Interactive Maps with Folium

import math
from pathlib import Path

import folium
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster

pd.set_option("display.max_columns", None)

plt.rcParams["figure.facecolor"] = (1, 1, 1, 0)  # RGBA tuple with alpha=0
plt.rcParams["axes.facecolor"] = (1, 1, 1, 0)  # RGBA tuple with alpha=0

PROJECT_ROOT = Path.cwd().parent.parent

We begin by creating a relatively simple map with folium.Map().

m_1 = folium.Map(location=[42.32, -71.0589], tiles="openstreetmap", zoom_start=10)

m_1
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Several arguments customize the appearance of the map:

Boston Crime Data

crimes = pd.read_csv(
    f"{PROJECT_ROOT}/data/kaggle_geospatial/crimes-in-boston/crimes-in-boston/crime.csv", encoding="latin-1"
)

# Drop rows with missing locations
crimes.dropna(subset=["Lat", "Long", "DISTRICT"], inplace=True)

# Focus on major crimes in 2018
crimes = crimes[
    crimes.OFFENSE_CODE_GROUP.isin(
        [
            "Larceny",
            "Auto Theft",
            "Robbery",
            "Larceny From Motor Vehicle",
            "Residential Burglary",
            "Simple Assault",
            "Harassment",
            "Ballistics",
            "Aggravated Assault",
            "Other Burglary",
            "Arson",
            "Commercial Burglary",
            "HOME INVASION",
            "Homicide",
            "Criminal Harassment",
            "Manslaughter",
        ]
    )
]
crimes = crimes[crimes.YEAR >= 2018]

crimes.head()
INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART STREET Lat Long Location
0 I182070945 619 Larceny LARCENY ALL OTHERS D14 808 NaN 2018-09-02 13:00:00 2018 9 Sunday 13 Part One LINCOLN ST 42.357791 -71.139371 (42.35779134, -71.13937053)
6 I182070933 724 Auto Theft AUTO THEFT B2 330 NaN 2018-09-03 21:25:00 2018 9 Monday 21 Part One NORMANDY ST 42.306072 -71.082733 (42.30607218, -71.08273260)
8 I182070931 301 Robbery ROBBERY - STREET C6 177 NaN 2018-09-03 20:48:00 2018 9 Monday 20 Part One MASSACHUSETTS AVE 42.331521 -71.070853 (42.33152148, -71.07085307)
19 I182070915 614 Larceny From Motor Vehicle LARCENY THEFT FROM MV - NON-ACCESSORY B2 181 NaN 2018-09-02 18:00:00 2018 9 Sunday 18 Part One SHIRLEY ST 42.325695 -71.068168 (42.32569490, -71.06816778)
24 I182070908 522 Residential Burglary BURGLARY - RESIDENTIAL - NO FORCE B2 911 NaN 2018-09-03 18:38:00 2018 9 Monday 18 Part One ANNUNCIATION RD 42.335062 -71.093168 (42.33506218, -71.09316781)

Plotting points

daytime_robberies = crimes[((crimes.OFFENSE_CODE_GROUP == "Robbery") & (crimes.HOUR.isin(range(9, 18))))]

folium.Marker

m_2 = folium.Map(location=[42.32, -71.0589], tiles="cartodbpositron", zoom_start=12)

for idx, row in daytime_robberies.iterrows():
    Marker([row["Lat"], row["Long"]]).add_to(m_2)

m_2
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folium.plugins.MarkerCluster

If we have a lot of markers to add, folium.plugins.MarkerCluster() can help to declutter the map. Each marker is added to a MarkerCluster object.

m_3 = folium.Map(location=[42.32, -71.0589], tiles="cartodbpositron", zoom_start=12)

mc = MarkerCluster()
for idx, row in daytime_robberies.iterrows():
    if not math.isnan(row["Long"]) and not math.isnan(row["Lat"]):
        mc.add_child(Marker([row["Lat"], row["Long"]]))
m_3.add_child(mc)

m_3
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Bubble Maps

A bubble map uses circles instead of markers. By varying the size and color of each circle, we can also show the relationship between location and two other variables.

We create a bubble map by using folium.Circle() to iteratively add circles. In the code cell below, robberies that occurred in hours 9-12 are plotted in green, whereas robberies from hours 13-17 are plotted in red.

m_4 = folium.Map(location=[42.32, -71.0589], tiles="cartodbpositron", zoom_start=12)


def color_producer(val):
    if val <= 12:
        return "forestgreen"
    else:
        return "darkred"


for i in range(0, len(daytime_robberies)):
    Circle(
        location=[daytime_robberies.iloc[i]["Lat"], daytime_robberies.iloc[i]["Long"]],
        radius=20,
        color=color_producer(daytime_robberies.iloc[i]["HOUR"]),
    ).add_to(m_4)

m_4
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Note that folium.Circle() takes several arguments:

  • location is a list containing the center of the circle, in latitude and longitude.
  • radius sets the radius of the circle.
    • Note that in a traditional bubble map, the radius of each circle is allowed to vary. We can implement this by defining a function similar to the color_producer() function that is used to vary the color of each circle.
  • color sets the color of each circle.
    • The color_producer() function is used to visualize the effect of the hour on robbery location.

Heatmaps

To create a heatmap, we use folium.plugins.HeatMap(). This shows the density of crime in different areas of the city, where red areas have relatively more criminal incidents.

As we’d expect for a big city, most of the crime happens near the center.

m_5 = folium.Map(location=[42.32, -71.0589], tiles="cartodbpositron", zoom_start=11)

HeatMap(data=crimes[["Lat", "Long"]], radius=10).add_to(m_5)

m_5
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As you can see in the code cell above, folium.plugins.HeatMap() takes a couple of arguments:

  • data is a DataFrame containing the locations that we’d like to plot.
  • radius controls the smoothness of the heatmap. Higher values make the heatmap look smoother (i.e., with fewer gaps).

Choropleth Maps

To understand how crime varies by police district, we’ll create a choropleth map.

As a first step, we create a GeoDataFrame where each district is assigned a different row, and the geometry column contains the geographical boundaries.

districts_full = gpd.read_file(
    f"{PROJECT_ROOT}/data/kaggle_geospatial/Police_Districts/Police_Districts/Police_Districts.shp"
)
districts = districts_full[["DISTRICT", "geometry"]].set_index("DISTRICT")
districts.head()
geometry
DISTRICT
A15 MULTIPOLYGON (((-71.07416 42.39051, -71.07415 ...
A7 MULTIPOLYGON (((-70.99644 42.39557, -70.99644 ...
A1 POLYGON ((-71.05200 42.36884, -71.05169 42.368...
C6 POLYGON ((-71.04406 42.35403, -71.04412 42.353...
D4 POLYGON ((-71.07416 42.35724, -71.07359 42.357...

We also create a Pandas Series that shows the number of crimes in each district.

plot_dict = crimes.DISTRICT.value_counts()
plot_dict.head()
DISTRICT
D4     2885
B2     2231
A1     2130
C11    1899
B3     1421
Name: count, dtype: int64

It’s very important that plot_dict has the same index as districts - this is how the code knows how to match the geographical boundaries with appropriate colors.

Using the folium.Choropleth() class, we can create a choropleth map.

m_6 = folium.Map(location=[42.32, -71.0589], tiles="cartodbpositron", zoom_start=11)

Choropleth(
    geo_data=districts.__geo_interface__,
    data=plot_dict,
    key_on="feature.id",
    fill_color="YlGnBu",
    legend_name="Major criminal incidents (Jan-Aug 2018)",
).add_to(m_6)

m_6
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