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
281084 2005 103.508 30.450 6 [255, 24, 255, 255]
295327 2005 104.117 29.650 3 [255, 12, 255, 255]
316084 1999 102.192 29.625 4 [255, 16, 255, 255]
69956 2009 105.442 30.650 5 [255, 20, 255, 255]
61382 2009 104.550 31.183 4 [255, 16, 255, 255]
83199 2009 103.750 29.667 5 [255, 20, 255, 255]
119350 2003 104.267 31.392 4 [255, 16, 255, 255]
186183 2007 104.392 31.325 4 [255, 16, 255, 255]
272552 2005 104.075 30.858 8 [255, 32, 255, 255]
222418 2011 104.375 31.400 6 [255, 24, 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)