There’s one element that most data sets have in common — location. And this element is what allows organizations and entities to nicely organize data points on spatial analysis maps. The advantages of having data revealed on maps are the insights about available objects and their whereabouts.
With the help of spatial data analysis, you can do just that: put the wealth of data at your disposal on the map to gain meaningful insights and make profitable decisions. But, in order to be able to perform such an analysis, it is important to be educated on how to work with GIS tools.
Spatial Analysis: How does it work?
The answer to this question lies in the spatial analysis definition. SA is a technique that studies objects and entities by utilizing geographic, geometric, and topological characteristics. In order to present data visually, datasets are maintained with the help of layers that provide insights into an industry’s state, ongoing trends, and predictions. Moreover, layers identify the challenges difficult for people to detect.
Each business deals with a unique scope of challenges by employing advanced spatial analysis. This means, that there’s no one universal approach to handling data with SA. Depending on goals, issues, and datasets at hand, there are multiple types of spatial analysis.
Suitability analysis
This technique is arguably the most widely adopted in the GIS. The workflow with this approach is universal regardless of the field of application. As the initial step, specialists identify essential criteria. Next, these criteria are applied to available data, and the results are acquired. This type of spatial analysis is used in geography, areas planning, solving manufacturing challenges, public security, ensuring economic growth and sustainability.
Spatial Clustering Analysis
The use of this type of SA ranges from locating outbreaks of diseases and polluted vital natural resources to gaining insights from buyers’ behavior. Governments and NGOs adopt this technique for humanitarian purposes, for example, to distribute aid. Businesses in their turn, utilize it for unleashing their economic opportunities. Such industries as insurance, retail, banking, real estate, telecom, transport, and others find this type of spatial clustering vital for their business.
Exploratory Analysis
This is a perfect approach to data management for those seeking more information about patterns and data distribution. This technique can be implemented either independently or serve as a jumping-off point for different analysis types. The sectors that use it include healthcare, retail, public works, natural resources, and more.
Cost Distance Analysis
This technique is the most pivotal for those who try to calculate their routes and need more information than merely miles count. This approach can empower travelers with the knowledge of what is the most cost-efficient way to get from point A to point B. Moreover, it also helps businesses discover a network of routes that interconnect certain geographical points.
Spatial-temporal analysis
This approach to spatial analysis marries time and space. It is adopted to follow changes that take place in specific areas over a definite period of time. This SA type has become widely popular across industries. The leading spheres that benefit the most from the spatial-temporal analysis are epidemiology, disaster management, environmental science, and transportation.
Welcome SA into your workflow
Spatial analysis with its powerful capabilities and multiple techniques has already proven its value to numerous businesses. If you are ready to follow their lead and start gaining beneficial insights for effective decision-making and data-driven business expansion, then now is the time to incorporate SA. Check out the services Aspectum provides and reach out to our manager to discuss how exactly these GIS tools will drive your business forward.