Local Air Conditioning Estimates for the United States

This layer contains Local Air Conditioning Estimates (LACE) for the United States for 2023. Data shown by nation, state, county, and census tract. This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2023 ACS data product: Local Air Conditioning Estimates (LACE) Data downloaded from: [https://www.census.gov/data/experimental-data-products/lace.html] Date of download: May 19th, 2026 From Census' Local Air Conditioning Estimates (LACE) [Quick Guide]: One of the most frequent suggestions from data users was to include lack of air conditioning as a component of social vulnerability to extreme heat. However, air conditioning is not measured in our primary sources, the ACS and the PEP. The Census Bureau’s most comprehensive estimates of air conditioning are from the American Housing Survey (AHS), which is nationally representative but was not designed to produce reliable estimates at granular levels of geography. The [Local Air Conditioning Estimates (LACE) ]provide national, state, county, and census tract level estimates of the number and percent of occupied housing units that have an air conditioning unit. The estimates are created using a novel methodology, cross-survey modeling. This technique allows us to use data from the American Housing Survey to train a machine learning model. That model is then applied to data from the American Community Survey (ACS) to estimate how respondents in the ACS likely would have responded to AHS’s Air Conditioning question. Experimental data products from the Census Bureau are innovative statistical products created using new data sources or methodologies which can benefit data users in the absence of other relevant products. One goal of experimental products is to seek feedback from data users and stakeholders on the quality and usefulness of the new products. The genesis for LACE was another experimental data product, the [Community Resilience Estimates (CRE)] for Heat. While the standard CRE measures the social vulnerability that inhibits community resilience, the CRE for Heat measures social vulnerability specifically in the context of extreme heat exposure. Data Processing Notes: * Boundaries come from the [US Census TIGER geodatabases]. Then the water and/or coastlines were clipped out for cartographic and mapping purposes. The water cutouts are derived from a subset of the [2020 Areal Hydrography boundaries offered by TIGER]. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). * The States layer contains 51 records - all US states and Washington D.C. The 51 state boundaries were dissolved together to create a nation-wide layer that does not include Puerto Rico and other territories. * The water_tract field was dropped and water tracts were removed. * Two Census tracts in Louisiana (22075050100 and 22087030105) with were simplified to reduce their vertex count. * Field alias names were created based on the File Layout listed in the [Quick Guide.] Field definitions were based on American Housing Survey documentation. * Negative values (e.g., -2222...) have been set to null. These negative values exist in the raw data to indicate the following situations: * The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. * Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. * The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. * The estimate is controlled. A statistical test for sampling variability is not appropriate. * The data for this geographic area cannot be displayed because the number of sample cases is too small.