Energy Justice Analysis of Climate Data with ClimRR

Sam Foreman

Chicago Temperature Analysis

Sam Foreman

Argonne National Laboratory

August 1, 2023

Target Goals

Repeat this analysis with:
- Different climate variables
- Another state / geographic area
- Another climate scenario
Any interesting trends or insights to be drawn from the data

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.data import DATA_DIR
from ClimRR import get_logger, set_plot_style
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)

console.print("Data: \n" + f"\n".join(list(data.keys())))

Data: 
FireWeatherIndex_Wildfire
HeatingDegreeDays
AnnualTemperatureMinimum
SeasonalTemperatureMaximum
ConsecutiveDayswithNoPrecipitation
SeasonalTemperatureMinimum
WindSpeed
AnnualTemperatureMaximum
Precipitation_inches_AnnualTotal

# counties = gpd.read_file(
#     'https://public.opendatasoft.com/api/explore/v2.1/catalog/datasets/georef-united-states-of-america-county/exports/shp?lang=en&timezone=America%2FChicago'
# )
from ClimRR.data import load_counties
counties = load_counties(crs=shape.crs)

counties.head(n=5)

	year	ste_code	ste_name	coty_code	coty_name	coty_area_	coty_type	coty_name_	coty_fp_co	coty_gnis_	geometry
0	2022	['29']	['Missouri']	['29081']	['Harrison']	USA	county	['Harrison County']	081	00758495	POLYGON ((-10489884.973 4949400.757, -10487772...
1	2022	['29']	['Missouri']	['29099']	['Jefferson']	USA	county	['Jefferson County']	099	00758504	POLYGON ((-10089996.419 4590245.129, -10090028...
2	2022	['29']	['Missouri']	['29145']	['Newton']	USA	county	['Newton County']	145	00758527	POLYGON ((-10470623.473 4445817.195, -10470640...
3	2022	['29']	['Missouri']	['29223']	['Wayne']	USA	county	['Wayne County']	223	00758564	POLYGON ((-10105533.614 4446123.627, -10105492...
4	2022	['30']	['Montana']	['30053']	['Lincoln']	USA	county	['Lincoln County']	053	01720038	POLYGON ((-12771356.786 6274948.609, -12771395...

illinois = counties[counties['ste_name'] == "['Illinois']"]
console.log(f"illinois.shape={illinois.shape}")
illinois.head(n=5)

[07:47:47] illinois.shape=(102, 11)

	year	ste_code	ste_name	coty_code	coty_name	coty_area_	coty_type	coty_name_	coty_fp_co	coty_gnis_	geometry
31	2022	['17']	['Illinois']	['17025']	['Clay']	USA	county	['Clay County']	025	00424214	POLYGON ((-9873318.708 4709448.559, -9873005.6...
32	2022	['17']	['Illinois']	['17065']	['Hamilton']	USA	county	['Hamilton County']	065	00424234	POLYGON ((-9874775.991 4566267.709, -9874777.2...
33	2022	['17']	['Illinois']	['17077']	['Jackson']	USA	county	['Jackson County']	077	00424240	POLYGON ((-9927204.688 4572408.800, -9925575.1...
97	2022	['17']	['Illinois']	['17001']	['Adams']	USA	county	['Adams County']	001	00424202	POLYGON ((-10120274.096 4894041.102, -10120277...
98	2022	['17']	['Illinois']	['17067']	['Hancock']	USA	county	['Hancock County']	067	00424235	POLYGON ((-10120274.096 4894041.102, -10120442...

fig, ax = plt.subplots(figsize=(10, 7))
ax = illinois.boundary.plot(ax=ax, color='#666666', linewidth=0.8)
ax.set_axis_off()

illinois_hdd = data["HeatingDegreeDays"].clip(illinois)
illinois_hdd.head(n=2)

	OBJECTID	Crossmodel	Shape_Leng	Shape_Area	geometry	hist	rcp85_midc	mid85_hist	boundary	centroid
21130	21131	R137C419	61562.240069	2.368692e+08	POLYGON ((-9925519.402 4437348.204, -9922237.2...	5618.240234	5188.899902	-429.347992	LINESTRING (-9910499.397 4433932.370, -9913921...	POINT (-9919714.441 4428138.497)
15883	15884	R137C418	61585.714048	2.370499e+08	MULTIPOLYGON (((-9940550.883 4440740.008, -993...	5668.370117	5196.740234	-471.623993	LINESTRING (-9943924.960 4425714.844, -9940550...	POINT (-9934728.187 4431536.593)

_, ax = plt.subplots(figsize=(10, 7))
ax = illinois_hdd.boundary.plot(color='#666666', linewidth=0.8, ax=ax, alpha=0.2)
ax = illinois_hdd.plot(column='hist', ax=ax, legend=True)
ax.set_axis_off()
ax.set_title('Historical Heating Degree Days across Illinois')
plt.tight_layout()

fig, ax = plt.subplots(ncols=3, figsize=(16, 7), sharey='row')
ax0 = illinois_hdd.plot('hist', ax=ax[0], legend=True)
ax1 = illinois_hdd.plot('rcp85_midc', ax=ax[1], legend=True)
ax2 = illinois_hdd.plot('mid85_hist', ax=ax[2], legend=True)
ax0.set_title("HDD: Historical")
ax1.set_title("HDD: Mid-Century RCP8.5")
ax2.set_title("HDD: (Mid-Century - Historical) RCP8.5")
ax0.set_axis_off()
ax1.set_axis_off()
ax2.set_axis_off()
plt.tight_layout()

_, ax = plt.subplots(figsize=(10, 7))
ax = illinois_hdd.boundary.plot(color='#666666', linewidth=0.8, ax=ax, alpha=0.2)
ax = illinois_hdd.plot(column='rcp85_midc', ax=ax, legend=True)
ax.set_axis_off()
ax.set_title('RCP8.5 Mid-Century Heating Degree Days across Illinois')
plt.tight_layout()

_, ax = plt.subplots(figsize=(10, 7))
ax = illinois_hdd.boundary.plot(color='#666666', linewidth=0.8, ax=ax, alpha=0.2)
ax = illinois_hdd.plot(column='mid85_hist', ax=ax, legend=True)
ax.set_axis_off()
ax.set_title('RCP8.5 End-Century Heating Degree Days across Illinois')
plt.tight_layout()

fig, ax = plt.subplots(figsize=(10, 7))
wind_il = data['WindSpeed'].clip(
    illinois,
    # how='intersection'
)
# ax = wind_il.boundary.plot(ax=ax, color='#666666', linewidth=0.8)
ax = wind_il.plot(column='hist', ax=ax, linewidth=0.8, legend=True)
ax.set_axis_off()

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

# wind_il.explore(column='hist')
wind_il.head(n=2)

	OBJECTID	Crossmodel	Shape_Leng	Shape_Area	geometry	hist	rcp45_midc	rcp45_endc	rcp85_midc	rcp85_endc	mid45_hist	end45_hist	mid85_hist	end85_hist	mid85_45	end85_45	boundary	centroid
21130	21131	R137C419	61562.240069	2.368692e+08	POLYGON ((-9925519.402 4437348.204, -9922237.2...	7.36933	7.40137	7.53169	7.46885	7.43563	0.032043	0.162359	0.099523	0.066305	0.067481	-0.096054	LINESTRING (-9910499.397 4433932.370, -9913921...	POINT (-9919714.441 4428138.497)
15883	15884	R137C418	61585.714048	2.370499e+08	MULTIPOLYGON (((-9940550.883 4440740.008, -993...	7.76390	7.78540	7.90540	7.86042	7.82429	0.021496	0.141499	0.096518	0.060394	0.075023	-0.081105	LINESTRING (-9943924.960 4425714.844, -9940550...	POINT (-9934728.187 4431536.593)

_, ax = plt.subplots()
ax = wind_il.plot(column='hist', ax=ax, legend=True)
_ = ax.set_title('WindSpeed, historical')
ax.set_axis_off()

fig, ax = plt.subplots()
ax = wind_il.plot(column='rcp45_midc', ax=ax, legend=True)
ax.set_axis_off()
_ = ax.set_title('WindSpeed, Mid-Century [RCP45]')

fig, ax = plt.subplots()
_ = ax.set_title('WindSpeed, End-Century [RCP45]')
# _ = ax.legend(loc='best')
ax = wind_il.plot(column='rcp45_endc', ax=ax, legend=True)
ax.set_axis_off()

fig, ax = plt.subplots(ncols=3, figsize=(16, 7))
ax0 = wind_il.plot('hist', ax=ax[0], legend=True)
ax1 = wind_il.plot('rcp45_midc', ax=ax[1], legend=True)
ax2 = wind_il.plot('rcp45_endc', ax=ax[2], legend=True)
ax0.set_title("Wind Speed: Historical")
ax1.set_title("Wind Speed: Mid-Century RCP4.5")
ax2.set_title("Wind Speed: End-Century RCP4.5")
ax0.set_axis_off()
ax1.set_axis_off()
ax2.set_axis_off()
plt.tight_layout()

fig, ax = plt.subplots(ncols=3, figsize=(16, 7))
ax0 = wind_il.plot('hist', ax=ax[0], legend=True)
ax1 = wind_il.plot('rcp85_midc', ax=ax[1], legend=True)
ax2 = wind_il.plot('rcp85_endc', ax=ax[2], legend=True)
ax0.set_title("Wind Speed: Historical")
ax1.set_title("Wind Speed: Mid-Century RCP8.5")
ax2.set_title("Wind Speed: End-Century RCP8.5")
ax0.set_axis_off()
ax1.set_axis_off()
ax2.set_axis_off()
plt.tight_layout()

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.