Leaflet.js choropleth map color by count using geoJSON datasource

I have a Django web application that needed an interactive map with shapes corresponding to Canadian postal code FSA areas that were different colors based on how many properties were in each FSA. It ended up looking something like the screenshot below.


This exercise turned out to be relatively easy using the awesome open-source Javascript map library Leaflet.js.

I used this Leaflet.js tutorial as the foundation for my map.

One of the biggest challenges was finding a suitable data source for the FSAs. Chad Skelton (now former) data journalist at the Vancouver Sun wrote a helpful blog post about his experience getting a suitable FSA data source. I ended up using his BC FSA data source for my map.

Statistics Canada hosts a Canada Post FSA boundary files for all of Canada. As Chad Skelton notes these have boundaries that extend out into the ocean among other challenges.

Here is a summary of the steps that I followed to get my choropleth map:

1. Find and download FSA boundary file. See above.

2. Convert FSA boundary file to geoJSON from SHP file using qGIS.

3. Create Django queryset to create data source for counts of properties by FSA to be added to the Leaflet map layer.

4. Create Leaflet.js map in HTML page basically the HTML DIV that holds the map and separate Javascript script that loads Leaflet.js, the FSA geoJSON boundary data and processes it to create the desired map.

Find and download FSA boundary file.

See above.

Convert FSA boundary file to geoJSON from SHP file using qGIS.

Go to http://www.qgis.org/en/site/ and download qGIS. Its free and open source.

Use qGIS to convert the data file from Canada Post or other source to geoJSON format. Lots of blog posts and documentation about how to use qGIS for this just a Google search away.

My geoJSON data source looked like this:

var bcData = {
    "type": "FeatureCollection",
    "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4269" } },
    "features": [
    { "type": "Feature", "properties": { "CFSAUID": "V0A", "PRUID": "59", "PRNAME": "British Columbia \/ Colombie-Britannique" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -115.49499542, 50.780018587000029 ], [ -115.50032807, 50.77718343600003 ], [ -115.49722732099997, 50.772528975000057 ], [ -115.49321284, 50.770504059000075 ], [ -115.49393662599999, 50.768143038000062 ], [ -115.50289288699997, 50.762270941000054 ], [ -115.50846411599997, 50.754243300000041 ], [ -115.5104796, 50.753297703000044 ], [ -115.51397592099994, 50.748953800000038 ], [ -115.51861431199995, 50.745737989000077 ], [ -115.52586378899997, 50.743771099000071 ], [ -115.53026371899995, 50.74397910700003 ], [ -115.53451319199996,


Create Django queryset to create data source for counts of properties by FSA to be added to the Leaflet map layer.

I used a SQL query in the Django View to get count of properties by FSA.

This dataset looks like this in the template. These results have only one FSA, if it had more it would have more FSA / count pairs.

   var fsa_array = [["V3J", 19]];

Below is code for  the Django view query to create the fsa_array FSA / counts data source.

    cursor = connection.cursor()
    "select fsa, count(*) \
    from properties \
    group by fsa \
    order by fsa;")
    fsas_cursor = list(cursor.fetchall())

    fsas_array = [(x[0].encode('utf8'), int(x[1])) for x in fsas_cursor]

My Javascript largely retains the Leaflet tutorial code with some modifications:

1. How the legend colors and intervals are assigned is changed but otherwise legend functions the same.

2. Significantly changed how the color for each FSA is assigned. The tutorial had the color in its geoJSON file so only had to reference it directly. My colors were coming from the View so I had to change code to include new function to match FSA’s in both my Django view data and the geoJSON FSA boundary file and return the appropriate color based on the Django View data set count.

var map = L.map('map',{scrollWheelZoom:false}).setView([ active_city_center_lat, active_city_center_lon], active_city_zoom);

map.once('focus', function() { map.scrollWheelZoom.enable(); });

var fsa_array = fsas_array_safe;

L.tileLayer('https://api.tiles.mapbox.com/v4/{id}/{z}/{x}/{y}.png?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpandmbXliNDBjZWd2M2x6bDk3c2ZtOTkifQ._QA7i5Mpkd_m30IGElHziw', {
    maxZoom: 18,
    attribution: 'Map data © OpenStreetMap contributors, ' +
        'CC-BY-SA, ' +
        'Imagery © Mapbox',
    id: 'mapbox.light'

// control that shows state info on hover
var info = L.control();

info.onAdd = function (map) {
    this._div = L.DomUtil.create('div', 'info');
    return this._div;

info.update = function (props) {
    this._div.innerHTML = (props ?
        '' + props.CFSAUID + ' ' + getFSACount(props.CFSAUID) + ' lonely homes' 
        : 'Hover over each postal area to see lonely home counts to date.');


// get color 
function getColor(n) {
    return n > 30 ? '#b10026'
           : n > 25 ? '#e31a1c' 
           : n > 25 ? '#fc4e2a' 
           : n > 20 ? '#fd8d3c'
           : n > 15  ? '#feb24c'
           : n > 10  ? '#fed976'
           : n > 5  ? '#ffeda0'
           : n > 0  ? '#ffffcc'
           : '#ffffff';

function getFSACount(CFSAUID) {
    var fsaCount;
    for (var i = 0; i < fsa_array.length; i++) {
        if (fsa_array[i][0] === CFSAUID) {
            fsaCount = ' has ' + fsa_array[i][1];
    if (fsaCount == null) {
         fsaCount = ' has no '; 
    return fsaCount;

function getFSAColor(CFSAUID) {
    var color;
    for (var i = 0; i < fsa_array.length; i++) {
    if (fsa_array[i][0] === CFSAUID) {
        color = getColor(fsa_array[i][1]);
        //console.log(fsa_array[i][1] + '-' + color)
    return color;
function style(feature) {
    return {
        weight: 1,
        opacity: 1,
        color: 'white',
        dashArray: '3',
        fillOpacity: 0.7,
        fillColor: getFSAColor(feature.properties.CFSAUID)

function highlightFeature(e) {
    var layer = e.target;
        weight: 2,
        color: '#333',
        dashArray: '',
        fillOpacity: 0.7

    if (!L.Browser.ie && !L.Browser.opera) {


var geojson;

function resetHighlight(e) {

function zoomToFeature(e) {

function onEachFeature(feature, layer) {
        mouseover: highlightFeature,
        mouseout: resetHighlight,
        click: zoomToFeature

geojson = L.geoJson(bcData, {
    style: style,
    onEachFeature: onEachFeature

var legend = L.control({position: 'bottomright'});

legend.onAdd = function (map) {

    var div = L.DomUtil.create('div', 'info legend'),
        grades = [0, 1, 5, 10, 15, 20, 25, 30],
        labels = [],
        from, to;

    for (var i = 0; i < grades.length; i++) {
        from = grades[i];
        if (i === 0) {
            var_from_to = grades[i];
            var_color = getColor(from);
        } else {
            var_from_to =  from + (grades[i + 1] ? '–' + grades[i + 1] : '+') ;
            var_color = getColor(from + 1);
            ' ' +

    div.innerHTML = labels.join('
'); return div; }; legend.addTo(map);

That is pretty much all there is to creating very nice looking interactive free open-source choropleth maps for your Django website application!

Python get image color palette

I created a web application that included screenshots of about 190 country’s national statistics agencies website home page. I created the site to host the results of comparisons of each country’s website home page features.

One of the features I wanted to include was the top 5 colors used on each home page. For example here is an image of the Central Statistical Office of Zambia website home page.


I wanted a list of the top 5 colors used in the web page, in my case I wanted these as a list of  rgb color values that I could save to a database and use to create a color palette image.

For example the Central Statistical Office of Zambia website home page top 5 rgb colors were:

  1. 138, 187, 223
  2. 174, 212, 235
  3. 101, 166, 216
  4. 93, 92, 88
  5. 242, 245, 247

These 5 colors were not equally distributed on the home page and when plotted as stacked bar plot and saved as an image, looked like this:zambia

How to identify top 5 colors on webpage

Identifying dominant colors in an image is a common task for a variety of use cases so there was a number of options available.

The general technique essentially involves reducing an image to a list of pixels and then identifying each pixel’s color and the relative proportion of that color to all other colors and then taking the top 5 pixel counts to get top 5 colors.

However a web page can have many similar colors for example many shades of blue. So  identification of colors involves a statistical categorization and enumeration to group similar colors into one category, for example, group all shades of blue into one ‘blue’ category.

There are a couple of different statistical methods to do this categorization that are discussed below.

Modified Median Cut Quantization

This method involves counting image pixels by color and charting them on a histogram from which peaks are counted to get dominant colors.

This method is used in a Python module called color-thief-py. This module uses Pillow to process the image and modified median cut quantization

I used this module in the code below where I loop through a folder of screenshots to open image and then pass it to color-thief-py to process. Then the top 5 colors’ rgb strings are written into my database as a list so they can be used later.

    import os, os.path
    from PIL import Image
    import psycopg2
    from colorthief import ColorThief

    conn_string = \
        "host='localhost' \
        dbname='databasename' \
        user='username' \
    conn = psycopg2.connect(conn_string)     
    cur = conn.cursor()

    ## dev paths
    screenshots_path = 'C:/screenshots/'

    screenshots_dir = os.listdir(screenshots_path)
    for screenshot in screenshots_dir:
        if screenshot != 'Thumbs.db':
            img = Image.open(screenshots_path + screenshot)
            width, height = img.size
            quantized = img.quantize(colors=5, kmeans=3)
            palette = quantized.getpalette()[:15]
            convert_rgb = quantized.convert('RGB')
            colors = convert_rgb.getcolors(width*height)
            color_str = str(sorted(colors, reverse=True))
            color_str = str([x[1] for x in colors])
            print screenshot + ' ' + str(img.size[1]) + ' ' + color_str
            cur.execute("UPDATE screenshots \
            set color_palette = %s,  \
            height = %s \
            WHERE filename like %s", \
            (str(color_str),img.size[1], '%' + screenshot + '%',))

K-means clustering

Another statistical method to identify dominant colors in an image is using K-means clustering to group image pixels by rgb values into centroids. Centroid’s with highest counts are identified as dominant colors.

This method is used in the process I found on this awesome blog post on pyimagesearch.com.

This method also uses Python module sklearn k-means to identify the dominant colors. It produced very similar results to the other method.

This code from the pyimagesearch website used Python module matplotlib to create plot of the palette and saved these as images. I modified the code to instead simply save the rgb color values as strings to insert into database and save the matplotlib palette rendered plot as an image. I  added plt.close() after each loop to close the rendered plot after the plot image was saved because if they aren’t closed, they accumulate in memory and crashed the program.

# python color_kmeans.py --image images/jp.png --clusters 3

# import the necessary packages
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
import utils
import cv2
import os, os.path
import csv

# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required = True, help = "Path to the image")
#ap.add_argument("-c", "--clusters", required = True, type = int, help = "# of clusters")
#args = vars(ap.parse_args())

screenshots_path = 'screenshots/'

screenshot_palette = list()

## Create csv file to write results to
file = csv.writer(open('palettes.csv', 'wb'))

screenshots_dir = os.listdir(screenshots_path)
for screenshot in screenshots_dir:
    if screenshot != 'Thumbs.db':
        print screenshot

        # load the image and convert it from BGR to RGB so that
        # we can dispaly it with matplotlib
        image = cv2.imread(screenshots_path + screenshot)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # show our image

        # reshape the image to be a list of pixels
        image = image.reshape((image.shape[0] * image.shape[1], 3))

        # cluster the pixel intensities
        clt = KMeans(n_clusters = 3)

        # build a histogram of clusters and then create a figure
        # representing the number of pixels labeled to each color
        hist = utils.centroid_histogram(clt)
        bar = utils.plot_colors(hist, clt.cluster_centers_)
        #color_palette = utils.plot_colors(hist, clt.cluster_centers_)
        #print color_palette
        #row = (screenshot, [tuple(x) for x in color_palette])
        #print row
        #print row[0]
        #print row[1]
        # show our color bar
        plt.savefig('palettes/' + screenshot)
        #        row[0].encode('utf-8', 'ignore'),
        #        row[1],
        #        ])