October 25, 2021. Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth's surface. Non-spatial data, on the other hand, is data that is independent of geographic location.
Spatial data provides the location information of the features whereas non-spatial data describes characteristics of the features. Non-spatial data is also known as attribute data. A combination of both data is known as geospatial data.
Some examples of non-spatial data could be: Lists of reference values (such as Country codes or equipment manufacturers). Postal addresses. Aggregated features such as National Roads which store the road name and reference a set of spatial road segments.
Spatial data can have any number of attributes about a location. For example, this may be a map, photographs, historical information or anything else that may be deemed necessary.
There are four typical data types that we use in GIS: integer, float/real, text/string, and date.
1.4 Data Model. The Spatial data model is a hierarchical structure consisting of elements, geometries, and layers, which correspond to representations of spatial data.
Spatial data are of two types according to the storing technique, namely, raster data and vector data.
The two primary spatial data types are Geometric and Geographic data. Geographic data is data that can be mapped to a sphere (the sphere in question is usually planet earth). Geographic data typically refers to longitude and latitude related to the location of an object on earth.
Spatial data is information about where observations are in relation to each other. Usually, this means that one of the dimensions associated with each observation describes that record's position in space.
answer “what?” This kind of data is independent of geographic location. They contain information about a particular object but don't define its location.
non·spa·tial ˌnän-ˈspā-shəl. : not spatial: such as. : not relating to, occupying, or having the character of space. nonspatial data. : not relating to or involved in the perception of relationships (as of objects) in space.
Generally speaking, Spatial data represents the location, size and shape of an object on earth surface such as mountain, plain, township, people etc. it also provides all the attributes of an entity that is being represented. Non Spatial data cannot be related to a location on the earth surface.
The main difference between attribute data and spatial data is that the attribute data describes the characteristics of a geographical feature while spatial data describes the absolute and relative location of geographic features.
Both “geospatial” and “GIS” are concerned with mapping data or locations. The term “spatial” is even broader in scope. In most situations, you can interchangeably use any of these terms without getting yourself in trouble.
Spatial data is linked to geographical locations such as cities, towns, and so on. A spatial database is designed to store and query information about objects. These are the objects that have a geometric space definition.
Geospatial data helps us to understand the world, and it can also help us to discover hidden spaces. Underground networks and caves for example, can be hard to access and explore. With geospatial data, it's possible to create a digital model or photographic interpretation of an area that has never been explored before.
GIS data can be separated into two categories: spatially referenced data which is represented by vector and raster forms (including imagery) and attribute tables which is represented in tabular format.
Important characteristics of spatial data are its measurement level, map scale and associated topological information. Nominal, ordinal, interval and ratio are the four levels of measurement for populating the spatial data matrix; they hold different amounts of information and determine what analysis can be performed.
Spatial data consists of spatial objects made up of points, lines, regions, rectangles, surfaces, volumes, and even data of higher dimension which includes time. Examples of spatial data include cities, rivers, roads, counties, states, crop coverages, mountain ranges, parts in a CAD system, etc.
Explanation One way to obtain spatial data is by direct observation of relevant geographic phenomena. This can be done through ground-based field surveys or by using remote sensors on satellites or aircraft.
Time is supported in spatial data in a variety of ways. Time information can be stored as an attribute (feature classes, stand-alone tables, and mosaic datasets), or it can be stored internally (such as in netCDF data). The following sections describe data that can be visualized through time.
Six types of spatial analysis are queries and reasoning, measurements, transformations, descriptive summaries, optimization, and hypothesis testing.
Non-spatial measure. describes and compares the masses of objects.
GIS can use any information that includes location. The location can be expressed in many different ways, such as latitude and longitude, address, or ZIP code. Many different types of information can be compared and contrasted using GIS.