FAA EAGLE Avgas Transition: Considerations for Impacts on Alaskan Supply Chains

Federal bodies have called for a directed transition away from 100 octane low lead aviation gas (100LL avgas) due to public health concerns. Leaded avgas currently powers piston engine aircraft in general aviation and air taxi fleets, serving both recreational and commercial purposes. In considering the unleaded avgas transition, we must acknowledge that public policy frequently generates unintended consequences that reduce anticipated net benefits for subgroups of the population. Particular attention should be placed on regions which are heavily reliant on piston aircraft for core commercial services to remote environments, and where infrastructure adjustments are highly complex and costly. Alaska is one such key context. This brief outlines considerations for potential core supply chain impacts in this remote, aviation-dependent environment and which communities are particularly exposed. While Alaska is 48th in total population, the state is 1st in total volume of intra-state air cargo delivery. Over 80% of the state's communities lie off the road system, and piston engine aircraft are an important component of that commercial fleet. Leveraging granularity in the Bureau of Transport Statistics (BTS) T-100 database, we find that over 50% of carriers reporting intra-Alaska flights had at least one piston engine aircraft in their fleet. In 2023, T-100 data recorded 130,850 commercial piston aircraft flights transporting 201,729 passengers and 30.6M lbs of cargo between Alaskan communities. For non-hub ‘bush’ communities, almost 50% of all commercial flights, 30% of passengers, and 20% of recorded cargo were delivered by piston aircraft. We map community reliance across the state, with particular importance found for off-road destinations in the Southeast, Southwest, and Kodiak. A complete tabular breakdown of piston-engine market shares is generated for all Alaskan destination communities. We conclude by providing key economic questions for Alaska to address ahead of a fuel transition. Assuring the technical performance of unleaded fuel alternatives in Alaskan environments is foundational. Then, to most efficiently utilize the preparation window, policymakers and sector leadership should understand the impact of increased fuel expenses on overall linehaul cost per ton-mile, the share of cost increases borne by service communities, impacts on route viability, and the potentially complex process of staging any necessary support infrastructure such as fuel storage to off-road communities in Alaska's narrow barge season.This report was prepared by the Institute of Social and Economic Research (ISER), University of Alaska Anchorage (UAA) through broader funding support from the State of Alaska via G00014956 "UAA Drone Program"

Alaska Misdemeanor Assault Arrest Rates, by Place: 1985-2022

This fact sheet presents Alaska misdemeanor assault arrest rates per 100,000 Anchorage residents and 100,000 residents outside of Anchorage, from 1985-2022

How has Alaska’s K-12 education spending changed? Trends from 2017-2023

Education funding in Alaska, as in most states, is one of the largest allocations in the state operating budget. In 2022, K-12 schools in Alaska spent $20,191 per student for current operations, which was 29$15,633. However, many things are more expensive in Alaska than they are in other parts of the nation, and this is also true for operating schools. After adjusting Alaska’s spending for its higher relative costs, we find that Alaska’s per-pupil current expenditures fall below the national average by 15%. In the five years between 2017 and 2022 (the first year we conducted this analysis and the most recent year with full data available, respectively), per pupil current spending in other US states increased by 26%, whereas Alaska’s spending increased by only 13

Data Submission Package for Manuscript 'Progress on the world's primate hotspots and coldspots: Modeling ensemble Super SDMs in cloud-computers based on digital citizen-science Big Data and 200+ predictors for more sustainable conservation planning'

Describing where distribution hotspots and coldspots are located with certainty is crucial for any science-based species management and governance. Thus, here we created the world’s first Super Species Distribution Models (SDMs) including all primate species and the best-available predictor set. These Super SDMs are conducted using modern Machine Learning ensembles like Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). For the global cold/ hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. We quantified the global primate hotspots for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. The global primate coldspots are Antarctica, the Arctic, most temperate regions, and Oceania past the Wallace line. We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world’s primates occur (or not). This shows us where the focus for most future research and conservation management efforts should be, using state-of-the-art digital data indication tools with reason. Those areas should be considered of the highest conservation priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival.Ye

Impact of the COVID-19 Pandemic on Travel Mode Choices and Fatal Crash Rates

The COVID-19 pandemic caused unprecedented disruptions to human mobility and transportation systems worldwide, significantly altering travel behavior and mode choices. This study investigates these changes within the Pacific Northwest region of the United States, encompassing a mix of urban and rural contexts with diverse socio- demographic characteristics. Using survey data from 807 respondents, we analyze transportation patterns before and during the pandemic, focusing on shifts in mode shares and probabilities of switching travel modes. The analysis incorporates McNemar’s test, logistic regression, and latent class analysis (LCA) to evaluate the extent of these shifts and identify key influencing factors. The results reveal a substantial reduction in public transport usage, reflecting heightened concerns over health risks and limited operational capacity during the pandemic. In contrast, there was a notable increase in the use of private vehicles and active transportation modes, such as walking and cycling. Demographic variables, including age, income, employment status, and gender, played significant roles in shaping travel behavior, with younger and lower-income individuals exhibiting higher probabilities of mode change. The latent class analysis highlighted distinct behavioral clusters, indicating that travel behavior responses were not uniform across populations. A logistic regression model further underscored the importance of pre-pandemic travel habits, socio-economic conditions, and pandemic-related concerns in influencing mode choice decisions. Additionally, traffic safety outcomes showed notable variations, with overall crash rates decreasing during the lockdowns but fatality rates rising due to riskier driving behaviors, such as speeding on roads. Crash patterns varied across urban and rural areas, with urban crashes experiencing a slight decline in proportion, while rural crashes increased

Alaska Misdemeanor Assault Arrest Rates: 1985-2022

This fact sheet presents Alaska misdemeanor assault arrest rates per 100,000 Alaska residents from 1985-2022

Data Submission Package for Manuscript 'Progress on the world's primate hotspots and coldspots: Modeling ensemble Super SDMs in cloud-computers based on digital citizen-science Big Data and 200+ predictors for more sustainable conservation planning'2

Describing where distribution hotspots and coldspots are located with certainty is crucial for any science-based species management and governance. Thus, here we created the world’s first Super Species Distribution Models (SDMs) including all primate species and the best-available predictor set. These Super SDMs are conducted using modern Machine Learning ensembles like Maxent, TreeNet, RandomForest, CART, CART Boosting and Bagging, and MARS with the utilization of cloud supercomputers (as an add-on option for more powerful models). For the global cold/ hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest environmental predictor set of 201 layers. For this analysis, all occurrences have been merged into one multi-species (400+ species) pixel-based analysis. We quantified the global primate hotspots for Central and Northern South America, West Africa, East Africa, Southeast Asia, Central Asia, and Southern Africa. The global primate coldspots are Antarctica, the Arctic, most temperate regions, and Oceania past the Wallace line. We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world’s primates occur (or not). This shows us where the focus for most future research and conservation management efforts should be, using state-of-the-art digital data indication tools with reason. Those areas should be considered of the highest conservation priority, ideally following ‘no killing zones’ and sustainable land stewardship approaches if primates are to have a chance of survival.Ye

2024 Alaska Seismicity Summary

The Alaska Earthquake Center reported 39,836 seismic events in Alaska and neighboring regions in 2024. The largest earthquakes were two magnitude 6.3 events that were part of a swarm of M6 events on December 8-9 in the Andreanof Islands region of Alaska. The first occurred on December 8 at 19:57:07 UTC, and the second occurred at 00:15:30 on December 9, followed by an M6.1 23 minutes later. Other strong earthquakes include two M6.0 events, one on May 19 and one on July 19, both south of Yunaska Island in the Islands of Four Mountains region of the Aleutians, and the strongest mainland earthquake, an M5.9, off the coast of Port Alexander in Southeast Alaska on January 12. We continued to monitor the 2020 M7.8 Simeonof sequence, but all other previous sequences and swarms have dropped below one event per day and are no longer being tracked. Numerous short-lived swarms occurred in 2024 and will be discussed below.1. Abstract 2. Introduction 3. Notable seismic events 3.1. December 8-9 Adak Island Swarm 3.2. January 19 M5.3 Salcha Earthquake 3.3. Kaktovik Swarm 3.4. Ulaneak Creek Swarm 3.5. Landslides 3.6. Volcanic Events 4. Ongoing aftershock sequences and swarms 4.1. 2020 M7.8 Simeonof aftershock sequence 5. Glacial seismicity and Wright Glacier cluster 6. Acknowledgments 7. Reference

Alaska Misdemeanor Assault Arrest Rates, by Sex: 1985-2022

This fact sheet presents Alaska misdemeanor assault arrest rates per 100,000 males and 100,000 females, from 1985-2022

School Travel Behaviors in Rural Communities: Pandemic-Related Impacts

The global pandemic, which started around early 2020, significantly disrupted life for many families, and the trip to and from school was not immune to these disruptions. Parents and children alike made travel adjustments depending on their preferences with regard to personal health and safety, social distancing, and aversion to risk. Each school district and individual school also made decisions with regard to in-person or remote learning during this period of uncertainty. In this study, the research team examines how the pandemic affected school transportation for hundreds of families across the Pacific Northwest. An online survey was developed and administered with the help of Qualtrics, an experience management company. Over 600 responses were gathered to assess school transportation-related travel decisions. In addition to collecting demographic data about the respondents, the survey also asked about travel mode choices and characteristics of the trip to and from school. The collective results were then analyzed to determine which factors directly contributed to pandemic-related changes in travel behavior. The study concluded that the demographic factors of parent education level, household income, and age of child were all statistically significant variables that affected behavioral change, though the place of household residence, whether rural or urban, was determined to be an insignificant variable. Additionally, common travel assumptions associated with rural students, when compared with urban students, were confirmed. These factors included a greater reliance on a yellow school bus and lesser availability of critical infrastructure