Writing Assignment #2: On Broadway

Evaluated Project: On Broadway (Project Website: http://on-broadway.nyc)

In this digital era, one who has access to the internet leaves digital footprints in the cyberspace. In order to observe the world we can’t see in conventional ways, we now need a different lens: the cultural analytics approach. Cultural analytics examines the large collection of cultural data through computation and visualization, and as a result, it provides a visual presentation of the footprints we left in the intangible virtual world. To demonstrate this approach, I chose Dr. Lev Manovich’s project On Broadway, in which his team mapped Broadway, a renowned street in the real world, in the digital virtual world.

There are two primary inspirations that need to be taken into consideration when we evaluate this project: the emerging geo-coded cultural data and the need for a new representation of the modern city in the digital era. The Chinese writer Zhou Shuren once wrote: “For actually the earth had no roads to begin with, but when many men pass one way, a road is made.” His remark, interestingly, applies to the emerging “digital roads” which are built upon the geo-information in correlation to the social media commonly used by people nowadays. Popular social media such as Instagram, Twitter and Facebook etc. give their users an option to share their geographical locations, and empirically, the users tend to do so. Benefitting from such geo-coded public cultural data, a traveller can preview a landscape and popular local activities simply by searching popular social media. For example, if one wants to visit Times Square, he could get a general idea about the site by looking up the photos on Instagram and Google Earth; if he wants to check in a hotel or find a restaurant or movie theatre, he could look up them on Yelp or Foursquare. Therefore, the visual representation of cultural data becomes more important than the traditional representation of the city, such as maps. How then can we create such a visual representation of a city or a part of the city by analyzing an enormous quantity of geo-coded cultural data?

There are three essential steps in a typical cultural analytics project: collecting cultural data, analyzing the data collection through computing technology, and creating digital visual representations. In addition, research would further analyze the representations to find correlations or explain the meanings behind the patterns. They usually post the final representations on websites to maximise the accessibility for the mass public.

In this project, On Broadway, Dr. Lev Manovich and his team first sliced the 13.5 mile region of Broadway into sample areas which measure 30 meter in length and 100 meter in width. Then they collect the data of each sample region from six credible sources: geo-coded cultural data from Instagram, Twitter, Foursquare and NYC Taxi and Limousine Commission (TLC); Economic indicators from the American Community Survey (ACS); street view images from Google Street View.

The team was most likely separated the collected cultural data into two categories  according to their properties: visual and numerical data. In the visual data category, the team collected the sample images of facades, the top view of the street, and sample photos posted by users on Instagram in each of the locations. The team further analyzed the major color theme of those images using software, possibly the FeatureExtractor, which is provided by Software Studies Initiative and which has the capability to extract the RGB colors from images. In the numerical data category, the team uses the programming languages such as Javascript to help calculate numerical data in real time. That is, in the later representation, the numerical data will calculate simultaneously when an audience defines the range,  and will give the maximum interactivity and manipulability to the audience. I will further explain the advantage of such a method in a later description to of the final visual presentation.

The final visual presentation of this project is an application accessible on the project’s website: http://on-broadway.nyc/app/#. The representation can be best described as a scroll with 13 different registers including:

Landmarks, streetview facades images, facade colors, taxi dropoffs per day, taxi pickups per day, streetview top images, Foursquare checkins per day, Twitter messages per day, Instagram photos per day, median household income per year, sample Instagram photos in the region, and Instagram photo colors.

This representation can be zoomed in and out by the user. When zoomed in, the representation will show the detailed statistics of a zoomed range of locations, and such statistics are calculated in real time using Javascript embedded in the Hypertext Markup Language (HTML), built in the webpage. The higher the number is, the brighter the color of the dot which represents the statistics will be. When zoomed out, the representation will show the averaged statistics on the left corner of the register, while the dots are compressed into a strip with different colors to represent the change in regional statistics.

This spectacular final visual representation of Broadway fulfills the need for a new representation of the city. It takes the cultural analytics approach to the geo-coded cultural data by blending the computational technology and visual data analysis together. Furthermore, it shows severe social inequalities in the city; the neighbourhoods which are  in radius of Broadway are almost divided into two major regions: the affluent area ranging from southern tip of the city to the Morningside Heights, and the poorer area in the north part of the city. The pattern also applies to the booming tourism supported by prosperous social media involvement which have a direct spatial correlation to the wealth gap. Thus, this visual representation initiated with intention of mapping the digital footprint people leave on the internet, especially through social media, results in finding a social pattern which can’t be demonstrated by the conventional qualitative humanities approach.

 

Streetview facades

Facade colors

Taxi dropoffs

Taxi pickups

Streetview top

Foursquare checkins

Twitter messages

Instagram photos

Median Household Income

Instagram photo colors

Instagram photos

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