International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 2 Issue 9 ǁ September. 2014 ǁ PP.35-40 www.ijres.org 35 | Page Risk governance for traffic accidents by Geostatistical Analyst methods Ismail Bulent Gundogdu Department of Geomatics, Engineering Faculty, Selcuk University. Abstract : Geographical Information Systems (GIS) are indispensable tool for administrating big datasets based on location of measured point. The values related to space may vary with both time and location. GIS-supported Geostatistical Analyst (GA) can evaluate datasets by analysing the locations of points. Maps produced using probability and prediction methods must be the base products for city planning. This study develops methods to obtain maps to determine traffic hot zones in Konya, Turkey, by applying GA supported by GIS. By applying GA, this study differs from previous studies which have determined the hot spots using linear analysis. In this study, unlike preceding studies, the aim is to determine new safe routes and zones with the help of GA. Another, different aim is to map and determine graduated hot or safe zones using number of mortalities criterion (AC1), number of injured people criterion (AC2), number of accidents with damage only criterion (AC3), and total number of accidents criterion (AC4). Keywords: Geostatistical Analyst; GIS; traffic accidents; probability maps; prediction maps; kriging I. Introduction In this paper, geostatistical approaches have been used to study traffic accident datasets in a case study of the city of Konya. The paper attempts to examine the characteristics of the spatial distribution of traffic accident data according to the accident criteria (AC) using kriging methods and to analyse their implications for junction spatial structure. The paper is organised as follows: after the next section on the data and methodology, I set up and interpret the accident mortality numbers, numbers of injured, numbers of accidents with damage only, and the total number of distribution models. Then I analyse the implications of traffic accident data models for urban spatial structure. The last section is the conclusion. A report from the World Health Organization (WHO) and the World Bank (WB) (2004) on road traffic accidents and injuries estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured worldwide (Gundogdu, 2010). According to estimations of the WB traffic accidents will be the third most frequent reason of deaths in 2020. The Turkish Statistical Institute (TUIK) facts reveal that 665 618 traffic accidents, 2629 of which resulted in mortality (3393 deaths), 77 644 of which resulted in injuries (135 441 injuries) and 585 345 of which resulted in economical damage, occurred in Turkey in last seven years. The most specified analysis and plotting can be carried out using GIS nowadays. Large number of recent studies such as (Cho 2003; Hansen and Lauritsen, 2010; Hill et al., 2011; Parasannakumar et al., 2011; Gu et al., 2013; Yu and Abdel-Aty, 2013 ) using the techniques that have been developed over the last fifty years. No studies of traffic accidents that applied GA were encountered. However, some studies discussed similar problems: risk reduction in urban planning (Wamsler, 2006), transportation planning, discussed in an editorial article (Journal of Transportation Planning), the relationship between noise pollution and traffic (Seto et al., 2007), and urban traffic flow (Stathopoulos and Dimitriou, 2008). This study differs from others in that the mapping is carried out according to the different AC. So, in this phase, GA must be explained in detail. II. Geostatistical Analyst In this chapter, the connection with the basis of GA is explained to guide the application. Geostatistics is a branch of practical statistics. Geostatistics was developed by George Matheron of the Centre de Morophologie Mathematicque in Fontainebleau in France. Evaluation of the traffic accident dataset according to different criteria is also not possible with known statistical methods because the effects of sampling location are not taken into consideration in the calculations