Using Big Data for Human Mobility Patterns - Examining how Twitter data can be used in the study of human movement across space

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dc.contributor.authorStolf Jeuken, Gustavo
dc.contributor.departmentChalmers tekniska högskola / Institutionen för energi och miljösv
dc.contributor.departmentChalmers University of Technology / Department of Energy and Environmenten
dc.date.accessioned2019-07-03T14:30:34Z
dc.date.available2019-07-03T14:30:34Z
dc.date.issued2017
dc.description.abstractDemands for transportation are growing at a fast pace in countries that are experiencing rapid economic growth and urbanisation, such as China, India, Brazil, and Africa. Understanding the spatial and temporal distribution of people and the activities they participate is essential for urban planning, travel demand forecasting, and infrastructure investment. This thesis explores ways in which Twitter data can be useful to understand some important aspects of human mobility, including total travel distance, patterns of mobility and communities. Raw Twitter data was processed to extract relevant information on space and time dimensions and we compare the results across all studied geographies. This information is also fed into a Continuous Time Random Walk (CTRW) model to estimate the average annual distance travelled by people on the same geographies, and we use travel survey data to validate our results. Origin-Destination Matrices (ODM) are generated and the patterns of mobility are visualised on a map and with Rose Diagrams. Finally we use a community detection algorithm to better understand its dynamics of these networks. The validity of our estimates may critically depend on the mathematical models we selected and careful interpretations of the results. Important future work can include continued refinements of our mathematical models to accurately represent total travel distance, identify biases, and further understand how demographics and characteristics of urban infrastructure affect travel demands and mobility patterns.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/250155
dc.language.isoeng
dc.relation.ispartofseriesRapportserie för Avdelningen för fysisk resursteori : FRT 2017:07
dc.setspec.uppsokLifeEarthScience
dc.subjectBuilding Futures
dc.subjectInformations- och kommunikationsteknik
dc.subjectTransport
dc.subjectHållbar utveckling
dc.subjectData- och informationsvetenskap
dc.subjectBuilding Futures
dc.subjectInformation & Communication Technology
dc.subjectTransport
dc.subjectSustainable Development
dc.subjectComputer and Information Science
dc.titleUsing Big Data for Human Mobility Patterns - Examining how Twitter data can be used in the study of human movement across space
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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