Artificial Intelligence and Planning Practice

PAS Memo 111

By David Wasserman, AICP, Michael Flaxman


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The term "artificial intelligence" (AI) conjures images of autonomous vehicles maneuvering through streets, smartphone assistants that answer your questions, or androids exploring final frontiers.

At a basic level, however, AI can be understood as the multidisciplinary endeavor to approximate human reasoning with computation. For planners, it represents an emerging toolbox that enables a range of new capabilities. Whether AI primarily benefits entire communities or narrow interests, though, depends on planners' abilities to engage with the challenges and opportunities surrounding its civic applications. Naively applied, these technologies can automate discrimination, create unaccountable processes, and create a false certainty about what the future holds.

This PAS Memo intends to equip planners with an understanding of AI concepts and their potential uses for practice. And because planners have a responsibility to understand the implications of the technologies they choose to deploy and help to ensure that those technologies are used responsibly, it discusses important considerations regarding AI applications and their roles in larger trends connected to digital governance and civic data in planning.


Page Count
Date Published
April 1, 2022
Adobe PDF
American Planning Association

About the Authors

David Wasserman, AICP
David Wasserman works at the intersection of urban informatics, 3D visualization, geospatial analytics, and visual storytelling. He brings years of experience and passion to applying scientific computing, spatial analysis, and scenario-focused storytelling toward the development of effective transportation planning solutions aimed at improving communities. His current areas of focus are enabling data-informed scenario planning, identifying how to align community goals to metrics to track progress towards them, incorporating civic data science into projects with web-delivery and computer vision derived datasets, and generating accessibility metrics that can identify the possible benefits of projects and who they go to.

Michael Flaxman
Michael Flaxman, PHD, is the spatial data science practice lead at After 20 years of working within the domain of spatial environmental planning, he now actively works to develop the next generation of geospatial computing technologies at His main goal is to continue to develop spatial scenario planning tools, ultimately to bring the benefits of sustainable environmental planning to a much wider global audience.