In [1]:

import folium
import pandas as pd

In [2]:

# define the world map
world_map = folium.Map()

# display world map
world_map

Out[2]:

In [3]:

# San Francisco latitude and longitude values
latitude = 37.77
longitude = -122.42

# Create map and display it
san_map = folium.Map(location=[latitude, longitude], zoom_start=12)

# Display the map of San Francisco
san_map

Out[3]:

In [4]:

# Change tiles of map
san_map = folium.Map(location=[latitude, longitude], zoom_start=12, tiles='Stamen Toner')
san_map

Out[4]:

In [5]:

# Read Dataset 
cdata = pd.read_csv('https://cocl.us/sanfran_crime_dataset')
cdata.head()

Out[5]:

	IncidntNum	Category	Descript	DayOfWeek	Date	Time	PdDistrict	Resolution	Address	X	Y	Location	PdId
0	120058272	WEAPON LAWS	POSS OF PROHIBITED WEAPON	Friday	01/29/2016 12:00:00 AM	11:00	SOUTHERN	ARREST, BOOKED	800 Block of BRYANT ST	-122.403405	37.775421	(37.775420706711, -122.403404791479)	12005827212120
1	120058272	WEAPON LAWS	FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE	Friday	01/29/2016 12:00:00 AM	11:00	SOUTHERN	ARREST, BOOKED	800 Block of BRYANT ST	-122.403405	37.775421	(37.775420706711, -122.403404791479)	12005827212168
2	141059263	WARRANTS	WARRANT ARREST	Monday	04/25/2016 12:00:00 AM	14:59	BAYVIEW	ARREST, BOOKED	KEITH ST / SHAFTER AV	-122.388856	37.729981	(37.7299809672996, -122.388856204292)	14105926363010
3	160013662	NON-CRIMINAL	LOST PROPERTY	Tuesday	01/05/2016 12:00:00 AM	23:50	TENDERLOIN	NONE	JONES ST / OFARRELL ST	-122.412971	37.785788	(37.7857883766888, -122.412970537591)	16001366271000
4	160002740	NON-CRIMINAL	LOST PROPERTY	Friday	01/01/2016 12:00:00 AM	00:30	MISSION	NONE	16TH ST / MISSION ST	-122.419672	37.765050	(37.7650501214668, -122.419671780296)	16000274071000

In [6]:

# get the first 200 crimes in the cdata
limit = 200
data = cdata.iloc[0:limit, :]

# Instantiate a feature group for the incidents in the dataframe
incidents = folium.map.FeatureGroup()

# Loop through the 200 crimes and add each to the incidents feature group
for lat, lng, in zip(cdata.Y, data.X):
    incidents.add_child(
        folium.CircleMarker(
            [lat, lng],
            radius=7, # define how big you want the circle markers to be
            color='yellow',
            fill=True,
            fill_color='red',
            fill_opacity=0.4
        )
    )

# Add incidents to map
san_map = folium.Map(location=[latitude, longitude], zoom_start=12)
san_map.add_child(incidents)

Out[6]:

In [7]:

# add pop-up text to each marker on the map
latitudes = list(data.Y)
longitudes = list(data.X)
labels = list(data.Category)

for lat, lng, label in zip(latitudes, longitudes, labels):
    folium.Marker([lat, lng], popup=label).add_to(san_map)    
    
# add incidents to map
san_map.add_child(incidents)

Out[7]:

In [8]:

from folium import plugins

# let's start again with a clean copy of the map of San Francisco
san_map = folium.Map(location = [latitude, longitude], zoom_start = 12)

# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(san_map)

# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(data.Y, data.X, cdata.Category):
    folium.Marker(
        location=[lat, lng],
        icon=None,
        popup=label,
    ).add_to(incidents)

# add incidents to map
san_map.add_child(incidents)

Out[8]:

In [9]:

import json
import requests

url = 'https://cocl.us/sanfran_geojson'
san_geo = f'{url}'
san_map = folium.Map(location=[37.77, -122.4], zoom_start=12)
folium.GeoJson(
    san_geo,
    style_function=lambda feature: {
        'fillColor': '#ffff00',
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(san_map)

#display map
san_map

Out[9]:

In [10]:

# Count crime numbers in each neighborhood
disdata = pd.DataFrame(cdata['PdDistrict'].value_counts())
disdata.reset_index(inplace=True)
disdata.rename(columns={'index':'Neighborhood','PdDistrict':'Count'},inplace=True)
disdata

Out[10]:

	Neighborhood	Count
0	SOUTHERN	28445
1	NORTHERN	20100
2	MISSION	19503
3	CENTRAL	17666
4	BAYVIEW	14303
5	INGLESIDE	11594
6	TARAVAL	11325
7	TENDERLOIN	9942
8	RICHMOND	8922
9	PARK	8699

In [11]:

san_map = folium.Map(location=[37.77, -122.4], zoom_start=12)

# Create Choropleth map
folium.Choropleth(
    geo_data=san_geo,
    data=disdata,
    columns=['Neighborhood','Count'],
    key_on='feature.properties.DISTRICT',
    #fill_color='red',
    fill_color='YlOrRd',
    fill_opacity=0.7,
    line_opacity=0.2,
    highlight=True,
    legend_name='Crime Counts in San Francisco'
).add_to(san_map)

san_map

Out[11]:

In [12]:

san_map = folium.Map(location=[37.77, -122.4], zoom_start=12)

# Create Choropleth map
folium.Choropleth(
    geo_data=san_geo,
    data=disdata,
    columns=['Neighborhood','Count'],
    key_on='feature.properties.DISTRICT',
    #fill_color='red',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    highlight=True,
    legend_name='Crime Counts in San Francisco'
).add_to(san_map)

san_map

Out[12]:

In [13]:

from folium.plugins import HeatMap

# let's start again with a clean copy of the map of San Francisco
san_map = folium.Map(location = [latitude, longitude], zoom_start = 12)

# Convert data format
heatdata = data[['Y','X']].values.tolist()

# add incidents to map
HeatMap(heatdata).add_to(san_map)

san_map

Out[13]: