There are four typical data types that we use in GIS: integer, float/real, text/string, and date.
Non-spatial data are stored in GIS as tables. Such tables are known as non-spatial (attribute) tables. A non-spatial table is represented by rows and columns in which each row shows a spatial feature and each column represents a characteristic.
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.
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.
: 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.
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.
Types of spatial patterns represented on maps include absolute and relative distance and direction, clustering, dispersal, and elevation.
Spatial data are of two types according to the storing technique, namely, raster data and vector data.
These data types are usually called spatial data types, such as point, line, and region but also include more complex types like partitions and graphs (networks).
GIS applications include both hardware and software systems. These applications may include cartographic data, photographic data, digital data, or data in spreadsheets. Cartographic data are already in map form, and may include such information as the location of rivers, roads, hills, and valleys.
The non-spatial data in a GIS are presented in tables that make up a database linked to the map. The geographic features in the table are presented in horizontal rows, where each row represents a single record. The attributes of the features in the table are listed in vertical columns, with field names at the top.
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.
The elements include an overview describing the purpose and usage, as well as specific quality elements reporting on the lineage, positional accuracy, attribute accuracy, logical consistency and completeness.
Two approaches or models have been widely adopted for representing the spatial data within GIS ; The cartographic map model and the geo-relational model.
Six types of spatial analysis are queries and reasoning, measurements, transformations, descriptive summaries, optimization, and hypothesis testing.
Spatial distribution can be measured as the density of the population in a given area. The three main types of population spatial distribution are uniform, clumped, and random. Examples of the types of spatial distribution: uniform, random, and clumped.
Geographic phenomena are often classified according to the spatial dimension best used to describe their nature. These include points, lines, areas, and volumes (3D).
In landscape ecology, spatial patterns refer to how we define the arrangement, structure, and placement of objects within any given landscape. This can include anything from patches of forestry, to river banks, to the landscape of man-made settlements like towns.
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.
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.
Special category data includes data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and genetic data, biometric data, data concerning health or data concerning a person's sex life or sexual orientation.