Differences Between Spatial And Non-spatial Data

GIS is a set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world for a particular purpose. Data is the most important component of GIS. Data are the observations we make from monitoring the real world and processed to give them meaning and turn them into information.

There are two types of data;

  1. Spatial Data
  2. Non-spatial Data

Key Differences Between Spatial and Non-spatial Data

Spatial Data

  • It answers where things are.
  • It describes the absolute and relative location of geographical objects.
  • It is stored in a shapefile or geodatabase.
  • Generally multi-dimensional and auto-correlated.
  • Satellite maps and scanned images help to obtain spatial data.
  • Relationships among spatial attributes are implicit. For example, boundaries 1 and 2 could be neighbors, but cannot be explicitly represented.
  • Types of spatial data: Raster Data – Composed of grids or pixels and identified by rows and columns. Vector Data – Composed of points, lines, and polygons.
  • Examples of spatial data are maps, photographs, satellite images, scanned images, roads rivers, contours, etc.

Non-spatial Data

  • It answers what and how much things are.
  • Characteristics of geographical features that are qualitative or quantitative in nature.
  • It is stored in a database table.
  • Generally one-dimensional and independent.
  • Forest managers, fire departments, environmental groups, and online media helps to obtain non-spatial data.
  • Relationships among non-spatial attributes are explicit. For example, two different attributes may be a part of, a subclass of, a member of, or represented in the form of arithmetic values or orders.
  • Types of non-spatial data: Nominal Data, Ordinal Data, Interval Data, Ratio Data
  • Examples of non-spatial data are names, phone numbers, area, postal code, rainfall, population, etc.
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  1. Spatial data is represented on maps by points, lines and polygons.
    Non-special data makes maps meaningful by highlighting the characteristics of special data.

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