Interactive Mapmaking with Python

Sangarshanan

Data + Pandas <3

In [1]:
import pandas
df = pandas.read_csv('data/cities.csv')
df.head()
Out[1]:
name longitude latitude
0 Vatican City 12.453387 41.903282
1 San Marino 12.441770 43.936096
2 Vaduz 9.516669 47.133724
3 Luxembourg 6.130003 49.611660
4 Palikir 158.149974 6.916644

Data with a location component

Geometries

  • Point (latitude, longitude)
  • Polygon [point1, point2]

We can also have linestrings, multipolygons, circles etc

Enter GeoPandas

  • Work with a Familiar interface (Dataframes), in this case Geodataframes
  • Read/ write GIS data (Fiona) formats like shapefile, geojson, kml etc
  • Perform spatial operations like merge/join/overlay etc (Shapely)
  • Plot em on a map (Matplotlib)

Also a whole lot of other things like handling projections, recently added vectorized geometrical operations, Indexing with rtree... and more such goodies

Interested in more, https://github.com/jorisvandenbossche/geopandas-tutorial has a comprehensive tutorial by the maintainer

In [2]:
df.head()
Out[2]:
name longitude latitude
0 Vatican City 12.453387 41.903282
1 San Marino 12.441770 43.936096
2 Vaduz 9.516669 47.133724
3 Luxembourg 6.130003 49.611660
4 Palikir 158.149974 6.916644
In [3]:
import geopandas
# Converting latitude, longitude to geometry object
gdf = geopandas.GeoDataFrame(
    df, geometry=geopandas.points_from_xy(df.longitude, df.latitude))
# setting the projection
gdf.crs = 'epsg:4326'
gdf.head()
Out[3]:
name longitude latitude geometry
0 Vatican City 12.453387 41.903282 POINT (12.45339 41.90328)
1 San Marino 12.441770 43.936096 POINT (12.44177 43.93610)
2 Vaduz 9.516669 47.133724 POINT (9.51667 47.13372)
3 Luxembourg 6.130003 49.611660 POINT (6.13000 49.61166)
4 Palikir 158.149974 6.916644 POINT (158.14997 6.91664)

How do I plot em ?

With geopandas, its as simple as .plot()

In [29]:
import matplotlib.pyplot as plt
gdf.plot()
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f07cabfb710>

Italian Trulli

Italian Trulli

Folium Plots

In [5]:
gdf.head(1)
Out[5]:
name longitude latitude geometry
0 Vatican City 12.453387 41.903282 POINT (12.45339 41.90328)
In [6]:
# import the library
import folium
# Create a map with a center and zoom level
mapa = folium.Map(location= [-15.783333, -47.866667],
                  zoom_start= 1,
                  tiles= "OpenStreetMap")
mapa
Out[6]:
In [7]:
# Add the geodataframe as a geojson feature
points = folium.features.GeoJson(gdf, 
                                 # tooltip with the name
                                tooltip=folium.GeoJsonTooltip(fields=['name']))
# Adding the feature to the canvas we created
mapa.add_child(points)
mapa
Out[7]:

Onward to Polygons

In [8]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.head(2)
Out[8]:
pop_est continent name iso_a3 gdp_md_est geometry
0 920938 Oceania Fiji FJI 8374.0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 53950935 Africa Tanzania TZA 150600.0 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
In [9]:
#https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html
world.plot(figsize=(20,5), column= 'gdp_md_est', cmap='YlGnBu', legend=True)
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f07d3faecc0>
In [10]:
import branca.colormap as cm
colormap = cm.linear.YlGnBu_09.to_step(data=world['gdp_md_est'], n=9)
colormap
Out[10]:
16.021140000.0
In [11]:
m = folium.Map()
# Setting the style
style_function = lambda x: {
    # How to fill color of the polygon
    'fillColor': colormap(x['properties']['gdp_md_est']),
    # Color of Polygon
    'color': 'black',
    # Weight of the border (Around the Polygon)
    'weight': 0.5,
    # Opacity of filled color
    'fillOpacity': 0.75
}
In [12]:
# Creating a geojson map with the style
folium.GeoJson(
    world,
    tooltip=folium.GeoJsonTooltip(fields= ["name", "gdp_md_est"]),
    style_function=style_function
).add_to(m)
# Add the legend to the same canvas
colormap.add_to(m)
m
Out[12]:

Marker Clusters

In [13]:
from folium.plugins import MarkerCluster
locations = []
# City location geometries to a list of latlongs pairs  
for idx, row in gdf.iterrows():
    locations.append([row['geometry'].y, row['geometry'].x])
# Empty canvas
m = folium.Map()
# Markercluster
m.add_child(MarkerCluster(locations=locations))
m
Out[13]:

Heatmap

In [14]:
from folium.plugins import HeatMap
m = folium.Map()
m.add_child(HeatMap(locations, radius=15))
m
Out[14]:

Mo data Mo Problems

GitHub Logo

kepler.gl

Kepler.gl is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets. Built on top of Mapbox GL and deck.gl, kepler.gl can render millions of points representing thousands of trips and perform spatial aggregations on the fly.

Jupyter Notebook > 5.3

pip install keplergl

JupyterLab

jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter

meme

Kepler uses config to customize its maps

{
  "version": "v1",
  "config": {
    "visState": {
      "filters": [
        {
          "dataId": "earthquakes",
          "id": "vo18yorx",
        }
      ],
      "layers": [
        {
          "id": "hty62yd",
          "type": "point",
          "config": {
            "dataId": "earthquakes",
            "label": "Point",
            "color": [
              23,
              184,
              190
            ],
            "columns": {
              "lat": "Latitude",
              "lng": "Longitude",
              "altitude": null
            },
.....

The UX flow is composed of five layers

image.png

Filters, Timelines and other cool perks

Kepler.gl GIF

Kepler.gl GIF

GitHub Logo

Bokeh

In [30]:
from bokeh.io import output_notebook
from bokeh.plotting import figure, output_file, show
from bokeh.tile_providers import CARTODBPOSITRON, get_provider
output_notebook()
tile_provider = get_provider(CARTODBPOSITRON)
p = figure(x_range=(-2000000, 6000000), y_range=(-1000000, 7000000),
           x_axis_type="mercator", y_axis_type="mercator")
p.add_tile(tile_provider)
show(p)

bokeh

Plotly

In [16]:
import plotly.express as px
df = px.data.carshare()
fig = px.scatter_mapbox(df, lat="centroid_lat", lon="centroid_lon", color="peak_hour", size="car_hours",
                  color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10,
                  mapbox_style="carto-positron")

bokeh

In [17]:
import geopatra
gdf.folium.plot(zoom=2)
Out[17]:

Yet another one, why ?

  • Different libraries have different APIs
  • All of them are awesome and have something new and exciting and offer
  • Netflix Syndrome
  • I wanna be able to switch between them without having to remember all the interfaces/ spend time googling

Chloropeth maps

In [18]:
world.folium.chloropeth(color_by= 'pop_est',
          color= 'green',
          zoom= 1,
          style = {'color': 'black',
                    'weight': 1,
                    'dashArray': '10, 5',
                    'fillOpacity': 0.5,
                  })
Out[18]:

Circle Plots

In [19]:
gdf.folium.circle(radius=10, fill=True, fill_color='red', zoom=100, color='blue')
Out[19]:

Markercluster

In [20]:
gdf.folium.markercluster(zoom=1, tooltip=["name"])
Out[20]:

Weighted Markercluster

In [21]:
import random
gdf['value'] = [int(random.randint(10, 1000)) for i in range(len(gdf))]
gdf.folium.markercluster(zoom=1, metric='sum', weight='value')
Out[21]:

Heatmap

In [22]:
gdf.folium.heatmap(style={'min_opacity': 0.3}, zoom=5)
Out[22]:

Kepler.gl

In [24]:
from IPython.display import IFrame
kmap1 = gdf.kepler.plot()
User Guide: https://docs.kepler.gl/docs/keplergl-jupyter

meme

meme

Thank You : )

Flowers