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
56025 2009 104.633 31.667 5 [255, 20, 255, 255]
235576 2011 103.708 30.692 12 [255, 48, 255, 255]
315528 1999 104.325 29.750 5 [255, 20, 255, 255]
186758 2007 104.275 31.283 6 [255, 24, 255, 255]
52093 1995 103.433 29.908 4 [255, 16, 255, 255]
277042 2005 104.208 30.633 22 [255, 88, 255, 255]
266630 2005 104.325 31.200 6 [255, 24, 255, 255]
254887 2011 104.358 29.717 5 [255, 20, 255, 255]
1359 1993 104.825 31.433 5 [255, 20, 255, 255]
265312 2005 104.933 31.300 3 [255, 12, 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()
Out[4]:
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)