Recreating John Snow's Ghost Map

In 1854, Dr. John Snow used spatial analysis by mapping patterns and occurrences of cholera outbreaks in Soho, London. He mapped the deaths in the neighbourhood and determined that a vast majority occurred around one particular water well and that those that died used that well.

In this Python project, you will reanalyze the data and recreate his famous map.

1. Dr. John Snow

Dr. John Snow (1813-1858) was a famous British physician and is widely recognized as a legendary figure in the history of public health and a leading pioneer in the development of anesthesia. Some even say one of the greatest physicians of all time.

deaths = pd.read_csv('datasets/deaths.csv')

newcols = {
    'Death': 'death_count',
    'X coordinate': 'x_latitude', 
    'Y coordinate': 'y_longitude' 
    }
    
deaths.rename(newcols, inplace=True,axis='columns')
print(deaths.shape)

>>> (489, 3)

print(deaths.head())

	death_count	x_latitude	y_longitude
0	1	51.513418	-0.137930
1	1	51.513418	-0.137930
2	1	51.513418	-0.137930
3	1	51.513361	-0.137883
4	1	51.513361	-0.137883

2. Cholera attacks!

Originally he formulated and published his theory that cholera is spread by water or food in an essay, which received negative reviews, but we know he was right.

print(deaths.describe())

	death_count	x_latitude	y_longitude
count	489.0	489.000000	489.000000
mean	1.0	51.513398	-0.136403
std	0.0	0.000705	0.001503
min	1.0	51.511856	-0.140074
25%	1.0	51.512964	-0.137562
50%	1.0	51.513359	-0.136226
75%	1.0	51.513875	-0.135344
max	1.0	51.515834	-0.132933

3. You know nothing, John Snow!

His medical colleagues simply said: "You know nothing, John Snow!" However, a reviewer made a helpful suggestion in terms of what evidence would be compelling: the crucial natural experiment would be to find people living side by side with lifestyles similar in all respects except for the water source.

4. Snow's Map

We now know how John Snow did it and have the data too, so let's recreate his map using modern techniques.

import folium

locations = deaths[['x_latitude', 'y_longitude']]
deaths_list = locations.values.tolist()

map = folium.Map(location=[51.5132119,-0.13666], tiles='Stamen Toner', zoom_start=17)
for point in range(0, len(deaths_list)):
    folium.CircleMarker(deaths_list[point], radius=8, color='red', fill=True, fill_color='red', opacity = 0.4).add_to(map)
map

5. It's the pump!

After marking the deaths on the map, what John Snow saw was not a random pattern.The majority of the deaths were concentrated at the corner of Broad Street (now Broadwick Street) and Cambridge Street (now Lexington Street). A cluster of deaths around the junction of these streets was the epicenter of the outbreak, but what was there? Yes, a water pump.

pumps = pd.read_csv('datasets/pumps.csv')
locations_pumps = pumps[['X coordinate', 'Y coordinate']]
pumps_list = locations_pumps.values.tolist()

map1 = map
for point in range(0, len(pumps_list)):
    folium.Marker(pumps_list[point], popup=pumps['Pump Name'][point]).add_to(map1)
map1

6. The picture worth a thousand words

Removing the handle from the pump prevented any more of the infected water from being collected. The spring below the pump was later found to have been contaminated with sewage. This act was later recognized as an early example of epidemiology, public health medicine and the application of science (the germ theory of disease) in a real-life crisis.

import bokeh
from bokeh.plotting import output_notebook, figure, show
output_notebook(bokeh.resources.INLINE)

# Set up figure
p = figure(plot_width=900, plot_height=450, x_axis_type='datetime', tools='lasso_select, box_zoom, save, reset, wheel_zoom',
          toolbar_location='above', x_axis_label='Date', y_axis_label='Number of Deaths/Attacks', 
          title='Number of Cholera Deaths/Attacks before and after 8th of September 1854 (removing the pump handle)')

# Plot on figure
p.line(dates['date'], dates['deaths'], color='red', alpha=1, line_width=3, legend='Cholera Deaths')
p.circle(dates['date'], dates['deaths'], color='black', nonselection_fill_alpha=0.2, nonselection_fill_color='grey')
p.line(dates['date'], dates['attacks'], color='black', alpha=1, line_width=2, legend='Cholera Attacks')

show(p)

john_snow_knows_nothing = False