2023 marked the hottest summer on record. And this heat is not felt equally across the United States, cities, or even across neighborhoods. In a changing climate, it's crucial to understand how biophysical conditions impact different communities and urban settings. A pivotal figure in this narrative is the Home Owners' Loan Corporation (HOLC) of the 1930s, which fostered the redlining phenomenon: a racially biased grading system that informed lending risks and property investment potential for two decades, sowing deep-seated socio-economic rifts and spawning far-reaching environmental consequences.
Grades of Heat utilizes digital HOLC maps and a decade of satellite imagery analysis to unveil the thermal legacy of redlining at a city level. It offers users the chance to explore detailed temperature and vegetation data through interactive map and trend views, revealing the striking connection between historic HOLC grades and the experience of heat across the US.
Grades of Heat is produced and supported by the Brown Institute for Media Innovation.
During the 1930s, the Home Owners' Loan Corporation (HOLC) emerged as a crucial entity with the primary aim to refinance home mortgages and prevent foreclosure. However, it initiated a controversial practice of grading neighborhoods from A to D, which not only determined lending risks but also sculpted lasting perceptions and influenced policies significantly based on racial composition. These grading criteria seeped deep into the urban fabric, affecting both public policy and private investment, consequently positioning lower graded neighborhoods at the periphery of developmental priorities, with implications echoing through the decades. This legacy of mapping bore not just segregation but epitomized systemic biases with lingering socio-economic repercussions.
Grades of Heat utilizes digital versions of HOLC maps along with advanced satellite image processing techniques to reveal the complex environmental patterns that have developed in areas categorized by these historical gradings. The initiative, focusing on metrics like temperature and vegetation, intends to highlight the environmental consequences of these gradings. It underscores the fact that these environmental conditions have been shaped over time by various factors, including the influence of the HOLCs, which were initially designed as socio-economic tools.
The tool is built on analysis of ten years of Landsat 8 imagery spanning June-August from 2013-2023. Its analysis includes two core metrics, land surface temperature (LST) and normalized difference vegetation index (NDVI). As a note, during summers in urban environments, LST will appear more extreme than air temperature, due to what is known as the heat island effect. In this tool, the LST provided represents the mean temperature captured across the 50+ readings for each location. For context, NDVI represents vegetation and greenspace, be it a park, a row of trees, or a forest. There is a strong connection between NDVI and lower temperatures for an environment.
This project is meant to provide journalists, researchers, and the public an opportunity to explore the relationship between HOLC grades and temperature and vegetation at a city level -- to interrogate the thermal legacy of HOLCs. The platform offers two main views for a nuanced analysis: the Map View and the Trend View.
Map View
The Map View serves as an interactive tool to investigate the selected region in detail. Users can click on a city directly on the map or utilize the search function to select a specific city or state. Once a city is identified, the legend provides an overview of data associated with the city, offering detailed information and trends about temperature and NDVI.
Upon selecting a city, a thermal layer for that city is loaded, representing the mean temperature readings derived from a decade-long analysis of Landsat imagery. This layer is specially designed to underscore the significant hot spots across a city, drawing attention to the substantial role both concrete and greenspaces play in affecting temperature dynamics. Upon selecting a city, users can further zoom in to explore a detailed view of the urban area under investigation, thereby gaining insights into intricate patterns and deviations.
Trend View
It is hard to ignore the direct connection between HOLC grades and higher temperatures. The Trend view is meant to provide a simple interface to highlight this. In this view, users have two modes of engaging with the data: a lollipop chart and a scatter plot. In both view, cities are arranged along their means, with HOLCs presented based on their deviation from the mean. This view is meant to provide a key takeaway from the data: Grades A and B are routinely cooler than Grades C and D during summers.
At its core, Grades of Heat is an effort to illuminate the geophysical impacts of HOLCs on communities, particularly as our planet faces increasing temperatures. It's more than just numbers and analysis; it's about stories, histories, and lived experiences. Great reporting has been done about impacts of redlining on heat, but often at a single city with a single sample of temperature. This tool is meant to provide robust, processed data, built on thousands of satellite images over a ten-year span.
Interested in using data on a city? After selecting a city on the map, download links for that city will appear providing access to a dataset containing geographic and geophysical HOLC data specific to your selected city. This data encompasses mean NDVI and LST values extracted from a complete collection of Landsat 8 raster images taken across a 10-year period (2013-2023) for HOLCs across the US. Users also have access to the raw temperature values (in Celsius) in tif format. To access all data used for this tool, in its creation (raw data) and in its output, click on the Data + Methods button in the bottom right of most views.
If at any point you find yourself with questions, or you seek guidance on how to leverage this data to its fullest potential, please don't hesitate to contact us. Collaboration and knowledge sharing are essential to crafting narratives rooted in science but focused on people. If you would like additional data for a city, let us know.
This project was created by Michael Krisch and was supported by The Brown Institute for Media Innovation at Columbia Journalism School and Stanford Engineering School.
Select a city on the map or use the search bar in the lower right to find a state or HOLC to investigate.
Of the HOLCs represented here, 173 out of 202 Grade C and Grade D neighborhoods experienced hotter summer temperatures across 2013-2023 than bordering Grade A and Grade B neighborhoods.
Land Surface Temperature and Normalized Difference Vegetation Index data driving this tool are derived from ten summers of USGS satellite imagery captured June-August from 2013-2023.
Temperature (℉) across HOLCs
NDVI across HOLCs
Nearby Cities
Data Downloads
In this chart, cities are arranged alphabetically and aligned along their mean temperatures. For each city, grades are situated according to their variance from the mean. Cooler temperatures are to the left of the mean and hotter temperatures are to the right.
In scrolling through the list, a core trend emerges: it is cooler in the majority of grades A and B relative to their counterpart grades C and D.
In this chart, cities are arranged left to right based on mean temperatures of each city. For each city, grades are situated according to their variance from the mean. Cooler temperatures are to the bottom of the mean and hotter temperatures are to the top.
In this view, a core trend emerges: it is cooler in the majority of grades A and B relative to their counterpart grades C and D. A trend mirrored in the other chart view.
Home Owners' Loan Corporation (HOLC)
The HOLC was a U.S. government agency established in 1933 as part of the New Deal response to the Great Depression. Its primary mission was to assist homeowners facing foreclosure by refinancing their mortgages. However, it is infamously known for its role in institutionalizing redlining, a discriminatory practice wherein neighborhoods, often with large minority populations, were designated as high-risk for loan purposes.
The Home Owners' Loan Corporation utilized a grading system for neighborhoods which consisted of four designations: A, B, C, and D. Each grade reflected HOLC's assessment of the neighborhood's desirability and creditworthiness:
Land Surface Temperature (LST)
Land Surface Temperature (LST) represents the temperature of the Earth's surface and is a crucial metric obtained from remote sensing data, especially in the context of understanding urban heat and microclimates. Unlike air temperature which is commonly measured by weather stations at a standard height above the ground, LST provides a direct measure of the heat emitted from the ground, buildings, and other surfaces. In urban areas, LST can vary significantly across short distances due to variations in land cover, with built-up regions typically registering higher temperatures than vegetated areas. Monitoring LST is vital for urban planning and environmental health, as high LST values can exacerbate heat stress on populations, affect local air quality, and influence energy consumption patterns.
Normalized Difference Vegetation Index (NDVI)
The Normalized Difference Vegetation Index is a remote sensing metric used to quantify and monitor vegetation health, density, and greenness from multispectral imagery, and it's crucial for studies spanning both natural environments and urban areas. It is calculated by taking the difference between the near-infrared (NIR) and red reflectances and dividing by their sum: NDVI = (NIR - Red) / (NIR + Red). NDVI values range between -1 and 1. In urban contexts, where vegetation is intermixed with built structures, NDVI serves as a key indicator of urban greenery and heat island effects. Values close to 1 suggest dense, healthy vegetation, such as parks or green roofs; values around 0 might signify barren spaces or highly urbanized areas with minimal vegetation.
Processing for steps 1-3 were completed using GeoPandas in Python. Processing for steps 4-10 were completed using Google Earth Engine. There are varied approaches taken to derive land surface temperature readings from raw Landsat products. While this method uses steps defined by the NASA Applied Remote Sensing Training (ARSET) program and leverages pre-processed products for surface temperature and quality assurance, other methods were explored. For comparison, methods to derive LST using top of atmospheric (TOA) spectral radiance (Avdan & Jovanovska - 2016) were followed. LST values were on average 18.56 degrees hotter for each HOLC grade using steps defined by ARSET. Importantly, trends pertaining to LST, NDVI, and HOLCs did not change, regardless of method. Mean LST derived using TOA is found in the statistics available for download, for those interested.
This project was created by Michael Krisch and was supported by The Brown Institute for Media Innovation at Columbia Journalism School and Stanford Engineering School.
A special thanks to Grga Bašić at the University of Chicago, for inspiration and concept design for exploring the the use of heat as a proxy for vulnerability. And thanks to Shivani Agarwal at Columbia University, for support and guidance on scientific presentation and accuracy.