From bb9d19c44a8e73f50eb2531c7438dab0d715d060 Mon Sep 17 00:00:00 2001
From: suobset
Date: Sun, 19 Nov 2023 20:44:26 -0500
Subject: [PATCH] Push prelimnary version of map + test CSS
---
folium_map_with_plotly_choropleth.html | 0
index.html | 1 +
leaflet.ipynb | 0
leaflet.py | 75 +++--------
map.html | 178 +++++++++++++++++++++++++
styles.css | 30 +++++
6 files changed, 231 insertions(+), 53 deletions(-)
create mode 100644 folium_map_with_plotly_choropleth.html
create mode 100644 leaflet.ipynb
create mode 100644 map.html
create mode 100644 styles.css
diff --git a/folium_map_with_plotly_choropleth.html b/folium_map_with_plotly_choropleth.html
new file mode 100644
index 0000000..e69de29
diff --git a/index.html b/index.html
index 50473e3..b579b6e 100644
--- a/index.html
+++ b/index.html
@@ -27,6 +27,7 @@
+
diff --git a/leaflet.ipynb b/leaflet.ipynb
new file mode 100644
index 0000000..e69de29
diff --git a/leaflet.py b/leaflet.py
index 83326ef..dfbee73 100644
--- a/leaflet.py
+++ b/leaflet.py
@@ -1,66 +1,35 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Nov 19 18:59:36 2023
-
-@author: kushd
-"""
-
-import pandas as pd
import folium
-from geopy.geocoders import Nominatim
-from geopy.exc import GeocoderTimedOut
-from folium import plugins
-from folium.plugins import HeatMap
-import geopandas as gpd
-from shapely.geometry import Point
-
-data1 = pd.read_csv("./data/counties-age.csv")
-
-def geocode_address(address):
- geolocator = Nominatim(user_agent="my_geocoder")
- try:
- location = geolocator.geocode(address)
- if location:
- return location.latitude, location.longitude
- else:
- return None
- except GeocoderTimedOut:
- return geocode_address(address)
-
-data1["Location"] = data1["NAMELSAD"] + ", " + data1["STUSPS"]
-data1["Latitude"], data1["Longitude"] = zip(*data1["Location"].apply(geocode_address).dropna())
-
-# Geocode the county names to get coordinates
-data1["geometry"] = data1["Location"].apply(geocode_address).apply(Point)
-gdf = gpd.GeoDataFrame(data1, geometry="geometry")
-
-# Download U.S. counties shapefile from the Census Bureau
-counties_shapefile_url = "https://www2.census.gov/geo/tiger/GENZ2021/shp/cb_2021_us_county_20m.zip"
-counties_gdf = gpd.read_file(counties_shapefile_url)
+import pandas as pd
+from urllib.request import urlopen
+import json
-# Merge the county geometries with your data based on county names
-merged_gdf = pd.merge(counties_gdf, gdf, left_on="NAME", right_on="Location", how="inner")
+# Load GeoJSON data
+with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
+ counties = json.load(response)
-# Save the GeoJSON file
-merged_gdf.to_file("us-counties.json", driver="GeoJSON")
+# Load unemployment data
+df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
+ dtype={"fips": str})
-m = folium.Map(location=[center_lat, center_lon], zoom_start=6)
+# Create a Folium map centered at a location of your choice (e.g., USA center)
+map_center = [37.0902, -95.7129]
+m = folium.Map(location=map_center, zoom_start=4)
-# Choropleth map
+# Add choropleth layer to the map with blue to red color scale
folium.Choropleth(
- geo_data='us-counties.json', # Replace with the path to a US counties GeoJSON file
+ geo_data=counties,
name='choropleth',
- data=data1,
- columns=['Location', 'Population Age 65 and Older'],
- key_on='feature.properties.NAME', # Use the county name as the key
- fill_color='YlGnBu',
+ data=df,
+ columns=['fips', 'unemp'],
+ key_on='feature.id',
+ fill_color='RdBu',
fill_opacity=0.7,
line_opacity=0.2,
- legend_name='Population Age 65 and Older',
+ legend_name='Unemployment Rate (%)'
).add_to(m)
-# Display the map
+# Add layer control to toggle visibility
folium.LayerControl().add_to(m)
-# Save the map
-m.save("choropleth_map.html")
\ No newline at end of file
+# Save the map as an HTML file
+m.save('./map.html')
diff --git a/map.html b/map.html
new file mode 100644
index 0000000..8e8a1a8
--- /dev/null
+++ b/map.html
@@ -0,0 +1,178 @@
+
+
+