Open AccessBIG BICYCLE DATA PROCESSING: FROM PERSONAL DATA TO URBAN APPLICATIONS
Author(s) -
Chris Pettit,
Scott N. Lieske,
Simone Leão
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 38
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-2-173-2016
Subject(s) - data science , trace (psycholinguistics) , computer science , macro , context (archaeology) , big data , phone , focus (optics) , urban computing , key (lock) , mobile phone , movement (music) , data processing , human–computer interaction , data mining , geography , database , telecommunications , computer security , philosophy , linguistics , physics , archaeology , optics , programming language , aesthetics
Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog. We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing the data and conducting higher volume and more extensive analysis.