Data Analytics Bootcamp

I have always dreamed about doing some contribution towards improving the gender balance in technology, which as you may know, is far from ideal. Fortunately the opportunity arose, when Katrina Walker has invited me to teach the “Data Analytics”  bootcamp … Continue reading

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AAG2018: Innovations in Urban Analytics

Call for Papers, AAG2018: Innovations in Urban Analytics

We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.

Session Description

New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.

This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:

  • Agent-based modelling (ABM) and individual-based modelling;
  • Machine learning for urban analytics;
  • Innovations in consumer data analytics for understanding urban systems;
  • Real-time model calibration and data assimilation;
  • Spatio-temporal data analysis;
  • New data, case studies, demonstrators, and tools for the study of urban systems;
  • Complex systems analysis;
  • Geographic data mining and visualization;
  • Frequentist and Bayesian approaches to modelling cities.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: http://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).

Key Dates
  • 18 October, 2017: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 23 October, 2017: Session finalization and author notification.
  • 25 October, 2017: Final abstract submission to AAG, via the link above. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson (n.s.malleson@leeds.ac.uk). Neither the organizers nor the AAG will edit the abstracts.
  • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
  • 9-14 April, 2018: AAG Annual Meeting.
Session Organizers
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AAG2018: Innovations in Urban Analytics

Call for Papers, AAG2018: Innovations in Urban Analytics

We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.

Session Description

New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.

This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:

  • Agent-based modelling (ABM) and individual-based modelling;
  • Machine learning for urban analytics;
  • Innovations in consumer data analytics for understanding urban systems;
  • Real-time model calibration and data assimilation;
  • Spatio-temporal data analysis;
  • New data, case studies, demonstrators, and tools for the study of urban systems;
  • Complex systems analysis;
  • Geographic data mining and visualization;
  • Frequentist and Bayesian approaches to modelling cities.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: http://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).

Key Dates
  • 18 October, 2017: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 23 October, 2017: Session finalization and author notification.
  • 25 October, 2017: Final abstract submission to AAG, via the link above. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson (n.s.malleson@leeds.ac.uk). Neither the organizers nor the AAG will edit the abstracts.
  • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
  • 9-14 April, 2018: AAG Annual Meeting.
Session Organizers
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Big Data, Agents and the City

In the recently published book “Big Data for Regional Science” edited by Laurie Schintler and  Zhenhua Chen, Nick Malleson, Sarah Wise, and Alison Heppenstall and myself have a chapter entitled: Big Data, Agents and the City. In the chapter we discuss how big data can be used with respect to building more powerful agent-based models. Specifically how data from say social media could be used to inform agents behaviors and their dynamics; along with helping with the calibration and validation of such models with a emphasis on urban systems. 
Below you can read the abstract of the chapter, see some of the figures we used to support our discussion, along with the full reference and a pdf proof of the chapter. As always any thoughts or comments are welcome.

Abstract:

Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD.  In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.

Keywords: Agent-based models, Big Data, Emergence, Cities.

The growth in Agent-based modeling -from search results of Web of Science and Google Scholar.
Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users.

Full Reference:

Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)

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Big Data, Agents and the City

In the recently published book “Big Data for Regional Science” edited by Laurie Schintler and  Zhenhua Chen, Nick Malleson, Sarah Wise, and Alison Heppenstall and myself have a chapter entitled: Big Data, Agents and the City. In the chapter we discuss how big data can be used with respect to building more powerful agent-based models. Specifically how data from say social media could be used to inform agents behaviors and their dynamics; along with helping with the calibration and validation of such models with a emphasis on urban systems. 
Below you can read the abstract of the chapter, see some of the figures we used to support our discussion, along with the full reference and a pdf proof of the chapter. As always any thoughts or comments are welcome.

Abstract:

Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD.  In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.

Keywords: Agent-based models, Big Data, Emergence, Cities.

The growth in Agent-based modeling -from search results of Web of Science and Google Scholar.
Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users.

Full Reference:

Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)

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Big Data and Design: More Baboon, Less Unicorn

I recently had the pleasure of giving a Creative Mornings talk. Each month there is a new theme that the presenters need to refer to – mine was “fantasy” so I chose to open with one of my favourite fantasy creatures: the unicorn. It’s a talk about the creative process behind Oliver Uberti and I’s […]

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The Full Stack: Tools & Processes for Urban Data Scientists

Recently, I was asked to give talks at both UCL’s CASA and the ETH Future Cities Lab in Singapore for students and staff new to ‘urban data science’ and the sorts of workflows involved in collecting, processing, analysing, and reporting on … Continue reading 

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New Paper: User-Generated Big Data and Urban Morphology

Continuing our work with crowdsourcing and geosocial analysis we recently had a paper published in a special issue of the  Built Environment journal entitled “User-Generated Big Data and Urban Morphology.”
The theme of the special issue is: “Big Data and the City” which was guest edited by Mike Batty and includes 12 papers.  To quote from the website

“This cutting edge special issue responds to the latest digital revolution, setting out the state of the art of the new technologies around so-called Big Data, critically examining the hyperbole surrounding smartness and other claims, and relating it to age-old urban challenges. Big data is everywhere, largely generated by automated systems operating in real time that potentially tell us how cities are performing and changing. A product of the smart city, it is providing us with novel data sets that suggest ways in which we might plan better, and design more sustainable environments. The articles in this issue tell us how scientists and planners are using big data to better understand everything from new forms of mobility in transport systems to new uses of social media. Together, they reveal how visualization is fast becoming an integral part of developing a thorough understanding of our cities.”

Table of Contents

In our paper we discuss and show how crowdsourced data is leading to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics. Specifically how such data can provide us information pertaining to linked spaces and geosocial neighborhoods. We argue that a geosocial neighborhood is not defined by its administrative boundaries, planning zones, or physical barriers, but rather by its emergence as an organic self-organized social construct that is embedded in geographical spaces that are linked by human activity. Below is the abstract of the paper and some of the figures we have in it which showcase our work.

“Traditionally urban morphology has been the study of cities as human habitats through the analysis of their tangible, physical artefacts. Such artefacts are outcomes of complex social and economic forces, and their study is primarily driven by traditional modes of data collection (e.g. based on censuses, physical surveys, and mapping). The emergence of Web 2.0 and through its applications, platforms and mechanisms that foster user-generated contributions to be made, disseminated, and debated in cyberspace, is providing a new lens in the study of urban morphology. In this paper, we showcase ways in which user-generated ‘big data’ can be harvested and analyzed to generate snapshots and impressionistic views of the urban landscape in physical terms. We discuss and support through representative examples the potential of such analysis in revealing how urban spaces are perceived by the general public, establishing links between tangible artefacts and cyber-social elements. These links may be in the form of references to, observations about, or events that enrich and move beyond the traditional physical characteristics of various locations. This leads to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics.”

Keywords: Urban Morphology, Social Media, GeoSocial, Cities, Big Data.

City Infoscapes – Fusing Data from Physical (L1, L2), Social, Perceptual (L3) Spaces to Derive Place Abstractions (L4) for Different Locations (N1, N2).
Recreational Hotspots Composed of “Locals” and “Tourists” with Perceived Artifacts Indicating “Use” and “Need”. (A) High Line Park (B) Madison Square Garden.



Moving from Spatial Neighborhoods to Geosocial Neighborhoods via Links.

The Emergence of Geosocial Neighborhoods after the in the
Aftermath of the 2013 Boston Marathon Bombing

Full  Reference: 

Crooks, A.T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P. and Stefanidis A. (2016). “User-Generated Big Data and Urban Morphology,”  Built Environment, 42 (3): 396-414. (pdf)

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