Call for abstracts

Over the past decade, significant advancements in deep learning have launched a new “AI spring,” reigniting research interest in artificial intelligence within GIScience and quantitative geography. While large language models have been making headlines in newspapers worldwide, a broader range of foundation models and architectures (e.g., CNN, GNN GAN, LSTM, Transformer) have sparked new work in geospatial artificial intelligence (Janowicz et al., 2020; De Sabbata et al., 2023, Hu et al.  2024, Mai et al., 2025) and representation learning (Chen et al., 2025; Liu et al., 2025; Klemmer et al, 2025; Wang et al, 2025). For example, vision models have been applied to street-view imagery to explore urban perceptions and infer socio-economic outcomes (Biljecki and Ito 2021; Law et al 2019). The transformer architecture has been used for natural language processing (Berragan et al., 2023), image analysis and machine vision (Li et al, 2022). Graph neural networks have been employed for geodemographic classifications and question-answering with geographic knowledge graphs (De Sabbata and Liu, 2023; Mai et al, 2020). More recently, geospatial foundation models (Janowicz et al., 2025) have emerged, leveraging large-scale geographic data to support multi-modal geographic analysis.

This session aims to be a forum to discuss advances, opportunities, and challenges of the use of GeoAI in quantitative geography and geographic information science, showcasing the latest advancements in GeoAI theories, methods and applications within the realm of quantitative geographic studies. We invite submissions that engage with the research agenda recently proposed by Nelson et al. (2024) and focus on but not limited to the topics below:

• GeoAI methods, including but not limited to
  • Spatial Explicit Machine learning
  • Causal inference
  • Uncertainity
  • Agent-based modelling
  • Machine Vision
  • Natural language processing
  • Information retrieval
  • Foundation models
  • Explainable and interpretable AI
  • Reinforcement learning
  • Generative AI
• GeoAI applications, including but not limited to
  • Crowdsourcing, citizen science and volunteered geographic information
  • Data integration
  • Environment (Disaster Management and Resilience)
  • Health
  • Location-based services
  • Mapping, cartography and information visualisation
  • Spatial analysis and uncertainty
  • Transportation & Mobility
  • Urban analytics
  • Urban Planning
• Critical GIS, ethics and privacy in GeoAI
  • Bias and fairness
  • Responsible AI
  • Policy implications
  • Transparency and accountability
• Reproducibility and open science in GeoAI
  • Open Data for reproducibility and transparency
  • Standardising benchmarks and evaluation metrics

Call for abstracts

Artificial Intelligence (AI) has entered our daily life and work, transforming how many people learn about the world, as well as the way we approach data analysis in human, digital and physical geographies (Ash et al., 2019; Bennett and De Sabbata, 2023; Gao, 2023; Janowicz et al., 2022; Lin and Zhao, 2025; Maalsen et al., 2023; Osborne and Jones 2023). However, AI models and tools have their own geographies, shaped by their creation and deployment (Bender et al., 2021; Miceli and Posada, 2022; Weidinger et al., 2022), and which, in turn, generate and shape disinformation and misinformation, biases, and inequalities. The societal and ethical implications of AI are still underexplored in a continuously, rapidly changing technological and market environment. Crucially, many discussions are confined to (sub-)disciplinary silos, which hinder a broader understanding of this complex phenomenon.

Aligning itself to this year’s Chair’s theme “Geographies of inequalities: towards just places”, this session aims to be an open forum to bridge critical analysis of AI based on theories in human and digital geographies (Sieber et al., 2025; Walker and Winders, 2024), and quantitative geographic analysis on AI safety and alignment (De Sabbata et al., 2025; Janowicz et al., 2025; Li et al., 2024; Van de Weghe et al., 2025). We invite submissions that explore the inequalities of AI and their implications, with a particular focus on but not limited to the topics below:

• AI benchmarking
  • geo/spatial bias
  • geo/spatial diversity
  • geo/spatial reasoning
• AI ethics
  • exclusionary practices and discrimination
  • disinformation and misinformation
  • biases and inequalities
  • labour conditions and labour markets
• AI safety and trust
  • alignment
  • mechanistic interpretability

We welcome and ecourage submission exploring a range of methods, including but not limited:

• Contribution type
  • theoretical
  • empirical
  • case study
• Approach
  • critical
  • ethnographic
  • qualitative
  • quantitative
  • statistical
  • mixed methods

Annual International Conference 2025

The Geographic Information Science Research Group (GIScRG) of the RGS-IBG is glad to invite you to submit proposals for sponsorship of your RGS-IBG Annual International Conference 2026 sessions.

Call for abstracts

Geospatial Artificial Intelligence (GeoAI) draws from across disciplinary domains in that it situates itself in data science, environmental science and geography. It incorporates multi-modal geospatial data to inform use cases in policy such as environmental degradation and disaster management.

This session aims to explore these and other cross-disciplinary interstices among big data, large language models, Geographic Information Systems (GIS) and cartography. GIS education, which combines spatial analysis and cartographic design, represents a unique context in which Generative Artificial Intelligence tools can make a significant impact.

GIS education encompasses a variety of pedagogical approaches aimed at building both technical proficiency and spatial thinking (Duarte et al. 2022). Traditional methods are often complemented by laboratory-based learning, where students engage in hands-on exercises with GIS software like ArcGIS or QGIS, developing skills in spatial data manipulation, analysis, and visualization (Rickles and Ellul, 2015).

Contemporary approaches have integrated emerging technologies and pedagogies. For example, experiential learning incorporates fieldwork, enabling students to gather spatial data using GPS devices or drones. Additionally, the rise of WebGIS platforms and open data has democratized GIS education, making geospatial tools and datasets accessible to broader audiences (Ruibo, 2019).

This session is inspired by Muehlenhaus’s cartographically-focussed Generative Pre-training Transformer (GPT). It is envisaged that papers will represent a diversity of methodological stances: qualitative studies and preliminary explorations are particularly encouraged.

Possible topics include (but are not limited to):

• Descriptions and case studies of GeoAI and / or large language models to GIS and cartography / cartographic education;

• Examples of how geographical epistemologies have shaped and / or been shaped by the application of (what might loosely be termed as) AI agents / AI tools;

• Methodological approaches to such investigations; and

• Defining and designing for the nurturing of literacies of GeoAI and the ethical implications of the use of IoT and Data Science in such investigations.

Enquiries to Kenneth Y T Lim (voyager@mac.com / kenneth.lim@nie.edu.sg) are welcome.

Call for abstracts

Human interaction with urban environments has been a central topic in urban studies (Pattison, 1964). Geographers have explored the interactions between humans and their surroundings, investigating how individuals adapt to, modify, and influence their environment, while also considering how environmental factors shape human activities and cultural practices (Tuan, 1975). Recent advancements in GIS technologies, coupled with the increasing availability of diverse datasets—such as remote sensing imagery, geotagged search engine data, social media content, mobility data, and street-level imagery—have enabled novel approaches to studying place identity, residents’ mobility, and social perceptions (e.g., sense of belonging, community emotions).Moreover, the use of computational techniques, including machine learning and deep learning, has significantly improved our ability to interpret and model complex interactions.

This session aims to be a forum for discussing the integration of multi-source geospatial big data and GIS techniques to advance our understanding of human-urban interaction. We welcome submissions that explore innovative methodologies, theoretical perspectives, and practical applications in this field. Topics of interest include, but are not limited to:

• Multi-Source Geospatial Big Data for Extracting Human Behaviour/ Perception
  • Remote sensing
  • Street-level imagery
  • Social media data
  • Mobility data
• GIS, Machine Learning, and Deep Learning for Human-Environment Interaction
  • Human mobility
  • Human perception and social sensing
  • Social interaction
  • Transit-Oriented Development (TOD)

Call for abstracts

Over the past decade, significant advancements in deep learning have launched a new “AI spring,” reigniting research interest in artificial intelligence within GIScience and quantitative geography. While large language models have been making headlines in newspapers worldwide, a broader range of foundation models and architectures (e.g., CNN, GNN GAN, LSTM, Transformer) have sparked new work in GeoAI (Janowicz et al., 2020; De Sabbata et al., 2023, Hu et al. 2024, Mai et al., 2025). For example, vision models have been applied to street-view imagery to explore urban perceptions and infer socio-economic outcomes (Biljecki and Ito 2021; Law et al 2019). The transformer architecture has been used for natural language processing (Berragan et al., 2023), image analysis and machine vision (Li et al, 2022). Graph neural networks have been employed for geodemographic classifications and question-answering with geographic knowledge graphs (De Sabbata and Liu, 2023; Mai et al, 2020).More recently, Geo foundation models have emerged, leveraging large-scale geographic data to support multi-modal geographic analysis.

This session aims to be a forum to discuss advances, opportunities, and challenges of the use of GeoAI in quantitative geography and geographic information science, showcasing the latest advancements in GeoAI theories, methods and applications within the realm of quantitative geographic studies. We invite submissions that engage with the research agenda recently proposed by Nelson et al. (2024) and focus on but not limited to the topics below:

• GeoAI methods, including but not limited to
  • Spatial Explicit Machine learning
  • Causal inference
  • Uncertainity
  • Agent-based modelling
  • Machine Vision
  • Natural language processing
  • Information retrieval
  • Foundation models
  • Explainable and interpretable AI
  • Reinforcement learning
  • Generative AI
• GeoAI applications, including but not limited to
  • Crowdsourcing, citizen science and volunteered geographic information
  • Data integration
  • Environment (Disaster Management and Resilience)
  • Health
  • Location-based services
  • Mapping, cartography and information visualisation
  • Spatial analysis and uncertainty
  • Transportation & Mobility
  • Urban analytics
  • Urban Planning
• Critical GIS, ethics and privacy in GeoAI
  • Bias and fairness
  • Responsible AI
  • Policy implications
  • Transparency and accountability
• Reproducibility and open science in GeoAI
  • Open Data for reproducibility and transparency
  • Standardising benchmarks and evaluation metrics

Annual International Conference 2025

The Geographic Information Science Research Group (GIScRG) of the RGS-IBG is glad to invite you to submit proposals for sponsorship of your RGS-IBG Annual International Conference 2025 sessions.

Annual International Conference 2024

The Geographic Information Science Research Group (GIScRG) of the RGS-IBG is glad to invite you to submit proposals for sponsorship of your RGS-IBG Annual International Conference 2024 sessions. The GIScRG welcomes sessions that focus on, but not limited to:

• Geographic information science 
• Geographic information systems and tools
• Critical GIS, ethics and privacy
• Reproducibility and open science
• Mapping, cartography and information visualisation
• Spatial analysis and uncertainty
• GeoAI, including but not limited to
  • Machine learning
  • Agent-based modelling
  • Natural language processing
  • Information retrieval
  • Foundation models 
• Movement analysis
• Location-based services
• Crowdsourcing, citizen science and volunteered geographic information
• Application, including but not limited to
  • Environment
  • Health
  • Transportation
  • Urban analytics

We are also open to co-sponsorships with other Research Groups.

Obtaining a group sponsorship for your session is not required, but it will connect your session to the broader GIScience community of the RGS-IBG. We will promote your session along with the other sessions we sponsor, providing your session with broader publicity. The session will be part of our AIC programme, thus avoiding overlaps with other GIScience sessions. Please note, however, that sponsorships do not include financial support, although you might be able to apply for a guest pass.

Please send the information below to giscience.rgs [at] gmail.com before February 12th.

• Session title
• Session abstract (250 words)
• Session organisers’ name, affiliation and email
• Session format and how many timeslots you are requiring
• Whether you aim for a co-sponsorship with other groups
• (No need to include abstracts for paper contributions)

Timeline:

• Call of papers and sessions for the RGS-IBG Annual International Conference 2024
• Deadline for sponsorship applications from session organisers to GIScience RG: February 12th
• Decision on sponsorship applications from GIScience RG to session organisers: February 16th
• Deadline for submissions from session organisers to RGS: March 1st

The Geographic Information Science Research Group (GIScRG) of the RGS-IBG is glad to invite you to submit proposals for sponsorship of your RGS-IBG Annual International Conference 2023 sessions. The GIScRG welcomes sessions that focus on geographic information science and tools, cartography, spatial analysis, GeoAI, critical GIS and related disciplines. We are also open to co-sponsorships with other Research Groups.

Obtaining a group sponsorship for your session is not required, but it will connect your session to the broader GIScience community of the RGS-IBG. We will promote your session along with the other sessions we sponsor, providing your session with broader publicity. The session will be part of our AIC programme, thus avoiding overlaps with other GIScience sessions. Please note, however, that sponsorships do not include financial support, although you might be able to apply for a guest pass.

Please send us the following information through the form below before February 3rd.

• Session title

• Session abstract (250 words)

• Session organisers’ name, affiliation and email

• Session format and how many timeslots you are requiring

• Whether you aim for a co-sponsorship with other groups

• (No need to include abstracts for paper contributions)

Timeline:

• Call of papers and sessions for the RGS-IBG Annual International Conference 2023 is now live.

• Deadline for sponsorship applications from session organisers to GIScience RG: February 3rd.

• Decision on sponsorship applications from GIScience RG to session organisers: February 10th.

• Session organisers call for paper contributions, GIScience RG advertises sessions until March 10th.

• Deadline for submissions from session organisers to RGS: March 17th.

The Geographic Information Science Research Group (GIScRG) of the Royal Geographical Society (with IBG) is a group of academics and practitioners interested in promoting GIScience, Geographic Data Science and GI Technology in geographical research, teaching and the workplace.

The RGS-IBG Annual International Conference regularly attracts over 2,000 geographers from around the world. This year, the conference is taking place at Newcastle University, with in-person, online, and hybrid ways to participate.

This year the GIScRG is pleased to sponsor paper presentation sessions on the following topics that focus on geographic information science and tools, cartography, spatial analysis, critical GIS and related disciplines:

• Session 1: Innovative GI methods in safety and emergency services research
• Session 2: Deep learning applications in geography
• Session 3: Dealing with uncertainty: Quality and Representation in the context of Big Data
• Session 4: Ethics within GIScience, including (but not limited to) the Locus Charter
• Session 5: Building healthy learning spaces. What is next as we move from Emergency Education?
• Session 6: Geographies of misinformation

The deadline for abstract submission to each of these sessions is March 18th, 2022. Further to the paper sessions we also sponsor a World Café session:

• Session 7: Beyond the systems of geography information

Click on the link to access the details of each sessions and to find out how to contribute to the session. Please note that the RGS has strict limits to the contributions of each delegate (see Contribution limits). There is no formal limit to the number of sessions chaired or the number of co-authorships. However, sessions are normally understood as open to submission of papers. A session only composed by members of your own team is unlikely to be approved by the RGS.

When: Wednesday 2nd February 2022, 1:00 PM UTC/GMT

Register at: https://www.eventbrite.co.uk/e/rgs-ibg-giscience-webinar-accessibility-and-mobility-in-time-of-covid-19-tickets-250301507547

Accessibility and essential travel: public transport and the mobility of vulnerable groups during the COVID-19 pandemic
by Alfie Long

Abstract: The COVID-19 pandemic, and numerous lockdowns and restrictions, resulted in a significant decline in public transport demand and the replacement of usual out-of-home activities with online equivalents. Whilst restrictions have since eased in the UK, a simple return to pre-pandemic travel behaviour is unlikely. In order to support public transport authorities and operators in providing an inclusive public transport system that meets the needs of users, the field requires continued research into changes in public transport patronage by different population groups throughout the COVID-19 pandemic.
Using smart card travel data, we analyse demand for bus services during the COVID-19 pandemic to identify the groups of the older population that continued to use public transport services during lockdown periods and that quickly returned to public transport when restrictions were eased. We compare these trends according to demographic characteristics of passengers as well as accessibility.

Biography: Alfie Long is currently a PhD Student at UCL Department of Geography. His research is aimed at developing existing public transport big data infrastructure into simpler data formats that can be used to analyse the social equity of public transport mobilities.

Analysing the Impacts of Lockdown Restrictions on the London Bicycle Sharing System
By James Todd

Abstract: The COVID-19 pandemic has caused large scale disruptions to the daily lives of billions around the world. This is primarily a result of the implementation of Non-Pharmaceutical Interventions (NPI), in its various forms, by local and national governing bodies to help minimise the spread of the virus. In the most severe cases lockdown restrictions were imposed on populations, forcing them to quarantine within their own homes except for limited and specific purposes. Within this analysis we aim to quantify the impacts of these lockdown restrictions on the activity patterns observed within the docked London bicycle sharing system. Employing network and statistical analysis methods this work presents novel insights into the changes in cycling behaviour observed spatially, temporally and at a system-wide scale.

Biography: James is currently an Associate Lecturer and Ph.D. student within the Department of Geography at UCL. His research aims to analyse the utility and validity of new urban mobility data sources in helping to understand movement trends for Smart City applications. Over the past few years, James has worked extensively with bicycle sharing system data from over 600 cities, exploiting dock capacity information to create comparative heuristics.

https://youtu.be/cM3qIOOKkMU

Abstract: Movement patterns are the results of complex behaviors and processes which drive individuals’ movement in social and ecological systems. These patterns shape urban and natural ecosystem dynamics, structure human and wildlife social networks, and are instrumental to understanding human and wildlife’s behavioral responses to a changing environment. Ubiquitous tracking and the increasing access to movement data in both trajectory forms and aggregate indices have generated a tremendous interest in using data-driven approaches to model and understand behavioral responses to disruptive events such as the COVID-19 pandemic and to develop mitigation strategies. In this presentation, I argue for a human-centered approach to movement data science to analyze and map human responses to environmental disruptions.

Speaker: Somayeh Dodge, University of California, Santa Barbara, somayehdodge.info , @smayadodge.

Bio: Somayeh Dodge serves as Assistant Professor of Spatial Data Science and leads the MOVE Laboratory in the Department of Geography at the University of California, Santa Barbara. She received her PhD in Geography with a specialization in Geographic Information Science (GIScience) from the University of Zurich, Switzerland in 2011. She holds a MS degree in GIS Engineering and a BS degree in Geomatics Engineering from the KNT University of Technology, Iran. Somayeh’s research focuses on developing data analytics, knowledge discovery, modeling, and visualization techniques to study movement in human and ecological systems. She has published in a number of high-ranked international journals such as Methods in Ecology and Evolution, International Journal of Geographic Information Science, Philosophical Transactions of the Royal Society B, Journal of Spatial Information Science (JOSIS), Movement Ecology, Computers, Environment and Urban Systems (CEUS), Geographical Analysis, and Information Visualization. Her work is supported by the US National Science Foundation (NSF). Somayeh currently serves as the Co-Editor in Chief of the JOSIS as well as on the editorial board of multiple journals including Geographical Analysis, Cartography and Geographic Information Science, CEUS, Journal of Location Based Services, and The Professional Geographer. Dr Dodge’s work can be found at somayehdodge.info and @smayadodge on Twitter.

https://youtu.be/NWQRyAbR8LU

Abstract: There are many online and desktop tools designed to make mapping easy for anyone who wants to explore, analyze, and communicate the spatial patterns in their data. However, as geographers seem to enjoy reminding everyone, “spatial is special,” and the decision making that goes into finding, understanding, and communicating spatial patterns can be complex. In this presentation, I will address some of the technical, design, and analytics challenges that we face in designing self-service spatial analytics tools, as well as consideration of the broader challenges of how people think about maps that make the whole process more challenging (and more interesting).

Speaker: Sarah Battersby, Tableau, @mapsOverlord.

Bio: Sarah Battersby is a Principal Research Scientist on the Tableau Research team at Salesforce. Sarah’s primary area of focus is cartography, with an emphasis on cognition. Her work emphasizes how to help everyone visualize and use spatial information more effectively – no advanced degree in geospatial required. Sarah holds a PhD in GIScience from the University of California at Santa Barbara. She is a member of the International Cartographic Association Commission on Map Projections, and is a Past President of the Cartography and Geographic Information Society (CaGIS). Sarah’s work can be found on Twitter at @mapsOverlord.

When: Wednesday 2nd June 2021, 17:00PM UK / 16:00pm UTC

Register at: https://www.eventbrite.co.uk/e/rgs-ibg-giscience-webinar-mapping-movement-responses-to-env-disruptions-tickets-156566668119

Abstract: Movement patterns are the results of complex behaviors and processes which drive individuals’ movement in social and ecological systems. These patterns shape urban and natural ecosystem dynamics, structure human and wildlife social networks, and are instrumental to understanding human and wildlife’s behavioral responses to a changing environment. Ubiquitous tracking and the increasing access to movement data in both trajectory forms and aggregate indices have generated a tremendous interest in using data-driven approaches to model and understand behavioral responses to disruptive events such as the COVID-19 pandemic and to develop mitigation strategies. In this presentation, I argue for a human-centered approach to movement data science to analyze and map human responses to environmental disruptions.

Bio: Somayeh Dodge serves as Assistant Professor of Spatial Data Science and leads the MOVE Laboratory in the Department of Geography at the University of California, Santa Barbara. She received her PhD in Geography with a specialization in Geographic Information Science (GIScience) from the University of Zurich, Switzerland in 2011. She holds a MS degree in GIS Engineering and a BS degree in Geomatics Engineering from the KNT University of Technology, Iran. Somayeh’s research focuses on developing data analytics, knowledge discovery, modeling, and visualization techniques to study movement in human and ecological systems. She has published in a number of high-ranked international journals such as Methods in Ecology and Evolution, International Journal of Geographic Information Science, Philosophical Transactions of the Royal Society B, Journal of Spatial Information Science (JOSIS), Movement Ecology, Computers, Environment and Urban Systems (CEUS), Geographical Analysis, and Information Visualization. Her work is supported by the US National Science Foundation (NSF). Somayeh currently serves as the Co-Editor in Chief of the JOSIS as well as on the editorial board of multiple journals including Geographical Analysis, Cartography and Geographic Information Science, CEUS, Journal of Location Based Services, and The Professional Geographer. Dr Dodge’s work can be found at somayehdodge.info and @smayadodge.

https://youtu.be/Z-FeU_SiQIM

Abstract: Despite the continued growth of data collected about any number of social and spatial phenomena, as well as new analytical techniques to make sense of such data, many processes of particular interest to geographers remain resistant to datafication, quantification and spatial visualization. But the inherent fuzziness associated with trying to quantify and map emerging phenomena for whom precise data does not exist does not mean that using what data we do have is useless or should be avoided. Instead, as this presentation will argue, if we’re interested in uncovering the multifaceted inequalities that characterize our present moment, we must begin to embrace the inherent fuzziness of these new, partial data sources and attempt to make sense out of their partial perspective. In order to demonstrate the value of this disposition towards embracing the fuzziness of data, this presentation will discuss findings from two recent research projects focused on mapping otherwise opaque socio-spatial processes: (1) quantifying the presence of emerging forms of regionally-specific housing speculation in college towns of the American South that have previously gone unstudied, and (2) using geotagged social media data to develop more relational understandings of gentrification that focus on changes in the relationships between different people and neighborhoods in the city over time, rather than on the inherent or internal characteristics of neighborhoods themselves. Ultimately, this presentation points towards the need for continued experimentations with emerging data sources, analytical techniques and geographic theories to better understand the ever-evolving landscape of socio-spatial inequality in the world today.

Speaker: Taylor Shelton, Georgia State University, taylorshelton.info, @kyjts.

Bio: Dr. Taylor Shelton is an Assistant Professor in the Department of Geosciences at Georgia State University. Working at the intersection of critical human geography and geographic information science, Dr. Shelton is interested in how urban spaces and social inequalities are represented, reproduced and contested through maps and data. In particular, his work focuses on using mapping and data visualization to develop alternative understandings of urban inequalities, especially in relation to issues of housing speculation, property ownership and neighborhood segregation. Prior to joining Georgia State in 2020, Dr. Shelton held appointments at Mississippi State University, the University of Kentucky and the Georgia Institute of Technology. Before that, Dr. Shelton earned BA and MA degrees in geography from the University of Kentucky and his PhD from the Graduate School of Geography at Clark University. Dr Shelton’s work can be found at taylorshelton.info and @kyjts.

https://youtu.be/KHgjcrGmTnk

Abstract:After having grown into distinctly different sub-disciplines in the latter half of the 20th century, quantitative geography is now increasingly adopting methods and techniques from network analysis. Network analysis provides a different perspective on the world, through relations between people and places instead of the abstract space of GIS. This provides an exciting opportunity to break free(er) from the well-known constraints of the Cartesian grid common to most spatial techniques. Thrust forward by new types of geographical data and computational power, this combination of spatial + network analysis is especially incisive when applied to analyses of urban processes. In this talk, I will briefly discuss the diverging and converging histories of spatial and network analysis. I will then illustrate the current use and potential of network analytical methods in geography by drawing on my recent work on urban neighborhoods and systems.

Speaker: Ate Poorthuis, KU Leuven, atepoorthuis.com , @atepoorthuis.

Bio: Ate Poorthuis is an Assistant Professor of Big Data and Human-Environment Systems in the Department of Earth and Environmental Sciences at KU Leuven. His research explores the possibilities and limitations of big data, through quantitative analysis and visualization, to better understand how our cities work. He has particular interest in the practical application of these academic insights within urban planning and policy. Further information about Andrew’s work can be found at atepoorthuis.com and on Twitter @atepoorthuis.

When: Wednesday 19th May 2021, 13:00PM UK / 12:00pm UTC

Register at: https://www.eventbrite.co.uk/e/rgs-ibg-giscience-webinar-embracing-fuzziness-uncovering-inequalities-tickets-151440423383

Abstract: Despite the continued growth of data collected about any number of social and spatial phenomena, as well as new analytical techniques to make sense of such data, many processes of particular interest to geographers remain resistant to datafication, quantification and spatial visualization. But the inherent fuzziness associated with trying to quantify and map emerging phenomena for whom precise data does not exist does not mean that using what data we do have is useless or should be avoided. Instead, as this presentation will argue, if we’re interested in uncovering the multifaceted inequalities that characterize our present moment, we must begin to embrace the inherent fuzziness of these new, partial data sources and attempt to make sense out of their partial perspective. In order to demonstrate the value of this disposition towards embracing the fuzziness of data, this presentation will discuss findings from two recent research projects focused on mapping otherwise opaque socio-spatial processes: (1) quantifying the presence of emerging forms of regionally-specific housing speculation in college towns of the American South that have previously gone unstudied, and (2) using geotagged social media data to develop more relational understandings of gentrification that focus on changes in the relationships between different people and neighborhoods in the city over time, rather than on the inherent or internal characteristics of neighborhoods themselves. Ultimately, this presentation points towards the need for continued experimentations with emerging data sources, analytical techniques and geographic theories to better understand the ever-evolving landscape of socio-spatial inequality in the world today.

Dr. Taylor Shelton is an Assistant Professor in the Department of Geosciences at Georgia State University. Working at the intersection of critical human geography and geographic information science, Dr. Shelton is interested in how urban spaces and social inequalities are represented, reproduced and contested through maps and data. In particular, his work focuses on using mapping and data visualization to develop alternative understandings of urban inequalities, especially in relation to issues of housing speculation, property ownership and neighborhood segregation.

Prior to joining Georgia State in 2020, Dr. Shelton held appointments at Mississippi State University, the University of Kentucky and the Georgia Institute of Technology. Before that, Dr. Shelton earned BA and MA degrees in geography from the University of Kentucky and his PhD from the Graduate School of Geography at Clark University. Dr Shelton’s work can be found at taylorshelton.info and @kyjts.

When: Wednesday 5th May 2021, 17:00 pm UK / 16:00 pm UTC

Register at: https://www.eventbrite.co.uk/e/rgs-ibg-giscience-webinar-spatial-in-the-world-of-self-service-analytics-tickets-151306115665

Abstract: There are many online and desktop tools designed to make mapping easy for anyone who wants to explore, analyze, and communicate the spatial patterns in their data. However, as geographers seem to enjoy reminding everyone, “spatial is special,” and the decision making that goes into finding, understanding, and communicating spatial patterns can be complex.  In this presentation, I will address some of the technical, design, and analytics challenges that we face in designing self-service spatial analytics tools, as well as consideration of the broader challenges of how people think about maps that make the whole process more challenging (and more interesting).

Sarah Battersby is a Principal Research Scientist on the Tableau Research team at Salesforce.  Sarah’s primary area of focus is cartography, with an emphasis on cognition. Her work emphasizes how to help everyone visualize and use spatial information more effectively – no advanced degree in geospatial required.  Sarah holds a PhD in GIScience from the University of California at Santa Barbara. She is a member of the International Cartographic Association Commission on Map Projections, and is a Past President of the Cartography and Geographic Information Society (CaGIS). Sarah’s work can be found on Twitter at @mapsOverlord.

https://www.youtube.com/watch?v=VL8vJuDIl-U&ab_channel=RGS-IBGGIScienceResearchGroup

Abstract: The beginning of this century marked a milestone in human history. For the first time, more than half of the world’s population lived in urban areas. This trend is expected to continue into the foreseeable future with 6.7 billion people projected to live in cities by 2050. This rapid urbanization will place unprecedented pressures on urban systems and their ability to provide basic of services. To plan for this future, we need to better understand the inherent complexity of urban systems from social, economic and environmental perspectives. In this talk, I will explore how such understanding can be gained through the lens of computational social science (CSS): the interdisciplinary science of complex social systems and their investigation through computational modeling (e.g. agent-based models) and related techniques. Through a series of example applications, I will demonstrate how new forms of geographical data (e.g. crowdsourced, social media etc.) not only provide us with a novel way of analyzing urban environments but how such data can be integrated into geographically explicit agent-based models. In addition, I will highlight that by focusing on individual, or groups of individuals, leads to more aggregate patterns emerging and show how model outcomes can be validated by such datasets. After these demonstrations, I will outline the challenges associated with this program of research, as using such data is not without its difficulties. Together, this work provides a brief overview of the current state of analyzing and modeling urban environments through the lens of CSS.

Speaker: Andrew Crooks is a Professor at the University at Buffalo within the Department of Geography and a faculty member in the RENEW Institute. His research interests relate to exploring, understanding and the communication of the natural and socio-economic environments using geographical information systems (GIS), social network analysis (SNA), and Agent-based modeling methodologies. He is also a lead author of “Agent-based Modelling and Geographical Information Systems: A Practical Primer” which explains how to design and build ABM and how to link the models to Geographical Information Systems. Further information about Andrew’s work can be found at www.gisagents.org and on Twitter @AndyCrooks.