Analyzing the Production and Use of Fossil Fuels: A Case for Data Mining and GIS

Abstract

As technology progresses and data grows both larger and more complex, techniques are being developed to keep up with the exponential growth of information. The term “data mining” is a blanket term used to describe an approach to find anomalies and correlations in a large dataset. This approach involves leveraging data mining software to manipulate and prepare data, apply statistics to quantify trends and characteristics in the data from a high level, and potentially apply advanced techniques like machine learning to identify patterns that wouldn’t be apparent otherwise. In this case study, data mining aided a GIS in displaying substantial amounts of oil, gas, and coal data to make observations regarding two groups: OPEC and the largest non-OPEC fossil fuel producers from 1980 to 2020. To make more sophisticated observations and apply additional context to the trends observed in the data, populations and GDP data for the same period were included in the analysis to enrich the hydrocarbon production and consumption data and to help explain how these valuable resources are traded and consumed. This case study will apply appropriate data mining methods to feed data to a GIS and showcase trends that wouldn’t be apparent otherwise and will additionally identify topics for further research.