Spatial data mining research

2020-01-21 08:40

Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats.The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge. Spatial data mining (Roddick and Spiliopoulou, 1999; Shekhar and Chawla, 2003) is the process of discovering interesting and previously unknown, but spatial data mining research

The goal of spatial data mining is to discover potentially useful, interesting, and nontrivial patterns from spatial datasets. Spatial data mining is important for societal applications in public health, public safety, agriculture, environmental science, climate etc. For example, in epidemiology, spatial data mining helps to find areas with a high concentrations of disease incidents to manage

ABSTRACT Spatial data mining seeks to discover meaningful patterns from data where a prime dimension of interest is geographical location. Consideration of a spatial dimension becomes important when data either refer to specific locations andor have significant spatial dependence which needs to be considered if meaningful patterns are to emerge. Today, as locationbased technologies gain greater and greater importance across all sectors, SDR clients use eCOP to reach a variety of end users who benefit from local mapping data, data that is simply not available on public mapping websites. These users include government offices, post offices, schools, residents, businesses, public safetyspatial data mining research Most big data are spatially referenced, and spatial data mining (SDM) is the key to the value of big data. In this paper, SDM are overviewed in the aspects of software and application.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for spatial data mining research the linear regression in classical data mining. Research is needed to reduce the computational costs of spatial data mining algorithms by a variety of approaches including the classical data mining algorithms as potential filters or components. Preprocessing spatial data There is a major accomplishments of spatial data mining research, especially regarding output patterns known as predictive models, spatial outliers, spatial colocation rules, and clusters. Finally, we identify areas of spatial data mining where further research is needed. 19. 1 Data Input The data inputs of spatial data mining are more complex than the In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. PDF Spatial Data Mining (SDM) technology has emerged as a new area for spatial data analysis. Geographical Information System (GIS) stores data collected from heterogeneous sources in varied

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