Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.
Cite this paper
I. Sukaesih Sitanggang, "Spatial Multidimensional Association Rules Mining in Forest Fire Data," Journal of Data Analysis and Information Processing
, Vol. 1 No. 4, 2013, pp. 90-96. doi: 10.4236/jdaip.2013.14010
 J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” 2nd Edition, Morgan Kaufmann, 2006.
 M. Ester, H. Kriegel, and J. Sander, “Spatial Data Mining: A Database Approach,” Proceedings of the Symposium on Large Spatial Databases, Berlin, 15-18 July 1997, pp. 47- 66. http://dx.doi.org/10.1007/3-540-63238-7_24
 K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proceedings of the 4th International Symposium on Advances in Spatial Databases, Springer-Verlag, London, 1995, pp. 47-66. http://dx.doi.org/10.1007/3-540-60159-7_4
 A. Appice, M. Ceci, A. Lanza, F. A. Lisi and D. Malerba, “Discovery of Spatial Association Rules in Geo-Referenced Census Data: A Relational Mining Approach,” Intelligent Data Analysis, Vol. 7, No. 6, 2003, pp. 541-566.
 L. Wang, K. Xieb, T. Chena and X. Mab, “Efficient Discovery of Multilevel Spatial Association Rules Using Partitions,” Journal of Information and Software Technology, Vol. 47, No. 13, 2005, pp. 829-840.
 J. Mennis and J. W. Liu, “Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change,” Transactions in GIS, Vol. 9, No. 1, 2005, pp. 5-17.
 M. Berardi, M. Ceci and D. Malerba, “Mining Spatial Association Rules from Document Layout Structures,” Proceedings of the 3rd Workshop on Document Layout Interpretation and its Application, Edinburgh, 2 August 2003, pp. 9-13.
 D. Malerba and F. A. Lisi, “Discovering Associations between Spatial Objects: An ILP Application,” Proceedings of the 11th International Conference on Inductive Logic Programming, Springer-Verlag, London, 2001, pp. 156-163.
 V. Karasová, J. M. Krisp and K. Virrantaus, “Application of Spatial Association Rules for Improvement of a Risk Model for Fire and Rescue Services,” Proceedings on the 10th Scandinavian Research Conference on Geographical Information Science (ScanGIS), Stockholm, 13-15 June 2005, pp. 183-194.
 J. Niu, Y. Zhang, W. Feng and L. Ren, “Spatial Association Rules Mining for Land Use Based on Fuzzy Concept Lattice,” Proceedings of the 19th International Conference on Geoinformatics, Shanghai, 24-26 June 2011, pp. 1-6.
 P. K. R. Hilir, “Gambaran Umum Kabupaten,” 2010. http://www.rohilkab.go.id/?tampil=link&act=profil&id=4
 B. P. S. K. R. Hilir, “Hasil Sensus Penduduk 2010, Kabupaten Rokan Hilir, Data Agregat per Kabupaten/Kota,” 2010. http://sp2010.bps.go.id/files/ebook/1409.pdf
 Fire Information for Resource Management System (FIRMS), “Frequently Asked Questions,” 2013.
 R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of 20th International Conference on Very Large Data Bases, VLDB, 1994, pp. 487-499.
 R. Srikant and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables,” Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, 4-6 June 1996, pp. 1-12.