In [1]:
import pydeck as pdk
import pandas as pd

Plotting lights at night¶

NASA has collected global light emission data for over 30 years. The data set is a deeply fascinating one and has been used for news stories on the Syrian Civil War [1], North Korea [2], and economic growth [3].

In this notebook, we'll use a deck.gl HeatmapLayer to visualize some of the changes at different points in time.

Getting the data¶

The data for Chengdu, China, is cleaned and available below. Please note this data is meant for demonstration only.

In [2]:
LIGHTS_URL = 'https://raw.githubusercontent.com/ajduberstein/lights_at_night/master/chengdu_lights_at_night.csv'
df = pd.read_csv(LIGHTS_URL)
df.head()
Out[2]:
year lng lat brightness
0 1993 104.575 31.808 4
1 1993 104.583 31.808 4
2 1993 104.592 31.808 4
3 1993 104.600 31.808 4
4 1993 104.675 31.808 4

Setting the colors¶

pydeck does need to know the color for this data in advance of plotting it

In [3]:
df['color'] = df['brightness'].apply(lambda val: [255, val * 4,  255, 255])
df.sample(10)
Out[3]:
year lng lat brightness color
56182 2009 103.817 31.642 6 [255, 24, 255, 255]
112600 2001 103.650 29.717 5 [255, 20, 255, 255]
212788 2007 103.575 29.825 3 [255, 12, 255, 255]
137713 2003 103.800 30.292 3 [255, 12, 255, 255]
54477 1995 105.158 29.542 4 [255, 16, 255, 255]
34049 1997 105.683 29.683 6 [255, 24, 255, 255]
47662 1995 104.108 30.583 16 [255, 64, 255, 255]
2594 1993 104.050 31.225 4 [255, 16, 255, 255]
316246 1999 103.658 29.608 6 [255, 24, 255, 255]
24422 1997 105.442 30.775 4 [255, 16, 255, 255]

Plotting and interacting¶

We can plot this data set of light brightness by year, configuring a slider to filter the data as below:

In [4]:
plottable = df[df['year'] == 1993].to_dict(orient='records')

view_state = pdk.ViewState(
    latitude=31.0,
    longitude=104.5,
    zoom=8)
scatterplot = pdk.Layer(
    'HeatmapLayer',
    data=plottable,
    get_position=['lng', 'lat'],
    get_weight='brightness',
    opacity=0.5,
    pickable=False,
    get_radius=800)
r = pdk.Deck(
    layers=[scatterplot],
    initial_view_state=view_state,
    views=[pdk.View(type='MapView', controller=None)])
r.show()
In [5]:
import ipywidgets as widgets
from IPython.display import display
slider = widgets.IntSlider(1992, min=1993, max=2013, step=2)
def on_change(v):
    results = df[df['year'] == slider.value].to_dict(orient='records')
    scatterplot.data = results
    r.update()
    
slider.observe(on_change, names='value')
display(slider)