Difference between Spatial and Temporal Data Mining

Spatial data mining refers to the process of extraction of knowledge, spatial relationships and interesting patterns that are not specifically stored in a spatial database; on the other hand, temporal data mining refers to the process of extraction of knowledge about the occurrence of an event whether they follow, random, cyclic, seasonal variation, etc. Spatial means space, whereas temporal means time. In this article, we will learn Spatial and temporal data mining separately; after that, we will discuss the difference between them.

What is Spatial Data Mining?

The emergence of spatial data and extensive usage of spatial databases has led to spatial knowledge discovery. Spatial data mining can be understood as a process that determines some exciting and hypothetically valuable patterns from spatial databases.

Several tools are there that assist in extracting information from geospatial data. These tools play a vital role for organizations like NASA, the National Imagery and Mapping Agency (NIMA), the National Cancer Institute (NCI), and the United States Department of Transportation (USDOT) which tends to make big decisions based on large spatial datasets.

Earlier, some general-purpose data mining like Clementine See5/C5.0, and Enterprise Miner were used. These tools were utilized to analyze large commercial databases, and these tools were mainly designed for understanding the buying patterns of all customers from the database.

Besides, the general-purpose tools were preferably used to analyze scientific and engineering data, astronomical data, multimedia data, genomic data, and web data.

These are the given specific features of geographical data that prevent the use of general-purpose data mining algorithms are:

spatial relationships among the variables,
spatial structure of errors
observations that are not independent
spatial autocorrelation among the features
non-linear interaction in feature space.

Spatial data must have latitude or longitude, UTM easting or northing, or some other coordinates denoting a point’s location in space. Beyond that, spatial data can contain any number of attributes pertaining to a place. You can choose the types of attributes you want to describe a place. Government websites provide a resource by offering spatial data, but you need not be limited to what they have produced. You can produce your own.

Say, for example, you wanted to log information about every location you’ve visited in the past week. This might be useful to provide insight into your daily habits. You could capture your destination’s coordinates and list a number of attributes such as place name, the purpose of visit, duration of visit, and more. You can then create a shapefile in Quantum GIS or similar software with this information and use the software to query and visualize the data. For example, you could generate a heatmap of the most visited places or select all places you’ve visited within a radius of 8 miles from home.

Any data can be made spatial if it can be linked to a location, and one can even have spatiotemporal data linked to locations in both space and time. For example, when geolocating tweets from Twitter in the aftermath of a disaster, an animation might be generated that shows the spread of tweets from the epicentre of the event.

Spatial data mining tasks

These are the primary tasks of spatial data mining.

Spatial vs Temporal Data Mining

Classification:

Classification determines a set of rules which find the class of the specified object as per its attributes.

Association rules:

Association rules determine rules from the data sets, and it describes patterns that are usually in the database.

Characteristic rules:

Characteristic rules describe some parts of the data set.

Discriminate rules:

As the name suggests, discriminate rules describe the differences between two parts of the database, such as calculating the difference between two cities as per employment rate.

What is temporal data mining?

Temporal data mining refers to the process of extraction of non-trivial, implicit, and potentially important data from huge sets of temporal data. Temporal data are sequences of a primary data type, usually numerical values, and it deals with gathering useful knowledge from temporal data.

With the increase of stored data, the interest in finding hidden data has shattered in the last decade. The finding of hidden data has primarily been focused on classifying data, finding relationships, and data clustering. The major drawback that comes during the discovery process is treating data with temporal dependencies. The attributes related to the temporal data present in this type of dataset must be treated differently from other types of attributes. Therefore, most data mining techniques treat temporal data as an unordered collection of events, ignoring its temporal data.

Temporal data mining tasks

Data characterization and comparison
Cluster Analysis
Classification
Association rules
Prediction and trend analysis
Pattern Analysis