Results for “data collection” — Page 77

DOE OSTI2022

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Supervised Machine Learning Approach for Classifying Earth Science Publications

The data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding these collections' algorithms, their validation, and their applications. As NASA data centers collect these publications for public use, it is helpful to categorize them based on how they relate to their associated datasets. Specifically, whether the publication linked to the GES DISC dataset is using it for applicational research, describing the algorithm used for the dataset creation, validating the dataset, or providing a general overview of the data collection. Currently, this process requires simple manual labeling, and as such, it may be possible to solve via automation. To approach this problem, machine learning classifiers were developed to predict a publication's category. Manually labeled publications were used as the training data for the supervised machine learning algorithms, specifically Random Forest and Multinomial Naïve Bayes. After balancing the dataset and implementing the Multinomial Naïve Bayes algorithm, the classification accuracy achieved was substantially higher than the baseline accuracy, thus significantly improving the efficiency of publication labeling.

Rohan Dayal↗

NASA NTRS2016

Bluetooth(Registered Trademark) Heart Rate Monitors for Spaceflight

Heart rate monitoring is required during exercise for crewmembers aboard the International Space Station (ISS) and will be for future exploration missions. The cardiovascular system must be sufficiently stressed throughout a mission to maintain the ability to perform nominal and contingency/emergency tasks. High quality heart rate data is required to accurately determine the intensity of exercise performed by the crewmembers and show maintenance of VO2max. The quality of the data collected on ISS is subject to multiple limitations and is insufficient to meet current requirements. PURPOSE: To evaluate the performance of commercially available Bluetooth® heart rate monitors (BT_HRM) and their ability to provide high quality heart rate data to monitor crew health on board ISS and during future exploration missions. METHODS: Nineteen subjects completed 30 data collection sessions of various intensities on the treadmill and/or cycle. Subjects wore several BT_HRM technologies for each testing session. One electrode-based chest strap (CS) was worn, while one or more optical sensors (OS) was worn. Subjects were instrumented with a 12-lead ECG to compare the heart rate data from the Bluetooth sensors. Each BT_RHM data set was time matched to the ECG data and a +/-5bpm threshold was applied to the difference between the two data sets. Percent error was calculated based on the number of data points outside the threshold and the total number of data points. REULTS: The electrode-based chest straps performed better than the optical sensors. The best performing CS was CS1 (1.6%error), followed by CS4 (3.3%error), CS3 (6.4%error), and CS2 (9.2%error). The OS resulted in 10.4% error for OS1 and 14.9% error for OS2. CONCLUSIONS: The highest quality data came from CS1, unfortunately it has been discontinued by the manufacturer. The optical sensors have not been ruled out for use, but more investigation is needed to determine how to get the best quality data. CS2 will be used in an ISS Bluetooth validation study, because it simultaneously transmits Magnetic Pulse which is integrated with existing exercise hardware on ISS. The simultaneous data streams allow for beat to beat comparison between the current ISS standard and CS2.Upon Bluetooth(Registered Trademark) validation aboard ISS, down select of a new BT_HRM for operational use will be made.

Buxton, Roxanne E.↗

NASA NTRS2016

Bluetooth Heart Rate Monitors For Spaceflight

Heart rate monitoring is required for crewmembers during exercise aboard the International Space Station (ISS) and will be for future exploration missions. The cardiovascular system must be sufficiently stressed throughout a mission to maintain the ability to perform nominal and contingency/emergency tasks. High quality heart rate data are required to accurately determine the intensity of exercise performed by the crewmembers and show maintenance of VO2max. The quality of the data collected on ISS is subject to multiple limitations and is insufficient to meet current requirements. PURPOSE: To evaluate the performance of commercially available Bluetooth heart rate monitors (BT_HRM) and their ability to provide high quality heart rate data to monitor crew health aboard the ISS and during future exploration missions. METHODS: Nineteen subjects completed 30 data collection sessions of various intensities on the treadmill and/or cycle. Subjects wore several BT_HRM technologies for each testing session. One electrode-based chest strap (CS) was worn, while one or more optical sensors (OS) were worn. Subjects were instrumented with a 12-lead ECG to compare the heart rate data from the Bluetooth sensors. Each BT_HRM data set was time matched to the ECG data and a +/-5bpm threshold was applied to the difference between the 2 data sets. Percent error was calculated based on the number of data points outside the threshold and the total number of data points. RESULTS: The electrode-based chest straps performed better than the optical sensors. The best performing CS was CS1 (1.6% error), followed by CS4 (3.3% error), CS3 (6.4% error), and CS2 (9.2% error). The OS resulted in 10.4% error for OS1 and 14.9% error for OS2. CONCLUSIONS: The highest quality data came from CS1, but unfortunately it has been discontinued by the manufacturer. The optical sensors have not been ruled out for use, but more investigation is needed to determine how to obtain the best quality data. CS2 will be used in an ISS Bluetooth validation study, because it simultaneously transmits magnetic pulse that is integrated with existing exercise hardware on ISS. The simultaneous data streams allow for beat-to-beat comparison between the current ISS standard and CS2. Upon Bluetooth validation aboard ISS, the research team will down select a new BT_HRM for operational use.

Buxton, R. E.↗

NASA NTRSDate not supplied

Continued Environmental Microbiology Monitoring of The International Space Station (ISS) Veggie Unit Used for In-Flight, Crop-Based Food Systems

The International Space Station is a closed environment where rotating sets of Crewmembers live and work. This environment is monitored to ensure occupants’ health and safety during their spaceflight residency by routine Environmental Health System (EHS) collection of microbial samples including air, surface, and water. The microbial samples are collected, enumerated, and analyzed quarterly to monitor on-board system contamination and potential risks to crew health. Quarterly monitoring of the microorganisms in the ISS environment supports crew safety and contributes to a large set of microbial concentration and diversity data. The current in-inflight microbial requirements were developed using this historical data collected by the routine environmental monitoring. These in-flight microbial requirements have been established to maintain the health and safety of the spacecraft environment. This study leverages quarterly operational EHS sampling by collecting additional microbial samples from the surface of the Veggie plant production system on ISS. These samples will yield microbial concentration and diversity that can be compared and analyzed with nominal surface samples from the vehicle. The data collected in this study will aid in the development of requirements for spaceflight-based food production systems. Continued surface sampling of the internal and external surfaces of the Veggie system, along with collaboration from both Johnson Space Center (JSC) & Kennedy Space Center (KSC) scientists studying the microbiome of the veggie-crop systems, will be implemented as part of the future development of crop-based food system requirements for the ISS and beyond.

Christian Mena↗

DOE OSTI2022

Cryotrapping peroxide in the active site of human mitochondrial manganese superoxide dismutase crystals for neutron diffraction

Structurally identifying the enzymatic intermediates of redox proteins has been elusive due to difficulty in resolving the H atoms involved in catalysis and the susceptibility of ligand complexes to photoreduction from X-rays. Cryotrapping ligands for neutron protein crystallography combines two powerful tools that offer the advantage of directly identifying hydrogen positions in redox-enzyme intermediates without radiolytic perturbation of metal-containing active sites. However, translating cryogenic techniques from X-ray to neutron crystallography is not straightforward due to the large crystal volumes and long data-collection times. Here, methods have been developed to visualize the evasive peroxo complex of manganese superoxide dismutase (MnSOD) so that all atoms, including H atoms, could be visualized. The subsequent cryocooling and ligand-trapping methods resulted in neutron data collection to 2.30 Å resolution. The P 6 1 22 crystal form of MnSOD is challenging because it has some of the largest unit-cell dimensions ( a = b = 77.8, c = 236.8 Å) ever studied using high-resolution cryo-neutron crystallography. The resulting neutron diffraction data permitted the visualization of a dioxygen species bound to the MnSOD active-site metal that was indicative of successful cryotrapping.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗

DOE OSTI2025

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Teller 47 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory’s (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 47 along the Teller Road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDAR System was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, and lidar data and imagery can be found in NGA281 (https://doi.org/10.5440/1671794).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Correlation of Injury Simulation with Clinical Assessment of Traumatic Brain Injury

This report contains a summary of our efforts to correlate head injury simulations predicting intracranial fluid cavitation with clinical assessments of brain injury from blunt impact to the head. Magnetic resonance imaging (MRI) data, collected on traumatic brain injury (TBI) subjects by researchers at the MIND Institute of New Mexico, was acquired for the current work. Specific blunt impact TBI case histories were selected from the TBI data for further study and possible correlation with simulation. Both group and single-subject case histories were examined. We found one single-subject case that was particularly suited for correlation with simulation. Diffusion tensor image (DTI) analysis of the TBI subject identified white matter regions within the brain displaying reductions in fractional anisotropy (FA), an indicator of local damage to the white matter axonal structures. Analysis of functional magnetic resonance image (fMRI) data collected on this individual identified localized regions of the brain displaying hypoactivity, another indicator of brain injury. We conducted high fidelity simulations of head impact experienced by the TBI subject using the Sandia head-neck-torso model and the shock physics computer code CTH. Intracranial fluid cavitation predictions were compared with maps of DTI fractional anisotropy and fMRI hypoactivity to assess whether a possible correlation exists. The ultimate goal of this work is to assess whether one can correlate simulation predictions of intracranial fluid cavitation with the brain injured sites identified by the fMRI and DTI analyses. The outcome of this effort is described in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Kougarok 64 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Thermal Vacuum and Vibration Testing of the Differential Thermal Expansion Thermal Switch

The Extended Stroke, Reverse Operation, Differential Expansion Thermal Switch for Extreme Variable Environments uses materials with both positive and negative coefficients of thermal expansion (CTE) arranged in concentric cylinders so that the thermal switch (TS) operates passively, opening a gap between interfaces to create a low thermal contact when cold, and closing the gap between interfaces to create a high thermal contact when hot. This paper details the testing of the TS at Marshall Space Flight Center including the pre-vibe thermal vacuum cycle test, vibration test, post-vibe thermal vacuum cycle test, and long duration thermal vacuum cycle test. The pre- and post-vibe thermal cycle and the long duration cycle test tested to hot and cold plateau initial temperatures of 40 °C, and -173 °C, respectively. The pre- and post-vibe tests required plateau holds of at least 2 hours, whereas the long duration cycle test required plateau holds of at least 20 hours. The tests each used a rate of temperature change of < 2 °C/min when transitioning between plateaus. The data collected from each thermal test is compared to show the operation of the TS both before and after the vibration test to GEVS levels, and throughout the long duration cycle test. From the data collected, the total conductance through the TS interfaces is calculated for both on (TS closed – during hot plateau) and off (TS open – during cold plateau) configurations, and the turndown ratio is also calculated. Challenges and lessons learned from the testing are also discussed.

thermal control technology↗

DOE OSTI2020

Report on the recovery of legacy 1997-1998 Depth-of-Burial / Tunnel-and-Hole-Closure explosion data from the Kazakhstan former Soviet nuclear test site, Semipalatinsk.

In 1996, negotiations between the National Nuclear Center of the Republic of Kazakhstan (NNC), the U.S. Defense Special Weapons Administration (DSWA) and the U.S. Department of Energy led to an agreement for a joint series of experiments at the former Soviet nuclear test site Semipalatinsk (STS). These experiments had a dual purpose: to close holes and tunnels originally created for the purpose of testing nuclear weapons, and to characterize explosive sources at a former nuclear test site. This report covers the recovery of legacy data from the experiments conduct in 1997 and 1998. Two teams collected data. Los Alamos National Laboratory (LANL), in collaboration with the NNC, collected the near-source (local) data out to approximately 20 km. Lamont-Doherty Earth Observatory (LDEO), with funding from Lawrence Livermore Laboratory (LLNL), also in collaboration with NNC, collected regional data, 200 – 1000 km distant, leveraging the KZ regional seismic network that LDEO had recently installed in collaboration with NNC. The two teams also worked together to some extent, regarding instrumentation and planning. This report primarily addresses the near-source data collected by the LANL/NNC team.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Arctic shrub size and leaf traits, northern Alaska, summer 2017 (Version 2.0)

This data package contains leaf and size trait and environmental data collected from 170 plots of rapidly expanding shrub genera (Alnus, Betula, and Salix) and a widespread sedge (Eriophorum vaginatum) along a latitudinal and climate gradient in northern Alaska. The trait data were collected in summer 2017 and include leaf area, specific leaf area, leaf nitrogen concentration, leaf delta 15N, leaf delta 13C, shrub height, aboveground biomass, and the ratio of root biomass to aboveground biomass. These raw data support a submitted manuscript that examines the intraspecific variations of size, leaf and root traits of shrubs across the graminoid-dominated nutrient-poor arctic tundra and reveals the environmental drivers of deciduous shrub traits in tundra ecosystems (Fraterrigo et al., submitted). Data are presented by site (n=5) and patch (shrub or sedge plot) in separate csv files. The location data are provided in the “plot coordinates” file; all other files contain the data in the file title. Detailed methods are in the submitted manuscript. A companion data package contains root trait data collected simultaneously from the same plots (Fraterrigo and Chen, 2020). See the "Related references" section for more information.2023/07/20 Update: The latest version of this dataset publication is version 2.0. The latest version of the data package was updated to correct the leaf size trait.csv file.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗