Integrating geologic data from the Thompson Nickel Belt into a GIS

• Main Author: Michalak, Dianne
• Published: 2012
• Online Access: http://hdl.handle.net/1993/7343

As the geologic community moves towards a digital environment, many still have to be convinced that data management is as necessary as chemical analysis. In fact, assessing the data and software needs of a project from a systems perspective is ideally suited to a GIS expert. The expert is able to assemble very detailed datasets into a comprehensive overview of a region. The interaction between people and data has to be monitored to determine the success of a system. Identifying limitations in terms of processes and software and adapting solutions to meet the challenge is also part of the system approach. GIS project management issues particular to geological applications will be presented in the context of the CAMIRO Thompson Nickel Belt (TNB) project. Data are considered to be a resource and must be appropriately managed to realize its full potential. The benefits of designing and implementing a structured system of data management can be realized through time saving measures, efficient data storage and retrieval and the extraction of valuable information. Components of the system - people, processes, data, software and hardware - are examined with respect to the needs of the project. Application of spatial analysis techniques and data fusion were used to create mineral potential maps of nickel for the TNB. Measures of belief, probability, disbelief and uncertainty present a flexible way of quantifying data and are used to weight attributes in data layers. The data layers are then combined to produce mineral potential maps. The process of entering, manipulating, calculating and interpreting data influences potential results. This project identified issues that should be addressed in multidisciplinary projects. A realistic assessment of participants technical resources and timelines for data acquisition can increase use of data in GIS, hopefully evolving from simple visualization to more integrated analysis.