Extreme Winter Storms: Exploring the Impacts of Socioeconomic Inequalities in a Changing Climate

Contents

This repository (knitted HTML linked here: houston_extreme_storms) contains an analysis of blackout impacts during the Great Texas Freeze of 2021, evaluating socioeconomic disparities and spatial patterns of recovery across the Houston metropolitan area.

Techniques

This repository showcases a range of advanced technical skills and analytical methodologies, including:

1. Data Management

   • Wrangling and tidying data using tidyverse, here, and janitor to ensure clean, reproducible workflows (Wickham et al. 2019; Müller 2020; Firke 2023)

   • Visualizations with ggplot2 and tmap (Wickham 2016; Tennekes 2018)

   • Implementation of organized folder structures and detailed documentation to facilitate reproducibility and collaborative potential

   • Integration of error handling and validation checks to maintain data integrity, including conditional statements for spatial and statistical consistency

   • Development of generalizable functions to allow for reproducibility and flexibility in analyses

2. Geospatial Analysis

   • Manipulating spatial data with terra and sf, including advanced spatial operations such as intersections, raster manipulation, and spatial weighting (Hijmans 2024; Pebesma 2018)

   • Creating dynamic and interactive visualizations using tmap to explore and communicate geospatial data insights (Tennekes 2018)

   • Leveraging geospatial overlays and zonal statistics to assess the relationship between socioeconomic variables and blackout classifications

   • Standardizing spatial resolution and alignment across diverse datasets to ensure consistency in geospatial analyses

3. Analysis of Socioeconomic Disparities

   • Integrating census tract-level socioeconomic data to explore disparities in blackout status following extreme weather events

   • Employing spatially weighted methods to classify blackout status across multiple scales, from individual homes to census tracts

   • Comparing results from different classification approaches (e.g., individual home versus census tract level) to identify methodological differences and their implications for socioeconomic interpretations

Data

• The first dataset contains remote sensing images from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi satellite, which captures pre-storm and post-storm light intensity changes to evaluate blackout statuses spatially and temporally (NASA LAADS DAAC 2023).

• The second dataset is Open Street Map (OSM) data for home-level resolution, including road networks and infrastructure to account for essential services and accessibility (OpenStreetMap contributors 2023). This contains two main datasets:

  1. Roads:
     • Highways account for a large portion of the night lights observable from space, so to minimize falsely classifying areas that regularly experience reduced traffic levels as having experienced a blackout, we will ignore residential buildings that are within 200m of highways.
  2. Homes:
     • The OSM data contains information for all the buildings within the area, but we are only interested in considering residential buildings since we will be assigning socioeconomic status to these features based on census data.

• The final dataset contains 2019 census tract data collected by the US Census Bureau’s American Community Survey for the Houston metropolis. Census tract geometries were obtained from shapefiles, enabling spatial overlays and intersection analyses with blackout zones (US Census Bureau 2023).

Repository Structure

      .
      ├── README.md
      ├── README.rmd
      ├── bibs
      │   ├── combined.bib
      │   ├── packages.bib
      │   └── references.bib
      ├── code
      │   ├── custom.css
      │   ├── houston_extreme_storms.html
      │   └── houston_extreme_storms.qmd
      ├── data
      │   ├── ACS_2019_5YR_TRACT_48_TEXAS.gdb
      │   │   ├── a00000001.TablesByName.atx
      │   │   ├── a00000001.gdbindexes
      │   │   ├── a00000001.gdbtable
      │   │   ├── a00000001.gdbtablx
      │   │   └── ...
      │   ├── OSM
      │   │   ├── gis_osm_buildings_a_free_1.gpkg
      │   │   └── gis_osm_roads_free_1.gpkg
      │   └── VNP46A1
      │       ├── VNP46A1.A2021038.h08v05.001.2021039064328.tif
      │       ├── VNP46A1.A2021038.h08v06.001.2021039064329.tif
      │       └── ...
      ├── figures
      │   ├── animations
      │   │   └── nightlight_intensity_animation.gif
      │   ├── density_plots
      │   │   ├── houston_final_density_plot.png
      │   │   └── ...
      │   ├── maps
      │   │   ├── NL_02072021_crop_map.png
      │   │   ├── homes_interactive_map.html
      │   │   ├── lib
      │   │   │   ├── Proj4Leaflet-1.0.1
      │   │   │   │   └── proj4leaflet.js
      │   │   │   └── ...
      │   │   └── tracts_interactive_map.html
      │   └── tables
      │       ├── blackout_summary_kable.html
      │       ├── combined_table_kable.html
      │       └── ...
      ├── houston-storms.Rproj
      └── structure.txt

References

Firke, Sam. 2023. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://CRAN.R-project.org/package=janitor.

Hijmans, Robert J. 2024. Terra: Spatial Data Analysis. https://CRAN.R-project.org/package=terra.

Müller, Kirill. 2020. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.

NASA LAADS DAAC. 2023. “VIIRS: Missions and Measurements.” https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/.

OpenStreetMap contributors. 2023. “OpenStreetMap.” https://www.openstreetmap.org/#map=4/38.01/-95.84.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439–46. https://doi.org/10.32614/RJ-2018-009.

Tennekes, Martijn. 2018. “tmap: Thematic Maps in R.” Journal of Statistical Software 84 (6): 1–39. https://doi.org/10.18637/jss.v084.i06.

US Census Bureau. 2023. “American Community Survey (ACS).” https://www.census.gov/programs-surveys/acs.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.