Adding the value of NAVTECH road database: an implementation of spatial data mining techniques

2001 ◽  
Vol 11 (S1) ◽  
pp. 69-73 ◽  
Author(s):  
Lichun Sui ◽  
Liqiu Meng
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Data Mining Techniques

Availability of Spatial Data Mining Techniques

2012 ◽  
pp. 63-87 ◽  
Author(s):  
Shashi Shekhar ◽  
Vijay Gandhi ◽  
Pusheng Zhang ◽  
Ranga Raju Vatsavai
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Data Mining Techniques

scholarly journals Knowledge Discovery Web Service for Spatial Data Infrastructures

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Morteza Omidipoor ◽  
Ara Toomanian ◽  
Najmeh Neysani Samany ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Web Service ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
High Level

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.

Spatial Data Mining, Spatial Data Warehousing, and Spatial OLAP

2018 ◽  
pp. 97-132 ◽  
Author(s):  
Amira M. Idrees ◽  
Mostafa Lamlom Ahmed Khaled ◽  
Amal Hassan Ali Talkhan
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Data Warehousing ◽  
Spatial Data Mining ◽  
Complex Task ◽  
Basic Information ◽  
Geospatial Technology ◽  
Data Mining Techniques ◽  
Special Representation ◽  
Methods Of Analysis

Data mining is one of the current vital fields for all types of data including spatial data. An example of useful extracted patterns from spatial data is to describe changes in metropolitan poverty rates based on city distances from major highways. Geospatial is a term that has recently been gaining in popularity; moreover, many applications on geospatial have different uses in different fields such as geo-tagging, geospatial technology, and geo-fencing. Analyzing spatial data is considered a complex task due to its details as it is related to locations with a special representation such as longitude and latitude. Other attributes are involved in the description of objects which can be analyzed using different data mining techniques. In this chapter, a demonstration of the basic information is performed considering spatial data and spatial data mining including all aspects such as the different type of data, different methods of analysis, different mining techniques, and other related topics.

Spatial Data Mining, Spatial Data Warehousing, and Spatial OLAP

2019 ◽  
pp. 1425-1455
Author(s):  
Amira M. Idrees ◽  
Mostafa Lamlom Ahmed Khaled ◽  
Amal Hassan Ali Talkhan
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Data Warehousing ◽  
Spatial Data Mining ◽  
Complex Task ◽  
Basic Information ◽  
Geospatial Technology ◽  
Data Mining Techniques ◽  
Special Representation ◽  
Methods Of Analysis

Data mining is one of the current vital fields for all types of data including spatial data. An example of useful extracted patterns from spatial data is to describe changes in metropolitan poverty rates based on city distances from major highways. Geospatial is a term that has recently been gaining in popularity; moreover, many applications on geospatial have different uses in different fields such as geo-tagging, geospatial technology, and geo-fencing. Analyzing spatial data is considered a complex task due to its details as it is related to locations with a special representation such as longitude and latitude. Other attributes are involved in the description of objects which can be analyzed using different data mining techniques. In this chapter, a demonstration of the basic information is performed considering spatial data and spatial data mining including all aspects such as the different type of data, different methods of analysis, different mining techniques, and other related topics.

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