Chris Essig

Walkthroughs, tips and tricks from a data journalist in eastern Iowa

Archive for the ‘Maps’ Category

Leaflet formula: Counting markers within a radius (version 2)

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A couple of years ago, I open-sourced a project that allows you to count how many markers are within a circle using Leaflet. As part of the template, a user needs to enter an address to get a marker to show up on the map.

Two weeks ago, Jefferson Guerrón reached out to me because he was creating something similar that didn’t require a user to type in their address. He was using the plugin Leaflet.draw to get the marker on the map. But he was having a hard time getting a circle to show up around the marker and count the other markers within that circle.

After a few back-and-forth emails, we were able to come up with a solution. Here’s a look of the final project. And the code is available on Github.

If you have questions, don’t hesitate to e-mail or comment.

Written by csessig

December 14, 2016 at 5:15 pm

Building your first Leaflet.js map

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Earlier this year, I had the privilege of teaching a class on building your first Leaflet.js map at the NICAR conference. I just realized I forgot to post the code in this blog so I figured I’d post it now. Better later than never.

If you’re interested in building your first map, check out my Github repo for the presentation.

Written by csessig

August 17, 2016 at 7:58 am

Leaflet formula: Counting markers within a radius

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ssThe last project I worked on at The Courier was a comprehensive guide to mental health and disability services in NE Iowa. For the project, we first wanted to explain the MHDS system to our readers and how it works. It’s a relatively new system with a lot of moving parts and no good way to search for services. We really wanted to help residents and families in need of these services, so we designed everything — from the wording we used to the way items were laid out — to be focused on our readers.

After laying out the details of the new system, we wanted to allow our readers to search for services near them. A map seemed like an obvious choice; but we wanted to do more than just have them search for their address and plot a dot on the map.

(As a side note, we used Ben Welsh‘s jquery-geocodify plugin to geocode the addresses because it’s awesome.)

Instead, we wanted to show readers how many providers were close to them, as well as how to get ahold of the service provider and directions to the facility. For the former task, we used the following process:

First, the providers are plotted on the map. Then, we ask the reader to type in their address, which is plotted on the map using jquery-geocodify. They also have a second option for radius, which will be used to determine how many providers are within whatever radius they have given. They have four options: 5, 10, 25 and 50 miles.

Then, as the markers are getting plotted on the map, we run a few Javascript functions to calculate how many markers on the map are within the radius.

I’ve pulled out just this part of the mental health project and open-sourced it. You can view the full code over at my Github page.

The demo uses data on mental health providers, which is contained in this JSON fileand plots them on the map based on each provider’s latitude and longitude coordinates.

Here’s a quick run through the Javascript that makes all this run. It’s contained within the project’s script.js file. We’ll start from the bottom of the file and move our way up.

Note: This walkthrough assumes a decent understanding of Leaflet. I’m also using Leaflet-Awesome Markers and Font Awesome in this project.

First, we’ll add the base map tiles to our map. We’ll also  add the json_group to the map, which is a FeatureGroup we will create later in the file:

// Base map
var layer = new L.StamenTileLayer('toner-background');
var map = new L.Map('map', {
    center: new L.LatLng(42,-93.3),
    minZoom: 4,
    maxZoom: 10,
    zoom: 6,
    keyboard: false,
    boxZoom: false,
    doubleClickZoom: false,
    scrollWheelZoom: false,
    maxBounds: [[33.154799,-116.586914],[50.190089,-77.563477]]
// Add base layer to group
// Add our markers in our JSON file on the map

Next, we’ll add the code for jquery-geocodify. For the onSelect function, we will grab the location of reader’s address and then call a geocodePlaceMarkersOnMap function, which we will create later:

// jQuery Geocodify
var maxY = 43.749935;
var minY = 40.217754;
var minX = -96.459961;
var maxX = -90.175781;

var search_marker;
var search_icon = L.AwesomeMarkers.icon({
    icon: 'icon-circle',
    color: 'green'

    onSelect: function (result) {
        // Extract the location from the geocoder result
        var location = result.geometry.location;

        // Call function and place markers, circle on map
    initialText: 'Zip code, city, etc...',
    regionBias: 'US',
    // Lat, long information for Cedar Valley enter here
    viewportBias: new google.maps.LatLngBounds(
        new google.maps.LatLng(40.217754, -96.459961),
        new google.maps.LatLng(43.749935, -90.175781)
    width: 300,
    height: 26,
    fontSize: '14px',
    filterResults: function (results) {
        var filteredResults = [];
        $.each(results, function (i, val) {
            var location = val.geometry.location;
            if ( > minY && < maxY) {
                if (location.lng() > minX && location.lng() < maxX) {
        return filteredResults;

Next, we’ll loop through our providers.json file, which has a variable of json_data, and create a marker for each object in our array. We’ll then add it to our json_group FeatureGroup, which is defined at the top of our script file:

// This loops through the data in our JSON file
// And puts it on the map
_.each(json_data, function(num) {
    var dataLat = num['latitude'];
    var dataLong = num['longitude'];

    // Add to our marker
    var marker_location = new L.LatLng(dataLat, dataLong);

    // Options for our circle marker
    var layer_marker = L.circleMarker(marker_location, {
        radius: 7,
        fillColor: "#984ea3",
        color: "#FFFFFF",
        weight: 1,
        opacity: 1,
        fillOpacity: 0.8

    // Add events to marker
        // What happens when mouse hovers markers
        mouseover: function(e) {
            var layer_marker =;
                radius: 8,
                fillColor: "#FFFFFF",
                color: "#000000",
                weight: 1,
                opacity: 1,
                fillOpacity: 1
        // What happens when mouse leaves the marker
        mouseout: function(e) {
            var layer_marker =;
                radius: 7,
                fillColor: "#984ea3",
                color: "#FFFFFF",
                weight: 1,
                opacity: 1,
                fillOpacity: 0.8
    // Close event add for markers

// Close for loop
}, this);

The changeCircleRadius function is called anytime the radius is changed on the page. It first calls the pointsInCircle function, which we will define later, and then sets the radius of the circle using Leaflet’s setRadius function for circles.

Leaflet uses meters, so we will need to convert miles to meters using the milesToMeters function, which will be defined later. The mile’s value is contained within the DIV with the id of radius-selected. We’ll grab the value, which is the radius in miles, convert it to meters and then set the radius of the circle using Leaflet.

// Change circle radius when changed on page
function changeCircleRadius(e) {
    // Determine which geocode box is filled
    // And fire click event

    // This will determine how many markers are within the circle
    pointsInCircle(circle, milesToMeters( $('#radius-selected').val() ) )

    // Set radius of circle only if we already have one on the map
    if (circle) {
        circle.setRadius( milesToMeters( $('#radius-selected').val() ) );

$('select').change(function() {

The geocodePlaceMarkersOnMap function is called every time the reader enters an address into our geocoder and presses enter. It passes with it the location of their address, which we get from jquery-geocodify. With this function, we will create both a circle and a marker on top of the given address.

First, we set the view of the map based on this location and remove the circle on the map if there already is one. Then we actually create a circle and set how large it is based on the radius set by the user.

We then remove the marker if it’s already on the map and create a new one. When we create a new marker, we’ll set it to draggable and set its dragend method to reset the view of the map every time the reader drags the marker somewhere on the. It then calls the pointsInCircle function, which we will define next. Finally, we’ll call the same pointsInCircle function and add the marker to the map.

// This places marker, circle on map
function geocodePlaceMarkersOnMap(location) {
    // Center the map on the result
    map.setView(new L.LatLng(, location.lng()), 10);

    // Remove circle if one is already on map
    if(circle) {
    // Create circle around marker with our selected radius
    circle =[, location.lng()], milesToMeters( $('#radius-selected').val() ), {
        color: 'red',
        fillColor: '#f03',
        fillOpacity: 0.1,
        clickable: false
    // Remove marker if one is already on map
    if (search_marker) {
    // Create marker
    search_marker = L.marker([, location.lng()], {
        // Allow user to drag marker
        draggable: true,
        icon: search_icon

    // Reset map view on marker drag
    search_marker.on('dragend', function(event) {
        map.setView( ); 
        circle.setLatLng( );

        // This will determine how many markers are within the circle
        pointsInCircle( circle, milesToMeters( $('#radius-selected').val() ) );

        // Redraw: Leaflet function

        // Clear out address in geocoder

    // This will determine how many markers are within the circle
    // Called when points are initially loaded
    pointsInCircle( circle, milesToMeters( $('#radius-selected').val() ) );

    // Add marker to the map
// Close geocodePlaceMarkersOnMap

The pointsInCircle function is the last function called when one of two things happen: the user enters an address or the user drags the marker on the map. Its purpose is to find out how many markers on the map are within the circle we created and then display that number in the legend.

Fortunately, Leaflet has a really handy distanceTo method, which determines how many meters are within two points. It uses the two points’ latitude and longitude coordinates to find this out.

With the pointsInCircle function, we first capture the lat, long of the circle we’re putting on the map. We then loop through our providers.json file, find the latitude and longitude coordinates of each object and determine how far it is from our circle using the distanceTo method.

We then decide if that amount is higher or lower than the radius set by the user. If it is lower, we add it to a counter. When we’re done, that counter will equal the number of markers within the selected radius. It will be put on the map under the “Results” header.

Lastly, we will determine how many results we have and use the correct wording on the page. If we have one result, we’ll use the word “provider” (singular) in the map’s legend. Otherwise, we’ll use “providers.”

// This figures out how many points are within out circle
function pointsInCircle(circle, meters_user_set ) {
    if (circle !== undefined) {
        // Only run if we have an address entered
        // Lat, long of circle
        circle_lat_long = circle.getLatLng();

        // Singular, plural information about our JSON file
        // Which is getting put on the map
        var title_singular = 'provider';
        var title_plural = 'providers';

        var selected_provider = $('#dropdown_select').val();
        var counter_points_in_circle = 0;

        // Loop through each point in JSON file
        json_group.eachLayer(function (layer) {

            // Lat, long of current point
            layer_lat_long = layer.getLatLng();

            // Distance from our circle marker
            // To current point in meters
            distance_from_layer_circle = layer_lat_long.distanceTo(circle_lat_long);

            // See if meters is within raduis
            // The user has selected
            if (distance_from_layer_circle <= meters_user_set) {
                counter_points_in_circle += 1;

        // If we have just one result, we'll change the wording
        // So it reflects the category's singular form
        // I.E. facility not facilities
        if (counter_points_in_circle === 1) {
            $('#json_one_title').html( title_singular );
        // If not one, set to plural form of word
        } else {
            $('#json_one_title').html( title_plural );
        // Set number of results on main page
        $('#json_one_results').html( counter_points_in_circle );
// Close pointsInCircle

The final piece is to add the variables we’re using to the top of the script file. We’ll also add the milesToMeters function, which converts miles to meters.

// We'll append our markers to this global variable
var json_group = new L.FeatureGroup();
// This is the circle on the map that will be determine how many markers are around
var circle;
// Marker in the middle of the circle
var search_marker;
// Marker icon
var search_icon = L.AwesomeMarkers.icon({
    icon: 'icon-circle',
    color: 'red'

// Convert miles to meters to set radius of circle
function milesToMeters(miles) {
    return miles * 1069.344;

I hope this walkthrough helps fellow Leaflet users! If you have any questions, leave them in the comments.

Happy coding.

Written by csessig

June 22, 2014 at 1:32 pm

Using hexagon maps to show areas with high levels of reported crime

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Screenshot: Violent crime in WaterlooCrime maps are a big hit at news organizations these days. And with good reason: Good crime maps can tell your readers a whole lot about crime not only in their city but their backyard.

For the last year and a half, we’ve been doing just that. By collecting data every week from our local police departments, we’ve been able to provide our community a great, constantly updated resource on crime in Waterloo and Cedar Falls. And we’ve even dispelled some myths on where crime happens along the way.

The process, which I’ve blogged about in the past, has evolved since the project was first introduced. But basically it goes like this: I download PDFs of crime reports from the Waterloo and Cedar Falls police departments every week. Those PDFs are scraped using a Python script and exported into an CSV. The script pulls out reports of the most serious crimes and disregards minor reports like assistance calls.

The CSV is then uploaded to Google spreadsheets as a back up. We also use this website to get latitude, longitude coordinates from the addresses in the Google spreadsheet. Finally, the spreadsheet is converted into JSON using Mr. Data Converter and loaded onto our FTP server. We use Leaflet to map the JSON files.

We’ve broken the map into months and cities, meaning we have a map and a corresponding JSON file for each month for both Waterloo and Cedar Falls. We also have JSON files with all of the year’s crime reports for both Waterloo and Cedar Falls.

The resulting maps are great for readers looking for detailed information on crime in their neighborhood. It also provides plenty of filters so readers can see what areas have more shootings, assaults, burglaries, etc.

But it largely fails to give a good overview of what areas of the cities had the most reported crimes in 2013 for two reasons: 1) Because of the sheer number of crime reports (3,684 reports in 2013 for Waterloo alone) and the presence of multiple reports coming from the same address, it’s nearly impossible to simply look at the 2013 maps and see what areas of the cities had the most reports, and; 2) The JSON files containing all of the year’s reports runs well on modern browsers but runs more slowly outdated browsers (I’m looking at you, Internet Explorer), which aren’t equipped to iterate over JSON files with thousands of items.

As a result, I decided to create separate choropleth maps that serve one purpose: To show what areas of the cities had the most reported crimes. The cities would be divided into sections and areas with more crimes would be darker on the map, while the areas with fewer crimes would be lighter. Dividing the city into sections would also mean the resulting JSON file would have dozens of items instead of thousands. The map would serve a simple purpose and load quickly.

What would the areas of the cities actually look like? I’ve been fascinated with hex maps since I saw this gorgeous map from the L.A. Times on 9/11 response times. The map not only looked amazing but did an incredibly effective job displaying the data and telling the story. While we want our map to look nice, telling  its story and serving our journalistic purpose will always be our No. 1 goal. Fortunately, hex maps do both.

And even more fortunately, the process of creating hex maps got a whole lot easier when data journalist Kevin Schaul introduced a command line tool called binify that turns dot density shapefiles into shapefiles with hexagon shapes. Basically the map is cut into hexagon shapes, and all the points inside each hexagon are counted. Then each hexagon is given a corresponding count value for the number points that fall in between its borders.

I used binify to convert our 2013 crime maps into hex maps. As I mentioned earlier, we started out with CSVs with all the crimes. My workflow for creating the hex maps and then converting them into JSON files for Leaflet is in a gist below. It utilizes the fantastic command line tool og2ogr, which is used to convert map files, and binify.

# We start with a CSV called "crime_master_wloo_violent.csv" that has violent crime reports in Waterloo.
# Included in csv are columns for latitude ("lat") and longitude ("long"):

# Command to turn CSV into DBF with the same name
ogr2ogr -f "ESRI Shapefile" crime_master_wloo_violent.dbf crime_master_wloo_violent.csv

# Create a file called "crime_master_wloo_violent.vrt"
# Change "SrcLayer" to the name of the source
# Change OGRVRTLayer name to "wloo_violent"
# Add name of lat, long columns to "GeometryField"
  <OGRVRTLayer name="wloo_violent">
    <SrcDataSource relativeToVRT="1">./</SrcDataSource>
    <GeometryField encoding="PointFromColumns" x="long" y="lat"/>

# Create "violent" folder to stash shp file
mkdir violent

# Convert "vrt" file into shp and put it in our "violent" folder
ogr2ogr -f "ESRI Shapefile" violent crime_master_wloo_property.vrt

# Binify the "wloo_violent" shp into a shp called "wloo_violent_binify" with three options
# The first sets the number of hexagons across to 45
# The second sets the custom extent to a box around Waterloo
# Custom extent is basically the extent of the area that the overlay of hexagons will cover
# The third tells binify to ignore hexagons that have zero points
binify wloo_violent.shp wloo_violent_binify.shp -n 45 -E -92.431873 -92.260867 42.4097259 42.558403 --exclude-empty

# Convert binified shp into JSON file called "crime_data_review_violent"
ogr2ogr -f "GeoJSON" crime_data_review_violent.json wloo_violent_binify.shp

# Add this variable to JSON file
var crime_data_review_violent

# Here's the final files:

# And our JSON file:

# And the Javascript file that is used with Leaflet to run the map.
# Lines 113 through 291 are the ones related to pulling the data from the JSON files,
# coloring them, setting the legend and setting a mouseover for each hexagon

# And the map online:

The above shows the process I took for creating our hex map of violent crimes in Waterloo. I duplicated this process three more times to get four maps in all. One showed property crimes in Waterloo, while the other two show violent and property crime in Cedar Falls.

* Note: For more information on converting shapefiles with ogr2ogr, check out this blog post from Ben Balter. Also, this cheat sheet of ogr2ogr command line tools was invaluable as well.

Another great resource is this blog post on minifying GeoJSON files from Bjørn Sandvik. While the post deals with GeoJSON files specifically, the process works with JSON files as well. Minifying my JSON files made them about 50-150 KB smaller. While it’s not a drastic difference, every little bit counts.

To get choropleth maps up and running in Leaflet, check out this tutorial. You can also check out my Javascript code for our crime maps.

One last note: The map is responsive. Check it out.

Written by csessig

January 11, 2014 at 4:42 pm

Creating responsive maps with Leaflet, Google Docs

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wloo_history_teaserNote: This is cross-posted from Lee’s data journalism blog, which you can read over there by clicking here.

Quick: Name five people you know who don’t have a smartphone.

Stumped? Yeah, me too. The fact is more and more people have smartphones and are using them to keep up with the world.

What does that mean for news app developers? We need to be especially conscience of the mobile platform and make sure everything we build for the web is compatible on the smallest of screens.

One great way to do this is creating apps through responsive design. The idea behind responsive design is creating one web page for all users, as opposed to making separate pages for desktop and mobile computers.

Then we simply add, rearrange, subtract or tweak features on a web page based on the size of the browser the user has when they are viewing the app.

Maps can be difficult to manage on mobile platforms, especially when you add in legends, info boxes, etc. But they are not impossible. Fortunately Leaflet, an alternative to Google maps, is designed to work especially well on mobile platforms.

In this example, we will be loading data into a Google spreadsheet and using Leaflet to map the data on a responsive map.

1. Learn a little bit of Leaflet

Before we start, it would probably be best if you familiarize yourself with Leaflet. Fortunately, their website has some wonderful walk-throughs. I’d recommend going through this one before going anything further.

2. Grab the code

Now, go to my Github page and download the template.

The Readme file includes instructions on how to set up a basic map using Google spreadsheets and Tabletop.js, which is a wonderful tool that allows us to do all kinds of things with data in a Google spreadsheet, including map it using Leaflet.

3. Edit your index.html page

After you have followed the instructions on my Github page, you should have a map ready to go.

All you have to do is go into the index.html page and edit the title of the map, as well as add your own information into the “sidebar_content” div. Also make sure you add your name to the credits because you deserve credit for this awesome map you are putting together.

4. How does it work?

Now open up your map in a browser. If you rearrange the browser window size, you’ll notice that the map rearranges its size. The other components on the page also automatically readjust.

Some of this is done with the Bootstrap web framework, which was designed with responsive designing in mind.

I’ve also added my own CSS. One easy thing I’ve done with elements on the page is declare their widths and heights using percentages instead of pixels. This ensures that the components will automatically be adjusted regardless of the screen size.

Take a look at our css/styles.css file to get an idea of what I’m talking about: 

/* Body */
body {
    padding-left: 0px;
    padding-right: 0px;
  margin: 0;
    height: 100%;

html {
	height: 100%;

/* Map */
#map {
	position: absolute;
	float: left;
	top: 1%;
	height: 98%;
	width: 100%;
	z-index: 1;

The map’s height is 98 percent and width is 100 percent, ensuring it changes its size when the browser changes its size. If we set it to 600 pixels wide, the map would stay 600 pixels wide, even when the browser was adjusted.

– You’ll notice some other changes. For instance, if you have a wide screen, the map’s sidebar will be on the right side of the screen. We did this by using absolute positioning to place the sidebar and its content on the page:

/* Sidebar */
#sidebar {
  position: absolute;
	top: 2%;
	right: 1%;
	height: 96%;
	width: 30%;
	z-index: 2;
	border: 1px solid #999;
	padding-left: 1%;
	padding-right: 1%;
	background-color: #FFFFFF;

#sidebar h3 {
	line-height: 30px;

#sidebar_content {
	float: left;
	width: 30%;
	height: 70%;
	position: fixed;
	overflow: auto;
	padding-top: 5px;

The sidebar’s “right” position is set to 1 percent. This ensures that the sidebar will appear only 1 percent from the right side of the page. Additionally, its “top” position is set to 2 percent. This, effectively, pushes it to the top right corner of the screen.

We also used percentages to declare widths, heights and padding lengths for the sidebar.

– You’ll also notice when your browser is reduced drastically, the content of the sidebar disappears off the page. Instead, we have just the title of the sidebar at the top of the page. This is done with CSS media queries:

/* Styles from mobile devices */
@media (max-width: 625px) {
  #sidebar_content {
  	display: none;

  /* Sidebar */
  #sidebar {
		position: relative;
		margin-top: 0%;
		float: left;
		left: 0%;
		right: 0%;
		top: 0%;
		padding-left: 2%;
		padding-right: 2%;
		height: 35px;
		width: 96.5%;


Basically what the above code is saying is: If the browser is 625 pixels wide or smaller, apply the following CSS styles. These styles would therefore apply to almost all mobile phones. So what you are saying to the browser is: If this is a mobile browser, apply these styles to the elements on the page.

The first thing we do is hide the “sidebar_content” div, which is within our main “sidebar” div. Besides the “sidebar_content” div, we also have a div within the “sidebar” div called “sidebar_header” for our title. The template sets the title to “Tabletop to Leaflet” initially, although you should change that to match your project.

We hide the “sidebar_content” div with the property “display: none.” Hiding it ensures that the only thing left in our “sidebar” div is the title. Then the sidebar is pushed to the top left corner of the page using absolute positioning.

So what do we do with that information we have hidden? We put it in another div using some Javascript. Then we toggle that div from hidden to visible using a button with the class “toggle_description.” This toggle feature is enabled using jQuery.

From our js/script.js file:

// Toggle for 'About this map' and X buttons
// Only visible on mobile
isVisibleDescription = false;
// Grab header, then content of sidebar
sidebarHeader = $('#sidebar_header').html();
sidebarContent = $('#sidebar_content').html();
// Then grab credit information
creditsContent = $('#credits_content').html();
$('.toggle_description').click(function() {
  if (isVisibleDescription === false) {
		// Add Sidebar header into our description box
		// And 'Scroll to read more...' text on wide mobile screen
		$('#description_box_header').html(sidebarHeader + '<div id="scroll_more"><strong>Scroll to read more...</strong></div>');
		// Add the rest of our sidebar content, credit information
		$('#description_box_content').html(sidebarContent + '<br />' + 'Credits:' + creditsContent);
		isVisibleDescription = true;
	} else {
		isVisibleDescription = false;

The above code first grabs the information from the “sidebar_content” div, then places it in the “description_box” div. It also sets our toggle function, which is activated when the user clicks on the button with the class “toggle_description.”

– The “description_box” div is also styled similarly to the “sidebar” div. The big difference is the “description_box” div is hidden by default because we only want it shown if we are on a mobile phone. The button with the class “toggle_description” is also hidden by default.

From our css/styles.css file:

/* 'About this map' button, description box */
/* Mobile only */
.toggle_description {
  display: none;
	z-index: 8;
	position: relative;
	float: right;
    right: 0%;
    top: 0%;

#description_box_cover {
	display: none;
	z-index: 10;
	position: absolute;
	top: 0%;
	width: 100%;
	height: 100%;
    background-color: #444444;

#description_box {
	position: absolute;
	display: none;
	z-index: 11;
	width: 92%;
	height: 93%;
	padding-top: 1%;
	padding-left: 1%;
    padding-right: 1%;
    left: 2.5%;
    top: 2.5%;
    border: 1px solid #999;
    background-color: #FFFFFF;

#description_box h3 {
	padding-bottom: 0px;
	line-height: 15px;

Now we do the opposite with the “toggle_description” when compared to what we did with the “sidebar_content” div.

With the “sidebar_content” div, we had it shown by default then hidden on mobile phones using CSS media queries. With the button, we hide it by default and then show it on mobile phones using a CSS property of “display: inline.”

From our css/styles.css file:

/* Styles from mobile devices */
@media (max-width: 625px) {

	.toggle_description {
		display: inline;


As a noted above, when someone clicks on that button, jQuery toggles between hidden and shown on the button with the class “toggle_description”. It is hidden by default, so it is shown when the user first clicks  it. Then when the user clicks the blue X button (which also has the class of “toggle description”), the box disappears.

A similar philosophy is in place to hide and show the credits box.

That should give you a good idea of what is happening with this map. Feel free to fork the repo and create your own awesome maps.

Have any questions? Don’t hesitate to leave a comment.

Final infographics project

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For the last six weeks, I’ve taken a very awesome online course on data visualization called “Introduction to Infographics and Data Visualization” It is sponsored by the Knight Center for Journalism in Americas and taught by the extremely-talented Albert Cairo. If you have a quick second, check out his website because it’s phenomenal.

Anyways, we have reached the final week of the course and Cairo had us all make a final interactive project. He gave us free reign to do whatever we want. We just had to pick a topic we’re passionate about. Given that I was a cops and courts reporter for a year in Galesburg, Illinois before moving to Waterloo, Iowa, I am passionate about crime reporting. So I decided for my final project I’d examine states with high violent crime rates and see what other characteristics they have. Do they have higher unemployment rates? Or lower education rates? What about wage rates?

Obviously, this is the type of project that could be expanded upon. I limited my final project to just four topics mostly because of time constraints. I work a full time job, you know! Anyways, here’s my final project. Let me know if you have any suggestions for improvement.



Data: Information for the graphic was collected from four different sources, which are all listed when you click on the graphic. I took the spreadsheets from the listed websites and took out what I wanted, making CSVs for each of the four categories broken down in the interactive.

Map: The shapefile for the United States was taken from the U.S. Census Bureau’s website. Find it by going to this link, and selecting “States (and equivalent)” from the dropdown menu. I then simplified the shapefile by about 90 percent using this website. Simplifying basically makes the lines on the states’ polygons less precise but dramatically reduces the size of the file. This is important because people aren’t going to want to wait all day for your maps to load.

Merging data with map: I then opened that shapefile with an awesome, open-source mapping program called QGIS. I loaded up the four spreadsheets of data in QGIS as well using the green “Add vector layer” button (This is important! Don’t use the blue “Create a Layer from a Delimited Text File” button). The shapefile and all the spreadsheets will now show up on the right side of the screen in QGIS under “Layers.”

Each spreadsheet had a row for the state name, which matched the state names for each row in the shapefile.  It’s important these state names match exactly. For instance, for crime data from the FBI I had to lowercase the state names. So I turned “IOWA” into “Iowa” before loading it into QGIS because all the rows in the shapefile were lowercase (“Iowa” in the above example).

Then you can open the shapefile’s properties in QGIS and merge the data from the spreadsheets with the data in the shapefile using the “Joins” tab. Finally, right click on the shapefiles layer then “Save As” then export it as a GeoJSON file. We’ll use this with the wonderful mapping library Leaflet.


Leaflet: I used Leaflet to make the map. It’s awesome. Check it out. I won’t get into how I made the map interactive with Javascript because it’s copied very heavily off the this tutorial put out by Leaflet. Also check it out. The only thing I did differently was basically make separate functions (mentioned in the tutorial) for each of my four maps. There is probably (definitely) a better way to do this but I kind of ran out of time and went with what I had. If you’re looking for the full code, go here and click “View Page Source.”

Design: The buttons used are from Twitter’s Bootstrap. I used jQuery’s show/hide functions to show and hide all the element the page. This included DIVs for the legend, map and header.

GeoJSON: The last thing I did was modify my GeoJSON file. You’ll notice how the top 10 states for violent crime rates are highlighted in black on the maps to more easily compare their characteristics across the maps. Well, I went into the GeoJSON and put all those 10 states attributes at the bottom of the file. That way they are loaded last on the map and thus appear on top of the other states. If you don’t do this, the black outlines for the states don’t show up very well and look like crap. Here’s GeoJSON file for reference.

Hopefully that will help guide others. If you have any questions, feel free to drop me a line. Thanks!


Written by csessig

December 11, 2012 at 12:13 am

Going mobile with our news apps

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ImageIt’s been about two months since we launched our crime map for the city of Waterloo and so far, the response has been overwhelmingly positive.

I’ve been proud of our fans on Facebook, in particular, who are using the map to make informed opinions on crime in the area. One of our fans actually counted every dot on the map to counter another fan’s claim that crime only happens on the east side of town. He found crime was almost equally spread across the town.

In hopes that this data will be digested by an even larger audience, I launched the mobile/tablet equivalent of the map last week. The new app is fully responsive thanks to Twitter’s Bootstrap framework. If you haven’t check out Bootstrap, you really should. It makes designing websites for all platforms — desktops, tablets, mobile phones — about as easy as its going to get.

I also went in and modified  a lot of the CSS, changing things like the width of objects on the page from pixels to percentages. This ensures that the app looks great no matter how wide the screen you’re viewing it from is.

Serving up the tablet version of the map wasn’t particularly difficult. It’s basically the same map on our site without the header and footer, which seems to load slower on iPads. It’s also designed to be flexible regardless of how wide your tablet screen is.

The mobile version was more difficult. At this time, the mobile app does not have a map of the crimes. Instead, it’s just the color charts comparing different crimes and a table of all the crime data. I stripped out the map mostly because it’s difficult to click individual points on the map on small mobile screens. But screens continue to get bigger and nicer so hopefully this won’t be a problem in the future.

One pro tip: I set the padding-right CSS property on the table of crimes to 10 percent. This gives a nice cushion to the right of the table, making it easier for people to scroll past it on smartphones with touch screens.

For this project, I went about making the mobile version the completely wrong way: I opted to create just the desktop version at first and then go back and make one for tablets and phones.

Ideally I would have done both at the same time, which is easy to do with Bootstrap. And that’s exactly what I did for another project we launched this weekend on campaign finance reports. The project exams the finance reports for candidates running in four local races. The reports are made available by the Iowa Ethics and Campaign Disclosure Board, which fortunately allows users to export the data into CSV files.

We broke down the data and created bar charts using Bootstrap to compare the figures. The framework has several bar options to easily create the bar charts, which are worth checking out. The best part is they are designed to look good on all platforms.

We also have databases of all the contributions that are searchable. This allows readers to see exactly where the money is coming from.

For this project, I created the mobile and tablet equivalents of app as I was creating the desktop version. When viewing it on a desktop computer, the app is embedded on our website so it has the election header, footer and colors. The same app was created with responsive design in mind. So if you open it on a mobile phone, the app will look just as good on a tablet or smartphone as it does on a desktop computer.

Many studies show that more and more people are getting their news on smartphones. It is imperative that we keep those readers in mind when designed full-scale apps for our websites.

Hopefully we can continue this trend at the Courier and make sure our projects are reaching our full audience.

Written by csessig

October 21, 2012 at 1:58 pm