a region, based on the cumulative hourly change in the POIs' hours of operation. We then use these POIs' hours of operation to estimate non-residential population distribution opportunity and land use change over a 24-hour period. We validate our results through manual checks of randomly sampled individual buildings using multiple sources. Finally, we discuss the impact of our results on identifying population distribution opportunity and the reliability of estimating building use.



University of Auckland

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GeoComputation 2019