NACIS 2022

United States Communities in Harm's Way - Mapping the Intersection Between Wildfire Hazards and Vulnerable Communities
Zac Stanley, University of Kentucky
This talk will focus on my final MS project for the University of Kentucky New Maps Plus Program. The project is titled: "United States Communities in Harm's Way: Where are the Vulnerable in Relation to Wildfire Hazards?". It will explore the datasets, libraries and mapping techniques used to cartographically display where modeled wildfire hazard potential intersects with census designated places that have some degree of social vulnerability as defined by the centers for disease control. The result is an interactive bi-variate map that uses graduated symbols in concert with a sequential color scheme. Mapping libraries used include Leaflet and Mapbox.

Maps of the Arctic Alaska Boundary Area as Defined by the U.S. Arctic Research and Policy Act
Christopher Richmond, U.S Geological Survey
This project presents a series of general reference maps showing relevant geospatial features of the U.S. Arctic boundary as defined by the U.S. Congress since 1984. The first generation of the U.S. Arctic Research and Policy Act (ARPA) boundary maps was originally formatted and published in 2009 by a private firm contracted with the National Science Foundation and the U.S. Arctic Research Commission. Recognizing the steadily increasing relevance of Arctic issues to national and global affairs that requires more functional projections and online tools, the U.S. Geological Survey (USGS) Alaska Regional Office and the National Geospatial Technical Operations Center developed this updated series of ARPA boundary maps.

Landscape Metrics Show Potentials to Outperform Other Traditionally-used Ancillary Datasets in Dasymetric Mapping of Population
Heng Wan, Pacific Northwest National Laboratory
Population downscaling and interpolation methods are required to produce data which correspond to spatial units used in urban planning, demography, and environmental modeling. Previous approaches to imperviousness-based dasymetric mapping ignore cell-level patterning of imperviousness while landscape metrics derived from impervious cover percentage map offer a promising approach to capture these patterns. In this study, we incorporate landscape metrics into intelligent dasymetric mapping to downscale population from census tracts to block groups in four states, and then compare its performance against other dasymetric mapping methods based on traditionally-used datasets . The results show that landscape metrics outperform other models.

Slack channel: #nacis2022-rockisland