Spatial data cube: provides better support for spatial data mining

Zhang, Yuanzhi; Xie, Kun; Ma, Xiaolong; Dan, Xu; Cai, Cuo; Tang, Shiwei

doi:10.1109/igarss.2005.1525227

Cited by 3 publications

(1 citation statement)

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“…Spatial Data Mining techniques collectively used with GIS and satellite imagery in various studies to mine interesting facts associated in diverse domains' applications such as traffic risk analysis, fire accident analysis, analysis of forest extent changes, grading of agriculture land, analysis of railways, farming and forestry, warehouse, transport, tourism, military, geology, soil quality monitoring, water resource monitoring, and deforestation, land allocation, meteorology [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. This section throws the light on GIS data source, data formats, trends and applications in GIS.…”

Section: Fig1 Component Of Gismentioning

confidence: 99%

An Integrated Approach of GIS and Spatial Data Mining in Big Data

Goyal¹,

Sharma²,

Joshi³

2017

IJCA

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An explosive growth of spatial data has been demanding to Spatial Data Mining (SDM) technology, emerging as a innovative area for spatial data analysis. Geographical Information System (GIS) contains heterogeneous data from multidisciplinary sources in different formats. Geodatabase is the repository of GIS data, representing spatial attributes, with respect to location. Rapidly increasing satellite imagery and geodatabases generates huge data volume related to real world and natural resources such as soil, water, temperature, vegetation, forest cover etc. Inferring information from geodatabases has gained value using computational algorithms. The intent of this paper is to introduce with GIS, and spatial data mining, GIS and SDM tools, algorithmic approaches, issues and challenges, and role of spatial association rule mining in big data of GIS.

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