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The purpose of this research is to analyze fire risk level on the hospital in Ponorogo. Hospital is a community health service instantion should be kept away from fire disaster since many of the patients are vulnerable to become the victim. This research is descriptive – analytic with semi-quantitative approach. The scope involves 27 service units on Ponorogo Regional Hospital. Fire risk is analyzed by proportioning and scoring method of hazard, vulnerability and capacity identification result. The research results indicate 18.5% service unit of Ponorogo Regional Hospital has high fire risk. 59.3% medium risk, and 22.2% low risk. The existance of service unit with high fire risk is caused by hazard potential that does not well managed and fire protection systems that do not comply with the standard. The research result is expected to be used as reference in fire protection system improvement on Ponorogo Regional Hospital so that the fire risk can be minimalized.

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. 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