Spatial Pattern Mining for Soil Erosion Characterization

The protection and the maintenance of the exceptional environment of New Caledonia are major goals for this territory. Among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and its complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, this paper proposes a spatial approach based on co-location mining and GIS. Considering a set of Boolean spatial features, the goal of co-location mining is to find subsets of features often located together. This system provides useful and interpretable knowledge based on a new interestingness measure for co-locations and a new visualization of the discovered knowledge. The interestingness measure better reflects the importance of a co-location for the experts, and is completely integrated in the mining process. The visualization approach is a simple, concise and intuitive representation of the co-locations that takes into consideration the spatial nature of the underlying objects and the experts practice.

[1]  Sylvie Servigne,et al.  Managing Sensor Data Uncertainty: A Data Quality Approach , 2013, Int. J. Agric. Environ. Inf. Syst..

[2]  Michelangelo Ceci,et al.  Discovery of spatial association rules in geo-referenced census data: A relational mining approach , 2003, Intell. Data Anal..

[3]  Michel Riveill,et al.  Trusted Integration of Wireless Sensor Networks into Herd Control Monitoring Application , 2013, Int. J. Agric. Environ. Inf. Syst..

[4]  Guillaume Deffuant,et al.  A Multidimensional Model for Data Warehouses of Simulation Results , 2010, Int. J. Agric. Environ. Inf. Syst..

[5]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[6]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[7]  Shashi Shekhar,et al.  Zonal Co-location Pattern Discovery with Dynamic Parameters , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[8]  Gennady L. Andrienko,et al.  Knowledge-Based Visualization to Support Spatial Data Mining , 1999, IDA.

[9]  Michelangelo Ceci,et al.  Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach , 2007, PKDD.

[10]  Sandro Bimonte,et al.  Towards Spatial Decision Support System for Animals Traceability , 2010 .

[11]  Bhuvan Unhelkar,et al.  A Framework for Environmentally Responsible Business Strategies , 2011 .

[12]  Jeffrey W. White,et al.  The Role of Crop Systems Simulation in Agriculture and Environment , 2010, Int. J. Agric. Environ. Inf. Syst..

[13]  Pourang Irani,et al.  WiFIsViz: Effective Visualization of Frequent Itemsets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[14]  Donato Malerba,et al.  Inducing Multi-Level Association Rules from Multiple Relations , 2004, Machine Learning.

[15]  Jean-Marc Petit,et al.  Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases , 2004, Constraint-Based Mining and Inductive Databases.

[16]  Yan Huang,et al.  Discovering Spatial Co-location Patterns: A Summary of Results , 2001, SSTD.

[17]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[18]  Nikos Mamoulis,et al.  Mining frequent spatio-temporal sequential patterns , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[19]  Ferenc Bodon,et al.  Surprising Results of Trie-based FIM Algorithms , 2004, FIMI.

[20]  Jean-Marc Petit,et al.  The iZi Project: Easy Prototyping of Interesting Pattern Mining Algorithms , 2009, PAKDD Workshops.

[21]  Hércules Antonio do Prado,et al.  Computational Methods for Agricultural Research: Advances and Applications , 2010 .

[22]  Didier Josselin,et al.  The Weighted Fuzzy Barycenter: Definition and Application to Forest Fire Control in the PACA Region , 2013, Int. J. Agric. Environ. Inf. Syst..

[23]  Hui Xiong,et al.  Discovering colocation patterns from spatial data sets: a general approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[24]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[25]  Vania Bogorny,et al.  Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints , 2006, Sixth International Conference on Data Mining (ICDM'06).

[26]  Eduardo Bezerra,et al.  A Forecasting Method for Fertilizers Consumption in Brazil , 2013, Int. J. Agric. Environ. Inf. Syst..

[27]  Shashi Shekhar,et al.  A Joinless Approach for Mining Spatial Colocation Patterns , 2006, IEEE Transactions on Knowledge and Data Engineering.

[28]  Vittoria Pastore,et al.  Use of Remote Sensing Data for Landslide Change Detection: Montescaglioso Large Landslide (Basilicata, Southern Italy) , 2012, Int. J. Agric. Environ. Inf. Syst..

[29]  Tools for soil erosion mapping and hazard assessment: application to New Caledonia, SW Pacific , 2009 .

[30]  Longbing Cao.  Domain Driven Data Mining (D3M) , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[31]  Dawn J. Wright,et al.  Coastal Informatics: Web Atlas Design and Implementation , 2010 .

[32]  Frédéric Paran,et al.  Coupling Geographic Information System (GIS) and Multi-Criteria Analysis (MCA) for Modelling the Ecological Continuum in Participative Territorial Planning , 2011, Int. J. Agric. Environ. Inf. Syst..

[33]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.

[34]  S. Moges,et al.  Improving Spatio-Temporal Rainfall Interpolation Using Remote Sensing CCD Data in a Tropical Basin: A Geostatistical Modeling Approach , 2011 .