Disaster management and decision support systems in Turkey: Evaluation of AYDES

questionnaire was administered online to people working in the Disaster and Emergency Management Presidency (AFAD) and actively using the Disaster Management and Decision Support System (DMDS) software. The data obtained were subjected to various statistical analyses using Python programming language: Matplotlib and seaborn libraries were used to create graphs and visualize the data. Barlett Sphericity Test and Kaiser-Meyer-Olkin (KMO) Value functions were used to assess the suitability of the data for factor analysis. Afterwards, the FactorAnalyzer function was used to examine the factor structure of the data and the central tendency and dispersion measures of the data were calculated with Python's embedded describe function. The numpy, pandas and scipy libraries were used to process and analyze the data. Finally, the crosstab function in the pandas library was used to examine the relationship between two or more variables. These analysis methods and visualization techniques allowed the data to be examined in detail and enabled the usability level of AYDES software to be evaluated according to different demographic factors. The findings of the study show that between genders, women's SUS scores are higher than men's, SUS scores increase with increasing educational level, and SUS scores decrease with increasing age. In addition, it was observed that those with 5-10 years of experience in the organization had the highest ITS scores, while those with 16 years or more of experience had the lowest scores. It was found that SUS scores increased as the experience of using AYDES increased, but there was no significant difference between the groups with and without system usage training. The findings provide important clues for improving the user-friendly design of the software and training processes.