# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 3 Mine Planning Using GISs
### Ore Reserve Estimation  
In the ore reserve estimation phase, GIS-based technologies, such as spatial analysis and targeting, exposition, 3D data visualization, and processing, can be utilized to identify and assess the location, size, and geometry of mineral deposits buried underground. [PERSON] et al. [13] introduced a query framework for spatial targeting within a 3D GIS software environment. They developed query tools that can be used as a GIS-3D plug-in, which included a proximity query (to a \"probe\" object), property query (numeric attribute), shell query (containment within a closed surface), meta-data query, feature query (dome, depression, curvature), trend query (dip plane, vector), and intersection query (with a \"probe\" object). The developed plug-in also demonstrated the usefulness of software extensions to GOCAD(c). The 3D GIS capabilities of GOCAD(c) facilitate queries and easy attribute representation, and support spatial targeting to meet mining and mineral exploration needs. [PERSON] and [PERSON] [14] integrated geostatistics with GIS capabilities, such as spatial analysis, exposition, and 3D data visualization and processing. In this study, GIS-based visual and interactive analyses were used to examine the relationship between the data in the study area and the total data in the integrated spatial database. A case study was conducted in Turkey to evaluate the accuracy of kriging, a geostatistical method for estimating the shape, spatial location, and volume of open mines. The accuracy of each of the three normal co-kriging estimations was assessed to determine which method was the most accurate. Consequently, the estimate with three variables appears to be more accurate than that calculated with the two-variable models. These results show that the additional information obtained from additional auxiliary variables improves the accuracy of the estimates in the mine data. [PERSON] and [PERSON] [15] identified and classified weighting methods used in the mine exploration process. Weighting methods can be classified into two main groups: data-driven and knowledge-driven methods. In this study, six weighting methods were identified and scientifically assessed, namely ratio estimation, analytical hierarchy process (AHP), Delphi, weight of evidence, logistic regression, and artificial neural networks (ANNs). The first three are examples of knowledge-driven methods, whereas the last three are classified as data-driven methods. The evaluation of each weighting method on copper deposits in Iran showed that the ANN method is the most accurate because it can accurately predict all borehole characteristics. [PERSON] et al. [16] developed a new outlier top-cut method to quantify statistically significant data and presented the results of an analysis of borehole data from Au deposits. Generally, a geostatistical interpolation method, such as kriging, is used to estimate the amount of resources. However, if the data used in such an analysis deviate significantly from the mean value, this data should be excluded because it can significantly affect the results. In this process, the existing top-cut method can cause errors by excluding significant data, whereas the new methodology developed in this study can minimize the loss of data and analyze statistically significant outliers. The Getis-Ord Gi* statistic was calculated for each Au sample value in a borehole dataset by considering the 3D spatial coordinates of the borehole data.