Finding Pearls in London's Oysters

Reades, Jonathan; Zhong, Chen; Manley, Ed; Milton, Richard; Batty, Michael

doi:10.2148/benv.42.3.365

Cited by 24 publications

(26 citation statements)

References 17 publications

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“…Previous studies based on smart-card data have shown that flow volume data are particularly useful for revealing statistical human mobility patterns [2, 17, 19]. Similar analyses in combination with RTD derived that flow volume data can facilitate a better understanding of the HSR system.…”

Section: Resultsmentioning

confidence: 76%

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

et al. 2017

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Big data have contributed to deepen our understanding in regards to many human systems, particularly human mobility patterns and the structure and functioning of transportation systems. Resonating the recent call for ‘open big data,’ big data from various sources on a range of scales have become increasingly accessible to the public. However, open big data relevant to travelers within public transit tools remain scarce, hindering any further in-depth study on human mobility patterns. Here, we explore ticketing-website derived data that are publically available but have been largely neglected. We demonstrate the power, potential and limitations of this open big data, using the Chinese high-speed rail (HSR) system as an example. Using an application programming interface, we automatically collected the data on the remaining tickets (RTD) for scheduled trains at the last second before departure in order to retrieve information on unused transit capacity, occupancy rate of trains, and passenger flux at stations. We show that this information is highly useful in characterizing the spatiotemporal patterns of traveling behaviors on the Chinese HSR, such as weekend traveling behavior, imbalanced commuting behavior, and station functionality. Our work facilitates the understanding of human traveling patterns along the Chinese HSR, and the functionality of the largest HSR system in the world. We expect our work to attract attention regarding this unique open big data source for the study of analogous transportation systems.

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Section: Resultsmentioning

confidence: 76%

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

et al. 2017

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“…Although the data set is very rich in details, it has constraints in time extension due to TfL privacy policy. Regarding seasonal variation, according to related work using data from the same source (Reades et al, 2016) they do not significantly influence the data trends, therefore we are confident with the reliability of the data set used. About the spatial distribution of the records in the data set, we decided to concentrate our analysis within localised areas of approximately 400 metres around the stations of the rail network (Tube and Railways) to obtain spatially detailed results rather than aggregated ones for larger.…”

Section: Data Descriptionmentioning

confidence: 63%

“…The first data set contains information recorded through smart cards. We selected this type of data because it can reliably rep-resent human mobility through the records of individual journeys (Roth et al, 2011, Munizaga and Palma, 2012, Zhong et al, 2014 and it can provide a good estimation of the density of human activity in cities (Zhong et al, 2016, Reades et al, 2016. This transport data set is constituted of a large collection of nonaggregated records.…”

Section: Data Descriptionmentioning

confidence: 99%

Exploring Similarities and Variations of Human Mobility Patterns in the City of London

Sulis

Manley

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods.</p>

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“…Each movement is captured by the data and made available on a minute by minute basis which can be further aggregated into any appropriate but larger temporal unit. The data can be graphed in terms of desire lines between origins and destinations where each line pertains to the number of trips [ 17 ]. We show an example of this for a typical peak hour in figure 2 a .…”

Section: Movement In the High-frequency Citymentioning

confidence: 99%

Visualizing aggregate movement in cities

Batty

2018

Phil. Trans. R. Soc. B

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We argue here that despite the focus in cities on location and place, it is increasingly clear that a requisite understanding of how cities evolve and change depends on a thorough understanding of human movements at aggregate scales where we can observe emergent patterns in networks and flow systems. We argue that the location of activities must be understood as summations or syntheses of movements or flows, with a much clearer link between flows, activities and the networks that carry and support them. To this end, we introduce a generic class of models that enable aggregated flows of many different kinds of social and economic activity, ranging from the journey to work to email traffic, to be predicted using ideas from discrete choice theory in economics which has analogies to gravitation. We also argue that visualization is an essential construct in making sense of flows but that there are important limitations to illustrating pictorially systems with millions of component parts. To demonstrate these, we introduce a class of generic spatial interaction models and present two illustrations. Our first application is based on transit flows within the high-frequency city over very short time periods of minutes and hours for data from the London Underground. Our second application scales up these models from districts and cities to the nation, and we demonstrate how flows of people from home to work and vice versa define cities and related settlements at much coarser scales. We contrast this approach with more disaggregate, individual studies of flow systems in cities that we consider an essential complement to the ideas presented here.This article is part of the theme issue ‘Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour’.

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Finding Pearls in London's Oysters

Cited by 24 publications

References 17 publications

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

Exploring Similarities and Variations of Human Mobility Patterns in the City of London

Visualizing aggregate movement in cities

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