in collaboration with Juan Sebastián Moreno : Social Vulnerability to Heat in New York City

SOCIAL VULNERABILITY TO HEAT in new york city

Juan Sebastiรกn MORENO Zeineb SELLAMI MS Urban Planning Geographic Information Systems Columbia University GSAPP | Fall 2019

TABLE OF CONTENTS 1 2 3 4 5 6 7 8 9 10 11 12 13

introduction scope & limitations surface heat social vulnerability social vulnerability to heat coverage of NYCHA Cooling Centers modifiable areal unit problem neighborhood indicators coverage of NYC Public Libraries coverage of Cooling Centers & Public Libraries conclusion appendix | methodology references

“Global Warming at or above 2 degre preindustrialized levels will cause to be exposed globally to deadly hea 2

INTRODUCTION The effects of the climate emergency on cities have emphasized the influence of the built environment on the way people experience extreme temperatures. The Green New Deal has highlighted these consequences, utilizing evidence from the “Special Report on Global Warming of 1.5ºC” and the “November 2018 Fourth National Climate Assessment report:” “Global warming at or above 2 degrees Celsius beyond preindustrialized levels will cause [...] more than 350,000,000 more people to be exposed globally to deadly heat stress by 2050.” (1) Extreme heat is not evenly distributed in cities, since this phenomenon is heavily influenced by urban design, population density, and access to social infrastructure. In this sense, what the Green New Deal defined as systemic injustices (2) can also affect the creation of Urban Heat Islands (UHI), (3) since there is a close relationship between places with higher temperature and vulnerability. (4)

ees Celsius beyond more than 350 000 000 more people at stress by 2050.”

3

According to scientists from the National Oceanic and Atmospheric Ad-

ministration, July 2019 was the hottest month ever recorded on Earth.

In New York City, July 20, 2019 was the hottest day of the summer, with city wide temperatures reaching highs from 95 to 99 degrees fahrenheit and humidity levels at 70% or more. (5,6) Our research covers the five boroughs of New York City, focusing on that particular July day. The latest ACS estimates from 2017 allow us to evaluate demographic data about the people most vulnerable to these situations of extreme heat.

During periods of anticipated extreme heat, New York city provides access to a number of cooling centers dispersed throughout the city. “Cooling centers are air-conditioned spaces such as senior centers, community centers, public libraries, and other public facilities that typically operate during daytime hours and are free and open to the public.� (7) The first step consists of mapping relative surface temperatures using LandSat8 data. Meanwhile, aggregating a series of social indicators to census blocks, allow us to locate the census blocks with the most vulnerable populations. Once these two indicators are in place, the spatial relationship between the location of vulnerable populations and urban heat islands becomes apparent.

This leads to the following question : What is the proximity of New York City cooling facilities to census blocks with the highest levels of vulnerability to heat? Given future rises in temperature due to climate change, should the city increase the number of these facilities to alleviate heat in these neighborhoods?

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scope

Our research is not without limitations. Given the scope of our study,

we needed to define what constitutes an urban heat island. In practice, the horizontal features taken into account in land cover data are water, vegetation, density and impervious surfaces. Yet other variables could also be taken into consideration, such as the vertical reflectivity of building materials, the presence/absence of green roofs, as well as buildings’ greenhouse gas emissions. These elements would be worth considering in order to further this research. (8) Additionally, the surface temperature raster retrieved from the LandSat8 scan only shows relative heat. Converting this relative heat into actual temperatures in degrees Celsius or Fahrenheit was outside our purview.

On another note, New York City only makes the location of accessible cooling centers known during expected heatwaves. Given our timing, we could not retrieve data regarding the type and location of these designated cooling centers. For the purposes of our study, we used NYC Open Data’s Facilities database, operationalizing senior centers and community centers as ‘guaranteed’ cooling centers. Not knowing which of the New York City Public Libraries were previously used as cooling centers, and due to their shear numbers, we initially excluded them from our cooling centers. We later reincorporated them in our study.

& limitations

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using landsat 8 to evaluate heat

Saturday, July 20, 2019. The hottest day of the hottest month ever recorded in New York (and the world), with temperatures reaching 95ยบF. The United States Geological Survey recorded the scorching heat in New York City with a satellite image, allowing the general discomfort of New Yorkers to be a subject of spatial analysis.

We made ourselves familiar with LandSat data and accessed its images for surface temperature. After reassigning the cell size for this raster image, a step required in order to make it congruent to our social vulnerability map, we reclassified the cells: a score of 1 for the coolest cells and a score of 10 for the hottest.

This raster map shows the different effects of heat on the built environment: places such as John F. Kennedy International Airport concentrate surface temperature, while green spaces mitigate it. This is the case of Central Park in Manhattan and Prospect Park in Brooklyn.

There is a significant limitation to working with the LandSat8 image we employed: it constitutes a snapshot of urban heat in a very particular moment in this case, an unmerciful summer. In consequence, we cannot follow the patterns of urban heat during different times of the day, nor can we see the effects of urban heat islands during other days of the week.

bronx manhattan queens brooklyn staten island

6

0

5 Mi

score : 10

high surface temperature

low surface temperature score : 1

SURFACE HEAT Reclassified Raster Map

New York City July 20, 2019 7

Over 65 Over 6565 Over Poverty Poverty Non White

+ Non-white

8

Living Al

Income

Over 65

Non White

+ Over 65

+ No Highscool Diploma

=

Population Density

low vulnerability

score : 1

NoHSdiploma

Living Alone Living Alone Living Alone

+ Living Alone

After creating rasters for our seven social indicators, we then assigned them cell values ranging from 1 to 5 in each category. The resulting ranked map is composed of our seven equally weighted indicators, constituting our basis to spatialize social vulnerability in the city.

Population Density Density Population Population Density

Poverty Poverty Poverty NoHSdiploma Income Income

+ Poverty

The seven thematic maps portray the social indicators selected for our index.

The American Community Survey (ACS, 2017 five-year estimates) provides a per block definition of: the median household income, the percentage of

NoHSdiploma NoHSdiploma NoHSdiploma Living Alone Living Alone

Income Income Income Living Alone

Median Household Income

We initially assumed that socially vulnerable populations suffer disproportionately from the effects of heat in the city. To ascertain the influence of extreme heat on the most disenfranchised population, we constructed a social vulnerability index.

Non White Non White NoHSdiploma Non White Density NoHSdiploma Population

Ranked Thematic Maps

households under the federal poverty line, the percentage of households with people living alone, the percentage of households with at least one person over 65 years of age, the percentage of non-white population, the percentage of individuals without a high school diploma, and population density.

Poverty

SOCIAL VULNERABILITY indicators

high vulnerability 5 high

score : 5 Mi

5 5 5 high

Mi Mi

Mi

0

5 Mi

score : 35

high vulnerability

low vulnerability score : 7

SOCIAL VULNERABILITY Reclassified Raster Map

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ranked & weighted map analysis Once we set the inputs for our spatial analysis, we performed map algebra to add the surface temperature data to our social vulnerability index.

The first ranked map holds all variables equal. We did not trust this initial attempt. The results were painted with a broad stroke, and a large amount of neighborhoods were subsumed under the same classification categories without providing much specificity.

Ranked Map

Surface Temperature (x1) + Social Vulnerability (x1) score : 45

high vulnerability

low vulnerability score : 8

Similarly, we were unsatisfied with the level of detail the second map was providing. When assigning more weight to heat, despite having a more granular picture of the relation between surface temperature and social variables, we did not obtain specific information about neighborhoods especially vulnerable to heat.

We ultimately decided to assign our social vulnerability index double the weight than our temperature data.

This decision took into consideration a premise of our scope: we wanted to show the disproportionate effects of exposure to extreme heat in the city.

This third map highlights the most vulnerable neighborhoods during the hottest days of the year. Areas of the South Bronx, Queens, and the Lower East Side in Manhattan, clearly showed the spatial relationship between social vulnerability and heat.

Weighted Map 1

Surface Temperature (x2) + Social Vulnerability (x1) low 12 - 21 score : 55

high

0

5

10

22- 28 high 2935 vulnerability 36 - 40 41- 45

low vulnerability score : 9

0

10

5 Mi

Weighted Map 2

Surface Temperature (x1) + Social Vulnerability (x2) score : 80

high vulnerability

low vulnerability score : 15

SOCIAL VULNERABILITY TO HEAT 0

5 Mi

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NETWORK ANALYSIS 1

Percentage of covered Areas 0

5 Mi

by borough

bronx

25.1% manhattan

42.4% queens

0.3%

staten island

5.5%

brooklyn

20.2%

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To quench the hottest days of the summer, the city provides access to community and senior centers belonging to the New York City Housing Authority (NYCHA), transforming them into cooling centers.

To analyze their spatial distribution in relation to heat vulnerability, we performed a network analysis. This resulted in half-mile service areas within walking distance of the 246 corresponding facilities.

The resulting map illustrates how cooling centers perform an important service to many communities in need during events of extreme heat. The Lower East Side of Manhattan is almost entirely covered by service areas.

However, given the lack of facilities in other neighborhoods, these service areas seem insufficient. Census blocks in Queens and the Bronx, where some of the most vulnerable live, have littleto-no access to cooling centers.

Social Vulnerability to Heat Surface Temperature (x1) + Social Vulnerability (x2) score : 80

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Cooling Centers

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NYCHA Community & Senior Centers

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Distance from Cooling Center

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Social Vulnerability to Heat &

coverage of nycha cooling centers 0

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MODIFIABLE AREAL UNIT PROBLEM (1)

We intended to narrow our focus in order to analyze unserviced neighborhoods in the context of urban heat. However, when we looked at our findings with more detail, a modifiable areal unit problem (or MAUP) became apparent.

0

1 Mi

(2)

After overlaying the cooling center service areas to the census blocks with demographic and temperature data, the blocks that partially intersected the areas were all showing signs of vulnerability.

(Image 1) Some of them, however, were mostly covered by the service area of a cooling center. This introduced distortion to our analysis of the effects of cooling centers on specific neighborhoods.

(Image 2) For that reason, we calculated the ratio between the total area of the blocks and the area actually included inside the service area of 0 each cooling center.

(Image 3) For a block to be serviced by a cooling center, we decided that at least 50% of its area must be included within the service area.

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0

1 Mi

(3)

0.125

0.25

0.5 Miles

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0.5

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cool & scarce coverage Social Vulnerability to Heat Surface Temperature (x1) + Social Vulnerability (x2)

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score : 80

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score : 15

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Cooling Centers NYCHA Community & Senior Centers

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NEIGHBORHOOD INDICATORS 0

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5 Mi

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Fordham-Morris Heights, BX

Scarce Coverage

Percent of total Community Board 5 No Highscool Diploma

Living Alone

0 0.25 0.5

1 Miles

89.7%

20.5%

27.7%

Over 65

Below the poverty line

19.3%

40.5%

Non-white

Median HH Income $29,159 Population

136,610

Lower East side, MN

Cool Coverage

Median HH Income $55,472 Population

Percent of total Community Board 3

156,572

Non-white

No Highscool Diploma

Living Alone

56.9%

19.7%

47.8%

Over 65

Below the poverty line

28%

24.9% 17

NETWORK ANALYSIS 2

Percentage of Uncovered Areas 0

by borough

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bronx

65.4% manhattan

40.3% queens

74.9%

staten island

91.8%

brooklyn

62.2%

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Cooling centers in NYCHA facilities provide limited coverage to the neighborhoods in need of cooling during the summer. We considered that public library branches could perform this task, increasing the area covered to alleviate extreme heat.

In order to compare these areas, we started by creating a map with the same parameters to determine the coverage that libraries would provide: a network analysis with areas of a halfmile within walking distance of each facility.

Then we compared the area left uncovered by the two sets of facilities.

Overall, the unserviced area in the five boroughs would drop from 169,225 acres to 137,967.3 acres. In other words, using only libraries as cooling centers would reduce uncovered areas by 18.5%.

Social Vulnerability to Heat Surface Temperature (x1) + Social Vulnerability (x2) !

score : 80

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score : 15

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Social Vulnerability to Heat &

coverage of NYC PUBLIC LIBRARIES 0

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visualizing the gaps

Percentage of Uncovered Areas 0

bronx

54.4%

The city could expand the reach of cooling centers by adjusting library branches to serve their neighborhoods. The service areas of this combined set of facilities represent 34% of the area of New York.

In consequence, a substantial portion of the city would benefit from cooling centers during the summer.

This would be especially relevant in Queens, where current coverage represents 0.3% of the borough.

Adding libraries as cooling centers would result in an increase of 25.2% of the borough being serviced.

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by borough

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manhattan

29.2% queens

74.5%

staten island

87.6%

brooklyn

53.7%

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score : 80

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coverage of cooling centers & NYC PUBLIC LIBRARIES 0

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conclusion Surface heat does not manifest itself in an even fashion across New York City. Some neighborhoods - those with higher levels of social vulnerability - also show a similar vulnerability to heat during extreme weather events. The city has recognized this as a public health issue, and the cooling centers functioning in NYCHA facilities are mitigating the deleterious effects of urban heat islands.

Nevertheless, the coverage of cooling centers can be improved. We illustrated how using libraries in addition to community and senior centers can expand the services provided by the city to mitigate heat during the summer. This would be, by any measure, a palliative aimed at mitigating the conditions of vulnerability endured by disenfranchised communities all across the five boroughs.

Our research also suggests significant opportunities for further study.

For instance, there are additional variables that influence how a heat index is affected by the built environment. For instance, the presence of green roofs, the difference in construction height and building materials, and even the concentration of asphalt and other nonreflective surfaces, could all be taken into consideration for a more holistic analysis of urban heat islands.

Equally important is qualifying the analysis of vulnerable populations in the context of extreme weather. Census and survey data are but glimpses into how these neighborhoods perform in critical situations.

Conceptualizing the spatial relationship between social vulnerability and heat must also include research into the resources that each community can mobilize to be safe and overcome the challenges

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appendix

BASE MAP Borough boundaries, Greenspace, Water

LandSat 8 Surface te in Kelvin d

Clip to bo boundari

Clipped S Redefine

RECLASSIF

Ranked D HEAT VUL score 1-10

Our methodology will consist of creating a series of three main map outputs in order to answer our research questions. First, we will perform Map Algebra with our demographic data in order to define the census tracts with the most vulnerable populations, resulting in a Social Vulnerability map. Meanwhile, reclassifying surface temperature data from LandSat 8 produces a Relative Surface Temperature map, defining the locations of urban heat islands.

Aggregate the variables within these maps, produces an overall indication of vulnerability to heat by census block, otherwise defined as our Vulnerability to Heat map. Finally, we will perform a network analysis to determine the proximity to “cool� facilities, and determine locations where new facilities should be opened.

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methodology

MAP ALG Weighted

Weighted score 15-8

MAP ALG Weighted

Areas tha that lack C score 0-80

Raster to P and Spati

Download & prepare data. Confirm & correct coordinate systems ACS 2017 5-yr Estimates table: blocks popยบ, non-

8 emperature degrees

white, no diploma, households, living alone, over 65, med. income, HH below poverty

orough ies

Create & calculate population density field (people per acre)

Select and export ped. st.

SURFACE TEMP

Table join ACS data to census block boundaries. Export.

Lion Ped. Street

cell size = 20

ACS 2017 5-yr estimates blocks

FY: score 1-10

RASTERIZE (7 times). Create raster datasets per criterion variable.

Create NETWORK DATASET

2017 Census Blocks blocks (NY State)

RECLASSIFY by quintile : score 1-5

popยบ density (1-5)

Decision Map: LNERABILITY 0

% nonwhite (1-5)

% no diploma (1-5)

%1 person HH (1-5)

years (1-5)

% HH below poverty (1-5)

HH median income (1-5)

LION Pedestrian Streets

NYCHA Facilities Community & Senior Centers

NYC Facilities NYC Libraries

Add Location NYCHA Facilities Comm & Sen

Add Location NYC Libraries

SOLVE

SOLVE

1/2 mile walkability from Community Centers & Senior Centers

1/2 mile walkability from NYC Libraries

RASTERIZE

RASTERIZE

RECLASSIFY binary 0,1

RECLASSIFY binary 0,1

1/2 mile walkability from Community Centers & Senior Centers

1/2 mile walkability from NYC Libraries

Create SERVICE AREA (1/2 mile)

MAP ALGEBRA (Raster Calculator) Ranked (unweighted) Ranked Decision Map: SOCIAL VULNERABILITY score 7-35

GEBRA (Raster Calculator) d, Heat Vulnerability *1 and Social Vulnerability *2

d Decision Map : SOCIAL & HEAT VULNERABILITY 80

GEBRA (Raster Calculator) d, Social & Heat Vulnerability * Service Area

at are VULNERABLE and COOLING FACILITIES 0

Areas that are VULNERABLE and that lack COOLING FACILITIES score 0-80

Polygon ial Join into the Census Block boundaries to link scores to each block

Analysis of neighborhood indicators depending on their coverage Analysis of service area coverage in the five boroughs

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end notes (1) U.S. Congress, House, Recognizing the duty of the Federal Government to create a Green New Deal, H.R. 109, 116th Congress, 1st sess., introduced in House February 7, 2019, 2.

(2) “Whereas climate change, pollution, and environmental destruction have exacerbated systemic racial, regional, social, environmental, and economic injustices (referred to in this preamble as ‘systemic injustices’) by disproportionately affecting indigenous peoples, communities of color, migrant communities, deindustrialized communities, depopulated rural communities, the poor, low-in- come workers, women, the elderly, the unhoused, people with disabilities, and youth (referred to in this preamble as ‘frontline and vulnerable communities’),” U.S. Congress, House, Recognizing the duty of the Federal Government to create a Green New Deal, 4.

(3) “Urban heat island (UHI) describes the phenomenon that temperatures are higher in urban areas compared to surrounding rural areas. It is one of the research focuses in urban climatology and urban ecology because increasing temperatures in the urban area may lead to significant ecological and social consequences.” Xiaoma Li et al., “Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution?,” Landscape and Urban Planning 114 (2013), 1.

(4) “Studies that investigated UHI by incorporating LST and social data (e.g. Johnson and Wilson, 2009; Harlan et al., 2006) revealed that populations that are most vulnerable to heat and that have the fewest resources to fight against excess heat are often reside in warmer places within a city.” Ganlin Huang et al., “Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD,” Journal of Environmental Management 92 (2011), 1754.

(5) National Oceanic and Atmospheric Administration’s National Weather Service, available online at: https://w2.weather.gov/climate/ (6) Brady Dennis and Andrew Freedman, “Here’s how the hottest month in recorded history unfolded around the world,” Washington Post, August 5, 2019. Available online at: https://www.washingtonpost.com/climate-environment/2019/08/05/heres-how-hottest-month-recorded-history-unfolded-around-globe/ (7) NYC Emergency Management, “Extreme Heat,” available online at: https://www1.nyc.gov/site/em/ready/extreme-heat.page (8) “Temperatures of vertical surfaces, which constitute the main portion of urban building surfaces, are inaccessible by satellite and/ or aerial remote sensing. This information is essential in the accurate prediction of the urban storage flux. In the case of ground based remote sensing, the unknown surface emissivity can play a crucial role in the accurate determination of surface temperatures. Comprehensive databases of surface emissivity values for buildings in cities are not readily available.” Masoud Ghandehari et al., “Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer,” Scientific Reports 8:2224 (2018), 2.

references Dennis, Brady and Andrew Freedman, “Here’s how the hottest month in recorded history unfolded around the world,” Washington Post, August 5, 2019. Available online at: https://www.washingtonpost.com/climate-environment/2019/08/05/heres-how-hottest-month-recorded-history-unfolded-around-globe/ Ghandehari, Masoud, Thorsten Emig, and Milad Aghamohamadnia. “Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer.” Scientific Reports 8:2224 (2018): 1-10.

Huang, Ganlin, Weiqi Zhou, and M.L. Cadenasso. “Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD.” Journal of Environmental Management 92 (2011): 1753-1759.

Li, Xiaoma, Weiqi Zhou, and Zhiyun Ouyang. “Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution?.” Landscape and Urban Planning 114 (2013): 1-8.

National Oceanic and Atmospheric Administration’s National Weather Service, available online at: https://w2.weather.gov/climate/ New York City Emergency Management Department, “Extreme Heat,” available online at: https://www1.nyc.gov/site/em/ready/extreme-heat.page U.S. Congress. House. Recognizing the duty of the Federal Government to create a Green New Deal. H.R. 109. 116th Congress. 1st session. Introduced in House February 7, 2019.

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datasets New York City Department of City Planning. “New York City Borough Boundaries” [shapefile]. Bytes of the Big Apple, Issue 16D. October 27, 2016. <https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm> New York City Department of Information Technology & Telecommunications. Open Space (Parks) [shapefile]. 17 June 2014, updated August 27, 2016. <https://data.cityofnewyork.us/Recreation/Open-Space-Parks-/g84h-jbjm> U.S. Geological Survey, National Geospatial Program, 20161103, USGS National Hydrography Dataset (NHD) Best ResolutionHU4-0203 20170102 for HU-4 Subregion FileGDB 10.1 Model Version 2.2.1: U.S. Geological Survey. November 3, 2016. <https://www.sciencebase.gov/catalog/item/581d7618e4b0dee4cc8e6080> New York Public Library (NYPL), “Library” [dataset] made public November 18, 2014. Accessed via NYC Open Data, updated September 10, 2018. <https://data.cityofnewyork.us/Business/Library/p4pf-fyc4> Brooklyn Library (BPL), “BPL Branches” [dataset] made public October 10, 2011. Accessed via NYC Open Data, updated March 8, 2019. <https://data.cityofnewyork.us/Recreation/BPL-Branches/xmzf-uf2w> Queens Library (QBPL), “Queens Libraries” [dataset] made public November 18, 2011. Accessed via NYC Open Data, updated September 11, 2018. <https://data.cityofnewyork.us/Recreation/Queens-Libraries-Map-/swsf-ed7j> New York City Department of City Planning, “Facilities Database” [shapefile], January 2019. Accessed via NYC Open Data, <https:// www1.nyc.gov/site/planning/data-maps/open-data/dwn-selfac.page> US Census Bureau. 2013 TIGER/Line Shapefiles “Census Tracts, New York State” [shapefile]. <https://www.census.gov/geographies/ mapping-files/time-series/geo/tiger-line-file.2013.html> New York City Department of City Planning. 2017. LION v17A. [ESRI File Geodatabase]. <https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page> U.S. Census Bureau, 2009-2013, American Community Survey, 5-Year Estimates. Table S0601: “Selected Characteristics of the total and native populations in the United States”, New York City, New York State. [dataset]. Accessed via American FactFinder. 2013.

NYC Department of City Planning, Information Technology Division,”New York City, MapPLUTO 19v1”[dataset] BYTES of the BIG APPLE Issue MapPLUTO19v1. Created November 2018-July 2019, published September 23, 2019. <https://www1.nyc.gov/site/planning/data-maps/open-data.page> United States Geological Service, “Landsat Provisional Surface Temperature,” U.S. Landsat Analysis Ready Data (ARD), accessed through https://earthexplorer.usgs.gov/

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Source: University of Oregon Library

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