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MODULE 1: INTRODUCTION AND DATA STRUCTURE


Spatial vs. Non-spatial Data

Spatial Data

Data that define a location. These are in the form of graphic primitives that are
usually either points, lines, polygons or pixels.

   •   Spatial data includes location, shape, size, and orientation.
           o For example, consider a particular square:
                     its center (the intersection of its diagonals) specifies its
                     location
                     its shape is a square
                     the length of one of its sides specifies its size
                     the angle its diagonals make with, say, the x-axis specifies its
                     orientation.
   •   Spatial data includes spatial relationships. For example, the arrangement
       of ten bowling pins is spatial data.

Non-spatial Data

Data that relate to a specific, precisely defined location. The data are often
statistical but may be text, images or multi-media. These are linked in the GIS
to spatial data that define the location.

   •   Non-spatial data (also called attribute or characteristic data) is that
       information which is independent of all geometric considerations.
          o For example, a person’s height, mass, and age are non-spatial data
             because they are independent of the person’s location.
          o It’s interesting to note that, while mass is non-spatial data, weight is
             spatial data in the sense that something’s weight is very much
             dependent on its location.



It is possible to ignore the distinction between spatial and non-spatial data.
However, there are fundamental differences between them:

          o    spatial data are generally multi-dimensional and auto correlated.
          o    non-spatial data are generally one-dimensional and independent.



                                                                          Sumant Diwakar
MODULE 1: INTRODUCTION AND DATA STRUCTURE




These distinctions put spatial and non-spatial data into different philosophical
camps with far-reaching implications for conceptual, processing, and storage
issues.

         o   For example, sorting is perhaps the most common and important
             non-spatial data processing function that is performed.
         o   It is not obvious how to even sort locational data such that all points
             end up nearby their nearest neighbors.




                                                                        Sumant Diwakar

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Spatial vs non spatial

  • 1. MODULE 1: INTRODUCTION AND DATA STRUCTURE Spatial vs. Non-spatial Data Spatial Data Data that define a location. These are in the form of graphic primitives that are usually either points, lines, polygons or pixels. • Spatial data includes location, shape, size, and orientation. o For example, consider a particular square: its center (the intersection of its diagonals) specifies its location its shape is a square the length of one of its sides specifies its size the angle its diagonals make with, say, the x-axis specifies its orientation. • Spatial data includes spatial relationships. For example, the arrangement of ten bowling pins is spatial data. Non-spatial Data Data that relate to a specific, precisely defined location. The data are often statistical but may be text, images or multi-media. These are linked in the GIS to spatial data that define the location. • Non-spatial data (also called attribute or characteristic data) is that information which is independent of all geometric considerations. o For example, a person’s height, mass, and age are non-spatial data because they are independent of the person’s location. o It’s interesting to note that, while mass is non-spatial data, weight is spatial data in the sense that something’s weight is very much dependent on its location. It is possible to ignore the distinction between spatial and non-spatial data. However, there are fundamental differences between them: o spatial data are generally multi-dimensional and auto correlated. o non-spatial data are generally one-dimensional and independent. Sumant Diwakar
  • 2. MODULE 1: INTRODUCTION AND DATA STRUCTURE These distinctions put spatial and non-spatial data into different philosophical camps with far-reaching implications for conceptual, processing, and storage issues. o For example, sorting is perhaps the most common and important non-spatial data processing function that is performed. o It is not obvious how to even sort locational data such that all points end up nearby their nearest neighbors. Sumant Diwakar