Energy Justice Analysis of Climate Data with ClimRR

Sam Foreman

Chicago Analysis

August 1, 2023

Imports

%matplotlib inline
import matplotlib_inline
import matplotlib.pyplot as plt
import geopandas as gpd
import warnings

import matplotlib.pyplot as plt

# from enrich.console import Console, get_theme
matplotlib_inline.backend_inline.set_matplotlib_formats('svg')
from ClimRR import get_logger, set_plot_style
from ClimRR.data import DATA_DIR
set_plot_style()
log = get_logger('ClimRR')
from rich.console import Console as rConsole
from enrich.style import STYLES
from rich.theme import Theme

theme = Theme(STYLES)
log = get_logger('ClimRR')
console = rConsole(theme=theme, log_path=False, markup=True)

Using updated plot style for matplotlib

from ClimRR.data import load_shapefile, load_csvs

shape = load_shapefile()
data = load_csvs(shape)

data['FireWeatherIndex_Wildfire'].shape=(62834, 35)

data['HeatingDegreeDays'].shape=(62834, 10)

data['AnnualTemperatureMinimum'].shape=(62834, 18)

data['SeasonalTemperatureMaximum'].shape=(62834, 27)

data['ConsecutiveDayswithNoPrecipitation'].shape=(55896, 19)

data['SeasonalTemperatureMinimum'].shape=(62834, 27)

data['WindSpeed'].shape=(62834, 18)

data['AnnualTemperatureMaximum'].shape=(62834, 18)

data['Precipitation_inches_AnnualTotal'].shape=(55896, 18)

square = shape[shape["Crossmodel"] == 'R146C497']
fig, ax = plt.subplots(figsize=(4, 3))
ax = square.boundary.plot(ax=ax)
ax.set_axis_off()
plt.tight_layout()

import geopandas as gpd
import geodatasets
chipop = gpd.read_file(
    geodatasets.get_path('geoda.chicago_commpop')
).to_crs(square.crs)
chihealth = gpd.read_file(
    geodatasets.get_path('geoda.chicago_health')
).to_crs(square.crs)
chigroc = gpd.read_file(
    geodatasets.get_path('geoda.groceries')
).to_crs(square.crs)

We can inspect this data, looking at the chipop.boundary for example

chipop['boundary'] = chipop.boundary

fig, ax = plt.subplots(figsize=(10, 7))
ax = chipop.boundary.plot(linewidth=0.8, color='#838383', ax=ax)
ax.set_axis_off()
_ = ax.set_title('Chicago Neighborhoods')

Which we can use to plot the population (by neighborhood, in this case):

fig, ax = plt.subplots(figsize=(10, 7))
ax = chipop.to_crs(square.crs).plot(column="POP2010", legend=True, ax=ax)
ax.set_axis_off()
_ = ax.set_title(f"Chicago population by Neighborhood [2010]")

wtown = chipop[chipop["community"] == 'WEST TOWN']
humboldt = chipop[chipop["community"] == 'HUMBOLDT PARK']

humboldt.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

fig, ax = plt.subplots(figsize=(10, 7))
ax = humboldt.overlay(shape, how='intersection').plot(ax=ax, legend=True)
ax = (
    hp := chipop[chipop['community'] == 'HUMBOLDT PARK'].overlay(
        shape,
        how='intersection'
    )
).plot(ax=ax, legend=True)
ax = (
    lp := chipop[chipop['community'] == 'LINCOLN PARK'].overlay(
        shape,
        how='intersection'
    )
).plot(ax=ax, legend=True)
ax = chipop.boundary.plot(ax=ax, color='#666666', linewidth=0.8)
ax = lp.boundary.plot(color='red', ax=ax)
ax = hp.boundary.plot(color='red', ax=ax)
ax.set_axis_off()
_ = ax.set_title('Intersection of Humboldt Park & ClimRR data')

fig, ax = plt.subplots(figsize=(10, 7))
chiwind = data['WindSpeed'].overlay(
    chipop,
    how='intersection'
).overlay(chipop, how='union')
ax = chiwind.boundary.plot(ax=ax, color='#666666', linewidth=0.8)
ax.set_axis_off()

_, ax = plt.subplots(figsize=(10, 7))
ax = chipop.boundary.plot(color='#666666', linewidth=0.8, ax=ax, alpha=0.2)
ax = chiwind.plot(column='hist', ax=ax, legend=True)
ax.set_axis_off()
ax.set_title('Historical Wind Data across Chicago Neighborhoods')
plt.tight_layout()

chiwind.explore(column='hist')

Make this Notebook Trusted to load map: File -> Trust Notebook

_, ax = plt.subplots()
ax = chiwind.plot(column='hist', scheme='quantiles', k=8, ax=ax)
_ = ax.set_title('WindSpeed, historical')
ax.set_axis_off()
plt.tight_layout()

fig, ax = plt.subplots()
ax = chiwind.plot(column='rcp45_midc', scheme='quantiles', k=3, ax=ax)
ax.set_axis_off()
_ = ax.set_title('WindSpeed, Mid-Century [RCP45]')
plt.tight_layout()

fig, ax = plt.subplots()
ax = chiwind.plot(column='rcp45_endc', scheme='quantiles', k=3, ax=ax)
_ = ax.set_title('WindSpeed, End-Century [RCP45]')
plt.tight_layout()

fig, ax = plt.subplots(ncols=3, figsize=(16, 7))
ax0 = chiwind.plot('hist', ax=ax[0])
ax1 = chiwind.plot('rcp45_midc', ax=ax[1])
ax2 = chiwind.plot('rcp45_midc', ax=ax[2])
ax0.set_axis_off()
ax1.set_axis_off()
ax2.set_axis_off()

data['WindSpeed'].shape

(62834, 18)

selection = shape[0:5]

for index, row in selection.iterrows():
    # get the area of the polygon
    poly_area = row['geometry'].area
    console.print(f"Polygon area at {index} is {poly_area:.3f}")

Polygon area at 0 is 252927293.657

Polygon area at 1 is 235501313.715

Polygon area at 2 is 233416379.950

Polygon area at 3 is 261761834.191

Polygon area at 4 is 226073092.218

Citation

BibTeX citation:

@online{foreman2023,
  author = {Foreman, Sam},
  title = {Energy {Justice} {Analysis} of {Climate} {Data} with
    {ClimRR}},
  date = {2023-08-01},
  url = {https://saforem2.github.io/climate-analysis},
  langid = {en}
}

For attribution, please cite this work as:

Foreman, Sam. 2023. “Energy Justice Analysis of Climate Data with ClimRR.” Intro to HPC Bootcamp @ NERSC. August 1, 2023. https://saforem2.github.io/climate-analysis.