Relative space-based GIS data model to analyze the group dynamics of moving objects

Area of Science:

• Geographical Information Science (GIScience)
• Spatial Data Modeling
• Geospatial Analysis

Background:

• Traditional Geographic Information Systems (GIS) model moving objects in absolute space, leading to inefficient relative motion analysis.
• Existing GIS requires complex geo-computation for transforming between absolute and relative spatial references.
• There is a need for innovative GIS data models to directly handle the dynamic relative relationships of moving objects.

Purpose of the Study:

• To propose a relative space-based GIS data model for moving objects (RSMO).
• To develop algorithms for querying relationships and matching dynamic patterns of moving objects.
• To demonstrate the model's feasibility and computational advantages in real-world scenarios.

Main Methods:

• Development of the Relative Space-based Moving Objects (RSMO) data model.
• Implementation of relationship querying and relative relationship dynamic pattern matching algorithms.
• Experimental validation using scenarios like epidemic spreading, tracker finding, and crowd motion trend derivation.

Main Results:

• The RSMO model demonstrated significantly improved computational performance, with execution times 5-50% faster than absolute GIS methods.
• The model efficiently constructs, operates, and analyzes the dynamic relative relationships of moving objects.
• Experimental results confirmed the model's superior performance over traditional absolute methods in a commercial GIS software.

Conclusions:

• The proposed RSMO model effectively addresses the limitations of traditional GIS for analyzing moving object relationships.
• RSMO offers a promising approach for relative space-based geo-computation, analysis, and services.
• This innovation supports advancements in geodatabases, spatial indexing, and geospatial services for dynamic environments.