果冻影院

The 果冻影院 CASA Prize for the Best Paper on Spatial Analysis, in memory of Sinesio Alves Junior, has been awarded at GISRUK 2017.

Dr Adam Dennett presented the 2017 prize for the best paper on spatial analysis, in memory of Sinesio Alves Junior,听to Luis Francisco Mejia Garcia听for his paper entitled: 鈥淢odelling Spatial Behaviour in Music Festivals Using Mobile Generated Data and Machine Learning鈥澨�(with co-authors Guy Lansley and Ben Calnan).

CASA has presented this award annually at GISRUK since 2010 in memory of Sinesio Alves Junior (pictured above).听 Junior, as he was affectionately known, is a much missed friend who joined CASA in 2001 as a Doctoral Researcher and member of staff.

Previous winners of this prestigious GISRUK award have been:

Kaisa Lahtinen for "Using Geographically Weighted Regression to Explore Spatial Variation in Survey Behaviour" in听2016 held at the University of Greenwich

Alexandros Alexiou for "The Role of Geographical Context in Building Geodemographic Classifications" in听2015 held at the University of Leeds

Sarah Wise for "Agent Based Modelling and GIS: Exploring our New Tools in a Disaster Context" in听2014 held at the University of Glasgow

Dan Olner, Alison Heppenstall and Gordon Mitchell for "Modelling 听and 听visualising 听the 听impacts 听of 听increasing 听fuel 听costs on 听the UK 听spatial 听economy in听2013 held at the University of Liverpool

Suzanne Mavoa, Melody Oliver, Nicola Tavae and Karen Witten for 鈥淯sing GIS to integrate children鈥檚 walking interview data and objectively physical activity data鈥� in听2012 held at Lancaster University

Heshan Du, Suchith Anand, Mike Jackson, Didier Leibovici, Jeremy Morley and Glen Hart for "Geographical Information Integration from Disparate Sources" in听2011 held at Portsmouth University听and

Didier Leibovici, Lucy Bastin, Suchith Anand, Jerry Swan, Gobe Hobona and Mike Jackson for "Spatially Clustered Associations in Health GIS" in听2010 held at 果冻影院.

Below is a summary of the 2017 winning paper and you can access the full paper by clicking the link below:

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.听