Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

Built and social indices for hazards in Children's environments

Leveraging the capabilities of the Historical Spatial Data Infrastructure (HSDI) and composite indices we explore the importance of children's built and social environments on health. We apply contemporary GIS methods to a set of 2000 historical school records contextualized within an existing HSDI to establish seven variables measuring the relative quality of each child's built and social environments. We then combined these variables to create a composite index that assesses acute (short-term) health risks generated by their environments. Our results show that higher acute index values significantly correlated with higher presence of disease in the home. Further, higher income significantly correlated with lower acute index values, indicating that the relative quality of children's environments in our study area were constrained by familial wealth. This work demonstrates the importance of analyzing multiple activity spaces when assessing built and social environments, as well as the importance of spatial microdata.

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.