## 1 Introduction  
Road accidents are one of the leading causes of death, injury, and property damage worldwide [1]. The extremely high number of deaths annually and the accident fatality rate make road traffic fatalities not only a public health problem but also a socio-economic development problem, accounting for a 3% impact on GDP [2]. Road traffic injury is now the leading cause of death for children and young adults aged 5-29 years, indicating a need for a shift in the current child health agenda, which has largely neglected road safety [1]. More than half of all traffic deaths are among vulnerable road users: pedestrians, cyclists, and motorcyclists. The 2017 UN SDGs include specific targets for road safety, confirming security as an essential element of the health and development agenda: SDG3, to halve the number of global deaths and injuries from road traffic crashes; SDG11, to provide access to safe, affordable, accessible, and sustainable transport systems for all, improving road safety [3]. There is currently a vast amount of literature focusing on the analysis of road accidents from several perspectives: location [4, 5, 6, 7, 8, 9], weather conditions of occurrence [10], severity [11, 12, 13, 14, 15, 16], traffic problem areas in terms of infrastructure [17, 18, 19, 20], real time monitoring of incidents [21, 22, 23, 24], and also on data analysis methods used in the study and prediction of road accidents [17, 25, 26, 27]. Thus, the majority of research relies on statistics that are produced, gathered, kept, and publicly released by government bodies or their authorized representatives responsible for managing, enforcing, or reporting road accident data [15, 17, 20, 25, 28, 29, 30]. Open data and social media also represent increasingly used sources in accident analysis [31, 32, 33, 34, 35, 36]. The diversity of study objectives and data highlight the difficulty for scholars in developing a unified analytical or predictive model. This has also been influenced by the methods of developing, calibrating, and adapting models to the socio-economic, cultural and development conditions of the areas in where they have been proposed and tested [5; 32; 37; 38; 39]. The increasing availability of car crash data has enabled the development of a new research direction related to road accident prediction, in which machine learning analysis methods are used due to their ability to flexibly process and manage multi-dimensional data to determine crash severity [12; 40; 41; 42; 43; 44; 45]. The use of geographic information system (GIS) in road accident analysis has developed extensively due to the ability to analyze large volumes of geospatial data and to determine spatial patterns in their occurrence, and the most common methods are: Fuzzy-AHP to determine significant determinants of road accidents [46; 47]; accident characteristics analysis: location, user information, road type, vehicle, and crash severity using GIS [8; 9; 32; 48; 49; 50; 51; 52; 53]; clustering techniques in GIS to determine hotspots and blackspots using kernel density, adjacent neighbor distance, and K functions [19; 56; 57; 58; 59; 60; 61]; Getis and Moran's I-statistics [62]; and determining risk zones for vehicle crashes in GIS using neuro-fuzzy approaches [45]. In developing countries, the collection and organization of traffic data, especially regarding speeds at the time of accidents, are often inadequate. This makes it essential to create methodologies capable of compensating for missing data. Such methodologies would enable Geographic Information System (GIS) models to forecast and predict potential high-risk areas. Identifying these hotspots allows stakeholders to implement preventive measures effectively. The main objective of the presented study is to develop a complex spatial analysis model through which a methodology for analyzing vulnerability and risk associated with road accidents is proposed and developed under limited spatial data conditions.