Literature of relevance

[darribas]

Opening this thread to drop in references that may prove useful in the project. Land use, land use modelling, scenario building, reinforcement learning...

[darribas]

Systematizing heterogeneous expert knowledge, scenarios and goals via a goal-reasoning artificial intelligence agent for democratic urban land use planning

Cities, 2020. https://www.sciencedirect.com/science/article/pii/S0264275119312466

The tasks of democratic urban land use planning, as subjective-objective combined decision-making efforts that require considerable time and energy, have heretofore been accomplished mainly through deep human thought or by voting. In this paper, we introduce a goal-reasoning artificial intelligence (AI) agent that can assist with these tasks by combining traditional scenario planning, multicriteria decision analysis (MCDA) with a novel goal-oriented Monte Carlo tree search (G-MCTS) method. G-MCTS conducts goal-oriented searches to meet the needs of heterogeneous goals and provide the best land use solutions. We evaluated this method on a real-world planning case, and the results show that 1) the goal-reasoning AI agent is good at performing complex goal reasoning tasks with many heterogeneous expert knowledge; 2) different human planning manuscripts could be integrated into a better solution via a goal-reasoning AI agent; and 3) the goal-reasoning AI agent has the potential to make comprehensive decisions during a democratic political agenda. We conclude that the goal-reasoning AI agent, via an improved reinforcement learning (RL) method of G-MCTS, provides vast potential for assisting in subjective-objective combined urban land use planning and many other similar fields by weighing heterogeneous goals, reproducing human inspiration, and acting as a reflexive sociotechnical system.

[darribas]

Defining Transition Rules with Reinforcement Learning for Modeling Land Cover Change

Simulation, 2009. https://journals.sagepub.com/doi/abs/10.1177/0037549709103510

Spatio-temporal modeling provides the opportunity to simulate geographic processes of land use and land cover change (LUCC) by integrating geographic information systems (GIS) with various machine learning approaches to computing. Contemporary models are often developed using a training dataset to define a set of probabilistic transition rules that govern how a landscape changes over time. However, the use of training datasets can be problematic for spatio-temporal modeling, as they can limit the ability to incorporate system complexity and hinder the transferability of the model to different datasets. The purpose of this study is to evaluate a machine learning approach called reinforcement learning (RL) for defining transition rules for GIS-based models of land cover change due to natural resource extraction. Specifically, RL is evaluated based on its potential for constructing models independent of training datasets that can handle different levels of complexity and be transferred across different spatial extents. An RL model for Land Cover Change (RL-LCC) is developed for considering economic and ecological goals involved in natural resource management, and implemented using a hypothetical forest management scenario. Simulation results reveal that agents in the RL-LCC model are able to develop transition rules from their experience in their landscape in a variety of simulation scenarios that allow them to achieve their goals. This study demonstrates the benefits of integrating RL and GIS in order to address important issues of space, time and complexity.

[darribas]

From Frank:

https://www.tandfonline.com/doi/abs/10.1080/01944363.2019.1680311

[darribas]

Modeling Sustainability Scenarios in the Baltimore–Washington (DC) Region

https://www.tandfonline.com/doi/full/10.1080/01944363.2019.1680311

Problem, research strategy, and findings: Planners today are confronted with unprecedented uncertainty in economic, political, and technological environments, especially at the regional scale. An increasingly common approach to addressing such uncertainty is exploratory scenario analysis. To provide new insights into the methods and utility of such analyses, we conducted a scenario analysis of the Baltimore (MD)–Washington (DC) region by engaging a technical advisory committee and exercising a loosely coupled suite of advanced transportation, land use, and environmental impact models. Our analysis suggests the future is indeed uncertain and may evolve into plausible but quite different alternative scenarios. Key drivers of these scenarios include fuel prices; the rate and form of technological change, especially in the transportation sector; and the restrictiveness of land use controls.

Takeaway for practice: By developing exploratory scenario analyses and analyzing them using advanced computational models, planners can gain insights into how best to address uncertain development trends, such as how and to what degree planners can influence the adoption of electric and automated vehicles, how and where to guide development patterns through land use controls, and how best to respond to variation in the cost of energy, which could have dramatic impacts on the future sustainability of cities and regions. Although such scenario analyses cannot in most circumstances provide unambiguous robust or contingent policy prescriptions, they can provide important insights for raising public awareness and provide the foundation for further policy evaluation.

[darribas]

Interactive article on the Cambdridge-Oxford Arc project from Newcastle-Oxford:

https://nismod.github.io/arc-udm-vis/