GM Plot
Contents
8.4. GM Plot¶
To install gmplot, we can use pip by running pip or conda
pip install gmplot
conda install -c mlgill gmplot
After installing the package, we can now use it in Jupyter
Notebook. gmplot’s most unique use is it’s ability to save
geographical coordinates on Google Maps to a local file. Additionally,
it has a similar interface to matplotlib in regards to its generation
of the HTML to deliver all the additional data on top of Google Maps.
First, we have to import gmplot
import gmplot
8.4.1. Basic Plot¶
After doing so, we can generate a simple map using
gmplot.GoogleMapPlotter. This will create an .html file. I saved
mine to Jupyter Notebook but you can set your path to save it to
anywhere you choose.
# first two arguments are longitude and latitude and the third one is
# zoom resolution
gmap = gmplot.GoogleMapPlotter(17.438139, 78.39583, 18)
# Location where you want to save your file.
gmap.draw("map.html")
8.4.2. Polygon¶
Next we can draw a polygon on Google Maps to highlight a specific area
of interest. By using .polygon, we can create a highlighted border
around a specific area. The inputs are the longitude and latitude
points of the shape.
latitude_list = [ 17.4567417, 17.5587901, 17.6245545]
longitude_list = [ 78.2913637, 78.007699, 77.9266135 ]
gmap = gmplot.GoogleMapPlotter(17.438139, 78.3936413, 11)
gmap.polygon(latitude_list, longitude_list, color = 'cornflowerblue')
gmap.draw( "poly.html" )
8.4.3. Complete Map¶
A more complete map is shown below by using many longitude and latitude points to highlight specific areas of the map. First, we create a zip of the outline of Golden Gate Park and use .polygon as we did previously. Next, we add some attraction points use .scatter. Lastly, we can add a marker to give attention to a specific spot using .marker.
#Create the map plotter:
gmap = gmplot.GoogleMapPlotter(37.766956, -122.448481, 14)
#Outline the Golden Gate Park:
golden_gate_park = zip(*[
(37.771269, -122.511015),
(37.773495, -122.464830),
(37.774797, -122.454538),
(37.771988, -122.454018),
(37.773646, -122.440979),
(37.772742, -122.440797),
(37.771096, -122.453889),
(37.768669, -122.453518),
(37.766227, -122.460213),
(37.764028, -122.510347)
])
gmap.polygon(*golden_gate_park, color='cornflowerblue', edge_width=10)
#Highlight some attractions:
attractions_lats, attractions_lngs = zip(*[
(37.769901, -122.498331),
(37.768645, -122.475328),
(37.771478, -122.468677),
(37.769867, -122.466102),
(37.767187, -122.467496),
(37.770104, -122.470436)
])
gmap.scatter(attractions_lats, attractions_lngs, color='#3B0B39', size=40, marker=False)
#Mark a hidden gem:
gmap.marker(37.770776, -122.461689, color='cornflowerblue')
#Draw the map:
gmap.draw('GGP.html')
8.4.4. Heat Map¶
Another cool aspect of gmplot is the ability to create a heatmap. In this, I randomly generated 700 points around the world. Then I used .heatmap, which generates the points. You have the option to add markers to them, but it looks bad since so many points were generated in this specific example.
#Import libraries
import gmplot
import numpy as np
#generate the random points for heat map
latitude = (np.random.random_sample(size = 500) - 0.5) * 180
longitude = (np.random.random_sample(size = 500) - 0.5) * 360
#center of the map and zoom
gmap = gmplot.GoogleMapPlotter(0, 0, 2)
#plot heatmap
gmap.heatmap(latitude, longitude)
#save it to html
gmap.draw("heatmap.html")
8.4.5. NYC Data¶
import pandas as pd
nyc_crash = pd.read_csv("../data/nyc_mv_collisions_202201.csv")
nyc_crash.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7659 entries, 0 to 7658
Data columns (total 29 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CRASH DATE 7659 non-null object
1 CRASH TIME 7659 non-null object
2 BOROUGH 5025 non-null object
3 ZIP CODE 5025 non-null float64
4 LATITUDE 7097 non-null float64
5 LONGITUDE 7097 non-null float64
6 LOCATION 7097 non-null object
7 ON STREET NAME 5625 non-null object
8 CROSS STREET NAME 3620 non-null object
9 OFF STREET NAME 2034 non-null object
10 NUMBER OF PERSONS INJURED 7659 non-null int64
11 NUMBER OF PERSONS KILLED 7659 non-null int64
12 NUMBER OF PEDESTRIANS INJURED 7659 non-null int64
13 NUMBER OF PEDESTRIANS KILLED 7659 non-null int64
14 NUMBER OF CYCLIST INJURED 7659 non-null int64
15 NUMBER OF CYCLIST KILLED 7659 non-null int64
16 NUMBER OF MOTORIST INJURED 7659 non-null int64
17 NUMBER OF MOTORIST KILLED 7659 non-null int64
18 CONTRIBUTING FACTOR VEHICLE 1 7615 non-null object
19 CONTRIBUTING FACTOR VEHICLE 2 5624 non-null object
20 CONTRIBUTING FACTOR VEHICLE 3 824 non-null object
21 CONTRIBUTING FACTOR VEHICLE 4 225 non-null object
22 CONTRIBUTING FACTOR VEHICLE 5 80 non-null object
23 COLLISION_ID 7659 non-null int64
24 VEHICLE TYPE CODE 1 7539 non-null object
25 VEHICLE TYPE CODE 2 4748 non-null object
26 VEHICLE TYPE CODE 3 752 non-null object
27 VEHICLE TYPE CODE 4 207 non-null object
28 VEHICLE TYPE CODE 5 78 non-null object
dtypes: float64(3), int64(9), object(17)
memory usage: 1.7+ MB
nyc_crash_desc = nyc_crash.describe()
nyc_crash_desc
| ZIP CODE | LATITUDE | LONGITUDE | NUMBER OF PERSONS INJURED | NUMBER OF PERSONS KILLED | NUMBER OF PEDESTRIANS INJURED | NUMBER OF PEDESTRIANS KILLED | NUMBER OF CYCLIST INJURED | NUMBER OF CYCLIST KILLED | NUMBER OF MOTORIST INJURED | NUMBER OF MOTORIST KILLED | COLLISION_ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5025.000000 | 7097.000000 | 7097.000000 | 7659.000000 | 7659.000000 | 7659.000000 | 7659.000000 | 7659.000000 | 7659.0 | 7659.000000 | 7659.000000 | 7.659000e+03 |
| mean | 10908.957015 | 40.609463 | -73.705503 | 0.404753 | 0.002350 | 0.084345 | 0.001306 | 0.021935 | 0.0 | 0.287505 | 0.000914 | 4.495510e+06 |
| std | 514.740028 | 2.160598 | 3.919451 | 0.726622 | 0.048425 | 0.290332 | 0.036113 | 0.149131 | 0.0 | 0.692545 | 0.030220 | 2.338111e+03 |
| min | 10000.000000 | 0.000000 | -74.249980 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 4.491064e+06 |
| 25% | 10459.000000 | 40.665085 | -73.960370 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 4.493532e+06 |
| 50% | 11209.000000 | 40.712505 | -73.917310 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 4.495533e+06 |
| 75% | 11354.000000 | 40.786957 | -73.862274 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 4.497480e+06 |
| max | 11697.000000 | 40.909206 | 0.000000 | 8.000000 | 1.000000 | 3.000000 | 1.000000 | 2.000000 | 0.0 | 8.000000 | 1.000000 | 4.500594e+06 |
bronx_long = nyc_crash.loc[nyc_crash['BOROUGH'] == 'BRONX',
["CRASH DATE", "CRASH TIME", "LONGITUDE"]]
bronx_lat = nyc_crash.loc[nyc_crash['BOROUGH'] == 'BRONX',
["CRASH DATE", "CRASH TIME", "LATITUDE"]]
mybronx = pd.merge(bronx_lat, bronx_long)
lat = mybronx.dropna()
bronx_lat = lat["LATITUDE"]
bronx_lngs = lat["LONGITUDE"]
I chose to use the data for the Bronx borough as it was already cleaned. I zoomed in on the Bronx, and added in all of the crashes in that burough.
gmap = gmplot.GoogleMapPlotter(40.844782, -73.864827, 12)
gmap.scatter(bronx_lat, bronx_lngs, 'blue', size = 100, marker = False)
gmap.draw('bronxmap.html')