Spatial Land Use Modeling

Land use change (LUC) is one of the most impactful climate forcings that impact global warming and how the climate system changes. LUC describes the ways in which people have developed land to impact the Earth System. Primarily this refers to agricultural land use such as cultivating cropland and pastureland at a global scale (about 40% of Earth’s land area). Here we include videos from the AIMES/GLP joint working group on Large-Scale Behavioural Models of Land Use Change webinar series that is ongoing and intended to feature  a wide range of perspectives with interests in many aspects of human, individual, and collective behaviour in land system models.

LUC WEBINAR #1: Is the idea of large-scale behavioural modelling realistic?

Speaker: Dr. Peter Verburg (Vrije Universiteit Amsterdam)

Date aired: 13 November 2020

About: In this webinar you will hear about research efforts led by Dr. Peter Verburg that are pushing the bounds of modelling to represent human decision-making at continental and global extents. You will also be introduced to novel psychological frameworks developed by Dr. Elke Weber that could help make this possible and reveal new challenges. Together, their work provides new insights into large-scale behavioural modelling of land use change. The webinar concludes with a discussion of the reasons for developing such models, key issues that need to be considered in their design, and approaches that have particular promise for producing a new generation of models.

LUC Webinar #2: What can land use modellers learn from other disciplines?

Speaker: Robert Axtell (George Mason University), Brian Mac Namee (University College Dublin), Thomas Clemen (Hamburg University of Applied Sciences)

Date aired: April 30, 2021

About: First, Robert Axtell presented new work on large-scale agent based models in social science. Second, Brian Mac Namee described the use of machine learning and ABM in computer game design. Third, Thomas Clemen presented a simulation platform capable of modelling vast multi-agent systems.

The webinar concluded with a discussion among presenters and attendees about how these advances can be used to develop a new generation of land system science models.