Poisson Clustering Process on Hotspot in Peatland Area in Sumatera
Abstract: The increase in
peatland fire’s intensity has encouraged people to develop methods of preventing
wildfire. One of the prevention methods is recognizing the distribution pattern
of hotspot as one of forest and land fire indicators. We could determine the
area that has high fires density based on distribution patterns so any early
prevention steps could be performed in that area. This research proposed to
recognize the distribution pattern of hotspot clusters in the peatland areas in
Sumatera in the year 2014 using Kulldorff’s Scan Statistics (KSS) method with
Poisson model. This approach was specifically designed to detect clusters and
assess their significance via Monte Carlo replication. Results showed that the
method is reliable to detect the clusters of hotspots which have the accuracy
of 95%. Riau and South Sumatera province have the highest density of cluster
distributions of the hotspot. Based on the maturity level of peat, cluster
distributions of hotspot were mostly found in ‘hemic’ maturity level. Based on
peatland thickness, cluster distribution of hotspot was mostly found in ‘very
deep’ thickness.
Author: Annisa Puspa Kirana,
Imas Sukaesih Sitanggang, Lailan Syaufina
Journal Code: jptkomputergg150174
