GIS Based Spatial Modeling to Mapping and Estimation Relative Risk of Different Diseases Using Inverse Distance Weighting (IDW) Interpolation Algorithm and Evidential Belief Function (EBF) (Case study: Minor Part of Kirkuk City, Iraq)

Abstract

The health of the individual is one of the most important indicators of good living and quality of life for the community. Therefore, the contribution of developing of public health sector management and monitoring of diseases related to the cultural, economic, and social progress of any society. Moreover, the diseases occur from spatial factors where the distribution and concentration differ in diverse positions. Hence, GIS can be used as a decision support system in order to help the mangers, assess and monitoring of various types of diseases. Thus, this research aims to define a spatial distribution, prediction of risks and analysis of disease hazard areas in Kirkuk city, north east of Iraq using two models evidential belief function (EBF) and Inverse distance weighting (IDW). IDW determines the correlation between conditioning factors and disease occurrence. Consequently, EBF can be used to assess the effect of each class of conditioning factors on diseases occurrence. The result shows that Al-Wasity quarter reports the highest range of the patients who have the blood diseases (D89-D50) in 2017. Contrary, the northern parts of the city and some quarters in the center of the city (Tessen, Bagdad road, Al-Mansor) reflect the lowest range of the patients in blood diseases. Eye diseases (H59-H00) and its accessories have the same spatial distribution. The result also demonstrated that the GIS based spatial techniques is provided a prospect to simplify and measure the epidemic state of different diseases within specific areas(minor part of Kirkuk city), and lay a base to pursue future surveys into the environmental factors responsible for the augmented disease threat.

Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda

“…The IDW interpolation method, a commonly used technique, predicts values for unmeasured locations based on the values of surrounding measured locations. This method operates under two primary assumptions: the influence of an unknown value of a point increases with proximity to the control point and the degree of influence is directly proportional to the inverse of the distance between points [38,39]. In the process of interpolation, observation points receive weights that decrease as the distance from the new point increases, affecting their relative influence [40].…”

Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling

“…GISs are able to incorporate various information sources allowing data interpretation through various modeling and visualization techniques [29]. Hence, GISs can be considered decision support systems for the relevant authorities to perform assessments and decision making [30][31][32][33]. The use of spatial modeling and statistics has risen up-to-date [34].…”

“…where z i denotes the control value for the ith sample point, and w i is a weight that defines the relative importance of the specific control point z i in the interpolation process [30]. The IDW analysis is generated based on the concept of spatial dependence making it a reliable interpolation process for air pollution status prediction.…”

COVID‐19 in Chihuahua, Mexico: Assessing its spatial behaviour through the inverse distance weighted interpolation technique

“…One of the most accepted interpolation methods is Inverse Distance Weighting (IDW) due both to its technical practicality (Setianto & Triandini, 2013) and its multiple applications (Li & Heap, 2011). It is a widely used deterministic procedure, which has been employed to model the distribution patterns of diseases (Ajaj et al, 2018; Bhunia et al, 2013; Zeb et al, 2021). Furthermore, IDW has been applied to determine the spatial propagation of COVID‐19 (Kumar et al, 2020; Onovo et al, 2021).…”