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AirPolutantRateVSCancerCases_WFL1 (FeatureServer)

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Service Description:

Service ItemId: 5a699aad25074740a28801d5327e1b06

Has Versioned Data: false

Max Record Count: 2000

Supported query Formats: JSON

Supports applyEdits with GlobalIds: True

Supports Shared Templates: False

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Layers:

Description: This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.

The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. 

The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. 

Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. 

Citations:
van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020

van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.

Boundaries:
Data processing notes:
  1. NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.
  2. For each geography level, the following was performed: 
    1. Zonal Statistics were run against the mosaic as a multidimensional layer.
    2. A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.
    3. The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.
    4. The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.
  3. To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. 
    1. Within each hex bin, the total population figure and average PM 2.5 were multiplied.
    2. The hex bins were converted into centroids and summarized within the state and county boundaries.
    3. The summation of these values were then divided by the total population of each state/county. This population value was determined by summarizing the population values from the hex bins within each geography.


Copyright Text: NASA Socioeconomic Data and Applications Center (SEDAC), Kevin Butler (Esri)

Spatial Reference: 102100 (3857)

Initial Extent:
Full Extent:
Units: esriMeters

Child Resources:   Info

Supported Operations:   Query   ConvertFormat   Get Estimates