Wenyan Deng

Ph.D. Candidate

Massachusetts Institute of Technology

Folium: Interactive Maps

July 21, 2017

The case study

In last time's blog, we saw how to make a static choropleth map. This time, I overlay the choropleth map on an interactive platform using folium. See a copy of the final product in my GitHub repository. The final product of this project is shown in Figure 1.

Figure 1: Interactive map with Folium

Getting started

Open a jupyter notebook and import the following:

%matplotlib inline

import geopandas as gpd

import pandas as pd

import matplotlib.pyplot as plt

Load the geo-dataframe and rename the column so it makes sense at a glance:

geo_df = gpd.read_file("data1/LKA_adm1.shp")

geo_df.rename(columns = {"NAME_1" : "Districts"}, inplace = True)

geo_df.head()

Load the deaths dataframe created last time from the spatial join function:

deaths_geo_count = pd.read_excel("deaths_geo_count.xlsx")

Merge the deaths dataframe and the geo-dataframe by districts:

geo_merge = geo_df.merge(deaths_geo_count, on='Districts', how='left')

col_name =geo_merge.columns[0]

geo_merge.head()

Mapping

Import folium:

import folium

from folium.element import IFrame

from geopy.geocoders import Nominatim

Get the folium base map and set zoom position, then start mapping.

m = folium.Map([8, 79.8], tiles='cartodbpositron', zoom_start=8)

ft = "deaths_per_district"

cmap = folium.colormap.linear.YlOrRd.scale(geo_merge[ft].min(), geo_merge[ft].max())

folium.GeoJson(geo_merge, style_function=lambda feature: {

'fillColor': cmap(feature['properties'][ft]),

'fillOpacity' : 0.7,

'weight' : 1, 'color' : 'black' }).add_to(m)

cmap.caption = 'Deaths by district'

Next, if you want the map to also show the exact locations of deaths, import the original deaths files, which describes the names of the deceased, dates of death, and places of death. Use a "for" loop to add that information to markers as pop-ups.

deaths = pd.read_excel('SLA_RoH_Deng.xlsx')

deaths["DATE"] = deaths["DATE"].astype(str)

deaths["NAME"] = deaths["NAME"].astype(str)

deaths["PLACE"] = deaths["PLACE"].astype(str)

my_marker_cluster = folium.MarkerCluster().add_to(m)

for ix, row in deaths.iterrows():

text = "Name: " + row['NAME'] + "<br>" + "Location: " + str(row['PLACE']) + "<br>" + "Date: " + str(row['DATE'])

popup = folium.Popup(IFrame(text, width=300, height=100))

folium.Marker(location = [row['Latitude'],row['Longitude']], popup=popup).add_to(my_marker_cluster)

Save the map!

m.save("HeatMap.html")

You can zoom in and click on each point to view its associated pop-up (see figure below). Happy mapping!

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