• 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.
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.
Non-spatial data, simply, is data that contains 'what' instead of 'where'. As mentioned above, it is independent of geographic location. An attribute could contain both spatial and non-spatial data.
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.
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.
There are four typical data types that we use in GIS: integer, float/real, text/string, and date.
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.
Spatial data can be referred to as geographic data or geospatial data. Spatial data provides the information that identifies the location of features and boundaries on Earth. Spatial data can be processed and analysed using Geographical Information Systems (GIS) or Image Processing packages.
Spatial data are of two types according to the storing technique, namely, raster data and vector data.
(ˌnɒnˈspeɪʃəl ) adjective. not involving space. combining spatial information with non-spatial information such as facts and figures.
Examples of non-spatial data are names, phone numbers, area, postal code, rainfall, population, etc.
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.
Non-spatial measure. describes and compares the masses of objects.
A spatial-to-non spatial dimension is a dimension whose primitive-level data are spatial but whose generalization, starting at a certain high level, becomes non spatial.
So from geographical point of view a single vehicle (or a flock of birds) is a non-geographical feature. A compass and a scale bar on a map are just a map decorations and may not be considered as geographical or non-geographical features because they are not a real world objects.
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.
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.
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 is also known as geospatial data, spatial information or geographic information.
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.
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.
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.
• 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.
Spatial has broader meaning, encompassing the term geographic. Geographic data can be defined as a class of spatial data in which the frame is the surface and/or near-surface of the Earth. 'Geographic' is the right word for graphic presentation (e.g., maps) of features and phenomena on or near the Earth's surface.