Open AccessSpatiotemporal Dengue Disease Clustering by Means Local Spatiotemporal Moran’s Index
Author(s) -
I Gede Nyoman Mindra Jaya,
Yudhie Andriyana,
Bertho Tantular,
Zulhanif,
Budi Nurani Ruchjana
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/621/1/012017
Subject(s) - spatial analysis , cluster analysis , outlier , autocorrelation , data mining , index (typography) , resampling , statistics , computer science , geography , pattern recognition (psychology) , cartography , artificial intelligence , mathematics , world wide web
Spatiotemporal analysis has been used widely to explain some geographic phenomenon, especially in an epidemiology study. Spatial and temporal autocorrelation coefficients are usually used to assess the spatial and temporal dependencies in set geographic events. However, those statistics are usually computed separately and may lead to the misleading conclusion. Analysing spatiotemporal autocorrelation would be useful to understand the geographical evolution simultaneously. Spatiotemporal autocorrelation can be used to identify the spatiotemporal clustering and outlier via local spatiotemporal autocorrelation. This paper develops a method to estimate and test the local spatiotemporal autocorrelation based on the local spatial Moran’s Index. Randomization permutation test is used to obtain the p-value which is used to construct the disease clustering. The method was applied to identify the spatiotemporal clustering and outlier detection for dengue disease data in Bandung city. Based on image analysis, this method presents the better result compare than the local spatial Moran’s Index which is done for every time separately.