This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

FY2024 full and partial census tracts that qualify as Low-Moderate Income Areas (LMA) where 51% or more of the population are considered as having Low-Moderate Income. The low- and moderate-income summary data (LMISD) is based on the 2016-2020 American Community Survey (ACS). As of August 1, 2024, to qualify any new low- and moderate-income area (LMA) activities, Community Development Block Grant (CDBG) grantees should use this map and data.

For more information about LMA/LMI click the following link to open in new browser tab https://www.hudexchange.info/programs/cdbg/cdbg-low-moderate-income-data/

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are derived from the 2011-2015 American Community Survey (ACS) and based on Census 2010 geography.

To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Tract

The Rooftop Energy Potential of Low Income Communities in America REPLICA data set provides estimates of residential rooftop solar technical potential at the tract-level with emphasis on estimates for Low and Moderate Income LMI populations. In addition to technical potential REPLICA is comprised of 10 additional datasets at the tract-level to provide socio-demographic and market context. The model year vintage of REPLICA is 2015. The LMI solar potential estimates are made at the tract level grouped by Area Median Income AMI income tenure and building type. These estimates are based off of LiDAR data of 128 metropolitan areas statistical modeling and ACS 2011-2015 demographic data. The remaining datasets are supplemental datasets that can be used in conjunction with the technical potential data for general LMI solar analysis planning and policy making. The core dataset is a wide-format CSV file seeds_ii_replica.csv that can be tagged to a tract geometry using the GEOID or GISJOIN fields. In addition users can download geographic shapefiles for the main or supplemental datasets. This dataset was generated as part of the larger NREL-led SEEDSII Solar Energy Evolution and Diffusion Studies project and specifically for the NREL technical report titled Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States by Sigrin and Mooney 2018. This dataset is intended to give researchers planners advocates and policy-makers access to credible data to analyze low-income solar issues and potentially perform cost-benefit analysis for program design. To explore the data in an interactive web mapping environment use the NREL SolarForAll app.

ABOUT THIS TOOL:

The Better Building’s Clean Energy for Low Income Communities Accelerator (CELICA) was launched in 2016 to help state and local partners across the nation meet their goals for increasing uptake of energy efficiency and renewable energy technologies in low and moderate income communities. As a part of the Accelerator, DOE created this Low-Income Energy Affordability Data (LEAD) Tool to assist partners with understanding their LMI community characteristics. This can be utilized for low income and moderate income energy policy and program planning, as it provides interactive state, county and city level worksheets with graphs and data including number of households at different income levels and numbers of homeowners versus renters. It provides a breakdown based on fuel type, building type, and construction year. It also provides average monthly energy expenditures and energy burden (percentage of income spent on energy).

HOW TO USE:

The LEAD tool can be used to support program design and goal setting, and they can be paired with other data to improve LMI community energy benchmarking and program evaluation. Datasets are available for all 50 states, census divisions, and tract levels. You will have to enable macros in MS Excel to interact with the data. A description of each of the files and what states are included in each U.S. Census Division can be found in the file "DESCRIPTION OF FILES".

For more information, visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

This is a HUD datasetThe Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2011-2015 Data Updated: Every Five YearsReleased 2019This layer is used in the Map(s): CDBG Applicant Concentration 2024

The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level. Each dataset is broken out by income group, defined by the Area Median Income (AMI), by tenure, and by building type. The underlying LiDAR data used the estimation were collected in 2015-2017 and the American Community Survey (ACS) data are from 2011-2015. This Puerto Rico Solar-For-All dataset is intended to extend the SEEDSII Solar-For-All estimates for the US 50-states (https://www.nrel.gov/docs/fy18osti/70901.pdf) for the territory of Puerto Rico using updated methods. Please see the data documentation for details.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

This database represents a list of community solar projects, as well as the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects identified through various sources. The dataset is updated multiple times per year. The current version is the first file located below. Previous versions of the dataset published before June of 2024 can be found in the dataset below labeled “ARCHIVE_Sharing the Sun Community Solar Project Data_Before 06.24.“ The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects.Updated information can be submitted to Sudha Kannan (sudha.kannan@nrel.gov).

Abstract: Despite the proliferation of rooftop solar in the U.S., its deployment and associated benefits have not been distributed equitably. Many states have adopted targeted incentives to improve access to rooftop solar and increase its uptake amongst low- and moderate-income (LMI) communities. This paper examines the policy feedback effects of energy efficiency policies and electricity sector portfolio standards on the adoption and diffusion of LMI solar incentives across states. Event history analyses indicate that, between 2010 and 2019, the adoption and diffusion of the incentives has been conditional on the state’s portfolio standards, but independent of energy efficiency policies. Feedback effects from the portfolio standards in neighboring states are found to have a regressive impact on the likelihood of adoption. Hence, the feedback effects of previously adopted renewable energy policies are helping states to better serve vulnerable communities; however, there is no evidence of geographic clustering in the diffusion of incentives.

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2023. In addition, this dataset updated the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects, based on the most recent program data we collected as of March 2024. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

sam-liu-lmi/databricks-dolly-15k-alpaca-style dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset provides access to Qualified Census Tracts (QCTs) in Connecticut to assist in administration of American Rescue Plan (ARP) funds. The Secretary of HUD must designate QCTs, which are areas where either 50 percent or more of the households have an income less than 60 percent of the AMGI for such year or have a poverty rate of at least 25 percent. HUD designates QCTs based on new income and poverty data released in the American Community Survey (ACS). Specifically, HUD relies on the most recent three sets of ACS data to ensure that anomalous estimates, due to sampling, do not affect the QCT status of tracts. QCTs are identified for the purpose of Low-Income Housing Credits under IRC Section 42, with the purpose of increasing the availability of low-income rental housing by providing an income tax credit to certain owners of newly constructed or substantially rehabilitated low-income rental housing projects. Also included are the number of households from the 2010 census (the “p0150001” variable), the average poverty rate using the 2014-2018 ACS data (the “pov_rate_18” variable), and the ratio of Tract Average Household Size Adjusted Income Limit to Tract Median Household Income using the 2014-2018 ACS data (the “inc_factor_18” variable). For the last variable mentioned in the previous paragraph, the income limit is the limit for being considered a very low income household (size-adjusted and based on Area Mean Gross Income). This value is divided by the median household income for the given tract, to get a sense of how the limit and median incomes compare. For example, if ratio>1, it implies that the tract is very low income because the limit income is greater than the median income. This ratio is a compact way to include the separate variables for the household income limit and median household income for each tract.

A list of sites available for solar PV development under NYCHA's ACCESSolar program.

As part of NYCHA's NextGeneration Sustainability Agenda, NYCHA has pledged to put 25 MW of solar power on its properties. One of the avenues by which NYCHA aims to meet this goal is the ACcelerating Community Empowered Shared Solar (ACCESSolar) program, where smaller rooftops will be made available at low or no cost to community-based organizations and non-profits teaming up with solar developers. The ACCESSolar program will help maximize the solar potential of NYCHA’s portfolio. NYCHA also aims for this program to provide green jobs to residents, expand the availability of solar power to LMI populations, and facilitate first projects for start-up developers. ACCESSolar applicants will be able to submit an application online starting in late March 2018.

The ACCESSolar program was previously in development as Public Purpose Shared Solar (PPSS) and has been renamed to better reflect the program's goals.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.