Generating Maps with Python: “Maps with Markers”-Part 2
Maps with Markers
Let’s download and import the data on police department incidents using pandas `read_csv()` method.
Download the dataset and read it into a pandas dataframe:
df_incidents = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Police_Department_Incidents_-_Previous_Year__2016_.csv')print('Dataset downloaded and read into a pandas dataframe!')
Let’s take a look at the first five items in our dataset.
df_incidents.head()
So each row consists of 13 features:
1. IncidntNum: Incident Number
2. Category: Category of crime or incident
3. Descript: Description of the crime or incident
4. DayOfWeek: The day of the week on which the incident occurred
5. Date: The Date on which the incident occurred
6. Time: The time of day on which the incident occurred
7. PdDistrict: The police department district
8. Resolution: The resolution of the crime in terms of whether the perpetrator was arrested or not.
9. Address: The closest address to where the incident took place
10. X: The longitude value of the crime location
11. Y: The latitude value of the crime location
12. Location: A tuple of the latitude and the longitude values
13. PdId: The police department ID
Let’s find out how many entries there are in our dataset.
df_incidents.shape(150500, 13)
So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce the computational cost.
Let’s just work with the first 100 incidents in this dataset.
# get the first 100 crimes in the df_incidents dataframe
limit = 100
df_incidents = df_incidents.iloc[0:limit, :]Now that we reduced the data a little bit, let’s visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12.
# San Francisco latitude and longitude values
latitude = 37.77
longitude = -122.42# create map and display it
sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)# display the map of San Francisco
sanfran_map
Now let’s superimpose the locations of the crimes onto the map. The way to do that in Folium is to create a feature group with its own features and style and then add it to the sanfran_map.
#instantiate a feature group for the incidents in the dataframeincidents = folium.map.FeatureGroup()#loop through the 100 crimes and add each to the incidents feature groupfor lat, lng, in zip(df_incidents.Y, df_incidents.X):
incidents.add_child(
folium.features.CircleMarker(
[lat, lng],
radius=5, # define how big you want the circle markers to be
color=’yellow’,
fill=True,
fill_color=’blue’,
fill_opacity=0.6
)
)# add incidents to map
sanfran_map.add_child(incidents)
You can also add some pop-up text that would get displayed when you hover over a marker.
Let’s make each marker display the category of the crime when hovered over.
# instantiate a feature group for the incidents in the dataframe
incidents = folium.map.FeatureGroup()# loop through the 100 crimes and add each to the incidents feature groupfor lat, lng, in zip(df_incidents.Y, df_incidents.X):
incidents.add_child(
folium.features.CircleMarker(
[lat, lng],
radius=5, # define how big you want the circle markers to be
color=’yellow’,
fill=True,
fill_color=’red’,
fill_opacity=0.6
)
)# add pop-up text to each marker on the map
latitudes = list(df_incidents.Y)
longitudes = list(df_incidents.X)
labels = list(df_incidents.Category)for lat, lng, label in zip(latitudes, longitudes, labels):
folium.Marker([lat, lng], popup=label).add_to(sanfran_map)
# add incidents to map
sanfran_map.add_child(incidents)
Isn't this really cool? Now you are able to know what crime category occurred at each marker.
If you find the map to be so congested will all these markers, there is a remedy to this problem:
- The proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.
To implement this, we start off by instantiating a MarkerCluster object and adding all the data points in the dataframe to this object.
from folium import plugins# let’s start again with a clean copy of the map of San Francisco
sanfran_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(sanfran_map)# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):
folium.Marker(
location=[lat, lng],
icon=None,
popup=label,
).add_to(incidents)# display map
sanfran_map
Notice how when you zoom out all the way, all markers are grouped into one cluster, the global cluster, of 100 markers or crimes, which is the total number of crimes in our data frame. Once you start zooming in, the global cluster will start breaking up into smaller clusters. Zooming in all the way will result in individual markers.
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