Title: GAP: Geospatial Analyzing Patterns Methods and Renewable Energy applications
Authors: Taha Alfadul Taha Ali, Associate Professor of Information Technology (Geospatial Data Science)
Volume: 9
Issue: 8
Pages: 8-17
Publication Date: 2025/08/28
Abstract:
This paper illustrates the Analyzing Patterns Methods and renewable energy application. The objectives are Analyze, design and implement : Average Nearest Neighbor, High/Low Clustering, Incremental Geospatial Autocorrelation, Geospatial Autocorrelation, Multi-Distance Geospatial Cluster Analysis (Ripley's k-function). The methodologies are Analysis, Design, Develop, Implementation, and Evaluation for GIS Analyzing Patterns for renewable energy. There are many results such as GIS Analyze Patterns (Average Nearest Neighbor, High/Low Clustering, Incremental Geospatial Autocorrelation, Geospatial Autocorrelation, Multi-Distance Geospatial Cluster Analysis (Ripley's k-function)), Design Geodatabase (Average Nearest Neighbor, High/Low Clustering, Incremental Geospatial Autocorrelation, Geospatial Autocorrelation, Multi-Distance Geospatial Cluster Analysis (Ripley's k-function)), and implementations (Average Nearest Neighbor, High/Low Clustering, Incremental Geospatial Autocorrelation, Geospatial Autocorrelation, Multi-Distance Geospatial Cluster Analysis (Ripley's k-function)). In conclusion we recommendations and future researches: Geospatial Analyzing Patterns integrated GIS Data Cloud, GIS IoT, GIS AI, and GIS Cyber Security.