CS621B/C Spatial DatabasesResearch presentationSpatial data mining for Analysis and prediction ofnatural disaster

JUSTIN DONAGHY 11125993DEPARTMENT OF COMPUTER SCIENCENUI MAYNOOTHJUSTIN.DONAGHY.2012@MUMAIL.IE

Introduction Spatial data mining for Analysis and prediction of natural

disasters Spatial data mining is the application of data mining methods

to spatial data Goal of Spatial data mining is to find patterns in data with

respect to Geography.

Can we use Spatial mining techniques to predict Natural disasters around the world.

Natural disasters represent significant safety, economic, and security threats, and the formalized goal focused communities on developing adequate prevention, mitigation, response and recovery plans.

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Introduction

Why Spatial Data Mining? Spatial Data mining is to find interesting, 

potentially useful, non‐trivial patterns in  large spatial datasets. –A  huge volume of spatial data coming from an 

increasing number of geographical sensors and  satellites. “data  rich but knowledge poor” problem in 

spatial analysis

history

John Snow was the first to discover that the cause of the cholera in London was coming from a single pump. He did this by talking to survivors and finding what well they drank from, in collecting this spatial data , he was able to find a pattern and the pumpSo he became the first spatial data miner

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Spatial data mining Architecture

Problems with Spatial Data Mining

the spatial data mining algorithms are not efficient. Faced with massive database systems, spatial data mining process appears uncertain, the possibility of errors dimension model and problems to be solved are great, not only increases the algorithm of the search space, but also increased the blind searches possibility. And therefore it must be removed with the use of domain knowledge discovery tasks unrelated data, effectively reducing the dimension of the problem, design a more effective knowledge discovery algorithms.

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Problems with spatial data mining

There is no accepted standardized spatial data mining query language. One reason for the rapid development of database technology is the continuous improvement and development of a database query language, therefore, to continue to improve and develop spatial data mining is necessary to develop spatial data mining query language, digging the foundation for efficient spatial data.THIS IS NOT AN EXACT SCIENCE

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DATA MINING TECHNIQUES:

The various data mining techniques are: Statistics Clustering Visualization Association Classification & Prediction Outlier analysis Trend and evolution analysis

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European heatwave caused 35,000 deaths

Could this have been predicted using SPATIAL DATA MINING TECHNIQUES

Climate Data Mining

Climate data modelling

“it is very likely that hot extremes, heat waves, and heavy precipitation events will continue to become more frequent”. (IPPC, 2014)

The I.P.P.C

Future directions

DISTRIBUTED/COLLECTIVE DATA MINING UBIQUITOUS DATA MINING (UDM) HYPERTEXT AND HYPERMEDIA DATA MINING MULTIMEDIA DATA MINING SPATIAL AND GEOGRAPHIC DATA MINING TIME SERIES/SEQUENCE DATA MINING CONSTRAINT- BASED DATA MINING PHENOMENAL DATA MINING

Future trends

Geographic and spatial data mining: Geographical databases are becoming increasingly common and more detailed. They can be used for the extraction of implicit knowledge, spatial relationships and other patterns that are not explicit in them. One of the main challenges of this field will be the design and architecture of the data warehouses to store the information (given the very particular nature of the data), as well as the integration of heterogeneous data

Conclusion

Natural disasters are always going to be hard to predict but Spatial data mining may help to save lives in the future. With the development of more sophisticated techniques this could become more of an exact science

It is foreseeable that spatial data mining will not only promote space science, the development of computer science, but also will enhance human understanding of the world, the discovery of knowledge, in order to better transform the world.

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References

European Environment agency (2010) Mapping the impacts of natural hazards and technological accidents in Europe An overview of the last decade Luxembourg: Publications Office of the European Union, 2010.

Ganguly ,Auroop. R and Steinhaeuser ,Karsten (2008) Data Mining for Climate Change and Impacts [online] Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4733959 (accessed 8th December 2015).

International Conference on Circuit, Power and Computing Technologies (2015) ANALYSIS AND PREDICTION OF NATURAL DISASTER USING SPATIAL DATA MINING TECHNIQUE , Department Of Computer Science and Engineeering, Sathyabama University, Chennai

I.P.P.C (2013) Europe [online] Available at: http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-Chap23_FINAL.pdf (accessed 8th December 2015).

Otero, Abraham (2009) future trends in data mining [online] Available at: http://biolab.uspceu.com/datamining/pdf/FutureTrends.pdf (accessed 8th December 2015).

UCLA (2014)broad street pump outbreak [online] Available at: http://www.ph.ucla.edu/epi/snow/broadstreetpump.html (accessed 8th December 2015).

University of Minnesota(2010) Flood Prediction and Risk Assessment Using Advanced Geo-Visualization and Data Mining Techniques: A Case Study in the Red-Lake Valley [online] Available at: http://www.wseas.us/e-library/conferences/2014/Malaysia/ACACOS/ACACOS-02.pdf (accessed 8th December 2015).

2008 IEEE International Conference on Data Mining Workshops (2008) Data Mining for Climate Change and Impacts [online] Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4733959 (accessed 8th December 2015).

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Questions ?

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