National_Local_Child_Allowance_Mapping_CA.py

# -*- coding: utf-8 -*-
"""National_Local_Child_Allowance_Mapping_colab-CA.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1OUQEeExzQLRygV-PkpznU9Bj9O0TdAnY
"""

# Commented out IPython magic to ensure Python compatibility.
# %%time
#
# # Important library for many geopython libraries
# !apt install gdal-bin python-gdal python3-gdal
# # Install rtree - Geopandas requirment
# !apt install python3-rtree
# # Install Geopandas
# !pip install git+git://github.com/geopandas/geopandas.git
# # Install descartes - Geopandas requirment
# !pip install descartes
# # Install Folium for Geographic data visualization
# !pip install folium
# # Install plotlyExpress
# !pip install plotly_express

# pip install -U plotly

# pip install git+https://github.com/UBICenter/ubicenter.py

import pandas as pd
import numpy as np
import geopandas as gpd
from shapely.geometry import Point
import folium
import plotly_express as px
from urllib.request import urlopen
import json
from matplotlib.colors import LightSource
from geojson_rewind import rewind
import plotly.io as pio
import ubicenter

##Lower Chamber for CA
assembly = gpd.read_file("cb_2020_us_sldl_500k/cb_2020_us_sldl_500k.shp")
print(assembly.columns)
assembly = assembly[(assembly.STATEFP != "72")]
assembly["st_and_dist"] = assembly["STATEFP"] + assembly["SLDLST"]
##Upper Chamber for CA
senate = gpd.read_file("cb_2020_us_sldu_500k/cb_2020_us_sldu_500k.shp")
senate.columns
senate = senate[(senate.STATEFP != "72") & (senate.STATEFP != "11")]
senate["st_and_dist"] = senate["STATEFP"] + senate["SLDUST"]
assembly_pov = pd.read_csv("pov_all_lower_leg_district.csv")
assembly_pov.rename(
    columns={"lower_leg_district": "SLDLST", "st": "STATEFP"}, inplace=True
)
assembly_pov["STATEFP"] = assembly_pov["STATEFP"].astype(int)
assembly_pov["st_and_dist"] = assembly_pov["STATEFP"].astype(str).str.zfill(
    2
) + assembly_pov["SLDLST"].astype(str)
assembly["in_both"] = assembly.st_and_dist.isin(assembly_pov.st_and_dist)
assembly[(assembly["in_both"] == False)]
assembly_clean = assembly[(assembly["in_both"] == True)]
senate_pov = pd.read_csv("pov_all_upper_leg_district.csv")
senate_pov.rename(
    columns={"upper_leg_district": "SLDUST", "st": "STATEFP"}, inplace=True
)
senate_pov["STATEFP"] = senate_pov["STATEFP"].astype(int)
senate_pov["st_and_dist"] = senate_pov["STATEFP"].astype(str).str.zfill(2) + senate_pov[
    "SLDUST"
].astype(str)
senate["in_both"] = senate.st_and_dist.isin(senate_pov.st_and_dist)
senate_clean = senate[(senate.in_both == True)]
# put mapping and poverty rates in same order so they they having matching index numbers
assembly_clean = assembly_clean.sort_values("st_and_dist")
assembly_pov = assembly_pov.sort_values("st_and_dist")
senate_clean = senate_clean.sort_values("st_and_dist")
senate_pov = senate_pov.sort_values("st_and_dist")
# index for mapping
assembly_clean["ind"] = assembly_clean.reset_index().index
assembly_clean["ind"] = assembly_clean["ind"] + 1
assembly_pov["ind"] = assembly_pov.reset_index().index
assembly_pov["ind"] = assembly_pov["ind"] + 1
senate_clean["ind"] = senate_clean.reset_index().index
senate_clean["ind"] = senate_clean["ind"] + 1
senate_pov["ind"] = senate_pov.reset_index().index
senate_pov["ind"] = senate_pov["ind"] + 1
# make GEOJSON file
assembly_clean.to_file("GeoJSON_file", driver="GeoJSON")
with open("GeoJSON_file") as geofile:
    j_file_assembly = json.load(geofile)
senate_clean.to_file("GeoJSON_file1", driver="GeoJSON")
with open("GeoJSON_file1") as geofile:
    j_file_senate = json.load(geofile)
# id in GEOJSON
i = 1
for feature in j_file_assembly["features"]:
    feature["id"] = str(i)
    i += 1
i = 1
for feature in j_file_senate["features"]:
    feature["id"] = str(i)
    i += 1
assembly_pov["pct_change_100"] = (assembly_pov["pct_chg"] * -100).round(1)
assembly_pov["poverty_base_100"] = (assembly_pov["poverty_base"] * 100).round(1)
assembly_pov["poverty_reform_100"] = (assembly_pov["poverty_reform"] * 100).round(1)
assembly_pov["child_pct_change_100"] = (assembly_pov["child_pct_chg"] * -100).round(1)
assembly_pov["child_poverty_base_100"] = (
    assembly_pov["child_poverty_base"] * 100
).round(1)
assembly_pov["child_poverty_reform_100"] = (
    assembly_pov["child_poverty_reform"] * 100
).round(1)
assembly_pov["assembly_string"] = assembly_pov.SLDLST.astype(str)
senate_pov["pct_change_100"] = (senate_pov["pct_chg"] * -100).round(1)
senate_pov["poverty_base_100"] = (senate_pov["poverty_base"] * 100).round(1)
senate_pov["poverty_reform_100"] = (senate_pov["poverty_reform"] * 100).round(1)
senate_pov["child_pct_change_100"] = (senate_pov["child_pct_chg"] * -100).round(1)
senate_pov["child_poverty_base_100"] = (senate_pov["child_poverty_base"] * 100).round(1)
senate_pov["child_poverty_reform_100"] = (
    senate_pov["child_poverty_reform"] * 100
).round(1)
senate_pov["senate_string"] = senate_pov.SLDUST.astype(str)
state_names = pd.read_csv("us_state_fips.csv")
state_names["st"] = state_names[" st"]
assembly_pov.rename(columns={"STATEFP": "st"}, inplace=True)
assembly_pov_names = assembly_pov.merge(state_names, on="st")
senate_pov.rename(columns={"STATEFP": "st"}, inplace=True)
senate_pov_names = senate_pov.merge(state_names, on="st")
title_str_1 = "Change in child poverty from a $100 child allowance"
title_str_2 = "By Lower Legislative District"
title_str_3 = "By Upper Legislative District"
title_str_1_CA = "Change in child poverty from a $100 child allowance in California"
title_str_2_CA = "By Lower Legislative District"
title_str_3_CA = "By Upper Legislative District"
pov_child_assembly_map = px.choropleth(
    assembly_pov_names,
    color="child_pct_change_100",
    geojson=j_file_assembly,
    locations="ind",
    labels={
        "child_pct_change_100": "% change in child poverty",
        "child_poverty_base_100": "Base child poverty rate",
        "child_poverty_reform_100": "Reform child poverty rate",
        "SLDLST": "Assembly District Number",
    },
    color_continuous_scale="blues_r",
    scope="usa",
    hover_data={
        "child_pct_change_100",
        "child_poverty_base_100",
        "child_poverty_reform_100",
    },
)
pov_child_assembly_map.update_layout(hoverlabel=dict(bgcolor="white", font_size=16,))
pov_child_assembly_map.update_layout(
    title_text=title_str_1 + "<br>" + title_str_2, title_x=0.225
)
pov_child_assembly_map.update_traces(
    hovertemplate="<b>"
    + assembly_pov_names.stname
    + " "
    + assembly_pov_names.assembly_string
    + "</b>"
    + "<br>"
    + "Change in child poverty: "
    + assembly_pov_names.child_pct_change_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty base: "
    + assembly_pov_names.child_poverty_base_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty reform: "
    + assembly_pov_names.child_poverty_reform_100.astype(str)
    + "%"
)
pov_child_assembly_map.update(layout_coloraxis_showscale=False)
pov_child_assembly_map = ubicenter.format_fig(pov_child_assembly_map, show=False)
pov_child_assembly_map.write_html("lower_district.html")
assembly_pov_CA = assembly_pov_names[(assembly_pov_names.st == 6)]
pov_child_assembly_map_CA = px.choropleth(
    assembly_pov_CA,
    color="child_pct_change_100",
    geojson=j_file_assembly,
    locations="ind",
    labels={
        "child_pct_change_100": "% change in child poverty",
        "child_poverty_base_100": "Base child poverty rate",
        "child_poverty_reform_100": "Reform child poverty rate",
        "SLDLST": "Assembly District Number",
    },
    color_continuous_scale="blues_r",
    scope="usa",
    hover_data={
        "child_pct_change_100",
        "child_poverty_base_100",
        "child_poverty_reform_100",
    },
)
pov_child_assembly_map_CA.update_layout(
    hoverlabel=dict(bgcolor="white", font_size=16,),
    title_text=title_str_1_CA + "<br>" + title_str_2_CA,
    title_x=0.225,
)
pov_child_assembly_map_CA.update_traces(
    hovertemplate="<b>"
    + assembly_pov_CA.assembly_string
    + "</b>"
    + "<br>"
    + "Change in child poverty: "
    + assembly_pov_CA.child_pct_change_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty base: "
    + assembly_pov_CA.child_poverty_base_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty reform: "
    + assembly_pov_CA.child_poverty_reform_100.astype(str)
    + "%"
)
pov_child_assembly_map_CA.update(layout_coloraxis_showscale=False)
pov_child_assembly_map_CA.update_geos(fitbounds="locations", visible=False)
pov_child_assembly_map_CA = ubicenter.format_fig(pov_child_assembly_map_CA, show=False)
pov_child_assembly_map_CA.write_html("lower_district_CA.html")
pov_child_senate_map = px.choropleth(
    senate_pov_names,
    color="child_pct_change_100",
    geojson=j_file_senate,
    locations="ind",
    labels={
        "child_pct_change_100": "% change in child poverty",
        "child_poverty_base_100": "Base child poverty rate",
        "child_poverty_reform_100": "Reform child poverty rate",
        "SLDUST": "Senate District Number",
    },
    color_continuous_scale="blues_r",
    scope="usa",
    hover_data={
        "child_pct_change_100",
        "child_poverty_base_100",
        "child_poverty_reform_100",
    },
)
pov_child_senate_map.update_layout(
    hoverlabel=dict(bgcolor="white", font_size=16,),
    title_text=title_str_1 + "<br>" + title_str_3,
    title_x=0.225,
)
pov_child_senate_map.update_traces(
    hovertemplate="<b>"
    + senate_pov_names.stname
    + " "
    + senate_pov_names.senate_string
    + "</b>"
    + "<br>"
    + "Change in child poverty: "
    + senate_pov_names.child_pct_change_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty base: "
    + senate_pov_names.child_poverty_base_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty reform: "
    + senate_pov_names.child_poverty_reform_100.astype(str)
    + "%"
)
pov_child_senate_map.update(layout_coloraxis_showscale=False)
pov_child_senate_map = ubicenter.format_fig(pov_child_senate_map, show=False)
pov_child_senate_map.write_html("upper_district.html")
senate_pov_CA = senate_pov_names[(senate_pov_names.st == 6)]
pov_child_senate_map_CA = px.choropleth(
    senate_pov_CA,
    color="child_pct_change_100",
    geojson=j_file_senate,
    locations="ind",
    labels={
        "child_pct_change_100": "% change in child poverty",
        "child_poverty_base_100": "Base child poverty rate",
        "child_poverty_reform_100": "Reform child poverty rate",
        "SLDUST": "Senate District Number",
    },
    color_continuous_scale="blues_r",
    scope="usa",
    hover_data={
        "child_pct_change_100",
        "child_poverty_base_100",
        "child_poverty_reform_100",
    },
)
pov_child_senate_map_CA.update_layout(
    hoverlabel=dict(bgcolor="white", font_size=16,),
    title_text=title_str_1_CA + "<br>" + title_str_3_CA,
    title_x=0.225,
)
pov_child_senate_map_CA.update_traces(
    hovertemplate="<b>"
    + senate_pov_CA.stname
    + " "
    + senate_pov_CA.senate_string
    + "</b>"
    + "<br>"
    + "Change in child poverty: "
    + senate_pov_CA.child_pct_change_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty base: "
    + senate_pov_CA.child_poverty_base_100.astype(str)
    + "%"
    + "<br>"
    + "Child poverty reform: "
    + senate_pov_CA.child_poverty_reform_100.astype(str)
    + "%"
)
pov_child_senate_map_CA.update(layout_coloraxis_showscale=False)
pov_child_senate_map_CA = ubicenter.format_fig(pov_child_senate_map_CA, show=False)
pov_child_senate_map_CA.write_html("upper_district_CA.html")