Creating a longitudinal, data-driven 3D model of change over time in a postindustrial landscape using GIS and CityEngine. Issue 4 (29th January 2018)

• Record Type: Journal Article
• Authors: Arnold, John David McEwen; Lafreniere, Don
• Is Part Of: Journal of cultural heritage management and sustainable development. Volume 8:Issue 4(2018)
• Page Start: 434
• Page End: 447
• Publication Date: 2018-01-29
• Subjects: Industrial heritage -- Cultural landscapes -- 3D modelling -- CityEngine -- Historical GIS -- Historical spatial data infrastructure; Cultural property -- Protection -- Periodicals; Sustainable development -- Periodicals; Historic preservation -- Periodicals; 363.6905
• DOI: 10.1108/JCHMSD-08-2017-0055
• Languages: English
• ISSNs: 2044-1266

Abstract

Abstract : Purpose: The purpose of this paper is to create a longitudinal data-driven model of change over time in a postindustrial landscape, using the "Copper Country" of Michigan's Upper Peninsula as a case study. The models resulting from this project will support the heritage management and public education goals of the contemporary communities and Keweenaw National Historical Park that administer this nationally significant mining region through accessible, engaging, and interpretable digital heritage. Design/methodology/approach: The paper applies Esri's CityEngine procedural modeling software to an existing historical big data set. The Copper Country Historical Spatial Data Infrastructure, previously created by the HESA lab, contains over 120, 000 spatiotemporally specific building footprints and other built environment variables. This project constructed a pair of 3D digital landscapes comparing the built environments of 1917 and 1949, reflecting the formal and functional evolution of several of the most important copper mining, milling, and smelting districts of Michigan's Keweenaw Peninsula. Findings: This research discovered that CityEngine, while intended for rapid 3D modeling of the contemporary urban landscape, was sufficiently robust and flexible to be applied to modeling serial historic industrial landscapes. While this novel application required some additional coding and finish work, by harnessing this software to existing big data sets, 48, 000 individual buildings were rapidly visualized using several key variables. Originality/value: This paper presents a new and useful application of an existing 3D modeling software, helping to further illuminate and inform the management and conservation of the rich heritage of this still-evolving postindustrial landscape.