Choropleth maps: Adding tooltips and popups

!pip install geopandas

import folium
print(folium.__version__)

0.12.1

!pip install folium --upgrade

# Import libraries
import geopandas as gpd
import pandas as pd
import folium
import branca
import requests
import json

from folium.features import GeoJson, GeoJsonTooltip, GeoJsonPopup

url_data = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/US_Unemployment_Oct2012.csv'

state_data = pd.read_csv(url_data)

#print(state_data.head())

response = requests.get(r"https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json")
data = response.json()
states = gpd.GeoDataFrame.from_features(data, crs='EPSG:4326')

#print(states.head())

# Get match dataframe
response = requests.get('https://gist.githubusercontent.com/tvpmb/4734703/raw/b54d03154c339ed3047c66fefcece4727dfc931a/US%2520State%2520List')
abbrs = pd.read_json(response.text)


statesmerge = states.merge(abbrs, how='left', left_on='name', right_on='name')
statesmerge['geometry'] = statesmerge.geometry.simplify(.05)

#print(statesmerge.head())


statesmerge['unemployment'] = statesmerge.merge(state_data, how='left', left_on='alpha-2', right_on='State')['Unemployment']
print(statesmerge.head())

m = folium.Map(location=[35.3, -97.6], zoom_start=4)

###
# UPDATE to tooltip

popup = GeoJsonPopup(
    fields=['name','alpha-2', 'unemployment'],
    aliases=['State: ', 'Abbr: ' , "% Unemployment:"],
    localize=True,
    labels=True,
    style="background-color: yellow; border-radius: 90%",
)

tooltip = GeoJsonTooltip(
    fields=['name', "unemployment"],
    aliases=['State: ', "% Unemployment:"],
    localize=True,
    sticky=False,
    labels=True,
    style="""
        background-color: #F0EFEF;
        border: 2px solid black;
        border-radius: 3px;
        box-shadow: 3px;
    """,
    max_width=800,
)

###

g = folium.GeoJson(
    statesmerge,
    style_function=lambda x: {
        "color": "black",
        "fillOpacity": 0.4,
    },
    tooltip=tooltip,
    popup=popup
).add_to(m)

m 

                                            geometry  ... unemployment
0  POLYGON ((-87.35930 35.00118, -85.60667 34.984...  ...          7.1
1  MULTIPOLYGON (((-131.60202 55.11798, -131.5691...  ...          6.8
2  POLYGON ((-109.04250 37.00026, -109.04798 31.3...  ...          8.1
3  POLYGON ((-94.47384 36.50186, -90.15254 36.496...  ...          7.2
4  POLYGON ((-123.23326 42.00619, -120.00186 41.9...  ...         10.1

[5 rows x 4 columns]

Make this Notebook Trusted to load map: File -> Trust Notebook

Adding colormap

Based on: https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/GeoJsonPopupAndTooltip.ipynb

# Import libraries
import geopandas as gpd
import pandas as pd
import folium
import branca
import requests
import json

from folium.features import GeoJson, GeoJsonTooltip, GeoJsonPopup

url_data = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/US_Unemployment_Oct2012.csv'

state_data = pd.read_csv(url_data)

response = requests.get(r"https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json")
data = response.json()
states = gpd.GeoDataFrame.from_features(data, crs='EPSG:4326')

# Get match dataframe
response = requests.get('https://gist.githubusercontent.com/tvpmb/4734703/raw/b54d03154c339ed3047c66fefcece4727dfc931a/US%2520State%2520List')
abbrs = pd.read_json(response.text)


statesmerge = states.merge(abbrs, how='left', left_on='name', right_on='name')
statesmerge['geometry'] = statesmerge.geometry.simplify(.05)

#print(statesmerge.head())


statesmerge['unemployment'] = statesmerge.merge(state_data, how='left', left_on='alpha-2', right_on='State')['Unemployment']
print(statesmerge.columns)

####
# UPDATE COLORMAP
colormap = branca.colormap.StepColormap(
    vmin=statesmerge['unemployment'].quantile(0.0), 
    vmax=statesmerge['unemployment'].quantile(1), 
    colors=['red','orange', 'yellow','green','darkgreen'],
    caption="Unemployment in USA (%) - 2021",
)

####

m = folium.Map(location=[35.3, -97.6], zoom_start=4)

popup = GeoJsonPopup(
    fields=['name','alpha-2', 'unemployment'],
    aliases=['State: ', 'Abbr: ' , "% Unemployment:"],
    localize=True,
    labels=True,
    style="background-color: yellow;",
)

tooltip = GeoJsonTooltip(
    fields=['name', "unemployment"],
    aliases=['State: ', "% Unemployment:"],
    localize=True,
    sticky=False,
    labels=True,
    style="""
        background-color: #F0EFEF;
        border: 2px solid black;
        border-radius: 3px;
        box-shadow: 3px;
    """,
    max_width=800,
)

colormap.add_to(m)

g = folium.GeoJson(
    statesmerge,
    style_function=lambda x: {
        "fillColor": colormap(x["properties"]["unemployment"])
        if x["properties"]["unemployment"] is not None
        else "transparent",
        "color": "black",
        "fillOpacity": 0.4,
    },
     tooltip=tooltip,
    popup=popup
).add_to(m)

m.save("mychoropleth.html")
#m 

Index(['geometry', 'name', 'alpha-2', 'unemployment'], dtype='object')

from google.colab import files

files.download('mychoropleth.html')

pyDECK

!pip install pydeck

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MAPBOX_KEY = "pk.eyJ1IjoiZXJpY2tnbiIsImEiOiJjazJtMHRncTcwY3p3M2JxbDh4M2YxdnNrIn0.80yDIJPBHpZJvO4ZREpHJg"

import pydeck as pdk

# 2014 locations of car accidents in the UK'
UK_ACCIDENTS_DATA = ('https://raw.githubusercontent.com/uber-common/'
                     'deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv')

# Define a layer to display on a map
layer = pdk.Layer(
    'HexagonLayer',
    UK_ACCIDENTS_DATA,
    get_position=['lng', 'lat'],
    auto_highlight=True,
    elevation_scale=50,
    pickable=True,
    elevation_range=[0, 3000],
    extruded=True,                 
    coverage=1)

# Set the viewport location
view_state = pdk.ViewState(
    longitude=-1.415,
    latitude=52.2323,
    zoom=6,
    min_zoom=5,
    max_zoom=15,
    pitch=40.5,
    bearing=-27.36)

# Render
r = pdk.Deck(layers=[layer], initial_view_state=view_state)
r.to_html('demo.html', notebook_display=False)

from google.colab import files

files.download('demo.html')

"""
ArcLayer
========
Map of commutes to work within a segment of downtown San Francisco using a deck.gl ArcLayer.
Green indicates a start point, and red indicates the destination.
The data is collected by the US Census Bureau and viewable in the 2017 LODES data set: https://lehd.ces.census.gov/data/
"""

import pydeck as pdk
import pandas as pd

DATA_URL = "https://raw.githubusercontent.com/ajduberstein/sf_public_data/master/bay_area_commute_routes.csv"
# A bounding box for downtown San Francisco, to help filter this commuter data
DOWNTOWN_BOUNDING_BOX = [
    -122.43135291617365,
    37.766492914983864,
    -122.38706428091974,
    37.80583561830737,
]


def in_bounding_box(point):
    """Determine whether a point is in our downtown bounding box"""
    lng, lat = point
    in_lng_bounds = DOWNTOWN_BOUNDING_BOX[0] <= lng <= DOWNTOWN_BOUNDING_BOX[2]
    in_lat_bounds = DOWNTOWN_BOUNDING_BOX[1] <= lat <= DOWNTOWN_BOUNDING_BOX[3]
    return in_lng_bounds and in_lat_bounds


df = pd.read_csv(DATA_URL)
# Filter to bounding box
df = df[df[["lng_w", "lat_w"]].apply(lambda row: in_bounding_box(row), axis=1)]

GREEN_RGB = [0, 255, 0, 40]
RED_RGB = [255, 0, 0, 40]

# Specify a deck.gl ArcLayer
arc_layer = pdk.Layer(
    "ArcLayer",
    data=df,
    get_width="S000 * 2",
    get_source_position=["lng_h", "lat_h"],
    get_target_position=["lng_w", "lat_w"],
    get_tilt=15,
    get_source_color=RED_RGB,
    get_target_color=GREEN_RGB,
    pickable=True,
    auto_highlight=True,
)

view_state = pdk.ViewState(latitude=37.7576171, longitude=-122.5776844, bearing=45, pitch=50, zoom=8,)


TOOLTIP_TEXT = {"html": "{S000} jobs <br /> Home of commuter in RED; work location in GREEN"}
r = pdk.Deck(arc_layer, initial_view_state=view_state, tooltip=TOOLTIP_TEXT)
r.to_html("arc_layer.html", notebook_display=False)

from google.colab import files

files.download('arc_layer.html')

"""
TerrainLayer
===========

Extruded terrain using AWS Open Data Terrain Tiles and Mapbox Satellite imagery
"""

import pydeck as pdk
import os

# Import Mapbox API Key from environment
#MAPBOX_API_KEY = os.environ["MAPBOX_API_KEY"]
MAPBOX_API_KEY = MAPBOX_KEY

# AWS Open Data Terrain Tiles
TERRAIN_IMAGE = '"https://s3.amazonaws.com/elevation-tiles-prod/terrarium/{z}/{x}/{y}.png"'

# Define how to parse elevation tiles
ELEVATION_DECODER = {"rScaler": 256, "gScaler": 1, "bScaler": 1 / 256, "offset": -32768}

SURFACE_IMAGE = '"https://api.mapbox.com/v4/mapbox.satellite/{{z}}/{{x}}/{{y}}@2x.png?access_token={}"'.format(
    MAPBOX_API_KEY
)

terrain_layer = pdk.Layer(
    "TerrainLayer", data=None, elevation_decoder=ELEVATION_DECODER, texture=SURFACE_IMAGE, elevation_data=TERRAIN_IMAGE
)

view_state = pdk.ViewState(latitude=46.24, longitude=-122.18, zoom=11.5, bearing=140, pitch=60)

r = pdk.Deck(terrain_layer, initial_view_state=view_state)

r.to_html("terrain_layer.html", notebook_display=False)

from google.colab import files

files.download('terrain_layer.html')

"""
PathLayer
=========

Locations of the Bay Area Rapid Transit lines.
"""

import pandas as pd
import pydeck as pdk

DATA_URL = "https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/bart-lines.json"
df = pd.read_json(DATA_URL)


def hex_to_rgb(h):
    h = h.lstrip("#")
    return tuple(int(h[i : i + 2], 16) for i in (0, 2, 4))


df["color"] = df["color"].apply(hex_to_rgb)

view_state = pdk.ViewState(latitude=37.782556, longitude=-122.3484867, zoom=10)

layer = pdk.Layer(
    type="PathLayer",
    data=df,
    pickable=True,
    get_color="color",
    width_scale=20,
    width_min_pixels=2,
    get_path="path",
    get_width=5,
)

r = pdk.Deck(layers=[layer], mapbox_key=MAPBOX_KEY, initial_view_state=view_state, tooltip={"text": "{name}"})
r.to_html("path_layer.html", notebook_display=False)

from google.colab import files

files.download('path_layer.html')

More examples at: https://github.com/visgl/deck.gl/tree/master/bindings/pydeck/examples

OSMNX

!pip install osmnx

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ERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 6.0.2 which is incompatible.
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import osmnx as ox
%matplotlib inline
G = ox.graph_from_place('Yanahuara, Arequipa, Peru', network_type='drive')
fig, ax = ox.plot_graph(ox.project_graph(G))

import osmnx as ox
%matplotlib inline
G = ox.graph_from_place('Yanahuara, Arequipa, Peru', network_type='walk')
fig, ax = ox.plot_graph(ox.project_graph(G))

G = ox.graph_from_address('350 5th Ave, New York, New York', dist=2000, simplify=True, network_type='drive')
ox.plot_graph(G)

(<Figure size 576x576 with 1 Axes>, <AxesSubplot:>)

G = ox.graph_from_address('Bab Bhar, Tunis, Tunisia')
ox.plot_graph(G)

(<Figure size 576x576 with 1 Axes>, <AxesSubplot:>)

Getting started: https://geoffboeing.com/2017/02/python-getting-started/ For using in Google Colab: https://colab.research.google.com/drive/13OJuSO3BSvo8ACJEdYvBBsp0-A7By6HL#scrollTo=0jrkOFh7FEcT