Category: GIS

Opportunity: come and help us create the ExCiteS Social Enterprise!

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The Extreme Citizen Science group, set up about 8 years ago, has developed two main technological infrastructures – Sapelli software to allow data collection by low-literacy participants, and GeoKey, a data management system for community mapping. We have also developed an engagement approach that allows for the co-production of the data collection process, and for … Continue reading Opportunity: come and help us create the ExCiteS Social Enterprise!

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citizen science, Extreme Citizen Science, GIS, Social Enterprise, social entrepreneurship, UCL KT

Papers from PPGIS 2017 meeting: state of the art and examples from Poland and the Czech Republic

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About a year ago, the Adam Mickiewicz University in Poznań, Poland, hosted the PPGIS 2017 workshop (here are my notes from the first day and the second day). Today, four papers from the workshop were published in the journal Quaestiones Geographicae which was established in 1974 as an annual journal of the Faculty of Geographical and Geological … Continue reading Papers from PPGIS 2017 meeting: state of the art and examples from Poland and the Czech Republic

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Conference, crowdsourced geographic information, Crowdsourcing, Environmental information, GIS, GIS Usability, Participatory Mapping, Poznan, PPGIS, Public Participation GIS, VGI, Volunteered Geographic Information

Digital Representations of Place: Urban Overlays and Digital Justice

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Summary of the session on Digital Representation of Place at the RGS-IBG conference in Cardiff. The session aim was to address the following challenge: “Over the last few decades, our cities have become increasingly digital. Urban environments are layered with data and algorithms that fundamentally shape our geographic interactions: impacting how we perceive, move through, … Continue reading Digital Representations of Place: Urban Overlays and Digital Justice

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Conference, Digital divide, Digital Geographies, digital inclusion, Environmental information, GIS, RGS-IBG 2018, Smart Cities, Smart city

Developing mobile applications for environmental and biodiversity citizen science: considerations and recommendations

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The first outcome of the December 2016 workshop on apps, platforms, and portals for citizen science projects was the open access paper “Defining principles for mobile apps and platforms development in citizen science“, which came out in October 2017. The workshop, which was organised by Soledad Luna and Ulrike Sturm from the Berlin Museum for Natural History, has … Continue reading Developing mobile applications for environmental and biodiversity citizen science: considerations and recommendations

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apps, citizen science, ECSA, GIS, GIS Usability, HCI, Human–computer interaction, mobile app, smartphone apps, Usability, VGI

Modular Architectures Made Easier with docker-compose

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The Open GeoPortal is a Free and Open Source framework for rapidly discovering, previewing and retrieving curated geospatial data from multiple repositories. It implements a modular architecture, including a database, a search engine and several web applications. While it can … Continue reading →

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Docker, docker virtualization geospatial cloud foss4g, FOSS4G, Geospatial, GIS, Virtualisation

Citizen Science for Observing and Understanding the Earth

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Since the end of 2015, I’ve been using the following mapping of citizen science activities in a range of talks: The purpose of this way of presentation is to provide a way to guide my audience through the landscape of citizen science (see examples on SlideShare). The reason that I came up with it, is … Continue reading Citizen Science for Observing and Understanding the Earth

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Chapter, citizen science, crowdsourced geographic information, Earth Obesrvation, Earth Science, GIS, Papers, PPGIS, VGI, volunteer computing, Volunteered Geographic Information

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

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Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

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AGI, ambient geospatial information, Cities, GIS, informal settlements, OpenStreetMap, Remote Sensing, Slums, Volunteered Geographic Information

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

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Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

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AGI, ambient geospatial information, Cities, GIS, informal settlements, OpenStreetMap, Remote Sensing, Slums, Volunteered Geographic Information

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

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Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

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AGI, ambient geospatial information, Cities, GIS, informal settlements, OpenStreetMap, Remote Sensing, Slums, Volunteered Geographic Information

Citizen Science & Scientific Crowdsourcing – week 2 – Google Local Guides

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The first week of the “Introduction to Citizen Science and Scientific Crowdsourcing” course was dedicated to an introduction to the field of citizen science using the history, examples and typologies to demonstrate the breadth of the field. The second week was dedicated to the second half of the course name – crowdsourcing in general, and its … Continue reading Citizen Science & Scientific Crowdsourcing – week 2 – Google Local Guides

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citizen science, crowdsourced geographic information, Crowdsourcing, GIS, GIS Usability, Google, Google Maps, OpenStreetMap, teaching, Usability, VGI, Volunteered Geographic Information, Web 2.0

Online course – Introduction to Citizen Science and Scientific Crowdsourcing

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It’s a new year, and just the right time to announce that starting on the 11th January, UCL will run an 11 weeks hybrid (online and face to face) course called “Introduction to Citizen Science and Scientific Crowdsourcing“. This course aim is to introduce students to the theory and practice of citizen science and scientific crowdsourcing. … Continue reading Online course – Introduction to Citizen Science and Scientific Crowdsourcing

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citizen science, GIS, teaching

Chapter in Routledge Handbook of Mapping and Cartography – VGI and Beyond: From Data to Mapping

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Hot on the heels of the Routledge Handbook of Environmental Justice is the Routledge Handbook of Mapping and Cartography. The handbook was edited by Alex Kent (Canterbury Christ Church University) who is currently the President of the British Cartographic Society and Editor of The Cartographic Journal; and Peter Vujakovic (also from Canterbury Christ Church University) who edited The Cartographic … Continue reading Chapter in Routledge Handbook of Mapping and Cartography – VGI and Beyond: From Data to Mapping

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Book, Chapter, citizen science, COST ENERGIC, crowdsourced geographic information, GIS, Papers, PPGIS, VGI, Volunteered Geographic Information

Chapter in Routledge Handbook of Environmental Justice – Participatory GIS and community-based citizen science for environmental justice action

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The Routledge Handbook of Environmental Justice has been published in mid-September. This extensive book, of 670 pages is providing an extensive overview of scholarly research on environmental justice.  The book was edited by three experts in the area – Ryan Holifield from the University of Wisconsin-Milwaukee, Jayajit Chakraborty from the University of Texas at El Paso, and Gordon Walker … Continue reading Chapter in Routledge Handbook of Environmental Justice – Participatory GIS and community-based citizen science for environmental justice action

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citizen science, community mapping, environmental and society, environmental GIS, Environmental information, environmental justice, Extreme Citizen Science, GIS, Papers, participatory GIS, Participatory Mapping, PGIS, PPGIS, Public Participation GIS

AAG2018: Innovations in Urban Analytics

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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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AAG, ABM, Big Data, GeoComputation, GIS, Smart Cities, urban analytics, Urban Systems

AAG2018: Innovations in Urban Analytics

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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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AAG, ABM, Big Data, GeoComputation, GIS, Smart Cities, urban analytics, Urban Systems

Defining principles for mobile apps and platforms development in citizen science

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In December 2016, ECSA and the Natural History Museum in Berlin organised a  workshop on analysing apps, platforms, and portals for citizen science projects. Now, the report from the workshop has evolved into an open peer review paper on RIO Journal. RIO is worth noticing: is “The Research Ideas and Outcomes (RIO) journal” and what … Continue reading Defining principles for mobile apps and platforms development in citizen science

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citizen cyberscience, citizen science, Citizen Science apps, Conference, ECSA, European Citizen Science Association, GeoHCI, GIS, GIS Usability, Human–computer interaction, Papers, Platforms, Research projects, TogetherScience, Usability, VGI

Chapter in ‘Understanding Spatial Media’ on VGI & Citizen Science

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The book ‘Understanding Spatial Media‘ came out earlier this year. The project is the result of joint effort of the editors Rob Kitchin (NUI Maynooth, Ireland), Tracey P. Lauriault (Carleton University, Canada), and Matthew W. Wilson (University of Kentucky, USA). The book is filling the need to review and explain what happened in the part 20 years, with the increase use … Continue reading Chapter in ‘Understanding Spatial Media’ on VGI & Citizen Science

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Book, citizen science, Geospatial technology, GeoWeb, GIS, OpenStreetMap, Papers, PPGIS, VGI, Volunteered Geographic Information, Web 2.0

Agent-Based Modeling Chapter

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In the recently published “Comprehensive Geographic Information Systems” edited by Bo Huang, Alison Heppenstall, Nick Malleson and myself have a chapter entitled “Agent-based Modelling“1. Within the chapter, we provide a overview of agent-based modeling (ABM) especially for the geographical sciences. This includes a section on how ABM emerged i.e. “The Rise of the (Automated) Machines“, along with a discussion on what constitutes an agent. This is followed with steps to building an agent-based model, including: 1) the preparation and design; 2) model implementation 3) and how one goes about evaluating a model (i.e. verification, calibration and validation and how these are particularity challenging with respect to spatial agent-based models). We then discuss how we can integrate space and GIS into agent-based models and review a number of open-source ABM toolkits (e.g. GAMA, MASON, NetLogo) before concluding with challenges and opportunities that we see ahead of us, such as adding more complex behaviors to agent-based models, and how “big data” offers new avenues for multiscale calibration and validation of agent-based models.  If you are still reading this, below you can read the abstract of the paper and find the full reference to the chapter.

Abstract:

Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.

Full Reference: 

Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)

1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]↩

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ABM, ABM Examples, ABM Platforms, GIS

Agent-Based Modeling Chapter

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In the recently published “Comprehensive Geographic Information Systems” edited by Bo Huang, Alison Heppenstall, Nick Malleson and myself have a chapter entitled “Agent-based Modelling“1. Within the chapter, we provide a overview of agent-based modeling (ABM) especially for the geographical sciences. This includes a section on how ABM emerged i.e. “The Rise of the (Automated) Machines“, along with a discussion on what constitutes an agent. This is followed with steps to building an agent-based model, including: 1) the preparation and design; 2) model implementation 3) and how one goes about evaluating a model (i.e. verification, calibration and validation and how these are particularity challenging with respect to spatial agent-based models). We then discuss how we can integrate space and GIS into agent-based models and review a number of open-source ABM toolkits (e.g. GAMA, MASON, NetLogo) before concluding with challenges and opportunities that we see ahead of us, such as adding more complex behaviors to agent-based models, and how “big data” offers new avenues for multiscale calibration and validation of agent-based models.  If you are still reading this, below you can read the abstract of the paper and find the full reference to the chapter.

Abstract:

Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.

Full Reference: 

Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)

1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]↩

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ABM, ABM Examples, ABM Platforms, GIS

Changing departments – the pros and cons of being away from home discipline(s)

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Last weekend, I updated my Linkedin page to indicate that I’ve now completed the move between departments at UCL – from the Department of Civil, Environmental, and Geomatic Engineering to the Department of Geography. It’s not just me – the Extreme Citizen Science group will be now based at the Department of Geography. With this move, … Continue reading Changing departments – the pros and cons of being away from home discipline(s)

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Academia and business, academic career, citizen science, engineering, Extreme Citizen Science, Geography, GIS, GIS Usability, Interdisciplinary research, VGI

Crowdsourced: navigation & location-based services

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Once you switch the smartphone off from email and social media network, you can notice better when and how you’re crowdsourced. By this, I mean that use of applications to contribute data is sometimes clearer as the phone becomes less of communication technology and more of information technology (while most of the time it is … Continue reading Crowdsourced: navigation & location-based services

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GIS, GIS Usability, Google Maps, GPS, Navigation, SatNav, TripAdvisor, Usability, VGI, Volunteered Geographic Information, Waze

Big Data, Agents and the City

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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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ABM, Big Data, GIS, social media

Big Data, Agents and the City

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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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ABM, Big Data, GIS, social media
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