Project: Tracking Solar Power Usage around California¶
I would like to see the types of Solar Power installations and its accessibility throughout California, as renewable energy is becoming more and more prevalent. By visualizing this data, I aim to find trends and patterns in solar power installations that would help future infrastructure planning, solar energy and/or renewable energy policies, and strategies for increasing renewable energy usage as well.
The data sets I will be using are from the California Open Data Portal:
- https://data.ca.gov/dataset/solar-footprints-in-california/resource/85b0afc5-f75c-4805-9f8e-7cc98113d244 (solar footprint csv)
- https://gis.data.ca.gov/datasets/ce721c35ab7e4e4b89ef2080b4c331f6_0/explore?location=36.693449%2C-119.273117%2C5.98 (california counties shape file, taken from California Geo data portal, adjacent to the Open Data Portal)
In [5]:
%matplotlib inline
import os
os.environ["PROJ_LIB"] = r'/opt/conda/pkgs/proj4-5.2.0-he1b5a44_1006/share/proj/'
import numpy as np
import mpl_toolkits
import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import json
import folium
import mplleaflet
%matplotlib inline
In [6]:
#initial data to process
ca_counties = gpd.read_file('counties/California_Counties.shp') #use geopandas to read shapefile
print(ca_counties.head(5)) #extract and print the first five entries
solar_footprints = pd.read_csv('Solar_Footprints_V2_5065925295652909767.csv') #tracks california solar power usage + dev
In [7]:
#map install types to numbers to make analysis and plotting easier
install_type_numeric = {
'Rooftop': 1,
'Parking': 2,
'Ground': 3,
'None' : 4
}
solar_footprints['Install Type Numeric'] = solar_footprints['Install Type'].map(install_type_numeric)
#density of solar installations based on county in california
#isolate counties for solar footprints data
county_counts = solar_footprints['County'].value_counts().reset_index() #need to reset index according to value counts
county_counts.columns = ['County', 'Installation_Count']
count_data = ca_counties.merge(county_counts, left_on='NAME', right_on='County', how='left') #merge geo data with installation data
count_data['Installation_Count'] = count_data['Installation_Count'].fillna(0) #this is to makesure even the counties w/o data show up on the map
fig, ax = plt.subplots(1, 1, figsize=(15, 10))
count_data.plot(column='Installation_Count', ax=ax, legend=True, cmap='YlOrRd', edgecolor='0.8', linewidth=0.8,
legend_kwds={'label': 'Number of Installations'}) #plot count data with a heatmap legend to reference
ax.set_title('Solar Installation Density Across California Counties')
plt.show()
In [8]:
#in regards to solar installations:
install_type_counts = solar_footprints['Install Type'].value_counts()
print("Installation Types:")
print(install_type_counts)
#print largest and smallest solar installations
print(f"The county with the largest amount of solar installations is {county_counts['County'][0]} with {county_counts['Installation_Count'][0]} solar installations")
print(f"The county with the smallest amount of solar installations is {county_counts['County'][(len(county_counts)-1)]} with {county_counts['Installation_Count'][(len(county_counts)-1)]} solar installations")
#split into two sets, one for urban and one for rural
urban_data = solar_footprints[solar_footprints['Urban or Rural'] == 'Urban']
rural_data = solar_footprints[solar_footprints['Urban or Rural'] == 'Rural']
#find acres of land data per county
county_acres = solar_footprints.groupby('County')['Acres'].agg(['count','mean','min', 'max', 'sum'])
print(county_acres.sort_values('sum', ascending=False).head(5)[['sum', 'count']])
print("Average distance to electrical substation, Urban areas is", urban_data['Distance to Substation (Miles) CAISO'].mean())
print("Average distance to electrical substation, Rural areas is", rural_data['Distance to Substation (Miles) CAISO'].mean())
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urban_avg = urban_data.groupby('County')['Distance to Substation (Miles) CAISO'].mean() #find the mean distance to each substation per county
rural_avg = rural_data.groupby('County')['Distance to Substation (Miles) CAISO'].mean()
#merge data with geo data
urban_map = ca_counties.merge(urban_avg, left_on='NAME', right_index=True, how='left')
rural_map = ca_counties.merge(rural_avg, left_on='NAME', right_index=True, how='left')
#fill non existent data with 0 to show up on color map
urban_map['Distance to Substation (Miles) CAISO'] = urban_map['Distance to Substation (Miles) CAISO'].fillna(0)
rural_map['Distance to Substation (Miles) CAISO'] = rural_map['Distance to Substation (Miles) CAISO'].fillna(0)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
urban_map.plot(column='Distance to Substation (Miles) CAISO', ax=ax1, legend=True, cmap='YlOrRd', edgecolor='0.8')
ax1.set_title('Average Distance to Substation - Urban Areas')
rural_map.plot(column='Distance to Substation (Miles) CAISO', ax=ax2, legend=True, cmap='YlOrRd', edgecolor='0.8')
ax2.set_title('Average Distance to Substation - Rural Areas')
plt.show()
In [10]:
# type of installation crossed with urban or rural
urban_or_rural = solar_footprints.groupby(['Urban or Rural', 'Install Type']).size().unstack()
#plot stacked bar charts with legend
urban_or_rural.plot(kind='bar', stacked=True, figsize=(10, 6))
plt.title('Comparison of Installation Types used in Urban Vs. Rural Areas')
plt.xlabel('Area Type')
plt.ylabel('Frequency')
plt.legend(title='Installation Type', loc='upper left')
plt.show()
In [13]:
install_type_counts = solar_footprints.groupby(['County', 'Install Type']).size().unstack(fill_value=0)
popular_type = install_type_counts.idxmax(axis=1) #find the max in each county stack
#create a data frame to isolate the county and most popular installation type to use later
pop_type_counties = pd.DataFrame({
'County': popular_type.index,
'Most Popular Type': popular_type.values
})
#merge with geo data
counties_pop = ca_counties.merge(pop_type_counties, left_on='NAME', right_on='County', how='left')
#create a choropleth!
m = folium.Map(location=[37.8, -122], zoom_start=6)
folium.Choropleth(
geo_data=counties_pop,
name="choropleth",
data=solar_footprints,
columns=["County", "Install Type Numeric"], #using numeric install types
key_on="feature.properties.NAME",
fill_color="YlGn",
fill_opacity=0.6,
line_opacity=0.1,
legend_name="Install Types"
).add_to(m)
title_html = f'''
<h3 style="
position: absolute;
top: 20px;
left: 20px;
z-index:100000;
font-family: Arial;
font-size: 18px;
font-weight: bold;
color: #333333;
padding: 10px;
border-radius: 5px;
max-width: 300px;">
{"Average Solar Installation Type in Each California County"}
</h3>'''
m.get_root().html.add_child(folium.Element(title_html))
#style and highlight functions for the interactive pop ups
style_function = lambda x: {
'fillColor': '#ffffff',
'color':'#000000',
'fillOpacity': 0.1,
'weight': 0.1
}
highlight_function = lambda x: {
'fillColor': '#000000',
'color':'#000000',
'fillOpacity': 0.50,
'weight': 0.1
}
#create pop ups that portray county + most popular type when hovered over
folium.GeoJson(
counties_pop,
style_function=style_function,
highlight_function=highlight_function,
tooltip=folium.GeoJsonTooltip(
fields=['NAME', 'Most Popular Type'],
aliases=['County:', 'Most Popular Installation Type:'],
style=("background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;")
),
popup=folium.GeoJsonPopup(
fields=['NAME', 'Most Popular Type'],
aliases=['County:', 'Most Popular Installation Type:'],
style="background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;"
)
).add_to(m)
folium.LayerControl().add_to(m) #add everything to the map
#m
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