Cyber Cities: Social Media as a Tool for Understanding Cities

Arribas‐Bel, Daniel; Kourtit, Karima; Nijkamp, Peter; Steenbruggen, John

doi:10.1007/s12061-015-9154-2

Cited by 42 publications

(22 citation statements)

References 45 publications

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“…Part of the reason why new methodological approaches have been sought is that new forms of data required slightly different perspectives. Examples of such sources which are used to understand cities and their spatial structure include: location‐based services (e.g., Ferrari et al ; Arribas‐Bel et al ), smart transit cards (Roth et al ), real estate online listings (Rae ; Boeing and Waddell ; Rae and Sener ), or social media (e.g., Arribas‐Bel ), to name just a few. Given their pervasiveness and high degree of penetration among urban populations, a substantial amount of empirical research has utilized data from mobile phone operators with this purpose.…”

Section: The Spatial Structure Of Cities: “Old” and “New” Datamentioning

confidence: 99%

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Arribas‐Bel

Tranos

2017

Geographical Analysis

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Our understanding of the spatial structure of cities has been traditionally shaped by the availability of static data. In the last few years, thanks to improvements in geospatial technology as well as computing storage and power, there has been an explosion of geo‐referenced data, which monitor cities and urban activities in real time. Although this shift in the data landscape promises to change and augment the way we measure, understand, and act on cities, it poses significant methodological challenges and uncovers substantial gaps in the analytics required. This article contributes in this direction by delivering insights on two fundamental fronts: first, we compare several methods that conceptualize both space and time in rather different ways, highlighting their main advantages and limitations; second and more important, we propose a novel approach the Space‐Time Calendar that uncovers, characterizes and visualizes in an explicitly spatial way both fast and slow urban dynamics. We illustrate its advantages using a data set derived from over 2 years of mobile phone activity in Amsterdam (the Netherlands). Our findings highlight the advantages of the Space‐Time Calendar approach, but also the benefits of appropriately matching the methodological approach to the nature of the data at hand.

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Section: The Spatial Structure Of Cities: “Old” and “New” Datamentioning

confidence: 99%

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Arribas‐Bel

Tranos

2017

Geographical Analysis

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“…They include: the development of new area/neighborhood profiles using social media data [88][89][90]; estimates of the mobile population at risk of crime [91]; the identification of "important" places in peoples' lives from mobile telephone data [92]; the detection and delineating of events [93,94]; analysis of regular mobility patterns [95,96]; classification of areas based on their Twitter temporal profile [97]; and a wealth of others. However, examples applied in the context of urban modeling, let alone ABM specifically, are much scarcer.…”

Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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“…In Philadelphia, The Digital On-Ramps app attempts to link the unemployed with training and job opportunities ( Wiig, 2016 ). Arribas-Bel et al (2015) show how the geography of cultural diversity can be established through the language used in Twitter messages. As Letaifa (2015, p. 1416) suggests, “Smart people are the result of ethnic and social diversity, tolerance, creativity, and engagement.” There are also “networked publics” of do-it-yourself urban design proposals and virtual visions of local park planning ( Hollands, 2015 ), maintenance requests and community consultation, and use of social media to participate in graffiti, flash mobs and yarn bombing ( Foth et al, 2016 ).…”

Section: Social Innovationmentioning

confidence: 99%

Smart cities as a platform for technological and social innovation in productivity, sustainability, and livability: A conceptual framework

Kim

Sabri

Kent

2021

Smart Cities for Technological and Social Innovation

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Cyber Cities: Social Media as a Tool for Understanding Cities

Cited by 42 publications

References 45 publications

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Smart cities as a platform for technological and social innovation in productivity, sustainability, and livability: A conceptual framework

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