Thesis On Spatial Data Mining

Thesis On Spatial Data Mining-38
Spatial Distribution Of Malaria Indicator Tanzania by 2008. Geospatial Data Harmonization From Regional Level To European Level: A Use Case In Forest Fire Data.

Spatial Distribution Of Malaria Indicator Tanzania by 2008.

Taking advantage of the locality-preserving property of the space-filling Hilbert curve, the method is able to work with existing concept drift detection algorithms to automatically determine where and when in the spatiotemporal landscape that patterns are changing.

The framework was tested on the earthquake catalogue data around the Christchurch region.

Abdulhakim Abdi, “Modelling bird habitat associations to assess agricultural intensification in The Netherlands: a remote sensing approach” Vanessa Joy Anacta, Gender Differences in Cognitive Mapping Dwi Septi Cahyawati, Development of Spatial Data Infrastructure (case study in Heart of Borneo) Chunyuan Cai, Translation Encoding for OGC Services MD. Farm 2.0 Using Word Press to Manage Geocontent and Promote Regional Food Products.

Landsat Data and Spatial Metrics for Urban Landuse Change Detection.

The empirical results reveal that the local models improved the prediction accuracy of up to 9% on one of the tests when compared to a standard incremental model building approach based on a fixed size sliding window scheme.

The third method employs a Spiking Neural Network (SNN)-based system called Neu Cube to build an early event prediction system.It aims at making GIS tools more sensitive for large volumes of data stored inside GIS systems by integrating GIS with other computer sciences such as Expert system (ES) Data Warehouse (DW), Decision Support System (DSS), or Knowledge Discovery Database (KDD).One of the main branches of IGIS is the Geographic Knowledge Discovery (GKD) which tries to discover the implicit knowledge in the spatial databases. PPGIS and Web in practice for participatory planning. The Intersection of People, Technology and Local Space. Discovery and Retrieval of Geographic Data Using Google. Geospatial database generation from digital newspapers: Use case for risk and disaster domains. MONIRUZZAMAN, Impact of Climate Change in Bangladesh: Focusing on Water Logging and Mangrove Forest Change at South-West Coast Paulo Guilherme Mollin, Estimation of vegetation carbon stock in Portugal using land use/ land cover data Sushil Bhandari, Urban change monitoring using GIS and remote sensing tools in Kathmandu valley (Nepal) Saroj Koirala, Land use/ land cover change and its impact on soil erosion process in Begnas Tal Rupa Tal watershed using geospatial tools, Kaski district, Nepal Andrew Andrew Ferdinands, Testing native speakers of German and Portuguese on the understanding of topological operators-line-region relations in gv SIG Francis Molua Mwambo, Human and climatic change impact modelling on the habitat suitability for the chimpanzee (Pan troglodytes ellioti) – Case study: The proposed Mount Cameroon National Park Abargues, Carlos. Yikalo Hayelom Araya, Urban landuse change analysis and modelling: a case study of Setubal-Sesimbra, Portugal Tanmoy Das, Advanced Land Use Classification from High-Resolution Satellite Imagery Using Object Oriented Image Analysis (e Cognition) Rania Sabrah, Enriching Folksonomies Using Ontologies Ledjo Seferkolli, The Environmental Atlas of Albania Jia Wang, How Human Schematization and Systematic Distortions Take Effect On Sketch Map Formalizations Harshi Weerasinghe, Development of an interface for ontology-based transformation between Features of different Types Fanghong Ye, Adaptation of the Creative Commons Approach and the Roaming Concept to Spatial Data Infrastructures (SDI) Chintamani Kandel, Forest cover monitoring in the Bara district (Nepal) with remote sensing and geographic information systems Kathryn Elizabeth Clagett, Virtual globes as a platform for developing spatial literacy.Intelligent geographic information system (IGIS) is one of the promising topics in GIS field.To achieve this objective, the thesis proposes three different methods to deal with various types of data and employ distinct approaches to tackle common problems faced in spatiotemporal data mining.The first method deals with multiple time series and presents a development of a generic framework to extract knowledge in the form of temporal rules.

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