From Geospatial Data to Insight

Abstract

As technology advances, the potential applications for geospatial data will only continue to grow. However, conventional techniques for evaluating geographic data frequently involve manual interpretation or rule-based strategies, which take a long time and have a limited capacity to handle big datasets. Current technology has significantly enhanced geospatial analysis by providing powerful data collection, processing, and interpretation tools. This study used machine learning to analyze geospatial data and extract insights that would be difficult or impossible to obtain using traditional methods. Literature review, various Python libraries for geospatial data, building and evaluating machine learning models for algorithms like random forest, decision tree, linear regression, and K-means clustering using freely available geospatial data were presented. Machine learning makes analyzing geospatial data more effective for deriving deep understandings and extracting insights.