Typical examples of spatiotemporal data mining include discovering the evolutionary history of cities and lands, uncovering weather patterns, predicting earthquakes and hurricanes, and determining global warming trends.
Spatial Data Mining needs space information within the data. For example, any data with location coordinates can be treated as a Spatial Data set. Temporal Data Mining needs time information. For example, any data set containing the events over time can be treated as temporal data.
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.
It targets mining new patterns and unknown knowledge, which takes the temporal aspects of data. Examples: Finding hotspots, unusual locations. Examples: An association rules which seems - "Any person who buys motorcycle also buys helmet".
Use temporal data types to store date, time, and time-interval information. Although you can store this data in character strings, it is better to use temporal types for consistency and validation.
Temporal data is simply data that represents a state in time, such as the land-use patterns of Hong Kong in 1990, or total rainfall in Honolulu on July 1, 2009. Temporal data is collected to analyze weather patterns and other environmental variables, monitor traffic conditions, study demographic trends, and so on.
Any additional information, or non-spatial data, that describes a feature is referred to as an attribute. Spatial data can have any amount of additional attributes accompanying information about the location. For example, you might have a map displaying buildings within a city's downtown region.
These features embody either spatial or temporal information of a signal. Spatial features capture the change in space due to the movement, whereas temporal features represent time factors during the movement.
Spatiotemporal data analysis is an emerging research area due to the development and application of novel computational techniques allowing for the analysis of large spatiotemporal databases.
Spatial data are of two types according to the storing technique, namely, raster data and vector data.
Spatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process.
Temporal pattern: time and how things change over a period of time. Examples of temporal patterns include: even, uneven, fluctuating, cyclic, regular, irregular.
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.
There are four typical data types that we use in GIS: integer, float/real, text/string, and date.
Uses of Spatial Distribution
The sort of study you conduct may change based on your research needs. For example, an epidemiologist (someone who studies the impact of diseases) may conduct a spatial distribution study to track the dispersion of a virus.
A typical GIS involves 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.
The spatial information and the attribute information for these models are linked via a simple identification number that is given to each feature in a map. Three fundamental vector types exist in geographic information systems (GISs): points, lines, and polygons (Figure 4.8 "Points, Lines, and Polygons"). Points.
The main difference between the temporal and non-temporal data is a time constraint is appended with data representing when it is applicable or stored in the database. Data stored in conventional databases believe data should be valid at current time as for time instance “now”.
Trends over time, often captured through the use of longitudinal data sets, constitute temporal data that is useful to understanding changes in patterns of development or behavior over time.
Geographic data often has an important temporal component. Temporal data—information about features and attributes at different points in time—can help you explore phenomena as diverse as crime trends, the spread of an invasive species, and traffic accident patterns.
Data stored in a DBMS that supports temporal features differs from traditional data in that a time period is attached to the data to indicate when it was valid or changed in the database.