Data Visualization using Folium

import folium

from pyproj import crs
import geopandas as gpd
import matplotlib.pyplot as plt

Create a simple Interactive Map

m = folium.Map(location=[20.59,78.96],zoom_start=5,control_scale=True)

m

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Change the Style of the Map

m = folium.Map(
    location = [20.59,78.96],
    tiles = "Stamen Toner",
    zoom_start = 5,
    control_scale = True,
    prefer_canvas = True
)

m

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Adding layers to the Map - Pin location

#18.5786832,73.7666697
m = folium.Map(location=[20.59,78.96],
                zoom_start=5,control_scale=True)
folium.Marker(
    location = [18.5786832,73.7666697],
    popup='Sai Eshnaya Apartments',
    icon = folium.Icon(color='green',icon='ok-sign')
).add_to(m)

m

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Mark good resedential areas in Pune

points_fp = '../data/addresses.shp'
points = gpd.read_file(points_fp)

points.head()

	id	addr	geometry
0	1000	Boat Club Road, 411001, Pune, Maharastra	POINT (73.87826 18.53937)
1	1001	Koregaon, 415501, Pune, Maharastra	POINT (73.89299 18.53772)
2	1002	Kothrud, 411038, Pune, Maharastra	POINT (73.80767 18.50389)
3	1003	Balewadi, 411045, Pune, Maharastra	POINT (73.76912 18.57767)
4	1004	Baner, 411047, Pune, Maharastra	POINT (73.77686 18.56424)

points_gjson = folium.features.GeoJson(points, name='Good Residential Areas')

m = folium.Map(location=[18.5786832,73.7666697], tiles="cartodbpositron",
                zoom_start=8,
                control_scale=True)
points_gjson.add_to(m)
folium.LayerControl().add_to(m)
m

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Create a Heatmap of the locations

points["x"] = points["geometry"].apply(lambda geom: geom.x)
points["y"] = points["geometry"].apply(lambda geom: geom.y)

# Create a list of coordinate pairs
locations = list(zip(points["y"], points["x"]))

from folium.plugins import HeatMap

# Create a Map instance
m = folium.Map(
    location=[18.5786832,73.7666697], tiles="stamentoner", zoom_start=10, control_scale=True
)

# Add heatmap to map instance
# Available parameters: HeatMap(data, name=None, min_opacity=0.5, max_zoom=18, max_val=1.0, radius=25, blur=15, gradient=None, overlay=True, control=True, show=True)
HeatMap(locations).add_to(m)

# Alternative syntax:
# m.add_child(HeatMap(points_array, radius=15))

# Show map
m

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Create a Clustered point Map

from folium.plugins import MarkerCluster

# Create a Map instance
m = folium.Map(
    location=[18.5786832,73.7666697], tiles="cartodbpositron", zoom_start=12, control_scale=True
)

# Get x and y coordinates for each point
points["x"] = points["geometry"].apply(lambda geom: geom.x)
points["y"] = points["geometry"].apply(lambda geom: geom.y)

# Create a list of coordinate pairs
locations = list(zip(points["y"], points["x"]))

# Create a folium marker cluster
marker_cluster = MarkerCluster(locations)

# Add marker cluster to map
marker_cluster.add_to(m)

# Show map
m

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Create a Choropleth Map

import geopandas as gpd
from pyproj import CRS
import requests
import geojson

# Specify the url for web feature service
url = "https://kartta.hsy.fi/geoserver/wfs"

# Specify parameters (read data in json format).
# Available feature types in this particular data source: http://geo.stat.fi/geoserver/vaestoruutu/wfs?service=wfs&version=2.0.0&request=describeFeatureType
params = dict(
    service="WFS",
    version="2.0.0",
    request="GetFeature",
    typeName="asuminen_ja_maankaytto:Vaestotietoruudukko_2018",
    outputFormat="json",
)

# Fetch data from WFS using requests
r = requests.get(url, params=params)

# Create GeoDataFrame from geojson
data = gpd.GeoDataFrame.from_features(geojson.loads(r.content))

# Check the data
data.head()

	geometry	index	asukkaita	asvaljyys	ika0_9	ika10_19	ika20_29	ika30_39	ika40_49	ika50_59	ika60_69	ika70_79	ika_yli80
0	POLYGON ((25472499.995 6689749.005, 25472499.9...	688	9	28.0	99	99	99	99	99	99	99	99	99
1	POLYGON ((25472499.995 6685998.998, 25472499.9...	703	5	51.0	99	99	99	99	99	99	99	99	99
2	POLYGON ((25472499.995 6684249.004, 25472499.9...	710	8	44.0	99	99	99	99	99	99	99	99	99
3	POLYGON ((25472499.995 6683999.005, 25472499.9...	711	5	90.0	99	99	99	99	99	99	99	99	99
4	POLYGON ((25472499.995 6682998.998, 25472499.9...	715	11	41.0	99	99	99	99	99	99	99	99	99

from pyproj import CRS

# Define crs
data.crs = CRS.from_epsg(3879)

# Re-project to WGS84
data = data.to_crs(epsg=4326)

# Check layer crs definition
print(data.crs)

EPSG:4326

# Change the name of a column
data = data.rename(columns={"asukkaita": "pop18"})

data["geoid"] = data.index.astype(str)

# Select only needed columns
data = data[["geoid", "pop18", "geometry"]]

# Convert to geojson (not needed for the simple coropleth map!)
# pop_json = data.to_json()

# check data
data.head()

	geoid	pop18	geometry
0	0	9	POLYGON ((24.50236 60.31928, 24.50233 60.32152...
1	1	5	POLYGON ((24.50287 60.28562, 24.50284 60.28787...
2	2	8	POLYGON ((24.50311 60.26992, 24.50308 60.27216...
3	3	5	POLYGON ((24.50315 60.26767, 24.50311 60.26992...
4	4	11	POLYGON ((24.50328 60.25870, 24.50325 60.26094...

m = folium.Map(
    location=[60.25, 24.8], tiles="cartodbpositron", zoom_start=10, control_scale=True
)

# Plot a choropleth map
# Notice: 'geoid' column that we created earlier needs to be assigned always as the first column
folium.Choropleth(
    geo_data=data,
    name="Population in 2018",
    data=data,
    columns=["geoid", "pop18"],
    key_on="feature.id",
    fill_color="YlOrRd",
    fill_opacity=0.7,
    line_opacity=0.2,
    line_color="white",
    line_weight=0,
    highlight=False,
    smooth_factor=1.0,
    # threshold_scale=[100, 250, 500, 1000, 2000],
    legend_name="Population in Helsinki",
).add_to(m)

# Show map
m

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Create Choropleth Map with Interaction

# Convert points to GeoJson
folium.features.GeoJson(
    data,
    name="Labels",
    style_function=lambda x: {
        "color": "transparent",
        "fillColor": "transparent",
        "weight": 0,
    },
    tooltip=folium.features.GeoJsonTooltip(
        fields=["pop18"], aliases=["Population"], labels=True, sticky=False
    ),
).add_to(m)

m

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