ANALYSIS OF RAINY DAYS AND RAINFALL TO LANDSLIDE OCCURRENCE USING LOGISTIC REGRESSION IN PONOROGO EAST JAVA

Referred to data of Badan Nasional Penanggulangan Bencana (BNPB) and Kementerian Kesehatan Republik Indonesia (Kemenkes RI), almost landslide occurrence in Ponorogo always starts with high-intensity rain. This research aimed to determine simultaneously correlation and partial assessment impact of rainy days every month and monthly rainfall toward landslide occurrence in Ponorogo using logistic regression. The data collection was conducted through Badan Pusat Statistik (BPS) in the book of Ponorogo Regency in Figure on 2012 to 2016. The existing data shows that in sixty months have been twenty-six times landslides occurrence in Ponorogo districts. The data statistically analyzed in simultaneous proves that contribution of rainy days and rainfall to landslide were included adequate correlation (Nagelkerke R Square = 25.4 % and Cox & Snell R Square = 36.9 %) and in partial test proves that rainy days have significant impact (sig. = 0.024) and rainfall does not significant impact (sig. = 0.291) (α = 0.05) to landslide occurrence in Ponorogo regency. The rainy days per month were abled applied to predict for possible landslide elsewhere. Keywords: rainy days, rainfall, landslide, Ponorogo, logistic regression References Aditian, A., Kubota, T., & Shinohara, Y. (2018). Geomorphology Comparison of GIS-based landslide susceptibility models using frequency ratio , logistic regression , and arti fi cial neural network in a tertiary region of Ambon , Indonesia. Geomorphology Journal, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006 Agresti, A. (1996). An Introduction to Categorical Data Analysis. 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The Art and Power of Data Imaging

Data imaging and visual data assessment are veritable gold mines in the scientist’s quest to understand and accurately interpret numerical data. Graphical displays of various aspects of a dataset offer the analyst insight to the data that no mathematical computation or statistic can provide. It is difficult, at best, for even a skilled and observant statistician to understand the underlying structure of a dataset. Often, there is either too little data to get a good “picture” of the structure that might be present or there is so much data that one cannot readily assimilate it. Of course, the latter problem (too much data) is, in reality, no problem at all given the abilities of modern computers and software systems to manage large amounts of data. Advances in computer technology and the advent of the global positioning satellite system have enabled scientists from many fields of endeavor to collect and view data in its spatial context. Visual images constructed from spatially referenced data reveal the inherent richness and structure in the data and lead to more informed conclusions. So powerful is data collected with spatial context that a relatively new branch of mathematical statistics, geospatial statistics, has emerged. Geospatial statistics seek to exploit this context rich data form to better understand the spatial and co-relationships that might exist, but would be otherwise hidden in tabular data or obscured with classic statistical approaches. This paper (and accompanying presentation) will show the power that visual data assessment possesses to understand radiological scanning data and to make confident and accurate decisions based on the data images. It will challenge the traditional mathematical concept of detection limits for scanning. It will demonstrate that more data, even if the individual datum comprising the dataset is of “poorer quality” (i.e., has a larger uncertainty and, thus, a larger calculated minimum detection value), is significantly more powerful than a smaller dataset comprised of higher quality measurements. This presentation will cause the open-minded health physicist to rethink how they prescribe, collect, evaluate, and make decisions based upon radiological scan data.