Integrating Unsupervised Land Cover Analysis with Socioeconomic Change for Post-Industrial Cities: A Case Study of Ponca City, Oklahoma

Urban centers shaped by industrial histories often exhibit complex patterns of land cover change that are not well-captured by standard classification techniques. This study investigates post-industrial urban change in Ponca City, Oklahoma, using remote sensing, unsupervised machine learning, and socioeconomic contextualization. Using a Jupyter Notebook version 7.0.8 environment for Python libraries, Landsat imagery from 1990 to 2020 was analyzed to detect shifts in land cover patterns across a relatively small, heterogeneous landscape. Principal component analysis (PCA) was applied to reduce dimensionality and enhance pixel distinction across multiband reflectance data. Socioeconomic data and historical context were incorporated to interpret changes in land use alongside patterns of industrial reduction and urban redevelopment. Results revealed changes in five distinct land cover classes of urban, vegetative, and industrial land uses, with observable trends aligning with key periods of economic and infrastructural transition. The trends also aligned with socioeconomic changes of the city, with a larger reduction in industrial and commercial land cover than in residential and vegetation cover types. These findings demonstrate the utility of machine learning classification in small-scale, heterogeneous environments and provide a replicable methodological framework for smaller city municipalities to monitor urban change.