boston-heat-vulnerability

Abstract Summary:

Extreme heat is considered to be a chronic climate hazard that will influence the Boston’s climate change throughout the 21st century. Average summer temperature is expected to increase from 69 degrees Fahrenheit during 1980-2010 to 76 degrees Fahrenheit by 2050 with more days of extreme heat. The Boston urban area tends to be hotter than its surrounding suburban and rural areas because of the urban heat island effect. The urban heat island effect of the city further intensifies the severe health impacts of heat, specifically for certain socially vulnerable populations like older adults, children, people of color, low-income and disabilities. The purpose of this analysis is to determine the overall vulnerability of ZCTAs within the Boston Metropolitan Region, considering the following factors:

• Population below the poverty level
• Population over 25 years with less than a High School degree
• Age-dependency ratio indicating number of young children and older adults dependent on the working population
• Population that is non-White
• Mean land surface temperature
• Tree canopy cover

The first four factors indicate socio-economic vulnerability of the ZCTAs and the last two indicate environmental burdens. Indicators were identified based on a study done by Reid et. al. for Boston.The analysis will allow us to identify vulnerable populations and areas that require heat mitigation interventions. The project focuses on identifying the priority ZCTAs of Boston Metropolitan Region using demographic and environmental indicators. The analysis does not use weights. Rather, it normalizes all indicator values using mean and standard deviation and categorized the normalized values into five bins, where 1 indicates low vulnerability and 5 indicates high vulnerability. The bins act as vulnerability scores which are aggregated across all ZCTAs to identify the most vulnerable ones. The aggregate vulnerability scores range between 6 and 30 (higher the score, higher the vulnerability).

Analysis Overview:

• Import all dependencies (check the environment.yml file for this).
• Read in all the shapefiles, rasters and spreadsheets.
• Set the coordinate reference system for all vectors based on the rasters.
• Select the Boston MPO region and clip the ZCTA boundaries within it.
• Attribute join the spreadsheets with the ZCTA boundaries using the ZCTA codes. Summarize and map the fields that are use further in the analysis.
• Perform zonal statistics using the ZCTA shapefile to extract the mean land surface temperature(LST) and tree canopy cover(TCC) from the rasters.
• Create bins for classifying the demographic variables into five categories of vulnerability with 1 indicating leas vulnerability and 5 indicating highest vulnerability.
• Normalize the demographic and mean LST and TCC variables using mean and standard deviation. Classify the normalized values into five bins. The five bins numbered 1 to 5 act as the vulnerability scores which is used to calculate a final aggregate vulnerability for every ZCTA.
• Percentile rank the aggregate vulnerability and then visualize it. Report the top 80% and bottom 20% ZCTAs.

Data and Sources

Vector Shapefiles(.shp)

1. Massachusetts MPO boundaries: boston-data/RAW_DATA/Vectors/MPO_Boundaries.shp (Source: MassDOT)
2. Massachusetts ZCTA boundaries: boston-data/RAW_DATA/Vectors/mass_zcta.shp (Source: Census Bureau)

American Community Survey(2019) Spreadsheets(.csv) (Source: American Community Survey, 2019, Census Bureau)

1. Race: boston-data/RAW_DATA/ACS_Spreadsheets/ACSDT5Y2019.B02001_data_with_overlays.csv
2. Age-Dependency Ratio: boston-data/RAW_DATA/ACS_Spreadsheets/ACSST5Y2019.S0101_data_with_overlays.csv
3. Educational Attainment: boston-data/RAW_DATA/ACS_Spreadsheets/ACSST5Y2019.S1501_data_with_overlays.csv
4. Poverty: boston-data/RAW_DATA/ACS_Spreadsheets/ACSST5Y2019.S1701_data_with_overlays.csv

Raster Images(GeoTiff)

1. Land Surface Temperature: boston-data/RAW_DATA/Rasters/LandSurfaceTemperature/lst_bostonmetro.tif (Source: Landsat 8 OLI/TIRS 2018, US Geological Survey)
2. Tree Canopy Cover: boston-data/RAW_DATA/Rasters/TreeCanopyCover/NLCD_2016_Tree_Canopy_Boston.tif (Source: NLCD, 2016)

For processing the Land Surface Temperature raster, refer:

• Singh, Anisha, & Mishra, Varun Narayan. (2020). Estimation of changes in land surface temperature using multi-temporal Landsat data of Ghaziabad District, India. Geographical Phorum, 19(1), 45–59. https://doi.org/10.5775/FG.2020.040.I

Binder Link

https://mybinder.org/v2/gh/nchatt/boston-heat-vulnerability/f2c50f9f8b975afc4b8924233a982ff381d2eda8

Overview of Packages:

• numpy: Used for working with arrays and matrices.
• pandas: It is built on top of Numpy and used for data analysis.
• shapely: Used for working with vector geometries like points, lines and polygons
• geopandas: It is very similar to pandas and used for working on geodataframes, that is spatial data.
• matplotlib: Used for plotting graphs and maps.
• mapclassify: Used for mapping data based on schemes like 'Equal Interval', 'Quantiles'.
• contextily: For adding basemaps to matplotlib maps.
• folium: Used for creating interactive maps.
• rasterio: Used for raster processing.
• rasterstats: For calculating zonal statistics for the raster files used in the analysis.

References:

Reid, Colleen E., Marie S. O’Neill, Carina J. Gronlund, Shannon J. Brines, Ana V. Diez- Roux, Daniel G. Brown, and Joel David Schwartz. "Mapping Community Determinants of Heat Vulnerability." Environmental Health Perspectives 117, no. 11 (2009): 1730-1736.

Nayak, S.G, S. Shrestha, P.L Kinney, Z. Ross, S.C Sheridan, C.I Pantea, W.H Hsu, N. Muscatiello, and S.A Hwang. "Development of a Heat Vulnerability Index for New York State." Public Health 161 (2018): 127-37.

Laura Barron, Dominique Ruggieri, and Charles Branas. "Assessing Vulnerability to Heat: A Geospatial Analysis for the City of Philadelphia." Urban Science 2, no. 2 (2018): 38.

Singh, Anisha, & Mishra, Varun Narayan. (2020). Estimation of changes in land surface temperature using multi-temporal Landsat data of Ghaziabad District, India. Geographical Phorum, 19(1), 45–59. https://doi.org/10.5775/FG.2020.040.I

City of Boston. “Climate Projection Consensus”. https://www.boston.gov/departments/environment/preparing-climate-change