Annual Electric Usage By Block for New York City

The map represents the total annual building energy consumption at the block level (zoom levels 11-15) and at the taxlot level (zoom levels 16-18) for New York City, and is expressed in kilowatt hours (k Wh) per square meter of land area. The data comes from a mathematical model based on statistics, not private information from utilities, to estimate the annual energy consumption values of buildings throughout the five boroughs. To see the break down of the type of energy being used, for which purpose and in what quantity, hover over or click on a block or taxlot.


All Of NYC's Affordable Housing through the Furman Center's Data Search Tool

Search all of New York City's affordable housing by name, owner, year built, location, financing or physical information (for example by # of building violations in 2010).  Or, you can research all sorts of demographic information from Crime to Education to employment to health to all sorts of housing informtion, to property tax to population, ethnic demographics and transportation.

Online Marketing Group Affordable Housing

The Furman Center for Real Estate and Urban Policy collects a broad array of data on demographics, neighborhood conditions, transportation, housing stock and other aspects of the New York City real estate market. We make our data directly available to the public through our new Data Search Tool, and publish comprehensive analyses of these data in our periodic reports.

The Data Search Tool is a new online application that provides direct access to New York City data collected by the Furman Center. Users can select from a range of variables to create customized maps, download tables, and track trends over time. Users are able to overlay never-before available information on privately-owned, publicly -subsidized housing programs collected through the Furman Center’s Subsidized Housing Information Project (SHIP). Information about how to use the Data Search Tool is available in our online guide.

Online Marketing Group Affordable Housing

From the Furman Center

When the Visualization Eclipses The Data...

In his 2003 novel Pattern Recognition, William Gibson created a character named Cayce Pollard with an unusual psychosomatic affliction: She was allergic to brands. Even the logos on clothing were enough to make her skin crawl, but her worst reactions were triggered by the Michelin Tire mascot, Bibendum.

Although it’s mildly satirical, I can relate to this condition, since I have a similar visceral reaction to word clouds, especially those produced as data visualization for stories.

If you are fortunate enough to have no idea what a word cloud is, here is some background. A word cloud represents word usage in a document by resizing individual words in said document proportionally to how frequently they are used, and then jumbling them into some vaguely artistic arrangement. This technique first originated online in the 1990s as tag clouds (famously described as “the mullets of the Internet“), which were used to display the popularity of keywords in bookmarks.

More recently, a site named Wordle has made it radically simpler to generate such word clouds, ensuring their accelerated use as filler visualization, much to my personal pain.

So what’s so wrong with word clouds, anyway? To understand that, it helps to understand the principles we strive for in data journalism. At The New York Times, we strongly believe that visualization is reporting, with many of the same elements that would make a traditional story effective: a narrative that pares away extraneous information to find a story in the data; context to help the reader understand the basics of the subject; interviewing the data to find its flaws and be sure of our conclusions. Prettiness is a bonus; if it obliterates the ability to read the story of the visualization, it’s not worth adding some wild new visualization style or strange interface.

Of course, word clouds throw all these principles out the window. Here’s an example to illustrate. About six months ago, I had the privilege of giving a talk about how we visualized civilian deaths in the WikiLeaks War Logs at a meeting of the New York City Hacks/Hackers. I wanted my talk to be more than “look what I did!” but also to touch on some key principles of good data journalism. What better way to illustrate these principles than with a foil, a Goofus to my Gallant?

And I found one: the word cloud. Please compare these two visualizations — derived from the same data set — and the differences should be apparent:

I’m sorry to harp on Fast Company in particular here, since I’ve seen this pattern across many news organizations: reporters sidestepping their limited knowledge of the subject material by peering for patterns in a word cloud — like reading tea leaves at the bottom of a cup. What you’re left with is a shoddy visualization that fails all the principles I hold dear.

Every time I see a word cloud presented as insight, I die a little inside.

For starters, word clouds support only the crudest sorts of textual analysis, much like figuring out a protein by getting a count only of its amino acids. This can be wildly misleading; I created a word cloud of Tea Party feelings about Obama, and the two largest words were implausibly “like” and “policy,” mainly because the importuned word “don’t” was automatically excluded. (Fair enough: Such stopwords would otherwise dominate the word clouds.) A phrase or thematic analysis would reach more accurate conclusions. When looking at the word cloud of the War Logs, does the equal sizing of the words “car” and “blast” indicate a large number of reports about car bombs or just many reports about cars or explosions? How do I compare the relative frequency of lesser-used words? Also, doesn’t focusing on the occurrence of specific words instead of concepts or themes miss the fact that different reports about truck bombs might be use the words “truck,” “vehicle,” or even “bongo” (since the Kia Bongo is very popular in Iraq)?

Of course, the biggest problem with word clouds is that they are often applied to situations where textual analysis is not appropriate. One could argue that word clouds make sense when the point is to specifically analyze word usage (though I’d still suggest alternatives), but it’s ludicrous to make sense of a complex topic like the Iraq War by looking only at the words used to describe the events. Don’t confuse signifiers with what they signify.

And what about the readers? Word clouds leave them to figure out the context of the data by themselves. How is the reader to know from this word cloud that LN is a “Local National” or COP is “Combat Outpost” (and not a police officer)? Most interesting data requires some form of translation or explanation to bring the reader quickly up to speed, word clouds provide nothing in that regard.

Visualization is reporting, with many of the same elements that would make a traditional story effective.

Furthermore, where is the narrative? For our visualization, we chose to focus on one narrative out of the many within the Iraq War Logs, and we displayed the data to make that clear. Word clouds, on the other hand, require the reader to squint at them like stereograms until a narrative pops into place. In this case, you can figure out that the Iraq occupation involved a lot of IEDs and explosions. Which is likely news to nobody.

As an example of how this might lead the reader astray, we initially thought we saw surprising and dramatic rise in sectarian violence after the Surge, because of the word “sect” was appearing in many more reports. We soon figured out that what we were seeing had less to do with violence levels and more to do with bureaucracy: the adoption of new Army requirements requiring the reporting of the sect of detainees. Of course, the horrific violence we visualized in Baghdad was sectarian, but this was not something indicated in the text of the reports at the time. If we had visualized the violence in Baghdad as a series of word clouds for each year, we might have thought that the violence was not sectarian at all.

In conclusion: Every time I see a word cloud presented as insight, I die a little inside. Hopefully, by now, you can understand why. But if you are still sadistically inclined enough to make a word cloud of this piece, don’t worry. I’ve got you covered.

This is an insightful and rather shrewd criticism of word clouds, and I think it applies to much of the infographic, data-visualizaion obsessed tech culture we live in.

I find myself fascinated by many of the new and innovative ways to graphically represent data. Yet, as Jacob Harris points out, many of these sleek new techniques (if they don't miss the point entirely) strip supposedly core ideas from the very context that lend them meaning... and we are left with a aesthetically pleasing series of pretty graphs and pie charts that convey very little actual information (see my post on the Infographic Idiom).

And even though CNN, Fox and other news networks are now embracing new visualization tools, tag clouds are ultimately useless measures of political sentiment, because concepts themselves really cannot be reduced to their most elemental articulation; in a word.

The Color Of Your Website Has A Huge Impact On What People Buy

Most entrepreneurs think about the color of their site in terms of how colors look together and whether they’re generally appealing, but color actually has a lot more to do with what your visitors think about your site – and the actions they take – than you might realize.

There have been entire books written about the psychology of color in buying habits. It’s a topic that has been well studied, and most marketing degrees include at least some study of this topic. We don’t want to bore you with psychology talk though. So here are the basics of what various colors mean, who they appeal to, and who should use them.

First, a Few Stats

Some of these statistics have to do with the color of individual products, but they demonstrate very clearly the power color has in the minds of consumers. According to a recent KISSMetrics report:

  • 93% of consumers place color and appearance above other factors when making a buying decision.
  • 85% of shoppers state color as the primary factor in their decision to buy a product.
  • Brand recognition, which links directly to consumer confidence, is increased by 80% when the right colors are used.

What Does it Mean?

It’s important to know what specific colors mean to people. It’s been shown that certain colors invoke specific feelings in most people, so let’s see what colors really mean.

Yellow: Youthful and optimistic. Use it to grab attention. Usually not good as a background or primary site color.

Red: Energy. Creates urgency and increases heart rate. Good for appealing to impulse shoppers.

Blue: Creates feelings of trust and security. This is why many banks use it in their logos or marketing. Navy or dark blue  is used to market to the budget-conscious.

Green: Gives the impression of wealth. Relaxing and easy-going. Teal can be used to appeal to people on a budget.

Orange: Aggressive and excitement. Good for calls to action and impulse buying.

Pink: Feminine and romantic. Used to market to women and girls and traditional buyers.

Black: Powerful and sleek. Use it to market luxury products and appeal to impulse buyers.\

Purple: Soothing and calming. Often used to market anti-aging products.

Part of the study mentioned above found that what your site visitors see when they come to your site, from overall design to the colors used, really does make an impact on whether they buy from you. In fact, 52% of people surveyed who said they would not return to a site stated the reason as ‘aesthetics.’ If there was ever any question, that should confirm that the look of your site is one of the most important parts of your business.