GIS (Geographic Information System) analytics refer to the process of analyzing geographic data using specialized software tools to gain insights and make decisions about various real-world problems. GIS analytics involves collecting, managing, analyzing, and presenting spatial data in the form of maps, graphs, charts, and other visualizations to provide insights into patterns, trends, and relationships that may exist in the data.
GIS analytics can be used in a wide range of applications such as urban planning, environmental management, transportation, natural resource management, emergency management, and public health. By using GIS analytics, organizations can make more informed decisions and optimize their operations by identifying and understanding the spatial relationships among different data points.
Some common GIS analytics techniques include spatial analysis, network analysis, 3D modeling, and predictive modeling. Spatial analysis involves analyzing the spatial relationships among different data points to identify patterns and trends. Network analysis involves analyzing the connectivity and accessibility of different locations in a geographic area. 3D modeling involves creating a virtual representation of a geographic area to help visualize and analyze the data. Predictive modeling involves using statistical techniques to forecast future trends based on historical data.