Push prelimnary version of map + test CSS

index.html

@@ -27,6 +27,7 @@
         <div id="leaflet-map" alt="leaflet-map">
             <!-- placeholder img for design purposes-->
 	    <!-- <img src="images/map.png"> -->
+            <iframe src="./map.html"></iframe>
         </div> 
         <script src="main.js"></script>
     </body>

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")
+# Save the map as an HTML file
+m.save('./map.html')