Spatial and non spatial data:
Spatial data sets are primarily defined as those which are directly or indirectly referenced to a location on the surface of the earth. When a dataset cannot be related to a location on the surface of the earth is referred as non spatial data. GIS technologies are unique in their capability to combine the two data sets thereby bringing a paradigm shift in thinking how the planning and monitoring system may work. The power and potential of such systems is unlimited thereby providing huge opportunity to process information which can be used effectively.
The non spatial data are numbers, characters or logical type. The spatial data sets, however has primary data type as point, line or polygon and may be referenced to some specific grid system. Traditionally the information systems in past have created the huge data repositories which appear to be non spatial in nature. However, these may indirectly be referring to specific locations.
Let us elaborate the discussion with example of a data table containing different types of soils and their characteristics. Here data are typically non spatial in nature as it directly or indirectly, does not refer to any location. In another example consider a table containing population information for specific locations say city ,districts or provinces. The population though is non spatial type, has relationship with locations. For large number of locations, the scope for use of such data could be to understand name of location having largest or least populations or statistical annotations eg, mean, average or other values for all the locations. If the location data is expressed specially incorporating latitude/longitude or linked to specific shape of locations eg districts or provinces on the map, the scope of analysis of data enhances many folds. Representation of population in defined slabs on map can be represented in different colours and would enable demarcation of areas of highest population or least population or identification of hot spots as per the requirements. The maps in such cases may be composed in many different ways. Such output are not only colourful and interesting to look at but also may accommodate display of large tabular data on one page. The Overlaying of different layers of spatial data such as transport, water or other natural resources may further provide an additional dimension for analysis of population variation. The following hence may be concluded;
- Non spatial data are easy to create.
- Non spatial data referred to specific locations may be linked to spatial data using GIS technologies which enormously enhances scope for analysis.