4.2.1. Forms of Geographical Information

Two types of the data represent the geographical information. These are spatial and non–spatial data.

Geographical information is divided into two main categories: spatial and non-spatial data. Spatial data refers to information that has a geographic aspect, like coordinates or locations on a map, while non-spatial data refers to information that doesn't have a location associated with it, like statistics and descriptions. Understanding this distinction is crucial in the field of geography as it impacts how we collect, analyze, and interpret data.

Think of spatial data like the GPS coordinates of your house, while non-spatial data could be the number of bedrooms in your house. Both pieces of information are important, but they serve different purposes.

The spatial data are characterised by their positional, linear and areal forms of appearances.

Spatial data can be represented in different forms: positional, which indicates the exact coordinates; linear, which presents data in the form of lines (like roads or rivers); and areal, which represents areas (like land use in a town). Each of these forms provides unique insights into geographical patterns and relationships, helping us understand spatial distributions.

Imagine a map showing a city. The streets are the linear features, the parks are the areal features, and the specific locations of buildings represent positional features. Together, they give us a comprehensive view of the city's layout.

A database contains attributes and their value or class. The non-spatial data on the left display cycle parts, which can be located anywhere. The data record on the right is spatial because one of the attributes, the name of different states, which have a definite locations in a map.

In a geographic database, attributes are used to define characteristics of spatial data. For instance, a cycle shop might list parts (non-spatial data) without a specific location, while the states where these parts can be sold (spatial data) can be mapped. This helps in understanding where resources are available and how they relate to geographical locations.

Think of a library. The books themselves are like non-spatial data, while their location on the shelves (like in a specific section) is akin to spatial data. Knowing both allows you to find and understand the resources you have.

These data forms must be geometrically registered to a generally accepted and properly defined coordinate system and coded so that they can be stored in the internal database structure of GIS.

To effectively use spatial data in Geographic Information Systems (GIS), they need to be accurately aligned to a coordinate system. This ensures that all spatial data can be integrated correctly, visualized, and analyzed together within the GIS framework. Proper registration and coding allow for efficient storage and retrieval of data when conducting analyses.

Consider how a puzzle works. Each piece (data) must fit perfectly into the overall picture (GIS). If even one piece is out of alignment, the final image will be incomplete or wrong. Accurate spatial registration is like ensuring every puzzle piece is in the right place.

The choice of a source of geographical data for a GIS application is, however, largely governed by the application area in itself, the available budget, and the type of data structure, i.e., vector/raster.

Selecting the appropriate source for geographic data is essential for efficient GIS use. This decision is influenced by factors such as the specific requirements of the project area, the budget constraints, and whether the data is best represented in vector (points, lines, polygons) or raster (grid of pixels) format. Understanding these elements helps GIS professionals choose suitable data sources.

Think about planning a road trip. You wouldn't choose the same map or GPS data if you're traveling through a city versus vast rural areas. For urban travel, detailed street maps are necessary (vector data), whereas for countryside navigation, satellite images might be more useful (raster data).

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