of Tweets e.g. Home, Work, Retail, Leisure, other What are the key drivers of variation in certain areas and when do they happen Why variations in Tweet locations occur between Days-of-the-Week, Time- of-Day and Seasons Better understand the movement of people at a micro-level Using more than just residential location to improve service provision Colour key: Partially complete Not complete New lines of enquiry
Reduced to 1.5 million – placing a bounding box around Leeds Removed duplicates/robots etc Further reduction to dataset Using the full dataset proved ineffective when undertaking highly detailed analysis Most people only tweeted a few times Wanted to focus on frequent tweeters as they will provide greater geographic information Therefore removed all Tweets from any users who had a frequency below 200
Lack of key phrases to look for Things such as LUFC were among the most popular Issues with different spellings, abbreviations etc Decided the geolocations of the Tweets would be better suited to the study
the greatest potential to provide the information required Therefore require: A large number of Tweets Suitable spread across Leeds – not all at one point Top and Tail 15% either side based on combined standard deviation between X and Y coordinates of each user Results in final dataset of 708 users with 376,000 Tweets
away from the centre during non-working hours. The centre of Leeds maintains its status as the primary area for people to Tweet in. Eastern LSOAs are more residential - where people tweet during non-working hours
is crap for geo* But can provide testbed for ideas/code GPS data is much more promising Leading to the need to model 'activity spaces' and 'intrazonal flows' not captured by spatial interaction models Based on Brownian motion + Ecological mathematics and theory
A. Myers. ‘Robust State– Space Modeling of Animal Movement Data’. Ecology 86, no. 11 (1 November 2005): 2874–80. doi:10.1890/04-1852. Lovelace, Robin, Martin Clarke, Philip Cross, and Mark Birkin. ‘From Big Noise to Big Data: Towards the Verification of Large Datasets for Understanding Regional Retail Flows’. Geographical Analysis, 2015. Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. ‘The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning’. arXiv:1509.04425 [cs], 15 September 2015. http://arxiv.org/abs/1509.04425. Simini, Filippo, Marta C González, Amos Maritan, and Albert-László Barabási. ‘A Universal Model for Mobility and Migration Patterns.’ Nature, February 2012, 8– 12. doi:10.1038/nature10856.