Meaning of spatial analysis
The process of geospatial analysis encompasses the interpretation, exploration, and modeling of GIS data. Based on the number of tasks and their complexity, spatial analysis software processes the retrieved information. In a nutshell, visualization is the simplest approach, while a more detailed approach involves comprehensive analytics and specific tools for elaborating actionable insights.
In spatial analysis, distance and shape are measured, routes and transportations are mapped, and spatial correlations are established by comparing observed and historical locations of objects, events, and places.
Spatial analysis typically involves five steps: understanding your goal, preparing data, selecting tools and techniques, conducting research, and estimating results.
What are some benefits Of Spatial Analysis?
Spatial analysis in GIS facilitates decision-making in a variety of fields, from minor routine business issues to disaster relief efforts on a global scale. By tracing changes and understanding trends, georeferencing technology not only recognizes coordinates but also states the time.
As satellites capture locations that are difficult to reach or remote, spatial analysis software and techniques allow data to be handled with accuracy and speed that would never be possible using a human analyst. With computerized spatial data analytics, large data volumes can be processed almost instantly.
Multiple aspects of the situation are highlighted by GIS geospatial analysis:
Therefore, spatial analysis is useful for observing the current situation and providing actionable insights for the future in any situation that involves geographical position.
What is the importance of spatial analysis?
Businesses with distributed locations like retail, manufacturing, and banking have long examined location-specific performance. With an increasing number of connected devices, geodata and insights become more abundant. With visual mapping that combines location data from satellites, smartphones, wearables, vehicles, and even shipping cargo, your existing analytics and dashboards can be enhanced by providing new perspectives.
Spatial analytics is growing primarily on the back of the Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud data warehousing, and the integration of GIS with business intelligence (BI) tools.
READ MORE: Top 7 Geospatial Analytics Use Cases