UNDERSTANDING VERTICAL GROUND DISPLACEMENT FROM P-SBAS USING GETIS-ORD GI* STATISTIC

The Differential Synthetic Aperture Radar Interferometric (DInSAR) algorithm has already shown its importance in volcanicmonitoring. However, it is limited by atmospheric perturbations or temporal decorrelation, implying the existence of lowcoherencerecords that must be discarded. In vast studied areas, with thousands of points unevenly distributed, theinterpretation of the results is usually complicated. This text presents an analysis of the vertical component variation onTenerife island (Canary Islands), from 2005 to 2010, using Getis-Ord Gi* spatial statistic on ENVISAT DinSAR images.The ascending and descending images have been processed using the Parallel Small BAseline Subset (P-SBAS)algorithm, within ESA Grid Processing on Demand cloud environment. From Line of Sight results of both tracks, the verticaldeformation speeds have been calculated over 72,207 points with high coherence (> 0.7). Finally, the Gi* statistic hasbeen applied, obtaining a map with statistical significance, where the high values of Gi*, both positive and negative, implythe spatial clustering of likely ground movements. This map highlights areas with variable vertical kinematics on TenerifeIsland, contributing to understanding of its geodynamics. The displacements obtained coincide with previous studies, evenshowing possible new relationships between some phenomena that should be considered. The Gi* spatial statistic is anefficient and quick tool to extract information in a regional scale kinematic study.

Determining Spatial Patterns of Road Accidents at Expressway by Applying Getis-Ord Gi* Spatial Statistic

This paper was using Getis–Ord (Gi*) spatial statistics to identify hot spots on the controlled-access expressway. The application of the method was demonstrated through a case study by using the reported road accident cases in North-South Expressway (NSE). The method successfully identified the clusters of accidents from more than 47,359 accident records from 2016 to 2019. 25 hotspot locations were identified at this study area represents 26.81% of reported cases with the lengthiest hotspot is 31.2 km and the shortest is 300m. The largest and the second largest means of z score of hotspots were identified near to well-known high populated and busy city Kuala Lumpur with scores of 6.17243 and 6.074437. The largest z score means the more intense clustering at the location will be and statistically significance to reject the null hypothesis. This study also found that the accident hotspots tend to occur at the location where the continuous traffic flow is disturbed. There are 16 hotspot locations were identified which is equivalent to 64% from the total hotspots that occur at the location where were the existing of interchange, exit ramp, slip road, rest area or lay by spotted at the area. The interference of traffic flows including diverge and merge activities will affect the speed consistency and which if often, leads to sideswipe and rear accidents. By using GIS, the location of hotspots can be analyzed meticulously at the location. It can help in determining effective countermeasures based on the analysis of the causal factors