Abstract
A rapidly changing Arctic has impacted biophysical and human systems while creating new economic opportunities. Spatially identifying locations with development potential in this changing environment requires characterizing convergences in critical enabling/constraining factors occurring in a particular place. However, mapping techniques based on simple overlays of spatially heterogeneous data may result in visual clutter, compromising legibility, and increasing the likelihood of interpretation errors. To overcome these limitations, we introduce Pythia, a tool that combines geographic statistical analysis with a subtractive color model to enable bi- or tri-variate data analysis. Three case studies showcase this visualization tool. Case study 1 identifies locations where temperature and population are projected to increase by 2040. Case study 2 reveals locations with a significant presence of major roads and high NO2 concentrations but few hospitals and clinics. In case study 3, a combination of transportation infrastructure, protected areas, and travel and tourism infrastructure signals challenges for the future Alaskan tourism industry. Comparing these results allows for further geographic characterization of locations, aiding policymakers in identifying areas lacking resources and infrastructure, exploring possible futures, and supporting long-term strategic planning.
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