This research assesses shifts in tornado occurrence pattens in space and time employing continental United States tornado records with an Enhanced Fujita (EF) rating equal or greater than 1. In similar research, most researchers discard tornado records prior to 1980 due to factors including: magnitude anomalies related to development of the Fujita Scale, unpredictability in tornado reporting (escalating populace, storm spotters, and technologic improvements), and better data records from the Census Bureau. We therefore constructed two datasets using tornados recorded in the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database: 1950-2019 (dataset 1) and 1980-2019 (dataset 2). The goals for this study were to 1) determine whether spatiotemporal patterns of recorded tornado activity have shifted over time, and 2) determine whether inclusion of pre-1980 tornado data changes the findings from 1). This study employed Space-Time Pattern Mining (STPM) to construct four spacetime cubes (STC) in ArcGIS Pro. Emerging Hot Spot Analysis (EHS) was employed to identify the changes in tornado occurrence (number of incidents in a STC cell) and magnitude (sum of tornado EF ratings for all incidents in a STC cell). EHS displayed increased tornado activity in the Southeast and decreased activity for areas in the Great Plains for both occurrence and magnitude in both datasets. This is interpreted as significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift for both datasets. Similar findings for both datasets indicate that inclusion of the less reliable pre-1980’s tornado data does not change the results and we recommend that the practice of discarding pre-1980’s tornado data in tornado occurrence research be reconsidered.

This research estimated the spatial-temporal patterns of tornadoes in the continental United States from 1950-2019 using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. This study employed Space-Time Cube Analysis and Linear Kernel Density (Kernel Density Linear Process, (KDLP)) rather than the standard Kernel Density Estimation (KDE) approach; to evaluate whether tornado hotspot locations and intensities shift over time. The first phase of the study utilized KDLP to map changes in tornado hotspots and qualitatively assess decadal shifts in hotspot locations and intensities by occurrence and magnitude between decades using ArcGIS Pro and CrimeStat. Next an Emerging Hot Spot Analysis (EHSA) was employed to identify the changes in tornado occurrence and magnitude. ESHA results identified, by both occurrence and magnitude, significant intensifying hot spots in the Southeast region and diminishing hot spots in the Great Plains indicating an east-south-east shift.

The occurrence of wildlife vehicle collisions (WVCs) is an increasing problem in Sweden with a calculated increase of 45 percent from 2015 to 2022. The highest measured number of WVCs occurred in 2021 with over 67,000 reported incidents, where only the payment for the search of wounded animals were approximately 60 million Swedish crowns. The Swedish transport agency works actively with the problem by constructing several types of wildlife passages to mitigate the increasing problem, inter alia, ecoducts and fauna bridges.   The aim of this study was to evaluate the mitigation effect of wildlife passages, in this case ecoducts and fauna bridges. In addition, were also the spatial extent of the mitigation effect together with the relationship between mitigation effect and the annual daily traffic (ADT) evaluated.   The evaluation of mitigation effect was conducted by the usage of several types of geographical information systems (GIS) tools in the software ArcGIS Pro. A selection of seven passages was made, based on several requirements and each passage was assigned a study area with an area of 100 square kilometers. The mitigation effect was initially determined by conducting an Emerging hot spot analysis, categorizing the result into showing trend of decrease or showing no trend of decrease. The spatial extent of the mitigating effect was evaluated through the Emerging hot spot results while the relation between ADT and WVCs was evaluated in an overlay analysis.   Two of the passages were concluded as to having a mitigating effect, three passages were concluded as to showing no mitigating effect and two passages were excluded from further evaluation due to high uncertainty in the results. At the passages with stated mitigating effect were the spatial extent of effect shown in the whole study area. The result showed no evident correlation between ADT and mitigating effects which led to further reflections on the degree of influence that ADT has on the occurrence of WVCs.