From Geospatial Data to Insight: A Practical Guide to Machine Learning in Python for Real-World Problem-Solving

From Geospatial Data to Insight: A Practical Guide to Machine Learning in Python for Real-World Problem-Solving

Assefa Senbato Genale, Desalegn Aweke Wako, Tsion Ayalew Dessalegn
Copyright: © 2024 |Pages: 27
DOI: 10.4018/979-8-3693-6381-2.ch009
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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.
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Literature Review

Recently, in geospatial data analysis, machine learning techniques have become powerful methods for extracting valuable insights from geospatial data. In this section, we have reviewed the most recent and relevant literature.

Authors (Jena, Pradhan, Prasanajit, and Alamri, 2021) have proposed a deep learning approach and geospatial analysis for assessing earthquake risks in NE India. This paper addressed a significant issue in the region by providing an innovative approach to evaluate earthquake risk prone to seismic activities. They used a convolutional neural network model and compared it with a conventional machine learning model. This study used DEM and shapefile data, spatial analysis, and a complete earthquake catalog to assess the earthquake risks in the region. Finally, they concluded that the deep learning model was the best choice for evaluating earthquake risks in the region, using geospatial data collected from the chosen area in NE India. In addition, the authors analyze the challenges and limitations of the findings for the future direction.

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