Motivation:

• Producing analytics on multidimensional settlement characteristics (intensity, size, access) are pre-requisite for evidence-based policy making for spatial sustainability transition where applied geoinformatics and digital visualization can have the major contribution  
• Graph base knowledge graph can present the complex topic with enhanced readability and visualization
• Using graph-based data science algorithm (GDS) even can be adapted to explore multi-dimensional parameters
• GDS approaches have the scope of combining with ML/AI workflow

Specific Tasks:

• Conducting a brief literature review to conceptualize on graph-based data science algorithm (GDS) and applications
• Feasibility study in the adoption of GDSA (centrality, similarity, community detection) using Neo4J
• Using an open multi-temporal database on large cities in Germany
• Publish the results in an open web interface – e.g. GeoNode framework

Expected Output:

• Collection of key concepts, methods, tools, and application in focus of adoption of GDS in dealing with settlement-related geodata
• Prototype analytics and visualization applications for exploring settlement characteristics
• A written master thesis

Key Requirements:

• Familiar and interested in spatial science topics among others human settlement systems and built environment
• Knowledge of scripting language SQL, R/Python
• Skills in the English language to researching international literature

Staff involved: Dr. Sujit Sikder (s.sikder@ioer.de)