Contemporary income inequality outweighs historic redlining in shaping intra-urban heat disparities in Los Angeles

Domain: earth_science
Published: 2025-05-28
Editorial outcome: minor_revision

Abstract
The roots of intra-urban heat disparity in the U.S. often trace back to historical discriminatory practices, such as redlining, which categorized neighborhoods by race or ethnicity. In this study, we compare the relative impacts of historic redlining and current income inequality on thermal disparities in Los Angeles. A key innovation of our work is the use of land surface temperature data from the ECOSTRESS instrument aboard the International Space Station, enabling us to capture diurnal trends in urban thermal disparities. Our findings reveal that present-day income inequality is a stronger predictor of heat burden than the legacy of redlining. Additionally, land surface temperature disparities exhibit a seasonal hysteresis effect, intensifying during extreme heat events by 5−7 °C. Sociodemographic analysis highlights that African-American and Hispanic populations in historically and economically disadvantaged areas are often the most vulnerable. Our findings suggest that while the legacy of redlining may persist, the present-day heat disparities are not necessarily an immutable inheritance, where targeted investments and interventions can pave the way for a more thermally just future for these communities.

Introduction
Over the last few decades, research has unequivocally identified cities as distinctly warmer entities compared to their non-urban surroundings1,2. A large body of literature has also investigated the complexity of intra-urban thermal landscapes3,4 and their associations with present-day population distributions5–9. Although climate change exposes rapidly growing urban populations to enhanced heat hazards10,11, the inherent heterogeneity of cities further exacerbates the inequitable distribution of its worst impacts, especially evident in the U.S.12,13. For many U.S. cities, areas with poorer and predominantly non-white populations tend to be warmer5–9. These disparities in heat hazard have also been found to lead to unequal mortality/morbidity outcomes, making them an important policy focus for equitable urban planning14–17. Although the interplay of present-day socioeconomic factors, demographic characteristics, and land cover properties directly modulates the distribution of intra-urban heat exposure, emerging evidence suggests that the roots of this thermal disparity extend beyond contemporary factors8,18,19. Legacy issues stemming from residential segregation policies and historical decisions based on race/ethnicity urban planning have had an enduring impact on the spatial distribution of multifaceted vulnerabilities within cities20–22. Discussions around historic segregation in urban areas have frequently focused on the practice of redlining, often referred to as the Home Owners Loan Corporation (HOLC) grades23. It emerged in the United States in the twentieth century, as part of federal housing policies, where neighborhoods were classified based on their suitability for mortgage lending20,24,25. Often, such policies disproportionately targeted minority communities, particularly African American and Hispanic populations20,26,27. Urban neighborhoods were categorized and color-coded into four different groups according to perceived desirability, with A being the most desirable and D considered 'Hazardous' and marked in red, hence leading to the term redlining (Fig. 1a). This systemic bias denied C- and D-graded neighborhoods access to loans and investments, contributing to the enduring socioeconomic disparities. This lack of financial support and equitable opportunities led to a further decline in infrastructure, amenities, and overall neighborhood conditions, including outdoor green spaces that are still visible today28–30. The lack of shading and evaporative cooling from trees and green spaces, combined with excess impervious built-up surfaces, increases local temperatures within these neighborhoods31–33. As a result, residents of historically redlined areas are often exposed to higher temperatures, and this has been observed by previous studies in multiple U.S. cities13,29,34–39.

In this study, we focus on the Los Angeles (LA) county’s metropolitan region (extent shown in Fig. 1a), which is home to nearly 25 million residents. LA’ diverse topography, featuring a coastal plain rapidly rising into the San Gabriel Mountains, coupled with the strong coastal influence of the Pacific Ocean. This compressed diversity results in different Köppen climate classifications, such as Mediterranean (Csa, Csb), and semi-arid (BSh), to be found in close proximity here, which in turn leads to significant variations in temperature, precipitation, and vegetation over short distances40. For example, coastal areas experience a moderating marine influence with cooler temperatures and fog, while inland valleys can be significantly hotter and drier41. As elevation increases moving into the mountains, climate zones change dramatically. Heat waves can occur when these coastal processes are disrupted, leading to higher temperatures42. Over the last few decades, LA has witnessed a gradual increase in the frequency, intensity, and persistence of heat waves, posing a big challenge to its citizens and policymakers43. Additionally, LA was also subject to redlining in the past44. Fig. 1a, for example, displays the historically marked census tracts of LA23 (details in Methods). While the discriminatory practice was outlawed in 1968 with the passage of the Fair Housing Act45,46, its effects still linger and continue to shape patterns of inequality in many cities. Figure 1b and c, for example, illustrate how green cover is drastically different between an A- and D-graded neighborhood even today. Furthermore, significant segregation is still present across historically redlined and non-redlined neighborhoods, especially in White and Hispanic populations (Fig. 1d).

Most previous investigations into the intra-urban and intra-HOLC heat inequalities have relied on Land Surface Temperatures (LST) estimated for specific times of day from satellites in sun-synchronous orbits (e.g., Landsat at 10:00 am)29,34–39. Here, we leverage data from the ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) thermal instrument onboard the International Space Station (ISS), whose precessing orbit allows observations at different times during the day, providing comprehensive temporal coverage47,48 (see Methods). By leveraging ISS-borne LST observations, here, we aim to discern the diurnal and seasonal patterns that may have been overlooked in previous studies, providing a more detailed perspective on the temporal dynamics of heat inequalities and their potential implications for vulnerable and historically underserved communities.

Our exploration begins by investigating the diurnal trends in LST disparity between the two extremes of HOLC grades, D versus A, which contributes directly to the existing literature5–9 of heat disparities due to redlining observed in various U.S. cities. Acknowledging the intricate link between income inequality and historic redlining grades44,49,50, our paper then moves on to dissect the influence of income inequality on urban heat within each HOLC grade. Specifically, we aim to unravel the relative importance of present-day income inequalities versus century-old segregation practices. As part of this analysis, we delve into the areas that used to be historically redlined but are currently affluent. We then extend our investigation to study seasonal patterns and find an annual hysteresis effect wherein LST inequalities can have different magnitudes despite the same mean temperature, depending on time of year. Finally, we investigate the relative physiological heat vulnerability and socio-demographic profiles of the populations currently residing in these neighborhoods. Through this comprehensive analysis, we aim to contribute to a more holistic understanding of intra-urban thermal disparities, emphasizing the importance of considering both income classes and historic redlining in evaluating the recovery and potential vulnerabilities within communities.

Results
Utilizing the high-resolution standard Land Surface Temperature (LST) product from ECOSTRESS at 70-m spatial resolution, we explore distinctive urban features that manifest as hotspots at various times of the day47,48. For instance, during daytime hours, the large warehouses in central LA and Covina can be seen prominently due to higher rooftop temperatures, contrasting with the cooler, shaded road network within the urban canyon (Fig. 2a). Conversely, the nighttime data reveals paved surfaces such as road networks and the southern shipping dock, that retain heat from the daytime (Fig. 2b). We group the selected LST scenes into four seasons using the standard definition of seasons in the Northern hemisphere (Winter: Dec, Jan, Feb; Spring: Mar, Apr, May; Summer: Jun, Jul, Aug; Fall: Sep, Oct, Nov). The hours of day were clustered into four segments of time of day depending on how similar the spatial LST patterns were. The emerging four segments are labeled as follows: Morning (7 am to 11 am), Afternoon (11 am to 6 pm), Late evening (6 pm to 12 am), Past midnight (12 am to 7 am). The number of ECOSTRESS scenes used for each season and time segment are shown in Supplementary Fig. 1. We then compute the spatially gridded time-average of LSTs for the census tract shown in Fig. 1a for each of the sixteen combinations of season and time-of-day (Supplementary Fig. 1). This approach minimizes day-to-day variabilities or anomalies, enabling a focus on the expected value of seasonal LST signals, as illustrated in Fig. 2, and uncover the dependencies between LST and redlining disparity with respect to time of day and season.

From dawn to dusk: Temperature distributions across HOLC grades
First, we examine the variation in LST distribution across the four HOLC grades, presented as normalized and staggered density distribution plots in Fig. 3. We discuss a typical summer day for illustration and provide the corresponding figure panels for Spring, Fall, and Winter in Supplementary Fig. 2. In the hours past midnight and before sunrise, the A-graded neighborhoods experience slightly cooler temperatures (<1 °C) while there is negligible difference in LST among the B, C, and D grades (Fig. 3a). As the sun rises, temperature disparities begin to emerge for the different redlining grades. We use the Kolmogorov-Smirnov (KS) test to confirm that these differences are statistically significant (see Methods). We find that C- and D-graded neighborhoods are on average 3 °C warmer than their A-graded counterparts (Fig. 3b). These disparities peak during the afternoon hours (by 4–5 °C), as mean temperatures continue to rise (Fig. 3c), before diminishing to approximately 1−2 °C during late evening hours (Fig. 3d). Notably, C- and D-graded neighborhoods retain heat longer than the greener A- and B-graded counterparts because of the increased impervious fraction. As the disparity between A versus D-graded neighborhoods in LA is primarily driven by access to green spaces, this diurnal trend mimic that of the surface urban heat island effect51,52. To put this in perspective, we look at the percentage of census tracts and, consequently, people residing in neighborhoods with LSTs surpassing a specified threshold, say 40 °C for instance. We find that for A-graded neighborhoods, 25% of the population live in neighborhoods with LST above 40 °C, compared to ~75% in C- and D-graded neighborhoods (Supplementary Fig. 3). This contrast impacts over 1 million additional residents during the summer in the C- and D-graded census tracts assessed in this study (Fig. 4b).

Table 1 summarizes the mean differences in LST between A- and D-graded neighborhoods (ΔLST D−A = LST D − LST A) for four time periods within each season. We find statistically significant ΔLST D−A values for ~95% of the ECOSTRESS LST maps according to the KS test (Supplementary Fig. 4). The general diurnal trend remains consistent across all seasons, wherein the LST disparity is most pronounced during the afternoon hours, with summer and spring seasons displaying a ΔLST D−A in the range of 3–7 °C, whereas Fall and Winter seasons have a lower ΔLST D−A in the range of 2–4 °C. A detailed exploration of the drivers of seasonal differences is provided later.

Affluence as Insulation: Joint behavior of LST, income, and historic redlining
Over the past century, the city of LA has undergone substantial transformations, including the evolution of several neighborhoods that were historically graded as C and D, as is the case of Santa Monica, now an affluent community (Fig. 4a). This transformation introduces a nuanced interplay of socio-economic inequalities involving present-day income dynamics and historical segregation practices and leads to the question: how does the inequality in LST vary as a function of present-day income inequality within these historically graded neighborhoods?

To analyze these income inequalities, we first categorize census tracts into high-income (defined as above the 75th percentile), mid-income (25th-75th percentile), and low-income (below the 25th percentile) based on median household income (Supplementary Fig. 5). Given the evolution of present-day residential income distribution from past segregation practices44,49,50, there is significant overlap between the low and medium income classes with HOLC grades of C and D (Fig. 4b). Consequently, almost 35% of the population (~1.6 million people) residing in historically redlined areas fall into the low-income category. Moreover, within these census tracts, nearly 40 to 80% of households fall below poverty line (Supplementary Fig. 6). Conversely, 62% of the A- and B-graded census tracts can be considered as high-income, and ~10% of these currently fall into the low-income category.

Previous research has demonstrated a negative correlation between LST and median household income across LA census tracts53. In this study, we juxtapose another layer of complexity from the viewpoint of historic redlining and focus primarily on the temperature distribution across income categories within each HOLC grade. We find that LST as a function of income differs significantly even within each HOLC grade (Fig. 4c–f). In each of the HOLC grades, the LST within the high-income neighborhoods seem to deviate the most from LST distributions in the other two income categories. During summer afternoons, for example, the higher income neighborhoods within the D-graded census tracts are 7–8 °C cooler than their lower income counterparts (Fig. 4c). Alternatively, during the coldest time of the year, winter nights, the high income neighborhoods persistently stay warmer by 1–3 °C (Fig. 4f), which is consistent with the inversion of LST-income correlation reported during winter time in LA in other studies53.

If we now consider the neighborhoods that are in the high-income bracket within the C- and D-graded neighborhoods, they predominantly lie in areas that benefit from the cooling effects of coastal sea-breeze influences (e.g., Santa Monica), as highlighted in Fig. 4a. Additionally, some neighborhoods around Beverly Hills, which used to be historically redlined, have also dramatically increased in affluence, possibly due to the rise of LA’s Hollywood industry. This reorganization is driven by two complementary economic forces that are drivers of environmental gentrification54: siting55–57 and sorting58,59. Simply put, the naturally cooler and greener neighborhoods increased in affluence as they were preferentially sought out by people who could afford it58,59. In parallel, low-income neighborhoods are often selected as sites for industrial development, revealing disparities in siting behaviors and the impact of environmental awareness on the placement of facilities in communities of color55–57. While some neighborhoods serve as anecdotal examples of places that were able to invest in further greening as they became more affluent (as observed around the Hollywood region)60, establishing a clear causal link between income, urban greening, and heat impacts54 is beyond the scope of this study.

In summary, across the four examples shown here and those shown in Supplementary Fig. 7 for the other seasons and times of day, the LST distribution for low-income neighborhoods are not statistically different across different HOLC grades. The high and mid-income group, on the other hand, show statistically significant LST differences as a function of their HOLC grade. This is further illustrated in Supplementary Fig. 7 where we organize the LST distributions grouped by income category first, and then their HOLC grade. Therefore, we conclude that present-day income inequality is a stronger indicator of LST disparities than past redlining practices.

Greening to senescence: hysteretic dynamics of thermal disparities across Seasons
We now introduce a more nuanced LST disparity metric using a combination of both historic redlining and present-day income inequality, by computing the difference between the extreme cases identified above, ΔLST HOLC,income = LST D,Low − LST A,High. Compared to ΔLST D−A from Table 1, we find that the general diurnal and seasonal trend of ΔLST HOLC,income is still the same; but the magnitude of disparity is larger in each case, with summer afternoons’ LST disparities reaching as high as 5–7 °C (Table 2, and Supplementary Fig. 4). To evaluate how LST disparity responds to extreme heat events, we evaluate the correlation between ΔLST HOLC,income and mean LST for each of the 332 unique scenes collected from ECOSTRESS. Figure 5a shows a robust positive correlation (R2 = 0.83) that indicates the thermal disparities are more pronounced on hotter days, such as during heat wave events, with an increase of nearly 1 °C rise in ΔLST HOLC,income for every 6 °C rise in mean LST (Slope = 0.16). This demonstrates that historically D-graded and lower income communities live in neighborhoods with disproportionately higher LST compared to their historically A-graded and affluent counterparts, an effect that is more marked during a heat wave.

Shifting our focus away from the linear correlation, we find a clear hysteresis or a looping pattern when we group the data by seasons and time of day (Fig. 5b). If we consider the daily maximum temperatures that are corresponding to afternoon hours (shown in a solid line), the thermal disparity, ΔLST HOLC,income, increases from Winter to Summer as the mean LST increases. Additionally, although the mean LST in Spring and Fall are very similar, the respective ΔLST HOLC,income are distinctly different. In essence, this means that seasonal variation of ΔLST HOLC,income as a function of mean LST exhibits a time-dependent response between the forcing (i.e. mean temperature of a given day) and its effects on the system (i.e., thermal disparity, ΔLST HOLC,income, on the given day). We find that this hysteresis pattern is primarily driven by variations in vegetation activity, with differences in photosynthesis and evapotranspiration cooling between spring greening and fall senescence (Supplementary Fig. 8). Fall season tends to run hotter and drier in LA, with frequent heat waves occurring later in the year causing major wildfires43. This is particularly notable in greener and wealthier neighborhoods where the Normalized Difference Vegetation Index (NDVI) exhibits greater annual variation than the lower income, D-graded neighborhoods with year-long higher impervious land cover fraction (Supplementary Fig. 8). The same hysteresis pattern is also evident for daily minimum temperature at nighttime (shown in dashed line in Fig. 5b) as well as other times of day which are listed in Table 2 and shown in Supplementary Fig. 9. This finding aligns with the seasonality observed in city-averaged surface urban heat islands (SUHI), with higher SUHI intensity observed in spring and summer compared to SUHI in fall and winter61–64.

Beyond exposure: unpacking population vulnerability to extreme heat
The overall risk to residents is determined by both exposure to heat hazards and the population’s vulnerability, which denotes the likelihood of experiencing harm when exposed to such hazards65,66. Estimating heat exposure requires consideration of variables beyond surface temperature, such as near-surface air temperature (T air, which is typically measured at 2 meters above ground level), wind speed, solar irradiance, cloudiness, and humidity67. Some studies have used the Weather Research Forecasting Model (WRF)41,68 or in-situ T air measurements to estimate subsequent heat stress metrics69,70. While a comprehensive exposure assessment is beyond the scope of this study, we examined gridded T air from a recent global 1k m dataset71 to compare variations in T air across the HOLC grades. We observe similar overarching patterns in thermal disparities in the modeled T air as in LST, i.e. elevated T air disparities are maximum during summer, especially during afternoon hours by 1−2 °C (see Supplementary Fig. 10). This finding aligns with a recent study demonstrating that LST can capture the general direction of pervasive T air disparities across HOLC grades although the magnitude is generally lesser for T air compared to LST13. The patterns in moist heat stress metrics, which are particularly relevant in coastal cities like LA, may be more significantly different41,72, which requires further future investigation. Note that the modeled T air dataset we use is typically limited as it may underestimate actual urban thermal extremes71 and does not capture the nuanced differences in urban form and function due to local histories such as historic redlining. Therefore, remotely sensed LST remains our best option for obtaining fine-scale spatially continuous thermal disparities within the city. Additionally, since many thermal disparities within cities arise from differences in tree cover18, heat exposure metrics incorporating solar radiation and subsequent tree shading effects may exhibit greater disparity than seen in T air or moist heat stress alone73.

We now evaluate the relative extent to which LA neighborhoods would be vulnerable to heat, using the IPCC’s definition of vulnerability as ‘the propensity or predisposition to be adversely affected, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt’74. To this end, we evaluated the effective contribution of 26 variables that are indicative of increased vulnerability to heat impacts. These consider aspects such as preexisting health conditions75,76, living and working conditions77–79, financial situation17, and social isolation of the population80. The source of data is discussed in the Methods, and a complete list of variables is available in Supplementary Fig. 11b. By design, we exclude the variables related to median household income, ethnicity, and historical redline since we evaluate heat vulnerability as a function of these factors in Fig. 6.

To obtain a single condensed metric of heat vulnerability, we performed a principal component analysis (PCA) on the identified variables81. Through an in-depth exploration of variable loadings, we find that the first PC explains more than one-third of the observed variance (eigenvalue: 10.0, explained variance: ~35%). PC1 captures a multidimensional sensitivity to heat with strong and positive correlations with health variables such as obesity, diabetes, and asthma, as well as moderate positive correlations with socioeconomic factors such as extreme poverty, under-education, energy deficit households, predominantly outdoor workers, and overcrowded and inadequate housing conditions. PC2 (eigenvalue: 4.0, explained variance: ~13%) has strong correlations with individuals who live alone, renters, and those lacking access to personal vehicles or the ability to drive to work, reflecting an additional sensitivity to heat driven by social isolation and harsh working conditions. Lastly, PC3 (eigenvalue: 2.5, explained variance: ~8%) captures a segment of the population that is elderly, lives alone, and experiences adverse health conditions such as diabetes and stroke (further details in Supplementary Fig. 11b). Each PC is by definition orthogonal to the others, and thus they are best treated as separate dimensions of vulnerability. In the remainder of this work, we focus on the first PC only, given that it is the most comprehensive, and refer to it as the vulnerability score.

The spatial distribution of the vulnerability score is shown in Fig. 6a, revealing a higher vulnerability in central LA and valley regions. Despite its cosmopolitan nature and sociodemographic diversity, LA displays stark racial and ethnic geographic segregation even today. In Fig. 6b, we plot the majority ethnic group living in each census tract (the spatial map of percentage prevalence for each ethnicity is shown in Supplementary Fig. 12). We find that the juxtaposition of heat vulnerability with the ethnic and racial distributions of the city is particularly concerning as an environmental justice issue. Notably, we find that people of color are more vulnerable to heat impacts (Supplementary Fig. 13). For example, southern and south-eastern LA neighborhoods such as Boyle Heights, Lincoln Heights, East LA, and valley regions such as San Fernando and Covina are predominantly Hispanic. On the south-central side, where heat vulnerability is high, the majority of residents are African American. In addition, these neighborhoods are also at the intersection of historic redlining (Fig. 1a), lower household income (Fig. 4a), and higher afternoon LST (Supplementary Fig. 1). Viewed from the lens of income inequality and HOLC grade in Fig. 6c, we find that the vulnerability score is low for the high-income neighborhoods regardless of their redlining history. In the medium and low-income neighborhoods, the vulnerability score scales directly as a function of HOLC grade, with the low-income and D-grade neighborhoods being the most susceptible (racial and ethnic distribution sorted by both HOLC grades and income classes are given in Supplementary Fig. 14).

Redlined areas are also shown to be linked with the heightened risk of other adverse health outcomes such as asthma and cancer, as a result of exposure to adverse environmental conditions21,22. This is because they are often located near industrial zones, polluted areas, or highways, leading to poor air and water quality46,55–57. Here, we use CalEnvironScreen’s Pollution Burden82,83 (see Methods) to show that D-graded neighborhoods also experience higher overall pollution levels as a combination of these factors (Fig. 6d). Despite the absence of green spaces, mitigating indoor exposure to extreme heat is still feasible through the use of air conditioning, as exemplified in cities like Dubai. Perhaps unsurprisingly, our analysis of access to air conditioning and usage shows that access to AC as well correlates strongly with income84,85 (Fig. 6e, details in Methods). The AC usage only weakly correlates with HOLC grade in the middle-income category, and residents in low-income neighborhoods have limited AC usage despite HOLC grade. The widest distribution is observed in the historically D-graded high-income neighborhoods as they are mostly situated along the California coastline (Fig. 4a). The coastal cooling benefits explain why the electricity usage data reveals less AC usage, despite the affluence of these neighborhoods86.

Methods
Home Owner’s Loan Corporation (HOLC) redlining score
Redlining was a historically discriminatory practice that emerged in the United States during the 1930s and persisted for several decades. It involved the systematic denial of financial services, particularly loans and mortgages, to individuals and communities in certain neighborhoods, which was incidentally associated with income and racial/ethnic characteristics. We use Meier and Mitchell’s dataset23 for the historic HOLC map of LA, where they calculated a historic redlining score from the summed proportion of HOLC residential security grades multiplied by a weighting factor based on area within each census tract according to the 2020 census tract boundaries.

Land surface temperature from ECOSTRESS
Our primary dataset is the standard Land Surface Temperature (LST) product from ECOSTRESS with a spatial resolution of 70 × 70 meters (at nadir) acquired over the LA region since mid-2018. ECOSTRESS, or the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, was launched to the International Space Station in June 2018, and it currently provides the highest combined spatial (70 m at nadir), spectral (3/5 bands), and temporal (3–5 days) resolution thermal infrared data from space with observations available over the diurnal cycle47. The ECOSTRESS LST product has been validated to a total uncertainty of 1.07 K using 14 global sites covering a wide range of surface types and atmospheric conditions48. We process a total of 345 complete and cloud-free scenes within the Los Angeles region based on all five years of ECOSTRESS operation (2018-2023).

Kolmogorov-Smirnov statistic to test LST distribution dissimilarity
The Kolmogorov-Smirnov (KS) statistic is a non-parametric test used to assess the goodness of fit between an empirical distribution and a theoretical distribution or between two empirical distributions. It measures the maximum absolute difference between the cumulative distribution functions of the observed and expected data. KS statistic was chosen because the temperature distributions were non-normally distributed. The null hypothesis of the KS test is that the two distributions are identical, and a significant result indicates a rejection of this hypothesis, suggesting a significant difference exists between the distributions being compared.

Principal Component Analysis of vulnerability to heat
The social, demographic, and health data employed in the vulnerability index is obtained from four publicly available sources at the census tract level: the American Community Survey (ACS), CalEnviroScreen 4.0 (CES)82, the Climate and Economic Justice Screening Tool (CEJST), and CDC’s 500 Cities Project (CDC), encompassing variables measuring living conditions, social isolation, age and health, literacy, occupation, and transportation barriers that have been demonstrated to enhance the relative risk of heat mortality or morbidity17,75–80. We perform feature selection to remove redundant variables using a hierarchical clustering approach, eliminating variables that have correlations > 0.9, resulting in 26 variables being selected. We also perform feature scaling before starting the PCA. We run the PCA using the Python scikit-learn PCA package, which returns the relative influence of each variable on the principal components. Please see Supplementary Fig. 11 for further details.

CalEnvironScreen’s pollution burden score
CalEnviroScreen is a tool developed by the California Environmental Protection Agency to assess and identify communities disproportionately burdened by pollution82,83. It calculates pollution burdens by considering various factors such as air quality (ozone, particulate matter), toxic releases, impaired water bodies, solid waste sites, cleanup sites, traffic density, and pesticide use. It aims to address environmental justice concerns by identifying and assisting disadvantaged communities vulnerable to the adverse effects of pollution.

Estimates of air conditioning access and usage
The estimates of air conditioning prevalence at the census tract level were derived from various demographic factors, climate conditions, and geographic differences84. The logistic model was constructed using American Housing Survey metropolitan and national microdata spanning from 2013 to 2019 and was then applied to American Community Survey data to predict air conditioning prevalence for 115 metropolitan areas in the United States. This is closely related to the probability of AC ownership. In most regions of the U.S. where AC penetrations tend to be very high, this correlation is acceptable, but California has lower penetration than most US states because of the coastal influence, and this relationship breaks down86. We improve upon this estimate by assessing the electricity-temperature relationship of smart-meter records of 200,000 homes in Southern California over the years 2015-2016, using data provided by local utility Southern California Edison (SCE)85. As a result, our estimates more closely capture the actual usage of AC. Please see Supplementary Fig. 15 for further details.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a worthwhile study of Los Angeles (not clear in the title that it is a single city) about intra-urban heat island exposures.\nThe abstract describes its major contributions accurately: 1) compares temperature differences among areas that were\nredlined a century ago using HOLC maps and present-day income inequality; 2) uses superior LST data compared to older\nstudies that allow better diurnal and seasonal comparisons of temperature differences. The 4x4 grid study design of season\nand times of day/night is systematic, the analysis supports the finding that present-day income is more influential than\nredlining in predicting differences in heat exposure, and the writing is very clear.\nThe manuscript should review and acknowledge previous research on intra-urban heat temperature disparities among\nsocioeconomically diverse and racially/ethnically segregated neighborhoods. The second sentence of the Introduction (lines\n3- 4) is very misleading. Studies of intra-urban landscapes and inequities in extreme heat impacts are voluminous dating\nback to the early 2000s (e.g., US cities Phoenix, Philadelphia, Detroit, Chicago, Atlanta, New York, and others, as well as\ncities in Asia and Europe). Many US cities have developed heat vulnerability maps and action plans for mitigating heat\neffects in lower-income neighborhoods. Federal agencies such as the EPA Heat Islands and Equity, the CDC and NIH Heat\nEquity have long promoted urban heat/health equity to address problems in economically disadvantaged and racially\nsegregated neighborhoods. In line 26, the authors cite several redlining studies on heat, but again it is not acceptable to\nignore the large body of research on contemporary inequalities that underlie both the concept and methods of their study,\nincluding in their own city, Los Angeles, as well as very similar findings about neighborhood inequality. Some of the prior\nresearch shows differential impacts on heat mortality and morbidity, which this study does not do.\nThe manuscript would benefit from a brief section on LA climate, vegetation, and socioeconomics to establish a relevant\ncontext for the study. It is a rather unique combination of desert and coastal characteristics that differ from most other large\nUS cities, so generalizability to other places might be questioned. The map figures are not adequately marked to give\nreaders a sense of the expanse or “neighborhood” composition of the urban area. Sometimes the authors give interesting\nexamples of particular places, but they are not easy to see on the maps. Figure 1 or Figure 2 might be a good place to orient\nreaders with more prominent indications of places referred to in the text. Figure 4 is very hard to follow along with the text\nbecause it is attempting to depict relationships among three or four important variables. I do not see any red circles in Figure\n4a. I recommend reviewing this section with clarity in mind.\nThe definition of hysteresis in lines 164-166 is conveyed in technical terms that obscure the important substantive finding.\nThe authors would do well to explain the specific process in a more straightforward way.\nECOSTRESS is a welcome advance in measuring land surface temperatures. As the authors say in line 162, it is necessary\nto consider some other weather variables to estimate heat stress on humans. Tair at a 1 km scale is one way to do this, but it\nis still a rather crude estimate of microclimates. It should be acknowledged that other studies have used the Weather\nResearch Forecasting Model (WRF) and even individually-experienced temperature sensors to improve upon air\ntemperature measurements because air is more closely related to human stress than LST.\nIt is standard to use PCA to construct heat vulnerability indexes. The authors have included some atypical variables in their\nanalysis, which is good, but they don’t explain or label the factors. If I understand correctly, only the first factor that explains\n35% of the variance is used in the vulnerability index. More discussion of what this analysis shows about the social sources\nof heat vulnerability would be helpful. I don’t see an explanation for the choice to omit the other two factors that increase the\nexplained variance in the data. Also, please give clear color guidance in Supplemental Figure 11 for the significance and\ndirection of factor loadings.\nThe discussion of “access” to air conditioning in lines 231-238 is somewhat confusing. Does access mean having the\nequipment in the home, choosing to use it or not, or not using it because it is unaffordable? Considering other research on\nindoor heat deaths and social surveys of customers, it is likely that residents in low-income areas don’t use air conditioning\nbecause they can’t afford it. In Supplemental Figures 11 and 15, it looks like low usage areas are in downtown LA where\nAfrican Americans and Latinos live.\nThis brings me to my final comment that refers to Supplemental Figures 13 and 14. These figures show that African\nAmericans are as highly exposed to heat risks as Hispanics in LA. I believe the data indicates that lines 266-267 in the\nDiscussion should be amended accordingly.", "reviewer_2": "This article is an extremely important contribution to the literature about urban heat islands. It elucidates several important\ndrivers of inequitable vulnerabilities to climate change, in particular dissecting the relationship between historical redlining\nand current economic and racial disparities. The outcomes are well-articulated in the introduction and discussion. It should\nbe published ASAP.\nThe robust statistical analysis and referencing of prior research and source datasets are exceptional. While I do not conduct\nthis type of quantitative analysis in my own research, with a bit of effort, I could follow the analysis, with two exceptions, as\nexplained below.\nFirst, the explanation of how the first PC was used to create the vulnerability score should be strengthened. What exactly\ndoes the scale of -6 to 6 represent? And what does the red and blue color coding represent? The first question should be\nanswered more clearly at the end of the second paragraph in Section 2.4. The second question could be answered more\nclearly in the caption in Supplementary Figure 11b.\nSecond, a more minor addition I recommend, also in Section 2.4, is to explain the difference between LST and near-surface\nair temperature. In particular, the only place I could find an indication of the height above ground at which the air temperature\nwas taken is in the caption for Supplementary Figure 10. It would be helpful to readers if this distance were noted in the\ndescription of the dataset in the narrative in Section 2.4.\nFinally, I noted just a couple of line edits, as follows:\n• Line 32, sentence starting “Further…” needs editing for clarity, as it appears to be an incomplete sentence\n• Line 190, “maybe” should be “may be”", "reviewer_3": "The study aims to disentangle the effects of historic redlining and current income inequality on urban heat disparities. The\nresults indicate that present-day income inequality is a stronger indicator of heat burden than historic redlining. While the\nresearch addresses an important topic—intra-urban inequities—there are several critical issues that need to be addressed to\nstrengthen the manuscript.\nMajor comments:\n1. This study focuses solely on one city, Los Angeles. To attract more attention, the narrative should be framed from a\nbroader perspective. For example, it could include major cities across the United States or even globally. The implication of\nthis study is limited to a single city.\n2. Lines 3-4: Previous studies have extensively analyzed intra-urban inequalities. It is necessary to further expand the\nbackground information and include relevant citations. Additionally, it is important to highlight the limitations of the previous\nstudies and the innovative aspects of this research in the Abstract and Introduction.\n3. Lines 38-41 and Lines 283-292: What is the spatial coverage of ECOSTRESS observations? Can it be used for global or\nU.S.-wide studies? Why was Los Angeles chosen as the study subject? Moreover, this study focuses only on cloud-free land\nsurface temperatures. Considering the influence of clouds on temperature could impact the study results. For instance,\nhigher temperatures promote precipitation and cloud cover, thus reducing incoming solar radiation and mitigating urban\nwarming. This limitation should be acknowledged in the article.\n4. Land surface temperature does not accurately reflect human thermal sensation. It is necessary to supplement the study\nwith surface air temperature data from meteorological stations. Moreover, the temporal resolution and frequency of the\ndata are not clearly described in the manuscript. It is not clear what data has been used to derive the average LSTs.\nMinor comments:\n1. Line 83 and Lines 102-103: In addition to seasonal differences, the reasons for daytime and nighttime variations should\nalso be specifically discussed and analyzed.\n2. Lines 127-129: During winter, why are temperatures higher in high-income areas compared to low-income areas? Is it due\nto the effect of anthropogenic heat?\n3. Lines 263-264: How is a heatwave event defined in the study?\n4. The research indicates that present-day income inequality is a stronger indicator of heat burden than historic redlining. This content is not reflected in the title.\n5. The abstract should briefly introduce the background of historic redlining. Or at the beginning of the introduction."}, "author_response": "Response to reviewers for manuscript: NCOMMS-24-14163\nUnraveling the role of historic redlining and present-day inequities on\nintra-urban heat disparity\nNew Title: Present-day inequities in Los Angeles have a stronger role\non intra-urban heat disparities than historic redlining\nReviewer #1:\nThis is a worthwhile study of Los Angeles (not clear in the title that it is a single city)\nabout intra-urban heat island exposures. The abstract describes its major contributions\naccurately: 1) compares temperature differences among areas that were redlined a\ncentury ago using HOLC maps and present-day income inequality; 2) uses superior\nLST data compared to older studies that allow better diurnal and seasonal comparisons\nof temperature differences. The 4x4 grid study design of season and times of day/night\nis systematic, the analysis supports the finding that present-day income is more\ninfluential than redlining in predicting differences in heat exposure, and the writing is\nvery clear.\n1. The manuscript should review and acknowledge previous research on\nintra-urban heat temperature disparities among socioeconomically diverse and\nracially/ethnically segregated neighborhoods. The second sentence of the\nIntroduction (lines 3- 4) is very misleading. Studies of intra-urban landscapes and\ninequities in extreme heat impacts are voluminous dating back to the early 2000s\n(e.g., US cities Phoenix, Philadelphia, Detroit, Chicago, Atlanta, New York, and\nothers, as well as cities in Asia and Europe). Many US cities have developed\nheat vulnerability maps and action plans for mitigating heat effects in\nlower-income neighborhoods. Federal agencies such as the EPA Heat Islands\nand Equity, the CDC and NIH Heat Equity have long promoted urban heat/health\nequity to address problems in economically disadvantaged and racially\nsegregated neighborhoods. In line 26, the authors cite several redlining studies\non heat, but again it is not acceptable to ignore the large body of research on\ncontemporary inequalities that underlie both the concept and methods of their\nstudy, including in their own city, Los Angeles, as well as very similar findings\nabout neighborhood inequality. Some of the prior research shows differential\nimpacts on heat mortality and morbidity, which this study does not do.\nRESPONSE: We thank the reviewer for bringing up this important point. In the revised\nmanuscript, we have removed the second line of introduction as recommended and\nupdated the introduction to more comprehensively discuss the existing work on\ncontemporary inequalities in heat hazard, exposure, and risk within cities as written\nbelow (Citations in the updated manuscript): “Over the last few decades, research has\nunequivocally identified cities as distinctly warmer entities compared to their non-urban\nsurroundings. A large body of literature has looked at intra-urban thermal landscapes\nand their associations with present-day population distributions. While climate change\nexposes rapidly growing urban populations to enhanced heat hazards, the inherent\nheterogeneity of cities further exacerbates the inequitable distribution of its worst\nimpacts. For many cities, areas with poorer and predominantly non-white populations\ntend to be warmer. These disparities in heat hazard have been found to also lead to\nunequal mortality/morbidity outcomes, making them an important policy focus for\nequitable urban planning.” (Please see Lines 2-9, references 1-17)\n2. The manuscript would benefit from a brief section on LA climate, vegetation, and\nsocioeconomics to establish a relevant context for the study. It is a rather unique\ncombination of desert and coastal characteristics that differ from most other large\nUS cities, so generalizability to other places might be questioned. The map\nfigures are not adequately marked to give readers a sense of the expanse or\n“neighborhood” composition of the urban area. Sometimes the authors give\ninteresting examples of particular places, but they are not easy to see on the\nmaps. Figure 1 or Figure 2 might be a good place to orient readers with more\nprominent indications of places referred to in the text. Figure 4 is very hard to\nfollow along with the text because it is attempting to depict relationships among\nthree or four important variables. I do not see any red circles in Figure 4a. I\nrecommend reviewing this section with clarity in mind.\nRESPONSE: Our apologies for the oversight of not properly describing LA’s climate in\nthe manuscript and thank you for suggesting that. We have now added a section on LA\nclimatology and socio-economics in paragraph 3 of introduction (Lines 30-51 and also\npasted below, with citations in the updated manuscript) and acknowledged in the\ndiscussion section that the findings of LA may not apply to other US cities due to its\nunique geography (Lines 298-300). Regarding the local neighborhoods mentioned as\nexamples in the text, we think that labelling those on any figure may mislead a reader\ninto assuming that our focus is solely on those particular places, when we only intend\nfor them to serve as examples. In figure 4, there was indeed no red circle present, we\nhave also removed any mention of the red circle from figure caption or the text.\n“In this study, we focus on the Los Angeles (LA) region, home to nearly 25 million\nresidents, which features diverse microclimates and ecological zones shaped by\ncomplex topography and coastal influences. This leads to an uneven distribution of\nextreme heat events, such as heatwaves. The atmospheric circulation patterns that lead\nto most of Southern California's severe heatwaves are similar, typically involving a\nstrong high-pressure system over the Pacific Northwest, which creates a significant\nsurface pressure gradient between a high-pressure area over the Great Plains and a\nthermal low off the southwest coast of Los Angeles. The summer climate is largely\ninfluenced by a semi-permanent high-pressure system over the North Pacific Ocean,\nwhich deflects storm tracks and limits precipitation. The coastal upwelling of moist cold\nair due to dominant northwest winds during the summer leads to the formation of marine\nstratus clouds that cool daytime temperatures and warm nighttime temperatures.\nHowever, urbanization has been shown to slowly reduce fog and marine stratus by\ncausing increased nighttime warming. These patterns are further modified by local\nwinds, such as sea breezes and the occasional hot and dry Santa Ana winds during the\nFall season. Heatwaves can occur when these coastal processes are disrupted, leading\nto higher temperatures. Over the last few decades, LA has witnessed a gradual\nincrease in the frequency, intensity, and persistence of heatwaves posing a significant\nchallenge to its citizens and policymakers. Additionally, LA was also subject to redlining\nin the past. Figure 1a, for example, displays the historically marked census tracts of LA\n(details in the Methods section). While the discriminatory practice was outlawed in 1968\nwith the passage of the Fair Housing Act, its effects still linger and continue to shape\npatterns of inequality in many cities. Figures 1b and 1c, for example, illustrate how\ngreen cover is drastically different between an A- and D-grade neighborhood even\ntoday. Furthermore, significant segregation is still present across historically redlined\nand non-redlined neighborhoods, especially in the White and Hispanic populations\n(Figure 1d).”\n3. The definition of hysteresis in lines 164-166 is conveyed in technical terms that\nobscure the important substantive finding. The authors would do well to explain\nthe specific process in a more straightforward way.\nRESPONSE: We have now enhanced the explanation of the hysteresis phenomenon in\na much clearer way while also preserving the technical description of the phenomenon\n(Lines 181-188): “Shifting our focus away from the linear correlation, we find a clear\nhysteresis or looping pattern when we group the data by seasons and time of day\n(Figure 2b). If we consider the daily maximum temperatures corresponding to afternoon\nhours (shown in a solid line), the thermal disparity, ΔLST(HOLC, income), increases\nfrom winter to summer as the mean LST increases. Additionally, although the mean LST\nin spring and fall are very similar, the respective ΔLST(HOLC, income) values are\ndistinctly different. Essentially, this indicates that the seasonal variation of ΔLST(HOLC,\nincome) as a function of mean LST exhibits a time-dependent response between the\nforcing (i.e., mean temperature of a given day) and its effects on the system (i.e.,\nthermal disparity, ΔLST(HOLC, income), on the given day). We find that this hysteresis\npattern is primarily driven by variations in vegetation activity, with differences in\nphotosynthesis and evapotranspiration cooling between spring greening and fall\nsenescence (Supplementary Figure 8).”\n4. ECOSTRESS is a welcome advance in measuring land surface temperatures. As\nthe authors say in line 162, it is necessary to consider some other weather\nvariables to estimate heat stress on humans. Tair at a 1 km scale is one way to\ndo this, but it is still a rather crude estimate of microclimates. It should be\nacknowledged that other studies have used the Weather Research Forecasting\nModel (WRF) and even individually-experienced temperature sensors to improve\nupon air temperature measurements because air is more closely related to\nhuman stress than LST.\nRESPONSE: Yes, we have improved the discussion in section 2.4 Beyond Exposure\nwhere we discuss the limitation of LST in estimating exposure, and utility of T_air (air\ntemperature) as a more appropriate metric (Lines 200-207).\n5. It is standard to use PCA to construct heat vulnerability indexes. The authors\nhave included some atypical variables in their analysis, which is good, but they\ndon’t explain or label the factors. If I understand correctly, only the first factor that\nexplains 35% of the variance is used in the vulnerability index. More discussion\nof what this analysis shows about the social sources of heat vulnerability would\nbe helpful. I don’t see an explanation for the choice to omit the other two factors\nthat increase the explained variance in the data. Also, please give clear color\nguidance in Supplemental Figure 11 for the significance and direction of factor\nloadings.\nRESPONSE: In response to the reviewer’s request for clarification, we provide the\nfollowing interpretations of the first three principal components (PCs) from our analysis\nwhich has now been added to the latest version of our manuscript (Lines 226-240).\n\"To obtain a single condensed metric of heat vulnerability, we performed a principal\ncomponent analysis (PCA) on the identified variables. Through an in-depth exploration\nof variable loadings, we find that the first PC explains more than one-third of the\nobserved variance (eigenvalue: 10.0, explained variance: ~35%). PC1 captures a\nmultidimensional sensitivity to heat with strong and positive correlations with health\nvariables such as obesity, diabetes, and asthma, as well as moderate positive\ncorrelations with socioeconomic factors such as extreme poverty, under-education,\nenergy deficit households, predominantly outdoor workers, and overcrowded and\ninadequate housing conditions. PC2 (eigenvalue: 4.0, explained variance: ~13%) has\nstrong correlations with individuals who live alone, renters, and those lacking access to\npersonal vehicles or the ability to drive to work, reflecting an additional sensitivity to heat\ndriven by social isolation and harsh working conditions. Lastly, PC3 (eigenvalue: 2.5,\nexplained variance: ~8%) captures a segment of the population that is elderly, lives\nalone, and experiences adverse health conditions such as diabetes and stroke (further\ndetails in Supplementary Figure 11b). Each PC is by definition orthogonal to the others,\nand thus they are best treated as separate dimensions of vulnerability. In the remainder\nof this work, we focus on the first PC only, given that it is the most comprehensive, and\nrefer to it as the 'vulnerability score'.”\nTo provide additional context for our decision to retain only PC1 in the vulnerability\nindex: In our preliminary analysis, we explored the first few principal components (PCs)\nand examined their spatial distributions. Given that each PC is orthogonal to the others,\nthey represent separate dimensions of heat vulnerability. Importantly, each PC reflects\ncontributions from all the input variables, with factor loadings detailed in Supplementary\nFigure 11. Although we considered combining multiple PCs into a single vulnerability\nscore, we ultimately chose not to. While PC2 and PC3 did increase the explained\nvariance slightly, the gain was not substantial enough to outweigh the loss of\ninterpretability that comes with merging dimensions that capture different aspects of\nvulnerability. As such, we proceeded with PC1 as the primary vulnerability score. We\nhave also updated Supplementary Figure 11 to provide descriptions for each variable.\nThe color gradient has been clarified: red indicates a positive loading, blue a negative\nloading, and the shading reflects the strength of each relationship.\n6. The discussion of “access” to air conditioning in lines 231-238 is somewhat\nconfusing. Does access mean having the equipment in the home, choosing to\nuse it or not, or not using it because it is unaffordable? Considering other\nresearch on indoor heat deaths and social surveys of customers, it is likely that\nresidents in low-income areas don’t use air conditioning because they can’t\nafford it. In Supplemental Figures 11 and 15, it looks like low usage areas are in\ndowntown LA where African Americans and Latinos live.\nRESPONSE: Our data relies on estimates of “Probability of access to AC” from Romitti\net al (2022) that was derived from various demographic factors, climate conditions, and\ngeographic differences. This is more closely related to probability of ownership of ACs.\nIn most regions of the US where AC penetrations tend to be very high, this correlation is\nacceptable, but California has lower penetrations than most US states because of the\ncoastal influence, and this relationship breaks down. As the reviewer pointed out, this\ndoesn’t necessarily imply usage (for example, affluent houses in cooler micro-climates\nsuch as LA coasts and mountains where they can afford AC but need not use it, or poor\nhouses where despite an AC unit, the electricity bill might be untenable). Therefore, we\nimprove upon this estimate by assessing the patterns in their electricity consumption as\na function of mean temperature of a given day using smart-meter records of 200,000\nhomes in Southern California over the years 2015-2016 provided by local utility\nSouthern California Edison (SCE). As a result, our estimates capture the actual AC\nusage better (Chen et al, 2019; 2020). We have explained this better now in Section 4.6\nEstimates of Air Conditioning access and usage (Lines 356-366).\n7. This brings me to my final comment that refers to Supplemental Figures 13 and\n14. These figures show that African Americans are as highly exposed to heat\nrisks as Hispanics in LA. I believe the data indicates that lines 266-267 in the\nDiscussion should be amended accordingly.\nRESPONSE: Thanks for bringing this to our attention. We have amended the abstract\nand discussion section (Line 302) accordingly.\nReferences:\n● Romitti,Y.,SueWing,I.,Spangler,K.R.&Wellenius,G.A.Inequalityintheavailabilityof\nresidentialairconditioningacross115usmetropolitanareas.PNASnexus1,pgac210(2022).\n● Chen,M.,Sanders,K.T.&Ban-Weiss,G.A.Anewmethodutilizingsmartmeterdatafor\nidentifyingtheexistenceofairconditioninginresidentialhomes.Environ.Res.Lett.14,094004\n(2019).\n● Chen,M.,Ban-Weiss,G.A.&Sanders,K.T.Utilizingsmart-meterdatatoprojectimpactsof\nurbanwarmingonresidentialelectricityuseforvulnerablepopulationsinsoutherncalifornia.\nEnviron.Res.Lett.15,064001(2020).\nReviewer #2:\nThis article is an extremely important contribution to the literature about urban heat\nislands. It elucidates several important drivers of inequitable vulnerabilities to climate\nchange, in particular dissecting the relationship between historical redlining and current\neconomic and racial disparities. The outcomes are well-articulated in the introduction\nand discussion. It should be published ASAP.\nThe robust statistical analysis and referencing of prior research and source datasets are\nexceptional. While I do not conduct this type of quantitative analysis in my own\nresearch, with a bit of effort, I could follow the analysis, with two exceptions, as\nexplained below.\n1. First, the explanation of how the first PC was used to create the vulnerability\nscore should be strengthened. What exactly does the scale of -6 to 6 represent?\nAnd what does the red and blue color coding represent? The first question\nshould be answered more clearly at the end of the second paragraph in Section\n2.4. The second question could be answered more clearly in the caption in\nSupplementary Figure 11b.\nRESPONSE: We appreciate the reviewer’s suggestion and have made revisions to\naddress both points more clearly. As part of our preliminary analysis, we considered the\nfirst 3 principal components (PC’s) and analyzed their spatial distributions. Each PC is\northogonal to all others, and should be treated as a separate dimension. We considered\ncombining the PCs into a single ‘vulnerability score’, but the additions in explained\nvariance of PC2 and PC3 were not large enough to justify the loss of interpretability due\nto the combination of different dimensions, so we retained and performed the rest of our\nanalysis only with PC1 as the vulnerability score. We have strengthened this\nexplanation in the text now (Lines 226-240).\nRegarding the scale of -6 to 6: This scale represents the range of PC1 scores, with\neach score indicating a specific level of vulnerability relative to the overall data\ndistribution. A higher score (closer to 6) corresponds to greater vulnerability, while a\nlower score (closer to -6) indicates less vulnerability. We have updated the figure 6\ncaption to clarify that this range reflects standardized values based on the contributions\nof the variables loading onto PC1.\nAs for the red and blue color coding in Supplementary Figure 11b, we have revised the\ncaption to explain that red indicates a positive loading, meaning the variable contributes\nto increased vulnerability, while blue represents a negative loading, indicating that the\nvariable mitigates vulnerability. The degree of shading reflects the strength of each\nvariable’s contribution.\n2. Second, a more minor addition I recommend, also in Section 2.4, is to explain the\ndifference between LST and near-surface air temperature. In particular, the only\nplace I could find an indication of the height above ground at which the air\ntemperature was taken is in the caption for Supplementary Figure 10. It would be\nhelpful to readers if this distance were noted in the description of the dataset in\nthe narrative in Section 2.4.\nRESPONSE: Thank you for pointing that out. We have now added the following\nclarification regarding near surface air temperature to Section 2.4 which should be\nhelpful for the readers: Estimating heat exposure requires consideration of variables\nbeyond surface temperature, such as near-surface air temperature (T_air, which is\ntypically measured at 2 meters above ground level), wind speed, solar irradiance, and\nhumidity (Lines 201-204).\n3. Line 32, sentence starting “Further…” needs editing for clarity, as it appears to be\nan incomplete sentence\nRESPONSE: We have rewritten that sentence for improved clarity.\n4. Line 190, “maybe” should be “may be”\nRESPONSE: Thank you, we have corrected that now.\nReviewer #3:\nThe study aims to disentangle the effects of historic redlining and current income\ninequality on urban heat disparities. The results indicate that present-day income\ninequality is a stronger indicator of heat burden than historic redlining. While the\nresearch addresses an important topic—intra-urban inequities—there are several critical\nissues that need to be addressed to strengthen the manuscript.\nMajor comments:\n1. This study focuses solely on one city, Los Angeles. To attract more attention, the\nnarrative should be framed from a broader perspective. For example, it could\ninclude major cities across the United States or even globally. The implication of\nthis study is limited to a single city.\nRESPONSE: At this stage, it is indeed a single-city focussed paper, since the signals\nwe are looking at are small and is influenced by many factors (LA's climate, coastal\ninfluence, specific history as detailed in paragraph 3 of introduction) that may not be\ngeneralizable to all cities in the U.S. Moreover, redlining is not a global phenomenon\nand the urban population distributions in the U.S. is quite unlike that in other countries.\nHowever, this study provides a methodological framework to better separating out\nhistorical versus present inequalities for heat disparity studies over other cities. To avoid\nmiscommunication of the scope of our study, we have updated the title to explicitly\ninclude LA.\n2. Lines 3-4: Previous studies have extensively analyzed intra-urban inequalities. It\nis necessary to further expand the background information and include relevant\ncitations. Additionally, it is important to highlight the limitations of the previous\nstudies and the innovative aspects of this research in the Abstract and\nIntroduction.\nRESPONSE: Yes, we have now rewritten the introduction and abstract to reflect that\nbetter (Citations in the updated manuscript): “Over the last few decades, research has\nunequivocally identified cities as distinctly warmer entities compared to their non-urban\nsurroundings. A large body of literature has looked at intra-urban thermal landscapes\nand their associations with present-day population distributions. While climate change\nexposes rapidly growing urban populations to enhanced heat hazards, the inherent\nheterogeneity of cities further exacerbates the inequitable distribution of its worst\nimpacts. For many cities, areas with poorer and predominantly non-white populations\ntend to be warmer. These disparities in heat hazard have been found to also lead to\nunequal mortality/morbidity outcomes, making them an important policy focus for\nequitable urban planning.” (Lines 2-9, references 1-17)\nThe main limitations of previous studies and our key innovative use of ECOSTRESS is\nacknowledged in lines 52-61: “Most previous investigations into the intra-urban and\nintra-HOLC heat inequalities have relied on Land Surface Temperatures (LST)\nestimated for specific times of day from satellites in sun-synchronous orbits (e.g.,\nLandsat at 10:00 am). Here, we overcome this limitation in temporal resolution by using\ndata from the ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment\non Space Station) thermal instrument onboard the International Space Station (ISS)\nwhose precessing orbit allows observations at different times during the day, providing\ncomprehensive temporal coverage. By leveraging ISS-borne LST observations, we aim\nto discern the diurnal and seasonal patterns that may have been overlooked in previous\nstudies, providing a more detailed perspective on the temporal dynamics of heat\ninequalities and their potential implications for vulnerable and historically underserved\ncommunities.”\n3. Lines 38-41 and Lines 283-292: What is the spatial coverage of ECOSTRESS\nobservations? Can it be used for global or U.S.-wide studies? Why was Los\nAngeles chosen as the study subject? Moreover, this study focuses only on\ncloud-free land surface temperatures. Considering the influence of clouds on\ntemperature could impact the study results. For instance, higher temperatures\npromote precipitation and cloud cover, thus reducing incoming solar radiation and\nmitigating urban warming. This limitation should be acknowledged in the article.\nRESPONSE: While ECOSTRESS indeed has a global coverage, we chose to focus this\nresearch on a single city - LA for now because it is a large enough metropolitan city to\nhave high intra-urban climatological, topographical, and ecological diversity and a\nhistory of redlining (Paragraph 3 of introduction, Lines 30-51). Moreover, beyond the\nfine-scale socio-economic data, we have information about air conditioning usage here\nthrough our collaborator and co-author Kelly Sander’s research group at USC. Although\nredlining is not a global phenomenon, this study provides a methodological framework\nto better separating out historical versus present inequalities for heat disparity over\nother cities. Considering the influence of clouds on temperature is important and as they\nobscure surface temperature observations, it is a limitation of LST based analysis\nindeed. We have acknowledged that in Line 200-207.\n4. Land surface temperature does not accurately reflect human thermal sensation.\nIt is necessary to supplement the study with surface air temperature data from\nmeteorological stations. Moreover, the temporal resolution and frequency of the\ndata are not clearly described in the manuscript. It is not clear what data has\nbeen used to derive the average LSTs.\nRESPONSE: The reason we use LST data is because we do not have near-surface air\ntemperature data at the necessary spatial resolution (redlined neighborhood scale) for\nthis study. However, we do use a gridded air temperature data to contextualize some of\nour LST based results and discuss those in lines 200-216 and Supplementary figure 10.\nDetailed explanation of our data source is described in the Methods section (Section\n4.2, Lines 320-328) at the end of the paper as per the journal’s guideline. Our primary\ndataset is the standard Land Surface Temperature (LST) product from ECOSTRESS\nwith a spatial resolution of 70x70 meters (at nadir) acquired over the LA region since\nmid-2018. ECOSTRESS, or the ECOsystem Spaceborne Thermal Radiometer\nExperiment on Space Station, was launched to the International Space Station in June\n2018, and it currently provides the highest combined spatial (70m at nadir), spectral (3/5\nbands), and temporal (3-5 days) resolution thermal infrared data from space with\nobservations available over the diurnal cycle. As ECOSTRESS is not a polar orbiting\nsatellite, but an instrument mounted on the International Space Station, it doesn’t have\na regular frequency of revisit over any spot. However, the irregular overpass time is\nprecisely why we are able to leverage its irregular revisit time to obtain LST\nobservations at any time of day for this study.\nMinor comments:\n1. Line 83 and Lines 102-103: In addition to seasonal differences, the reasons for\ndaytime and nighttime variations should also be specifically discussed and\nanalyzed.\nRESPONSE: The diurnal variations are discussed and analyzed in Section 2.1 as well\nas Section 2.2\n2. Lines 127-129: During winter, why are temperatures higher in high-income areas\ncompared to low-income areas? Is it due to the effect of anthropogenic heat?\nRESPONSE: This is likely an effect of thermal regulation provided in the form of\nevapotranspiration by the local vegetation in the affluent neighborhoods that keeps the\nwinter nights warmer than the poorer neighborhoods (Lines 144-146). This was also\nobserved and discussed in details in another study (Yin, et al. 2023) by our co-author,\nDr. Yi Yin.\n3. Lines 263-264: How is a heatwave event defined in the study?\nRESPONSE: We use hotter than 95th percentile of the usual climatology as the\nheatwave threshold in this study. However, the focus isn’t a heatwave event here but\nany extremely hot day. We have edited this sentence to the following for improved\nclarity: We find that the thermal disparities escalate to up to 5-7°C during extreme heat\ndays such as heatwaves and exhibit a seasonal hysteresis pattern driven by the\nspringtime greening and falltime senescence of local vegetation (Lines 296-298).\n4. The research indicates that present-day income inequality is a stronger indicator\nof heat burden than historic redlining. This content is not reflected in the title.\nRESPONSE: This is indeed a valuable suggestion. We have updated the title to\nappropriately capture our most notable finding now: “Present-day inequities in Los\nAngeles have a stronger influence on intra-urban heat disparities than historic redlining”.\n5. The abstract should briefly introduce the background of historic redlining. Or at\nthe beginning of the introduction.\nRESPONSE: Paragraph 2 of the introduction (Lines 15-29) and the Methods Section\n4.1 (Lines 311-318) describes the background historic redlining and Line 1 and 2 of\nAbstract summarizes that.\nReferences:\n● Yin,Y.,He,L.,Wennberg,P.O.,&Frankenberg,C.(2023).Unequalexposuretoheatwavesin\nLosAngeles:Impactofunevengreenspaces.ScienceAdvances,9(17),eade8501."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for addressing the reviewers’ commentary. Their efforts have improved the manuscript. Reviewer\n1’s reactions to revisions made in response to my comments:\n1. Thank you for removing the misleading sentence in the first paragraph. Although the number of citations added does not\nfully reflect the large literature on intra-urban heat inequalities, it makes the necessary point for this manuscript.\n2. The new description of the LA climate and geography is not exactly what I had in mind. The complexities of the Southern\nCalifornia climate zones might be better appreciated by referring to the unusual close proximity of Koppen classifications;\ni.e., something like lines 278-279 would be beneficial in the introduction. Regarding comments about the locations of places\non the maps, a larger point is that we should have an exact definition of the boundaries of the study area. The “Los Angeles\nregion” is too vague – is it an MSA or some other construction that you are using? The use of terms like city and\nneighborhoods throughout the text deserves some clearer context because there are many cities in this region. Figure 2 is\nan example – for readers who are not familiar with SoCal, there is no orientation to where the city of LA is, let alone central\nLA, urban canyons, and shipping dock. Is a popout map an option? Figure 3 gives some markers about where places are\nlocated.\n3, 4. Thank you for the clarifications.\n5. I might have made a different decision about how to use the PCA. Collectively, I think we are learning this method is not\nthe best way to differentiate the different types of human vulnerability. Factor 1 points to a broad constellation of vulnerable\nindividuals but it is so general that it would not provide much guidance on protections. Nevertheless, that is not the point of\nthis study and we don't have anything better as a summary measure.\n6, 7. Thank you for the clarifications.", "reviewer_2": "The authors have improved their already-strong manuscript in their responses to the first round of reviewer comments,\nresulting in an excellent article that I look forward to seeing in publication. I have no further comments."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThank you to the authors for addressing the reviewers’ commentary. Their efforts have improved\nthe manuscript. Reviewer 1’s reactions to revisions made in response to my comments:\n1. Thank you for removing the misleading sentence in the first paragraph. Although the number of\ncitations added does not fully reflect the large literature on intra-urban heat inequalities, it makes\nthe necessary point for this manuscript.\nResponse: Thank you.\n2. The new description of the LA climate and geography is not exactly what I had in mind. The\ncomplexities of the Southern California climate zones might be better appreciated by referring to\nthe unusual close proximity of Koppen classifications; i.e., something like lines 278-279 would be\nbeneficial in the introduction. Regarding comments about the locations of places on the maps, a\nlarger point is that we should have an exact definition of the boundaries of the study area. The “Los\nAngeles region” is too vague – is it an MSA or some other construction that you are using? The use\nof terms like city and neighborhoods throughout the text deserves some clearer context because\nthere are many cities in this region. Figure 2 is an example – for readers who are not familiar with\nSoCal, there is no orientation to where the city of LA is, let alone central LA, urban canyons, and\nshipping dock. Is a popout map an option? Figure 3 gives some markers about where places are\nlocated.\nResponse: I see, thank you for that clarification, we have now rewritten the geographical\nand climatological background of LA with more of a focus on Koppen Geiger classification\n(Lines 30-35). We are using the census tracts within LA county and have clearly specified\nthat in Line 30. While a pop-out map is not an option at this stage, figure 2 is indeed a good\nopportunity to label some major landmarks to properly situate the reader. Thank you for\nthat suggestion. We have now labeled Figure 2 accordingly (please see below).\n3, 4. Thank you for the clarifications.\n5. I might have made a different decision about how to use the PCA. Collectively, I think we are\nlearning this method is not the best way to differentiate the different types of human vulnerability.\nFactor 1 points to a broad constellation of vulnerable individuals but it is so general that it would\nnot provide much guidance on protections. Nevertheless, that is not the point of this study and we\ndon't have anything better as a summary measure.\nResponse: We appreciate the reviewer’s observation about the first principal component\n(PC1). PC1 here (as well as all the other PCs) is an aggregate measure of vulnerability and the\nvarious contributions to its definition are shown in SI Figure 11b. Owing to the non-Gaussian\nnature of the variables that describe the vulnerability, we cannot expect a simpler\ndescription of PC1. As the reviewer also notes, our aim in using PCA was solely to create a\nlow-dimensional, aggregate measure of vulnerability to compare with other indices (income\nand redlining score). In this regard, the use of PCA meets the intended purpose.\n6, 7. Thank you for the clarifications."}]

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