Garcia, L;
Lansley, G;
Calnan, B;
(2017)
Modelling Spatial Behaviour in Music Festivals Using Mobile Generated Data and Machine Learning.
In:
GISRUK 2017 Proceedings.
Geographical Information Science Research UK (GISRUK): Manchester, UK.
Preview |
Text
GISRUK - Luis Mejia V4.pdf - Published Version Download (259kB) | Preview |
Abstract
This study explores the utility of location data collected from a mobile phone app as a means of modelling spatial behaviour for consumer analysis, focusing on data from a music festival. Our aim was to harvest geo-temporal variables from the app data to model when individuals visit catering services across the site. Using Random Forest and Artificial Neural Networks machine learning algorithms, we presented an efficient means of simulating the popularity of bar areas within the festival site across time. The research demonstrates that with an appropriate methodology, mobile app data can provide useful insight for service provision planning.
Type: | Proceedings paper |
---|---|
Title: | Modelling Spatial Behaviour in Music Festivals Using Mobile Generated Data and Machine Learning |
Event: | The 25th GIS Research UK (GISRUK) Conference |
Location: | The University of Manchester |
Dates: | 18 April 2017 - 21 April 2017 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://huckg.is/gisruk2017/GISRUK_2017_paper_67.pd... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | crowd dynamics, spatio-temporal, mobile data, machine learning, feature engineering |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS |
URI: | https://discovery.ucl.ac.uk/id/eprint/1556510 |
Archive Staff Only
View Item |