Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem

Area of Science:

• Digital health
• Epidemiology
• Geospatial data science

Background:

• Understanding spatial context is crucial for digital health challenges.
• Integrating diverse data sources (social, environmental, infrastructural) is essential for population health, epidemiology, and eHealth studies.
• A novel analytical framework is needed to leverage location for contextual knowledge.

Purpose of the Study:

• To develop a spatial data analytics framework integrating social, environmental, and infrastructural knowledge.
• To empower health researchers to use geospatial context for timely population health issue resolution.
• To offer an innovative technological approach and reduce the educational burden of spatial thinking for health researchers.

Main Methods:

• Developed a unique analytical tool using location as the key for data integration across source, geography, and time.
• Created a geospatial big table with over 162 million locations and 5549 attributes.
• Quantified contextual concepts (adjacency, proximity, distance) through geoanalytics, creating new attributes for analysis.

Main Results:

• Built the Geospatial Analytical Research Knowledgebase (GeoARK), a unique geospatial big data ecosystem.
• Enabled analytics across resolution groups, sources, and geographies for information extraction and insight generation.
• Demonstrated GeoARK's capability through case studies in telehealth, income inequality, and COVID-19 risk assessment.

Conclusions:

• Successfully integrated multifaceted data into a large, location-enabled database to understand health event context.
• The GeoARK system empowers health professionals to conduct complex research by synergizing health and geospatial information.
• Reduced the technical barrier of geospatial processing, shifting the challenge to developing spatial thinking skills among health researchers.