Modern Metrix

Color Forecast

This is awesome! French clothing company Pimkie has installed cameras on the streets of Milan, Paris and Antwerp (supposedly, Europe's most fashion-conscious cities) to capture images of people's clothing. The captured data is then converted into real-time "color forecast" on their webisite, so you can see what colors are trending right now in these cities. You can also look at the colors that were trending yesterday, last week, and last month. For example, these were the most popular colors in Paris in past 30 days:

Imagine taking this concept further, by capturing gender and age of passerby's to further enhance the forecast? Or capturing the images of shopping bags on the streets  and then projecting which stores are currently trending, on the giant screens, or on websites and in mobile apps?

Quantifying "Walkability"

Slate looks at how small start up Walk Score is attempting to quantify the "walkability" of geographical locations. 

Where Facebook Ads Work Better: States with the Highest and Lowest Click-Through Rates

This article in Mashable shares interesting statistics on geographical difference in click-through rates for Facebook ads,  provided by digital marketing agency:

"Facebook users in South Dakota, Tennessee and Colorado click through ads on Facebook at record rates. In South Dakota, Facebook users click on ads at a rate of 174% the national average.

Meanwhile, residents of Vermont, Alabama and Indiana are the least likely to take the bait. In Vermont, the click-through rate (CTR) is 57% the national average."

The differences are not explained. If you ask me, the first what comes to mind is demographics, most likely, education (states with less educated population have higher click-through rates). I also suspect that states with the higher click-through rates are are ealier stages of Facebook adoption (the novelty factor comes in play, hence more clicks). But that is just IMHO.

Why "Pace of Life" is Faster in the Cities

This short but fascinating article in The Atlantic explains why pedestrians in larger cities walk faster:

"Levine measured three "pace of life" variables — walking speed, work speed, and clock accuracy — though for the purposes of this post, in keeping with the previous literature, we'll focus on the first. The top ten cities for walking speed, according to Levine, were as follows: Dublin, Amsterdam, Bern/Zurich, London, Frankfurt, New York, Tokyo, Paris, Nairobi, and Rome.

With the exception of Nairobi — insert joke here about Kenyans crushing everyone at the New York City Marathon — the fastest walking cities were from wealthy nations. The statistical analysis confirmed this general perception: two of the three strongest social predictors of walking speed were a country's G.D.P. and its purchasing power parity (the other was its individualism). Indeed, when Levine considered all his "pace of life" metrics, he found that pace of life was swifter in "economically productive countries" like Western Europe and Japan than in undeveloped countries."

 

Neighborhood Lifestyle Metrics using Foursquare Data!

In this post on their blog, Foursquare engineering team describes how they used Foursquare activity data to compute lifestyle metrics of city neighborhods, and then used these metrics to compare neighborhoods across different cities:

"First, to collect data about neighborhoods, we built some Hive queries to access our large collection of check-ins (stored in S3) and count the number of check-ins per category for every neighborhood in the world. For example, the East Village of New York has 230k check-ins at bars, 57k check-ins at pizza places, 18k check-ins at yoga studios, and 34k check-ins at karaoke places (hipsters like to sing!).

We then used MATLAB to represent each neighborhood by a 400 dimensional vector which specifies the normalized probability distribution of checking in to a place in each category relative to the baseline distribution of the city. This approach allows us to compare neighborhoods with each other using a similarity metric such as cosine similarity, or KL divergence."

What an awesome hackday project!

To Rent or To Buy?

Check out this awesome interactive map that visualizes rent vs. buy ratios across 50 largest cities in the US. Yes, I live in New York City. Yes, I rent. No, I don't plan to buy!

New York City Council Frees NYC Public Data

This is great: NY City Council passed yesterday the Intro29A measure that requires all city agencies to make their public data available on NYCOpenData portal:

"Intro 29 would phase in over the next six years, starting with a technical manual to be released by DoITT this summer guiding city agencies on how to format and upload their data. Agencies would have until early next year to upload all public datasets currently on the internet to the new portal. By 2018, all public data would have to be posted. For the time being, DoITT is planning to power the new data portal with Socrata, the service that currently hosts NYC OpenData."

Accessing Your Personal Energy Consumption Data?

This is awesome: the Green Button initiative from the White House encourages utilities companies to share energy consumption data with consumers (think Mint for electricity and gas). 

 

 

Visualization of Annual Energy Consumption by NYC Buildings

Check out this awesome map visualizing annual energy consumption in NYC at the tax-lot (essentially, an apartment building) level. Interestingly, it's based not on data from Con Edison or other utility companies, but using estimates obtained from the model created by researchers from Columbia University (link to full article here). It would be interesting to validate these numbers against the ConEd data. 

Surveillance Cameras as a Source for Consumer Research Data

Think Google Analytics for brick and mortar stores, using in-store surveillance cameras:

"Prism's software can count people that come into a business, measure the length of the line at checkout, and produce static or animated visualizations showing how people moved around a store. It is designed so that it cannot identify or track individuals. One national wireless carrier is already using Prism's technology to generate heat maps of where visitors go in their showrooms, to compare the level of interest in different devices—valuable data to them and to the device makers."

I personally believe that data collected this way is more reliable than when using mobile phones to log and track people's behavior inside the stores.