Abstract
Land-use control is local and highly varied. State agencies struggle to assess plan contents. Similarly, advocacy groups and planning researchers wrestle with the length of planning documents and ability to compare across plans. The goal of this research is to (1) introduce Natural Language Processing techniques that can automate qualitative coding in planning research and (2) provide policy-relevant exploratory findings. We assembled a database of 461 California city-level General Plans, extracted the text, and used topic modeling to identify areas of emphasis (clusters of co-occurring words). We find that California city general plans address more than sixty topics, including greenhouse gas mitigation and Climate Action Planning. Through spatializing results, we find that a quarter of the topics in plans are regionally specific. We also quantify the rift and convergence of planning topics. The topics focused on housing have very little overlap with other planning topics. This is likely a factor of state requirements to update and evolve the Housing Elements every five years, but not other aspects of General Plans. This finding has policy implications as housing topics evolve away from other emphasis areas such as transportation and economic development. Furthermore, the topic modeling approach reveals that many cities have had a focus on environmental justice through Health and Wellness Elements well before the state mandate in 2019. Our searchable state-level database of general plans is the first for California-and nationally. We provide a model for others that wish to comprehensively assess and compare plan contents using machine learning.