| 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 |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.805
|
| 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 |
| email |
|
| full text |
 file.pdf (588,687 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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