There are two main data types when it comes to GIS: vector data and raster data. Vector data is data that uses points, lines and polygons to represent spatial features. Raster data on the other hand, uses a grid of cells with varying values to represent spatial features.

Vector data is great at representing objects with defined boundaries, like roads or landmarks. Raster data is better at representing a continuous surface, like land cover. Both methods have one thing in common, they are both a model of our real world. 
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But GIS doesn’t just deal with physical structures. GIS can be used to describe information like human population, animal movement and even disease outbreaks.

Information formation
GIS data can come in large volumes, so it’s important to keep it organised. To keep it organised, the data is split into layers - a concept taken from mapping - with each layer used to describe a specific feature.

For example, in GIS, you would have one layer of just roads and another of just buildings. The layers are then used for analysis and to derive relationships between them. Based on the example given, you could analyse the distance between buildings and roads from the two layers.

Layering is useful as it separates features of interest into common groups. Just like the example above, all the universities in the area are grouped under the layer ‘Universities’, train stations grouped under ‘Railway train locations’, and all roads are grouped under the layer ‘Roads’.

Of course, if the type of road is important in your analysis, you could break it down further into different layers like ‘Freeway roads’ and ‘Residential roads’. Once it’s broken down into common groups, it makes it easier to pick features to conduct analysis on.

Why use GIS? 
It can be difficult and overwhelming to deal with so much data in the real world. Creating a model based on the real world helps us focus on information that’s important to us.
 
GIS also helps us visualise vast quantities of data in a map or image that’s easy to understand. This helps us manage and plan important decisions around natural disaster management, urban planning, environmental conservation, and more.
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Bringing back the example of the visualisation of animal movement, it’s interesting to visualise how an animal moves through the environment and how far it moves within the habitat. This information is helpful for conservation scientists to ensure the habitat of endangered species is preserved and in allocating appropriately sized reserves for these species.