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id caadria2019_638
authors Willemse, Elias Jakobus, Tuncer, Bige and Bouffanais, Roland
year 2019
title Identifying Highly Dense Areas from Raw Location Data
source M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 805-814
summary In this paper we show how very high-volumes of raw WiFi-based location data of individuals can be used to identify dense activity locations within a neighbourhood. Key to our methods is the inference of the size of the area directly from the data, without having to use additional geographical information. To extract the density information, data-mining and machine learning techniques form activity-based transportation modelling are applied. These techniques are demonstrated on data from a large-scale experiment conducted in Singapore in which tens of thousands of school children carried a multi-sensor device for five consecutive days. By applying the techniques we were able to identify expected high-density areas of school pupils, specifically their school locations, using only the raw data, demonstrating the general applicability of the methods.
keywords ; Machine Learning, Big-data, Location-analysis
series CAADRIA
full text file.pdf (588,687 bytes)
references Content-type: text/plain
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