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Spatial Data

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Abstract

Climate change has increasingly become an issue at the forefront of society’s collective consciousness. Foreseeing the challenges that lie ahead, the United States passed the Global Change Research Act of 1990 that requires the U.S. Global Change Research Program (USGCRP) to deliver a report to Congress and the President to provide a comprehensive, integrated scientific assessment of the effects of global change every four years. The concept of global change is expansive, dealing with changes to the Earth’s system, such as oceans, atmosphere, oceans, among others. Changes in these foundational elements of life have profound impacts on all things in society such as agriculture, energy production and use, land and water resources, transportation, human health, and biological diversity (U.S. Global Change Research Program 1990). The principal output of the USGCRP is the National Climate Assessment—a report that brings together the best scientific minds and policy makers to make sense of the state of current research on climate.

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  • DOI: 10.1007/978-3-030-71352-2_12
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Notes

  1. 1.

    To access maps from CReSIS, visit https://cresis.ku.edu/content/research/maps.

  2. 2.

    The earth and planetary science communities tend to fashion creative acronyms from arbitrary letters. For example, one of NASA’s asteroid satellite missions was called OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer) while a specialized algorithm for astronomy was named GANDALF (Gas AND Absorption Line Fitting algorithm).

  3. 3.

    The data used to simulate these rasters is available on Github (https://github.com/DataScienceForPublicPolicy/build-simulation-data) under raster-simulation.Rmd.

  4. 4.

    Shapefiles were originally developed for use with a proprietary software. GeoJSON was developed to be an open-by-default format.

  5. 5.

    Data can be obtained from NASA Earthdata: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data.

  6. 6.

    sf stands for simple features. See the sf project’s main page (https://r-spatial.github.io/sf/index.html) for more information.

  7. 7.

    Before assuming WGS84, check the range of values. Decimal degrees will vary between specific ranges: latitude in degrees is -90 and +90 while longitude is between -180 and +180.

  8. 8.

    To calculate the distance between degrees longitude: \(\text {cosine}\left( \pi \times \text {latitude}/180 \right) \) km.

  9. 9.

    For the Chicago PD data, visit: https://data.cityofchicago.org/Public-Safety/Crimes-2018/3i3m-jwuy/data.

  10. 10.

    The function is also called read_sf.

  11. 11.

    Because our spatial join was not able to assign a district to some incidents, we may need to remove the non-joined observations with NA for dist_num as well as remove the geometry column from the output.

  12. 12.

    Given the size of the code snippets, this section’s geo-processing and visualization code is made available at the Github project under spatial-visualizations.

  13. 13.

    To take advantage of the programming capabilities, one needs to have skill in programming in Python.

  14. 14.

    To download the GeoDa tool, visit https://geodacenter.github.io/.

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Correspondence to Jeffrey C. Chen .

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Chen, J.C., Rubin, E.A., Cornwall, G.J. (2021). Spatial Data. In: Data Science for Public Policy. Springer Series in the Data Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-71352-2_12

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