Love Design? Join the WNYC Data News Team

Do you want to ...

  Inform the citizens of New York?

  Help people understand their world?

  Root out corruption?

  Make a mark on society?

  Craft beautiful online projects and visualizations?

  ( Like this diversity map, this stop & frisk project and this election tracker? )

WNYC is growing our Data News Team to make high-impact visualizations and projects, and to help WNYC reporters and producers present the facts, expose corruption and explain our world. We've been pioneers in the field of crowdsourcing, data journalism and mapping -- even winning some prestigious awards for our work.

Now we're kicking it up a notch. Like to join us? 

What we have:

  • An award-wining staff of reporters and producers
  • A committed, innovative digital staff
  • A mission to conduct journalism in the public interest
  • Millions of engaged, passionate and active listeners and readers

What you have:

  • A passion for news
  • An attention to detail, a respect for fairness and a hatred of inaccuracy
  • A user-centered approach to exploring information
  • An appreciation for clean lines, clear stories and use of white space
  • A genuine and friendly disposition, and an honest spirit of collaboration
  • A bias toward sharing what you know, and helping others build on it

What you'll do:

  • Huddle with reporters to figure out how we might help their stories with data, design and web technology
  • Work as a team to turn ideas into realities in days or weeks, tops
  • Learn from and build on successes and mistakes along the way
  • Have your work consumed online and talked about on air to millions of New Yorkers

Head over to our official aplication for Interaction Designer and tell us all about you.

The Thinking Behind WNYC's Vertical Timeline

Making a music stand, my father said, was a great challenge: Even though people had made them for centuries, it was still possible to blend beauty and function in a new way.

In journalism, the same is true for the timeline.

Presenting a chronological story online, beautifully and functionally, has been tricky. There are some great examples, such as the New York Times' chronology of the Iraq war, and the three-dimensional Middle East timeline from The Guardian.

ProPublica built the excellent TimelineSetter to put Times-like timelines in the hands of non-Times journalists, and we used it for a while. But TimelineSetter's horizontal layout got cramped in WNYC's article columns, and we longed for something that fit better.

Working with Balance Media and the WNYC web design team, we kicked around several ideas and settled on a vertical version. As it happened, Facebook's new vertical timeline had come out, inspiring a crop of JavaScript libraries we could work with.

We also decided to dispense with a journalistic convention that represented temporal gaps visually -- making months wider than weeks, for example -- and focus, instead, on seeing the sequence of events at once.

The live version at WNYC is here.

We also went with a center-spine orientation, which give it balance and allows the user to see more items at the same time. And the very cool Isotope code reshuffles the items to fit as they are closed, opened, resorted or even added.

Open to use

Finally we wanted it easy for us -- and you -- to use. So we wired it to Google Spreadsheets, allowing reporters and editors to easily enter and update the information. The wiring there is based on a previous project of ours called Tabletop.js.

And we made the source code openly available and scary-easy to use, and you can start by copying this Google spreadsheet template.

We usually build an HTML page just like the one in the code example, and then use a simple line of HTML to iframe it onto an article page. The only trick is to make sure the iframe is tall enough.

The code is free for non-commercial use; commercial use requires a $25 license fee for Isotope.

We hope folks will use the timeline, and come up with improvements. Let us know about either in the comments below or by writing me at john (at) johnkeefe (dot) net.

Mapping Campaign Contributions on the Fly

Our new Empire Blog reporter, Colby Hamilton, dropped by my desk the other day wondering whether we could map contributions to presumptive NYC mayoral candidates by zip code.

He was going to post about it after lunch. I said I'd be ready with a map.

Thanks to Fusion Tables and a little Ruby magic, I had one ready when his story was done shortly after lunch, and we updated it into the evening as the candidates' filings were made available by the NYC Campaign Finance Board.

How I Did It

For anyone looking to do something similar, here's what I did:

-- I downloaded each candidate's donation as an Excel spreadsheet from the homepage of the Campaign Finance Board.

-- I uploaded the spreadsheet to Google Fusion Tables (if it's an Excel file more than 1MB, you have to save it as comma-separated-values, or .CSV, before uploading).

-- I used Fusion Tables' fantastic aggregattion function -- View -> Aggregate -- to sum the contributions by zip code. Then I exported that file as a .CSV, which gave me a file for each candidate with the columns: ZIP, SUM and COUNT -- SUM being the total donations and COUNT the number of donations for the zip code.

-- I re-uploaded that aggregation export back to Fusion Tables. (If anyone knows how to save an aggregation in FT without exporting it and uploading it again, I'm all ears.)

-- Now that I had the contributions by zip code, I need the zip code shapes to go with them. The US Census has zip code shapefiles by state FIPS code, and for the entire United States. (Quick note: Census zip code data and US Postal Service zip codes aren't exactly the same, though we felt comfortable using the Census version for this project.)

-- I uploaded the New York state zip code shapefile to Fusion Tables, too, using Shpescape. (If you're working with New York State, you can save some work and just use mine.)

-- I opened the ZIP-SUM-COUNT file in Fusion Tables and merged it with the zip code shapefile, linking them with the ZIP field in the first file and ZTCA5CE10 in the second file.

-- Using Visualize -> Map, I could see all of the relevant zip codes for that candidate. By using the Configure Styles link, and then tinkering with Fill Color -> Buckets settings, I shaded the map according to total donations.

This map is ready to be embeded!

The Trouble with Tables

An admission, though: I didn't use the Fusion Tables embeddable map for this story. I did share the FT map with Colby, which let us know we had a couple of good stories. FT is great and fast for that. It also works in production with smaller data sets.

But the long time it takes Fusion Tables to populate a map with large data sets can make for a frustrating user experience. That's compounded by the fact there's no way (yet) to "listen" for a sign that the layer has fully loaded, which would let me display a "Please wait ..." sign until it did.

So in this case, I built my own KML, or Keyhole Markup Language, file (5 of them, actually; one for each candidate). I then compressed those files in to much smaller KMZ files, which are just "zipped" KML files, so they load quickly. I then used those files as layers with Google Maps' KmlLayer() constructor. I also used a "listener" to find out when the layer is fully loaded, and display an alert to the user until it is.

More to Come

As for how I built the KML file, I'm going to share that in another post once I clean up the Ruby code I used to automate the process. (If your project can't wait for that post, drop me a note and I'll try to help.)

But the basics are these:

1. The layout of a KML file, and the format for using different styles to color different shapes, is pretty straightforward and nicely documented. In my code, I changed the style name for a given shape based on the value of the "SUM" variable.

2. The hardest part of writing a KML file is defining each shape in the proper format. But the merged file I made linking the ZIP-SUM-COUNT data and the shapefile actually has that information! The "geometry" column of that table is straight KML! (Thank you, Shpescape.) Export that merged file as a .CSV, and you've got all of the building blocks for your map.

If you have ideas, improvements or questions about this post, please don't hesitate to drop a note in the comments or drop me a note via email.

Screaming for a Map: The New "Littles"

When I saw the NYC ancestry data, I immediately thought, "That screams MAP!"

Brian Lehrer Show producer Jody Avrigan had been working on a great project looking for the new "Littles" in New York City -- neighborhoods where people of a certain ancestry or ethnicity live. He had a spreadsheet; I wanted to visualize it.

The result may be my favorite map project so far:

Mostly, I built on what I'd learned making WNYC's Census Maps, adding a few of things:

• An on-map drop-down menu (here's the CSS code for that).

• Code that selects different data from a single Google Fusion Table

• Panning and zooming to the neighborhood I want to highlight.

• A better "Share or Embed" pop-up box using jquery.alerts.js

I also tried to clean up and refactor my original code to make it easier to read (and reuse).

You can see that code on GitHub. I tried to document it clearly, but post a note below if you have any questions or would like clarification.

UPDATE: In making this map, I used a new (to me) trick to remove the water areas from census tract shapes on the coastline.  Here's how I did it, if you're interested.

Making the WNYC Census Map

When the New York census numbers arrived this week, we were ready. WNYC quickly published an interactive, sharable map so New Yorkers -- and our reporters -- could explore the new data and see the stories.

We built the map with free tools and timely help from some smart, kind people.


The short story is that we mashed together population numbers and geographic shapes using Google Fusion Tables, and then used JavaScript and Fusion Tables' mapping features to make things pretty and interactive.

The long story is meandering and full of wrong turns. But here are the highlights, should anyone need a little navigation. Don't hesitate to contact me for more help and insight; I'm due to pay some forward.

Getting in Shape

First up: Shapes of the census tracts plotted on Earth. I downloaded New York's tracts from the U.S. Census Bureau's TIGER/Line Shapefile page. They also have counties, blocks, zip codes, and more.

Then I uploaded this "shapefile" -- actually collection of related files zipped together -- to Fusion Tables with a free, online tool called Shpescape. (Thanks to Google's Rebecca Shapley for sharing this key to my puzzle.)

Hello, Data

Census data is publicly available, but can be a hassle to handle. In fact, on the day each state's info was released, the files were available in a set that apparently requires one of two pricey programs -- SAS or Microsoft Access -- to assemble. 

So I got clean, assembled, comma-delimited files -- complete with 2000-to-2010 comparisons -- from the USA Today census team, which provided them as a courtesy to members of Investigative Reporters and Editors. Huge props to Anthony DeBarros and Paul Overberg, who crunched the New York numbers in a blazing 30 minutes.

By the way, IRE membership is $60 for professionals and $25 for students. Well worth it, and cheaper than either of those programs. If you're digging into census numbers and qualify, I recommend this route.

That said, every state's 2010 data is now available free from the Census Bureau's American Fact Finder. Navigating the site is a little tricky, and worth a separate post, but the bureau provides some tutorials, and there's very detailed PDF about each data field.

With data in hand, I uploaded it to Google Fusion Tables in another table.

Map Making

Next, I merged the shapes table and population table, using the unique tract ID to marry the data (the shape file calls it GEOID10. the IRE data calls it FULLTRACT). Note that the GEOID10 is formatted differently depending on whether you're using tracts, blocks, counties, etc., so be sure you've got the right match in both files.

Clicking Visualize -> Map shows a map. It'll be all default-red until you click on Configure Styles -> Fill Colors -> Gradient (or ->Buckets) and make different colors appear depending on values in the column of your choice.

Using the Share button makes the map viewable by others, and "Get embeddable link" does just that.

Adding Prettiness

I used the Fusion Tables "Configure info window" option to make custom pop-up bubbles on our maps. This actually required some nicer-looking data, such as a columns with rounded percentages and + or – signs. I added these using the free R statistical program, which I learned how to do from The New York Times' Amanda Cox at the 2011 Computer Assisted Reporting conference.)

Census tracts officially extend to the state lines, which made it look like a lot of people live in the Hudson River. So I had trimmed those tracts to the shorelines with a free mapping program called QGIS, using water shapefiles as a reference (those are here, in the drop-down menu).

After creating 12 merged Fusion Tables, I pulled them into one page using JavaScript and jQuery, with fantastic guidance from Joe Germuska at the Chicago Tribune (part of the team that built this great map).

The "Share/Embed this view" feature came together in two parts: 1) The JavaScript turns the current the latitude, longitude, zoom level and current map choice into a long URL that pops up when you click the Share/Embed link. 2) Using a nifty jQuery plug-in (updated link Dec. 2011), the map looks for those values in the URL that summoned it, and reorients to that map if they exist.

Prep Work

Clearly, not all of this could happen in a couple of hours on Data Day. I'd been tinkering, testing and tweaking for a few weeks using New Jersey's data, which came out much earlier.

I also wrote down, edited and revised every step I took to make the maps. So when the adrenaline was running I had a script to follow.

The WNYC Web Team also set up a slick, fresh project server, at, to host the html pages and track the traffic.

Fusion Function

Using Google Fusion Tables made it super easy to manage, map and serve up a lot of data. And the FT feedback team was fantastic about responding to questions and glitches I encountered along the way.

I did run into a couple of hiccups: slow load times and pop-up bubbles that failed to pop up. The first was a product of displaying so much data -- and I knew I was pushing things. The second was a Google glitch that their engineers managed to fix within a few hours, but was still spotty at times afterward.

Also, the Google Map engine starts dropping shapes when there are too many to show. So I funneled different counties' data into almost a dozen different layers, a workaround the Google folks showed me ahead of time.

That said, I had time to code and tweak lots of neat things because I didn't have to focus on building or running a database engine. Google's free services took care of that.

What Could Be Better?

Probably a lot. I wanted to let people to add comments, right on the map, but didn't have the chops or time to pull that off.

Another good thing would be a "Loading ..." indicator displayed while the map data is pulled into your browser, which I may yet add.

But what couldn't have been better was everything I learned, the help I got from other data folks and the support from my WNYC colleagues. Plus we gave New Yorkers a pretty nifty service and several great stories.

Need more details? Feel free to ask questions in the comments. Or drop me a line. I'll try to help, too.

Where's the Next Bus? I'll Tell You

When New York City released real-time bus info for a Brooklyn line, one of my colleagues wasn't happy.

Yes, she could use a smartphone to see buses on an MTA map. Yes, she could get location information by texting the code for her stop (she lives near the route). But none of this was simple enough.

"I want a phone number that will TELL me when the next bus is coming," she said.

I'll have it for you by the end of the day, I replied.

It was a bit of a gamble. More of a challenge to myself, really. But if "before midnight" counts as the end of the day, then I succeeded. Try it:

Dial 646-480-7193. When prompted, enter 308333 or any of the bus stop codes for the B63 line.

It's not journalism, but it is a working example of how someone can take public data and turn it into a useful tool, quickly. And at almost no cost.

How I Did It

First, I wrote a little program in Sinatra that sends a 6-digit bus stop code to the NYC Metropolitan Transit Authority API -- or application programming interface -- whenever someone hits my program's web address. (The API is a public portal to the live bus data. All you need is a little programmer know-how and a free key from the MTA. The technical details are right here.)

The API sends back 77 lines of information about the stop and the buses approaching it, including the one detail I want:


The next bus is three stops away. I use a Ruby tool called Nokogiri to find and extract just this number, which I drop into an amazingly simple web page. The page's entire output looks like this:

The next bus to arrive at 14th Street and Fifth Avenue heading north is 3 stops away.

That accomplished, I bought a phone number from Twilio for $1 a month, and $0.01/call. Twilio provides a telephone connection to web-based programs, which I first heard about during a demo at a TimesOpen event

I set my new phone number to hit my program's URL whenever someone calls. By wrapping the text in special tags, Twilio recognizes it as a cue to talk:

The next bus to arrive at 14th Street and Fifth Avenue heading north is 3 stops away.


I later used Twilio's <Gather> tags to build what's essentially a web form to capture digits entered on a phone for any bus stop. I also added error catching, for when no buses are coming, and programmed it to announce the location of the following bus if the next bus is arriving.

Some Hiccups

Turns out that Twilio reads text a little too fast to be understood on an NYC street corner. So I rewrote the output to introduce pauses:

The next bus to arrive at ... 14th Street ... and ... Fifth Avenue ... heading ... north ... is ... 3 ... stops ... away.

Also, there's a bug in the API system that sends back server errors in certain conditions. Word is that the MTA has actually fixed this on their development servers, and that fix is being pushed to the public system pretty soon. 

More to Come

I have a few enhancements up my sleeve, which should be done in a week or two. If you'd like to know when new tricks roll out, drop a note with "Bus Talk" in the subject line to john (at) I'd love to hear your thoughts on how it could work better, too.

I'll also release the source code after those nifty updates. Let me know, too, if you're interested in that.

Photo by jbrau13 on flickr.

Fast, Little Maps with Fusion Tables

Google Fusion Tables can handle huge amounts of data -- and seem designed for that. But a great little secret is that they're fantastic for making fast maps. Even little ones.

And it's surprisingly easy.

At WNYC, we used fusion tables for this quickie map of 63 taxi relief stands. My colleague Jim Colgan whipped together these plowed-streets maps (including the one below) from listeners' texted-in reports -- while he was sick in bed!

Some reasons we've been drawn to mapping with Google Fusion Tables:

Simple uploads. All you need is a comma-separated table (csv) or a spreadsheet made in Excel or Google Docs. Each "point" goes on a row. If you have even basic Excel skills, you're more than ready to go.

Embedded geocoding. Put addresses in one of your columns, and Google will geocode them for you -- doing the work of finding the latitude and longitude for your pin. If you already have the coordinates, that's fine, too. Here's the help page on this for more.

Customizable icons. You can designate one of your columns as the icon column, and use this map of available icons to pick names to put in that column for each point There are some really clear instructions for this

Custom popups. You can define what appears in a pin's pop-up bubble. Doing this is a little tricky, but just. In the "map" visualization, click on "Configure Info Window." I find the default templates confusing, so I choose "Custom" from the drop-down menu. You can then use text, html and the table info {in_curly_brackets} to craft a custom bubble.

Easy embeds. Zoom and position the map as you like it and then click the "Get Link" button for a link to what you see. Or click the blue "Get embeddable link" link to get the embed code. (Design note to Google folks: It's confusing that one of these is a button and one is an html link!)

Easy updates. You can add more data points easily, either with additional uploads or just typing your additions or fixes in your browser. 

Privacy controls. As with other Google products, you can click the "Share" button to control who can view and/or edit each table and map -- which is really nice for working in teams.

News maps on news time.  That's been working for us.

Update: Jim Colgan, who put together the snowplow map, talks about how he did it with the folks at Mobile Commons, who run the platform we use for texting projects.

Weaving a Patchwork Map ... in Real Time

We did something a little creative and unique at WNYC this past election night: We mapped the vote by "community type."

This revealed the diversity of the vote across New York State -- from the cities to the suburbs, boom towns and "service worker" centers -- in real time, on the air and on the WNYC home page.

And the diversity is striking. Despite Democratic wins in every statewide race, the Republicans running for state attorney general and comptroller "won" every community type outside "Industrial Metropolis" and "Campus & Careers" counties.

Patchwork Nation's Dante Chinni talked about this on air during WNYC's coverage election night, and has written more about it since.

The live map was a mashup of Patchwork Nation's unique take on the nation and the Associated Press's live vote totals. At the request of WNYC, Patchwork Nation programmers dove into the AP test results and quickly wove them into a new map based on PN's existing county maps -- customizing them for the event and adding real-time percentages by community type.

Bringing the Threads Together

In the months before the election, I had wondered how we might better understand the early returns -- those that come in typically between 9 p.m. and 10:30 p.m. -- which often don't match the final results. I wanted more clarity.

At a Hacks/Hackers Open-Source-a-Thon, I started playing with the election data with help from Al Shaw (then at TalkingPointsMemo, now at ProPublica) and Chrys Wu (of Hacks/Hackers and ONA fame).

That evolved into a little program I wrote in Sinatra that generated vote-total map at the left, shading counties darker as more of their precincts reported. It also helped me better understand how the data were structured, how to retrieve the numbers and what it might take to make a live map.

So when Chinni asked if WNYC had any county-level data sets we'd like to put through the Patchwork Nation treatment, I had the perfect candidate.

Open All Night: The Great Urban Hack NYC

For 26 hours this weekend, a bunch of journalists and coders got together to make lots of great things designed to help the citizens of New York City.

My blog post summarizing the event and all of the resulting projects is upon Hacks/Hackers.

I helped Jenny 8. Lee, Chrys Wu and Stephanie Pereira organize the event. Then I joined a team working with digital heaps of NYC taxi trip data to make data visualizations and start some other projects. My favorite one is here (with a detail below), which is a representation of taxi usage for 24 hours, set around a clock. Beautiful. It was built by Zoe Fraade-Blanar using Processing and data crunched by the other teammates.

Click image for full view

I came away from the event with many new connections, excitement about learning Processing, some more skills in Sinatra and a note to check out Bees with Machine Guns(!)