Predicting the Past: A Geospatial Analysis of Los Angeles Landmark Representation and Future Identification

Authors: Morgan Quirk*,
Topics: Spatial Analysis & Modeling, Ethnicity and Race, Sexuality
Keywords: historic preservation, predictive modeling, diversity, representation, social factors, equity, significance
Session Type: Paper
Day: 4/5/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Marriott Ballroom Salon 3, Marriott, Lobby Level
Presentation File: No File Uploaded

Los Angeles’ ever-changing urban environment presents a unique framework for analyzing trends in local landmark designation while challenging the paradigm for establishing historic significance. In applying both a historiographic and geospatial analysis to a value-based practice such as determining a site’s historic significance, we might begin to reveal an all too systematic approach for determination with little room for progress. Of the near twelve hundred Historic Cultural Monuments, only a small percentage are representative of marginalized groups. Many of those sites connected with diverse stories are not being recognized for their cultural associations. By employing similar predictive modeling strategies found in archaeological studies, this work aims to promote a more inclusive lens by using a weighted multi-criteria evaluation of both physical and social data to identify potentially diverse historic sites at a city-wide scale. Serving as a provocation rather than a comprehensive alternative to the work that many preservationists do to find meaning and curate public memory, we can address a critical issue of preservation equity at the local level while also challenging the very process that could perpetuate this lack of representation. The result is a series of analytical maps, visual data, and written evaluation of local landmarks that aims to present a comprehensive and collective look Los Angeles’ historic fabric–one that goes beyond what is captured in our current template for significance and is emblematic of the remarkably diverse landscape we all know it to be.

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