Cluster Analysis of Traffic Accident Patterns in Iraq: An Exploratory Statistical Study

Traffic accidents are considered one of the serious societal phenomena that are no less impactful than security threats such as terrorism, due to the significant losses they cause in lives and property, as well as the depletion of economic and human resources. This phenomenon has become a real challenge to sustainable development efforts, necessitating its analysis using advanced statistical methods to understand its dimensions, identify its patterns, and determine the factors influencing it.Based on this importance, this research examines recorded traffic accidents in Iraqi provinces, according to the Central Statistical Organization's data for 2024, in coordination with the Ministry of Interior.Cluster analysis was employed using the statistical software (SPSS Version 23) to explore the structural homogeneity among the provinces based on a set of relevant qualitative and quantitative variables. The researcher used three methods in the practical aspect: (1- Single linkage method, 2- Complete linkage method, 3- Ward's method). These methods were chosen because they yield less stringent clusters, especially Ward's method, which reduces variance when merging groups. The analysis of accidents was based on the nature of the accident (collision, overturning, run-over) as it is more accurate and available comprehensively for all provinces, whereas choosing fatalities or injuries might be linked to other reasons outside the current analysis scope or the type of road, which lacks temporal standardization or spatial accuracy in official records. The analysis was conducted with the aim of classifying the provinces into homogeneous clusters that reveal statistical variations in the pattern of accidents between different regions, thereby enabling the possibility of providing realistic recommendations based on scientific foundations to reduce these accidents or mitigate their impacts.The results of the analysis demonstrate the potential of exploratory statistics in supporting decision-makers and providing quantitative insights that help understand the dynamics of traffic accidents and guide preventive policies based on accurate and reliable data.