Geomapping with Python

1. Matplotlib

2. Bokeh

3. Plotly

conda install basemap

sudo apt-get install python-matplotlib
sudo apt-get install python-mpltoolkits.basemap

#import essential libraries.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import pandas as pd 

#Hope you have downloaded the data. Read it with pandas
df = pd.read_csv('housing.csv')

#initiate the figure with it's size
fig = plt.figure(figsize = (8, 8))

#initialize the Basemap with appropriate arguments. These will be discussed later.
m = Basemap(projection = 'lcc', resolution='f', lat_0 = 37.5, lon_0 = -119,width=1E6, height=1.2E6)
m.bluemarble()
m.drawmapboundary()
m.drawrivers()
m.drawcoastlines()
m.drawcountries()
m.drawstates()

# Scatter plot with latitude and longitude values customizing color with median_house_value and median_income
m.scatter(df['longitude'].values, 
		  df['latitude'].values,
		  latlon = True,
		  c = df.median_house_value.tolist(),
		  s = (10 * np.array(df.median_income.astype(int))),
		  cmap = 'viridis',
		  edgecolors = 'none')

#put a colorbar
plt.colorbar()

#save and show
plt.savefig('matplotlib_plot.png')
plt.show()

conda install bokeh

pip install bokeh

#necessary imports
from bokeh.plotting import figure, show, output_file
from bokeh.tile_providers import CARTODBPOSITRON
import pandas as pd
import numpy as np 
import math
from ast import literal_eval
from bokeh.palettes import Viridis5
from bokeh.models import ColumnDataSource,ColorBar,BasicTicker
from bokeh.models.mappers import ColorMapper, LinearColorMapper

#function to convert latitude and longitude into mercator projection mapping
def merc(Coords):
	Coordinates = literal_eval(Coords)
	lat = Coordinates[0]
	lon = Coordinates[1]
	r_major = 6378137.000
	x = r_major * math.radians(lon)
	scale = x/lon
	y = 180.0/math.pi * math.log(math.tan(math.pi/4.0 + lat * (math.pi/180.0)/2.0)) * scale
	return (x, y)

#supporting function
def make_tuple_str(x, y):
	t = (x, y)
	return str(t)

#read with pandas
df = pd.read_csv('housing.csv')

#converting latitude and longitudes of corners of california into mercator range
range0 = merc('(32.080577, -114.052642)')
range1 = merc('(42.356802, -124.753326)')
x_range = (range0[0],range1[0])
y_range = (range0[1], range1[1])

#now convert DataFrame longitude and latitude column into mercator coordinates
df['coords'] = df.apply(lambda x: make_tuple_str(x['latitude'], x['longitude']), axis = 1)
df['coords_latitude'] = df['coords'].apply(lambda x: merc(x)[0])
df['coords_longitude'] = df['coords'].apply(lambda x: merc(x)[1])

#prepare data
source = ColumnDataSource(
	data = dict(
		lat = df['coords_latitude'].tolist(),
		lon = df['coords_longitude'].tolist(),
		size = df.median_income.tolist(),
		color = df.median_house_value.tolist()
		)
	)

#initiate figure
p = figure(x_range = x_range, y_range = y_range, x_axis_type = "mercator", y_axis_type = "mercator")
p.add_tile(CARTODBPOSITRON)

#color palette
color_mapper = LinearColorMapper(palette = Viridis5)

#add a glyph
p.circle(x = 'lat', y = 'lon', 
		size = 'size',
		color = {'field':'color',
				 'transform':color_mapper},
		source = source 
		)
#put a colorbar to support
color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),
                     label_standoff=12, border_line_color=None, location=(0,0))

#layout
p.add_layout(color_bar, 'right')

#show it in web browser
output_file('example.html')

show(p)

pip install plotly

pip install plotly --upgrade

#imports
from plotly.offline import plot
import pandas as pd

#read data
df = pd.read_csv('housing.csv')

#make a data dictionary
data = [dict(
			type = 'scattergeo',
			lon = df['longitude'],
			lat = df['latitude'],
			mode = 'markers',
			marker = dict(
			size = df['median_income'],
			symbol = 'circle',
			line = dict(
						width=1,
						color='rgba(102, 102, 102)'
						),
			colorscale = 'Viridis',
			cmin = 0,
			color = df['median_house_value'],
			cmax = df['median_house_value'].max(),
			colorbar=dict(
						title="Median House Value"
						)

        ))]

#define the layout
layout = dict(
			title = 'California House pricing',
			geo = dict(
        			resolution = 50,
        			scope = 'usa',
					showland = True,
					landcolor = "rgb(250, 250, 250)",
					subunitcolor = "rgb(217, 217, 217)",
					countrycolor = "rgb(217, 217, 217)",
					countrywidth = 0.5,
					subunitwidth = 0.5,
					center = dict(
								lon = -121.0,
								lat = 35.0
								),
					projection = dict(
								scale = 0.4
								),
					lonaxis = dict(
								 range= [ -127.0, -114.0 ]
								 ),
					lataxis = dict(
								 range= [ 35.0, 38.0 ] 
								 ),
					),
			)
#add data and layout to figure
fig = dict(
		data=data,
		layout=layout
		)
#plot
plot(fig)