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DOE OSTI2024

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Climate change and federal aid disbursements after Hurricane Harvey: an extreme event attribution analysis

The role climate change plays in increasing the burden placed on governments and insurers to pay for recovery has not been extensively explored and is the focus of this study. This study examines the impacts of climate change attributed flooding on federal disaster aid disbursement in Harris County, Texas following Hurricane Harvey in 2017. Our approach uses flood models to estimate the amount of flood damages attributable and not attributable to climate change under two climate change attribution scenarios from peer reviewed studies: 20% and 38% increases in rainfall associated with the hurricane due to climate change. These estimates are combined with census tract-level disbursement data for FEMA’s National Flood Insurance Program (NFIP) and the Individual Assistance (IA) part of the Individuals and Households Program. We employ spatial lag regression models with direct and spatial spillover effects to analyze the relationship between a tract’s flood damages—both attributed and not attributed to climate change—and federal disaster aid. We find that both types of flood damage shape federal aid disbursements, but that climate change attributed damages tend to have larger effect sizes (elasticities) especially for IA. Specifically, for a 1% increase in additional climate change attributed damages per household in a census tract (under the 20% scenario), expected NFIP levels in that census tract are 0.26% higher and IA levels are 0.3% higher. Implications center on federal funding in an era of climate change.

FEMA↗

DOE OSTI2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators

Mobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is influenced by both intrinsic (i.e. personal motivations) and extrinsic (i.e., events like natural hazards or a pandemic like the COVID-19) factors. However, the intricate relationships between human mobility patterns and sociodemographic characteristics in the context of a pandemic are yet to be fully explored. Our goal is to overcome this gap by using human mobility data at the census block group level from mobile phones and combining those with social vulnerability indicators to examine the overall spread of COVID-19 at local spatial scales. We used 585,878 weekly visits to 37,871 points of interests (POIs) from Safegraph to quantify mobility indices and social distancing metrics in 2,820 census block groups in the city of Los Angeles (LA) - before and during lockdown as well as during the phase1 and phase 2 reopening. Finally, using supervised machine learning algorithms, we classified the census block groups in LA into High, Medium and Low categories that represented the vulnerability of these block groups based on the cumulative number of occurrences of COVID-19 cases till July 24, 2020. Our results indicate that the tree-based classifiers performed well in comparison to the Support Vector Machines and Multinomial Logit models. Gradient Boosting had the highest classification accuracy of 97.4% COVID-19 with an AUC score of 0.987. The block groups with high COVID-19 cases also had a high concentration of socially vulnerable populations, high human mobility index and a low social distancing index.

Roy, Avipsa↗

DOE OSTI2023

Incorporating Social Vulnerability Variables in Measures to Quantify Access to Opportunities

Herein this study quantifies access to travel opportunities to understand what societal factors are linked with local access and to identify communities with reduced access. We introduce a method to compare accessibility across all census tracts in the United States that can be used across geographically diverse communities ranging from sparsely to densely populated areas. This study considers six key opportunities which we consider essential for all communities (grocery stores, public schools, daycares, primary care doctors, pharmacies, and parks), and six additional destinations which can be viewed as a social safety net (homeless shelters, women’s shelters, food pantries, libraries, vocational schools, and banks). We quantify accessibility to these opportunities within a 15 min walk, transit trip, bicycle ride, and automobile drive for every census tract in the United States, and observe a decrease in vehicle miles traveled and vehicle ownership in tracts with increased walkability. Through analysis at the census tract level, this study incorporates variables of social vulnerability with these cumulative opportunity metrics to better understand diminished accessibility as attributed to social and racial inequities. As example findings, we find decreased access to financial services in communities with high minority and limited English speaking populations, no apparent change in access for childcare in communities with high percentages of single-parent families, and potentially increased or decreased access to women’s healthcare resources for Black women depending on the travel mode.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Tree damage and mortality measurements across seven ForestGEO plots in the tropics between Oct 2016 and Mar 2023

Annual records (raw data) on tree survival and structural completeness of 36,524 trees (2,467 species) collected across 29 censuses in seven tropical forests distributed across the Neotropics (Amacayacu, Colombia; Barro Colorado Island (BCI), Panamá; Yasuní, Ecuador) and Asia (Fushan, Taiwan; Huai Kha Khaeng (HKK), Thailand; Khao Chong (KC), Thailand; Pasoh, Malaysia). This dataset was used to compare aboveground biomass loss via damage to living trees relative to total AGB loss (mortality + damage). Variable definitions: site: name of the ForestGEO plot stemID.ams: Unique ID for the stem in the annual mortality surveys (ams) treeID.ams: Unique ID for the tree in the ams date.full.census: date of the previous full census of the plot, format: YYYY-MM-DD dbh.full.census: diameter at the breast height (dbh in mm) measured during the previous full census of the plot home: height of measurement of dbh (in m) meanWD: species-level wood density (g cm-3) date.ams: date of the ams, format: YYYY-MM-DD status: survival status of the tree (A: alive; D: dead; NF: not found; "?": unknown) H_considering_damage: living length of the main axis in meters; provides an estimate of the amount of remaining living tissues along the main axis of the stem (e.g., the height of breakage or the height discounting wood decay) b: the remaining proportion of branch volume within the living length (b∈[0,1]). weights.ind: frequency of the [size class x species] bins within the forest plot relative to their frequency in the sample. Necessary to extrapolate estimates to the whole plot.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Internet Access Index: Measuring the availability and household adoption of high-speed internet

Access to broadband or high-speed internet has taken on increased importance during the COVID-19 pandemic as many workplaces and schools shifted to a virtual environment. The Internet Access Index (IAI) was developed to better understand the challenges many households face in connecting to high-speed internet, whether due to lack of broadband availability from internet service providers or difficulties in subscribing to a broadband service. The IAI is calculated as the product of 3 factors. The first two factors gauge the quality and availability of high-speed internet and the third factor represents the public’s ability to subscribe to high-speed internet services. The IAI scores United States’ census tracts on a [0,1] scale, where values near 0 represent areas with less internet access and values approaching 1 represent more internet access. The maximum score (1.0) implies that all census blocks within a census tract have high speed internet available at the highest national speed (currently 1000 mbps), and that all households within the tract have a fixed broadband subscription of some type.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Microgrid Hardening Design Toolkit: Puerto Rico Use Case

This document provides a comprehensive example of the microgrid hardening framework and the Sandia developed Microgrid Hardening Design Toolkit v0.27 using a census tract in a coastal area of southern Puerto Rico as a case study. The census tract (72123953100) is located in the municipality of Salinas. Currently, the La Margarita neighborhood within this census tract is part of the Department of Energy’s Cohort 5 of the Energy Technology Innovation and Partnership Program (ETIPP). The neighborhood’s local energy cooperative, Abeyno Coop, has been operating several residential solar photovoltaic (PV) and battery energy storage system (BESS) installations (with around 30 rooftop solar systems as of 2026). As part of the ETIPP project, Abeyno Coop is planning to integrate a larger microgrid into the existing distribution feeder in the area, including solar PV and BESS to supply energy for homes and critical loads such as the medical facilities and the community center that provides emergency shelter and backup power during outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1974

TERSSE: Definition of the Total Earth Resources System for the Shuttle Era. Volume 6: An Early Shuttle Pallet Concept for the Earth Resources Program

A space shuttle sortie mission which can be performed inexpensively in the early shuttle era and which, if the necessary intermediate steps are accomplished provides a major technological advance for the user organization-the U.S. Bureau of Census is described. The orbital configuration created for the Urban Land Use/1980 Census mission is illustrated including sensors and ground support equipment along with the information flow for the mission. Factors discussed include: specific Census Bureau functions to be supported by the mission; hardware and flight operations necessary for implementation of the mission; and integration of the TERSSE pallet into a shuttle mission.

Source record↗

NASA NTRS1975

Remote sensing and urban public health

The applicability of remote sensing in the form of aerial photography to urban public health problems is examined. Environmental characteristics are analyzed to determine if health differences among areas could be predicted from the visual expression of remote sensing data. The analysis is carried out on a socioeconomic cross-sectional sample of census block groups. Six morbidity and mortality rates are the independent variables while environmental measures from aerial photographs and from the census constitute the two independent variable sets. It is found that environmental data collected by remote sensing are as good as census data in evaluating rates of health outcomes.

Rush, M.↗

NASA NTRS1975

Prediction of health levels by remote sensing

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Rush, M.↗

NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect. This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure in urban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗

NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect.This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure inurban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗

NASA NTRSDate not supplied

Wichita Climate: Using Satellite Data to Identify Neighborhoods Vulnerable to Extreme Heat for Equitable Climate Mitigation and Planning

Wichita, Kansas is facing a host of climate threats, one being extreme heat that is manifested through the urban heat island (UHI) effect. The uneven distribution of heat risk in Wichita across socioeconomic status is an environmental justice issue. We worked with the City of Wichita to map heat exposure, tree canopy, and heat risk in order to support the City's climate resilience initiatives. To visualize heat exposure, we quantified and mapped average summer heat from 2013–2021 using Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Land Surface Temperature (LST) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) night-time LST. To understand tree canopy cover gaps, we created a tree canopy map using 2021 PlanetScope imagery, which identified 20% more trees than the US Geological Survey’s (USGS) National Land Cover Database (NLCD) tree canopy coverage estimates for Wichita. To characterize high risk areas, we used socioeconomic census data and existing social vulnerability indices, highlighting populations that were exposed and vulnerable to extreme heat. The spatial analyses demonstrated that heat exposure is concentrated in the city center and southwest Wichita, areas that are also low in tree canopy coverage. The three census block groups and 17 census tracts with the highest heat risk primarily circle the city center, in areas home to more socially vulnerable populations and near enough to the dense urban center to feel significant urban heat island effects.

Brooke Laird↗

NASA NTRS2022

Wichita Climate: Using Satellite Data to Identify Neighborhoods Vulnerable to Extreme Heat for Equitable Climate Mitigation and Planning

Brooke Laird↗

DOE OSTI2023

Waffle Homes: Utilizing Aerial Imagery of Unfinished Buildings to Determine Average Room Size

A primary function of the Population Density Tables Project (PDT) at Oak Ridge National Laboratory is to produce residential population densities per 1000 sq. ft. for each country and their associated first-level administrative units. This is accomplished by utilizing the average size of different types of dwelling areas (urban, rural, single-family, multi-family, etc.) and the average household size provided by a country’s Census or statistical bureau records. This data is available for the majority of Europe, North America, and large swathes of Asia, but is less easily found in Africa and South America. In these regions, Censuses generally report dwelling area by number of rooms, which poses the challenging question of how we can translate number of rooms to dwelling size when no dwelling size areas are available with which to compare. Using sub-meter resolution satellite imagery of Accra, Ghana, this challenge can be tackled using imagery of roofless buildings currently under construction that show the interior floor plan of the dwelling. A sample of buildings from the different neighborhoods of Accra can be digitized to provide an estimate and range of average room sizes of dwellings. This average room size can then be translated to a total dwelling area using the "number of rooms occupied by a household" variable from the Ghanaian Census. This intermediate step between average dwelling size and number of rooms occupied, fills the missing link that prevents PDT from continually producing new population densities for countries where dwelling size is unavailable through any official means.

Woody, Carson↗

DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗