The application of a visual data mining framework to determine soil, climate and land use relationships

Vagh, Yunous

doi:10.1016/j.proeng.2012.01.1271

Cited by 3 publications

(4 citation statements)

References 9 publications

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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%

“…GIS and Spatial Data mining [11] Tourism Management integrating the ICT techniques with tourism association technique [12] Land allocation provide the selection a model for land use, illegal land fills Location prediction [13] Meteorology Estimation of rainfall for homogenous monsoon region genetic approach and correlation [14] forecast the rainfall of Rajasthan state Multiple liner regression [15] Regionwise rainfall fluctuation classification [16] Estimation of rainfall Spatial interpolation and association rule …”

Section: Issues Challenges and Solution Architecturementioning

confidence: 99%

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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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Section: Fig1 Component Of Gismentioning

confidence: 99%

Section: Issues Challenges and Solution Architecturementioning

confidence: 99%

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

Goyal¹,

Sharma²,

Joshi³

2017

IJCA

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“…Spatial Data mining techniques combined with widely used in various studies to mine interesting facts associated in domains Transport, Tourism, Soil quality monitoring, water resource monitoring, and deforestation [7], [9], [13], [17], [20][21][22], [24][25], [27][28][29], [31], [40][41][42], [44][45], [48][49], [54], [60], [62][63], [73], [75], [78], [82], [87].…”

Section: Fig 1 Components Of Gismentioning

confidence: 99%

“…Maintains the Bio-Diversity [18], [22], [75] Argiculture GIS and Saptial Data mining Used to access the soil quality, water resource management [69], [82], [86], [87] land allocation (MOLA)…”

Section: Association Technique Used To Integrate the Ict Technologiesmentioning

confidence: 99%

Spatial Data Mining Approaches for GIS – A Brief Review

Perumal

Velumani

Sadhasivam

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. 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 formats in the form of geodatabases representing spatial features, with respect to latitude and longitudinal positions. Geodatabases are increasing day by day generating huge volume of data from satellite images providing details related to orbit and from other sources for representing natural resources like water bodies, forest covers, soil quality monitoring etc. Recently GIS is used in analysis of traffic monitoring, tourist monitoring, health management, and bio-diversity conservation. Inferring information from geodatabases has gained importance using computational algorithms. The objective of this survey is to provide with a brief overview of GIS data formats data representation models, data sources, data mining algorithmic approaches, SDM tools, issues and challenges. Based on analysis of various literatures this paper outlines the issues and challenges of GIS data and architecture is proposed to meet the challenges of GIS data and viewed GIS as a Bigdata problem.

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