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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

README.md

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----
-title: Optimal EV Charging Stations
-emoji: 🐨
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Optimal location of EV charging stations
+Display Saarbrucken map using streamlit to showcase optimal location for future EV charging stations.

ev_germany.py

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+import streamlit as st
+import numpy as np
+import json
+import geopandas as gpd
+import pyproj
+import plotly.graph_objs as go
+import folium
+from streamlit_folium import st_folium
+import pandas as pd
+from folium.plugins import LocateControl, MarkerCluster
+from bs4 import BeautifulSoup
+from PIL import Image
+
+
+def load_poi_data(path, target_crs="epsg:3005"):
+    df = pd.read_csv(path)
+    poi_data = gpd.GeoDataFrame(
+        df.loc[:, [c for c in df.columns if c != "geometry"]],
+        geometry=gpd.GeoSeries.from_wkt(df["geometry"]),
+        crs="epsg:3005",  # Assuming the original CRS is EPSG:4326 (geographic CRS)
+    )
+    poi_data = poi_data.to_crs(target_crs)  # Reproject to the target CRS
+    poi_data['geometry_center'] = poi_data['geometry'].centroid
+    return poi_data
+
+def threshold(data):
+    threshold_scale = np.linspace(data.min(),
+                              data.max(),
+                              10, dtype=float)
+   # change the numpy array to a list
+    threshold_scale = threshold_scale.tolist() 
+    threshold_scale[-1] = threshold_scale[-1]
+    return threshold_scale
+
+#https://towardsdatascience.com/creating-interactive-maps-for-instagram-with-python-and-folium-68bc4691d075
+
+# NOTE: This must be the first command in your app, and must be set only once
+st.set_page_config( page_title="Optimal location of EV charging stations",
+    page_icon="https://img.icons8.com/external-phatplus-lineal-color-phatplus/64/external-ev-ev-car-phatplus-lineal-color-phatplus.png",
+    layout="wide")
+
+
+# Power location: https://img.icons8.com/external-tal-revivo-green-tal-revivo/100/external-power-location-on-map-for-quick-ev-charge-battery-green-tal-revivo.png
+# https://img.icons8.com/external-phatplus-lineal-color-phatplus/64/external-ev-ev-car-phatplus-lineal-color-phatplus.png", width=64
+# logo_url = "https://img.icons8.com/external-others-phat-plus/64/external-electric-electric-vehicles-color-line-others-phat-plus-14.png"
+logo_url = "https://img.icons8.com/external-phatplus-lineal-color-phatplus/64/external-ev-ev-car-phatplus-lineal-color-phatplus.png"
+st.image(logo_url, width=64)
+st.header("Optimal placement of EV charging stations in Saarbrücken, Germany")
+# st.write("[![Star](<https://img.shields.io/github/stars/><username>/<repo>.svg?logo=github&style=social)](<https://gitHub.com/><username>/<repo>)")
+st.markdown("Welcome to our mini-project.....")
+
+# col1, col2 = st.columns(2)
+
+# with col1:
+# Sidebar
+with st.expander("Select options to view"):
+    population_chk = st.checkbox(
+    "Population density"
+    )
+    parking_check = st.checkbox(
+    "Parking"
+    )
+    parkingSpaces_check = st.checkbox(
+    "Parking spaces"
+    )
+    restaurant_chk = st.checkbox(
+    "Restaurant"
+    )
+    residential_Chk = st.checkbox(
+    "Residential"
+    )
+    ev_stations_chk = st.checkbox(
+    "EV Charging Stations"
+    )
+
+
+
+
+    
+# Get cleaned data
+df_ev_data = pd.read_csv('Data/cleaned_ev_data_germany.csv')
+df_saarbrucken = df_ev_data[df_ev_data['City'] == 'Saarbrücken']
+
+# # Multiselect
+# cols = df_münchen['Operator'].unique()
+# st_ms = st.multiselect("Filter data by operators", cols)
+# mask_countries = df_münchen['Operator'].isin(st_ms)
+# st.write('You selected:', df_münchen[mask_countries])
+
+
+# create a map object of the city of München
+saarbrucken_map_markers = folium.Map(location=[49.24015720000001, 6.996932700000002])
+folium.Marker(
+    location=[49.24015720000001, 6.996932700000002],
+    popup="Saarbrucken",
+    icon=folium.Icon(color="red",icon="glyphicon glyphicon-pushpin"),
+).add_to(saarbrucken_map_markers)
+
+
+
+for  cp,type, lat, lng in zip( df_saarbrucken['Number of Charging Points'],df_saarbrucken['Charging Station Type'],df_saarbrucken['Latitude'], df_saarbrucken['Longitude']):
+    # ev_img = 'https://img.icons8.com/external-tal-revivo-color-tal-revivo/96/external-power-location-on-map-for-quick-ev-charge-battery-color-tal-revivo.png'
+    # ev_img = "https://img.icons8.com/external-icongeek26-linear-colour-icongeek26/64/external-EV-Power-Tank-ev-station-icongeek26-linear-colour-icongeek26.png"
+    ev_img ='https://img.icons8.com/external-tal-revivo-filled-tal-revivo/96/external-power-location-on-map-for-quick-ev-charge-battery-filled-tal-revivo.png'
+    # Create custom icon with ev icon image
+    custom_icon = folium.CustomIcon(ev_img, icon_size=(20, 20), popup_anchor=(0, -22))
+
+    # Define html inside marker pop-up
+    ev_html = folium.Html(f"""<p style="text-align: center;"><span style="font-family: ariel; font-size: 16px;"><b>Number of charging points:</b> {cp} <br> <b>Charging Station Type:</b> {type}</span></p>
+    <p style="text-align: center;">
+    """, script=True)
+    # Create pop-up with html content
+    popup = folium.Popup(ev_html, max_width=700)
+    
+    custom_marker = folium.Marker(location=[lat, lng], icon=custom_icon, popup=popup)
+    custom_marker.add_to(saarbrucken_map_markers)
+    # folium.CircleMarker(
+    #         [lat, lng],
+    #         radius=4,
+    #         color='purple',
+    #         opacity=0.4,
+    #         fill=True,
+    #         fill_color='blue').add_to(saarbrucken_map_markers)
+
+# Enable geolocation button on map.
+LocateControl(auto_start=False).add_to(saarbrucken_map_markers)
+
+# Get Curated data
+df_poi_data= load_poi_data('Data/all_city_data_with_pop.csv')
+df_saarbrucken_poi = df_poi_data[df_poi_data['city'] == 'Saarbrucken']
+
+#Plot centroids:
+df_saarbrucken_poi["latitude"]=df_saarbrucken_poi["geometry_center"].get_coordinates()["y"]
+df_saarbrucken_poi["longitude"]=df_saarbrucken_poi["geometry_center"].get_coordinates()["x"]
+
+for lat, lng, parking, park_space, restaurant,residential,evs in zip(df_saarbrucken_poi.latitude, 
+                            df_saarbrucken_poi.longitude,
+                            df_saarbrucken_poi.parking,
+                            df_saarbrucken_poi.parking_space,
+                            df_saarbrucken_poi.restaurant,
+                            df_saarbrucken_poi.residential,
+                            df_saarbrucken_poi.EV_stations):
+    park_icon = "https://img.icons8.com/ios-filled/100/parking.png"
+    parking_space ="https://img.icons8.com/external-bearicons-blue-bearicons/64/external-PARKING-SPACE-capsule-hotel-bearicons-blue-bearicons.png"
+    restaurant_icon ="https://img.icons8.com/color/96/restaurant-.png"
+    residential_icon="https://img.icons8.com/external-xnimrodx-lineal-xnimrodx/64/external-residential-city-xnimrodx-lineal-xnimrodx.png"
+    icons_size= 5
+    op =1
+    # folium.CircleMarker(
+    #         [lat, lng],
+    #         radius=4,
+    #         color='red',
+    #         opacity=0.4,
+    #         fill=True,
+    #         fill_color='blue').add_to(saarbrucken_map_markers)
+
+    if parking != 0 and parking_check==True:
+        custom_marker = folium.Marker(location=[lat, lng], popup=f"Parking: {parking}",opacity=op, icon=folium.CustomIcon(park_icon,icon_size=((parking+icons_size)/2, (parking+icons_size)/2), popup_anchor=(0, -22))).add_to(saarbrucken_map_markers)
+    if park_space != 0 and parkingSpaces_check ==True:
+        custom_marker = folium.Marker(location=[lat, lng], popup=f"Parking spaces: {park_space}",opacity=op,  icon=folium.CustomIcon(parking_space,icon_size=((park_space+icons_size)/2, (park_space+icons_size)/2), popup_anchor=(0, -22))).add_to(saarbrucken_map_markers)
+    if restaurant != 0 and restaurant_chk:
+        custom_marker = folium.Marker(location=[lat, lng], popup=f"Restaurant: {restaurant}",opacity=op,  icon=folium.CustomIcon(restaurant_icon,icon_size=((restaurant+icons_size)/2, (restaurant+icons_size)/2), popup_anchor=(0, -22))).add_to(saarbrucken_map_markers)
+    if residential != 0 and residential_Chk:
+        custom_marker = folium.Marker(location=[lat, lng], popup=f"Residential: {residential}", opacity=op, icon=folium.CustomIcon(residential_icon,icon_size=((residential+icons_size)/2, (residential+icons_size)/2), popup_anchor=(0, -22))).add_to(saarbrucken_map_markers)
+    if evs!=0 and ev_stations_chk:
+        folium.Circle(
+            location=[lat, lng],
+            radius=float(evs*20),
+            color='red',
+            fill=True,
+            fill_color='red',
+            opacity=1
+        ).add_to(saarbrucken_map_markers)
+
+
+
+# Make grid:
+if population_chk:
+    sim_geo = gpd.GeoSeries(df_saarbrucken_poi["geometry"]).simplify(tolerance=0.001)
+    geo_json = sim_geo.to_json()
+    geo_j = folium.GeoJson(data=geo_json, style_function=lambda x: {'lineColor': '#228B22', "fill_opacity":0.7,"fillColor": "orange"})
+    geo_j.add_to(saarbrucken_map_markers)
+
+
+    ev_saar = df_saarbrucken_poi["population"]
+    maps=folium.Choropleth(
+        geo_data=geo_json,
+        name="choropleth",
+        data=ev_saar,
+        columns=["population"],
+        key_on="feature.id",
+        # fill_color="YlGn",
+        # fill_opacity=0.5,
+        # line_opacity=.1,
+        # threshold_scale=threshold(df_saarbrucken_poi["population"]),
+        fill_color='YlOrRd',
+        fill_opacity=0.7,
+        line_opacity=0.2,
+        highlight=True,
+        legend_name="Population (%)"
+    ).add_to(saarbrucken_map_markers)
+    folium.LayerControl().add_to(saarbrucken_map_markers)
+
+
+
+# Map folium markers 
+st_folium(saarbrucken_map_markers,   width=1200, height=500, center=[49.24015720000001, 6.996932700000002], returned_objects=[],zoom=12)
+st.caption("Map of Saarbrücken")
+saarbrucken_map_markers.save("Mymap.html")
+
+
+# with col2:
+st.subheader("EDA")
+image = Image.open('Data/Number of different types of infrastructure in Saarbrucken.png')
+
+st.image(image, caption='Number of different types of infrastructure in Saarbrucken') 

requirements.txt

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+streamlit
+pandas
+geopandas
+plotly
+folium
+streamlit_folium
+folium.plugins
+bs4
+PIL
+numpy
+json