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more on: datadevelopment

Topic modeling to discover the thematic structure

and spatial-temporal patterns of building renovation

+ Yuan Lai, Constantine Kontokosta

Abstract

Topic modeling to discover the thematic structure and spatial-temporal patterns of building renovation and adaptive reuse in cities

Computers, Environment and Urban Systems

Volume 78, November 2019, 101383

Building alteration and redevelopment play a central role in the revitalization of developed cities, where the scarcity of available land limits the construction of new buildings. The adaptive reuse of existing space reflects the underlying socioeconomic dynamics of the city and can be a leading indicator of economic growth and diversification. However, the collective understanding of building alteration patterns is constrained by significant barriers to data accessibility and analysis. We present a data mining and knowledge discovery process for extracting, analyzing, and integrating building permit data for more than 2,500,000 alteration projects from seven major U.S. cities. We utilize natural language processing and topic modeling to discover the thematic structure of construction activities from permit descriptions and merge with other urban data to explore the dynamics of urban change. The knowledge discovery process proceeds in three steps: (1) text mining to identify popular words, popularity change, and their co-appearance likelihood; (2) topic modeling using latent Dirichlet allocation (LDA); and (3) integrating the topic modeling output with building information and ancillary data to discover the spatial, temporal, and thematic patterns of urban redevelopment and regeneration. The results demonstrate a generalizable approach that can be used to analyze unstructured text data extracted from permit records across varying database structures, permit typologies, and local contexts. Our machine learning methodology can assist cities to better monitor building alteration activity, analyze spatiotemporal patterns of redevelopment, and more fully understand the economic, social, and environmental implications of changes to the urban built environment.

 

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Yuan Lai is a PhD candidate in the NYU Tandon School of Engineering and a research affiliate of Dr. Constantine Kontokosta's Civic Analytics Program at the NYU Marron Institute of Urban Management.

Constantine E. Kontokosta, Ph.D., is an Associate Professor of Urban Science and Planning and Director of the Civic Analytics program at the NYU Marron Institute of Urban Management. He also directs the Urban Intelligence Lab and holds cross-appointments at the Center for Urban Science and Progress (CUSP) and the Department of Civil and Urban Engineering at the NYU Tandon School of Engineering, and is affiliated faculty at the NYU Wagner School of Public Service.

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