What is a Neighborhood?

For my master’s thesis, I am looking at neighborhoods and how to define, classify, and describe them.

First off, what is a neighborhood? Though there are conflicting definitions of what exactly constitute a neighborhood, most would agree that it is a geographically localized, somewhat homogeneous community within a larger city. If one had to name neighborhoods in New York City, it would be easy to rattle off names such as Upper East Side, Soho, East Village, Chinatown, Midtown, etc.  And when we picture these neighborhoods in our minds, each neighborhood often has a distinct feel or vibe to it that makes it easily identifiable.


Can you guess these neighborhoods? *Images taken from wikipedia.org

How do we have such a clear picture of what these neighborhoods are like? When thinking about what sort of characteristics differentiate one neighborhood from another, we think of the kind of places (restaurants, shops, stores, etc.), the kind of people (tourists, bankers, affluent people, young people, a certain ethnicity), and the kinds of activities that take place (working, shopping, partying, sightseeing) within this neighborhood. For instance, we would expect to see a lot more offices, working, and tourists in Midtown, but maybe more boutiques, shopping, and artists in Soho. Of course, there are many neighborhoods and characteristics of neighborhoods that are harder to guess off the top of our heads (NoLIta versus TriBeCa or NoHo?)

Another issue with neighborhoods is their fuzzy boundaries and ever-changing characteristics. People and places are not stationary, and over time, the characteristics and perhaps the boundaries of a neighborhood may change (gentrification, anyone?), or new neighborhoods emerge and old ones become consumed. For a recent example, see the shrinking of Little Italy. Who defines what the boundaries of a neighborhood are, anyway? These are often arbitrarily drawn by city officials going off of outdated information or natural boundaries, such as a river, that may no longer be there or do not represent cultural boundaries. Even worse, real estate agents have used these shifty boundaries to falsely stretch boundaries of more coveted neighborhoods or come up with new neighborhoods out of the blue to repackage and market a place. Hence, the aforementioned NoLIta, TriBeCa, NoHo, and now DUMBO, BoCoCa, BoHo, FiDi, and whatever else they have managed to come up with. Clearly, some names have stuck while others faded away, leading to the conclusion that in some cases, these names actually fulfilled a need and put a name to a newly formed, distinct neighborhood.

Surely there’s a better way? How can we systematically find neighborhoods, quantify their characteristics, and observe their changing or shifting states? With an eye to the characteristics I’ve mentioned that define neighborhoods, there are many popular social media websites out there now that give indication to some of these characteristics. For my study, I focus on Foursquare check-in data, specifically a data set that’s been collected from the Twitter API (check-ins from Foursquare forwarded to public Twitter accounts) from May 27th, 2010 to November 2nd, 2010 by the Cambridge NetOS group. This data set returns a list of places, a list of users, and a list of each time a user has checked into a place.

The characteristics that I chose to focus on were places, time, and tourist/local. Let me describe each in more detail, and how I collected these values.

Places – Foursquare has a given list of place categories that define all the places that people check in to. These include bars, Mexican restaurants, shoe stores, and many more. Categories are placed into a hierarchical tree, so that Mexican restaurants are under Restaurants and shoe stores are under Shops. For a full list of categories, visit here. Thus, every place has an associated place category tag.

Time – Here, I try to answer the question of: what time of the day are places busiest? By counting the volume of check-ins for every hour in a day for a place, I can pick out when they are most active. Then, by assigning chunks of hours in a day to categories such as Morning, Afternoon, Evening, and more, I can classify every place by the time category they are busiest.

Tourist/Local – To determine whether a place is touristy or local, I first must determine whether a user is a local or tourist. By counting the percentage of check-ins a user has in or around a city, I can make an educated guess of whether a user is a local of that city. From here, a place can be considered local or touristy based on the proportion of locals or tourists that visit this place.

From these tags associated with every place, I can cluster places based on their geographic location and the various characteristics just mentioned. The clustering method I use is called OPTICS and is a density-based hierarchical clustering algorithm that does not require an input of the number of clusters and also doesn’t require every point to be into a cluster. This makes sense for our look at neighborhoods, since we do not know the number of neighborhoods in advance, and neighborhoods are small. It would make little sense to define a “shopping” cluster that is the entire size of Manhattan, even though there are shops throughout the city. In this case, we are interested in highly-dense pockets for each characteristic. Because we have such a large number of characteristics, it would be difficult to perform OPTICS for each category, manually setting inputs into the algorithm to achieve a reasonable-looking set of clusters. By using an automatic clustering algorithm that is fine-tuned to each city, we greatly reduce the time it takes to cluster. Here I will share some preliminary results:

Here is an geographic plot of the places in New York City that are characterized as “Chinese Restaurant”. Highlighted is the cluster found by OPTICS of a dense area of Chinese restaurants – Chinatown.

Here is another plot that now shows places characterized as “Lunchtime”, or places that are their busiest from 11AM-1PM.

In this way, we can characterize the areas of a city by the clusters that are present. And, by overlapping clusters, we can find the areas of intersect that have homogeneous qualities across many characteristics, leading us to neighborhoods!

Here is a look at only the clusters corresponding to “Late Evening”, or 10PM to 2AM (in blue) and “Nightlife Spots” (in red). The purple is their overlap.

We see a lot of overlap in the clusters, leading us to the possibility that we could define neighborhood boundaries using this method. We also see that the nightlife spots are indeed busy in the late evening, as we would expect, but not all late evening clusters have dense nightlife spots. Thus, some characteristics of different neighborhoods emerge – this is the feel or vibe that we want to quantify. However, this is only an example from just looking at two characteristics. With more characteristics overlapping, we can do an even better job of finding neighborhoods and characterizing them based on place, time, and local/tourist.

This work is still in progress, and a web application for anyone to explore the various clusters is forthcoming. I touched earlier upon many more characteristics that can define a neighborhood that I have not delved into, including things like demographics of occupation, ethnicity, age, and more. Activities, such as working or shopping, were also not explicitly studied, though they may be inferred from the characteristics of place and time. In the future, these characteristics and more could be added to improve this study. Newer data sets could also be used to observe changes as a result of time and find if neighborhoods have moved, grown, shrunk, appeared or disappeared. The addition of cities (I am looking at New York City and London at the moment) is always good, though we are limited to only large cities that have enough volume in check-ins to do analysis on. An interesting related task would be to find neighborhoods that are similar across cities, as this post attempts. Comparisons of cities are also possible, as I happened upon striking differences between the check-in activity of New York City and London. Last, as more social media websites incorporate location-tagging, we could replicate this analysis on their data and possibly increase the size of our user demographic.


Where in the World Are You?

This is the question Twitter puts forth to every user on their profile page. But what are people actually putting down for their location? As I delved into hundreds of thousands of public profile location fields, the answer is: not always their actual location, and when it is, not often in an easily readable format for computers to understand. Along the way, I learned many insights both interesting and silly on how people express themselves through their location field.

When my research on local day-to-day patterns on Twitter began, one of the first steps we had to accomplish was to figure out where tweets were coming from. There were two ways to do this.

  1. If a tweet is sent from a phone or a browser that has geotagging enabled, or if a tweet is imported from a tagging service such as FourSquare, then it contains geo coordinates. How I took these coordinates and turned them into proper city names (reverse geocoding) is a subject for another post.
  2. Attempting to make sense of that location field in every user’s profile and translate it to an actual city name.

However, a cursory glance at a sample of locations that users input will show how difficult it can be to identify the name of a city from the many and various creative ways Twitter users express themselves through their location field.

Here’s a random peek:

Sevilla Andalucía España
252 — dmv
Above The Clouds
Lion City!
last exit to sumerland
At Northies you will find me
SomeWhere ….
Boston! Green all day!
canada, eh?
boston & new york .
SJC – SP – Brasil
Monumen Nasional, Jakarta
a bed.
Caracas Venezuela
Makassar, Sulawesi Selatan
Swimming Pool
Ur timeline
boston & new york .
in the shop doing hair
A place Quiet and Cozy
Sweden!!ღ ♫
Mi paraiso

Luckily, Twitter provides a Search API that allows you to collect tweets that are located within a given radius of a point (exactly how Twitter comes up with the geo for these tweets I am not entirely sure). The Social Media lab at Rutgers has been collecting these tweets for 57 selected cities mostly in the U.S. for over a year, amassing a data set of over 700 million tweets. The logical next step for me was to go through the tagged tweets, look at the location field from their user profiles, and find the most popular terms for each city. Demonstrating how difficult parsing these location fields are, there were many terms that were simply flat out wrong or very questionable/vague, and I had to go through a lot of pruning.

Here are a small number of user locations that had been tagged to New York, NY, but that I took out for one reason or another:

Waverly Place
Thames Street
Off the Wall
Third World
you’r world
Financial Freedom
Sapphire World
hogwarts on waverly place.
Tha Wurld
1st Place
your sin! my city : D
Probably In The Mall
around the corner!
Nick World
eight prince
Donghae’s room

A lot of knowing what was an error and what wasn’t an error required me to delve into my knowledge of cities and popular trivia. For instance, I knew to take out Waverly Place, because it probably meant that the user was a fan of the show Wizards of Waverly Place on Disney as opposed to living in Waverly Place in NYC. Other terms were complete question marks to me, and required searching on the web and urbandictionary.com.

Some interesting/random things I have found/mused about while doing this filtering:

  • Denver, CO is also commonly known as the “Mile High City.”
  • From Atlanta, GA? How about Hotlanta? Also sometimes referred to as  “Black Hollywood.”
  • For the longest time, I kept seeing varying versions of “DMV” popping up, and I would be so confused. To me, DMV stands for Department of Motor Vehicles. Google agreed with me as well, but a quick search on urbandictionary revealed that, sure enough, it stands for “D.C. Maryland Virginia.”
  • The many, many ways to incorporate “Bieber” into one’s location. One ex: Bieberlulu (Honalulu, HI). Also, an alarming number of users located in “biebers pants.”
  • “Springfield” – enough information to infer Springfield, IL? Probably not, considering there are 34 cities in the U.S. with this name and 36 townships. Oh, and the Simpsons live in “Springfield.”
  • “AdventureLand” – mistake? Or…a sprawling family resort in Des Moines, IA.
  • Why is “New Yawkkk” way more popular than, say, “New Yawkk,” “New Yawwk,” or “New Yawkkkk”? Perhaps it has something to with the fact that Snooki (@Sn00ki) from Jersey Shore lists “New Yawkkk” as her location?
  • More people list their location as “Quahog,” fictitious home of Family Guy, than “Cape Cod, MA.” Also Twitter seems to think Quahog is in MA.
  • A cool nickname for Long Island, a borough of NYC – “Strong Island.”
  • A not so cool nickname for Salt Lake City, UT – “SL, UT.”
  • “Bunny Ranch” – not a mistake by Twitter but actually a “famous” brothel in Carson City, NV.
  • Nashville, TN is also known as “Music City.” Apparently, it’s also known for money. “Cashville” and “Na$hville” were very popular.
  • “Noho” – confusingly standing for both NOrth HOuston (NYC) and NOrth HOllywood (LA). Other ambiguities: “Chinatown,” “Downtown,” “Uptown,” etc.
  • I found lots and lots examples of users listing a whole string of places for a location. For instance, something like “NY x LA” or, even crazier – “NY,LA,ATL,MIA,RIO,LON,PAR,LAG.” I wonder how many of these are large companies or actual globetrotters as opposed to the occasional vacationer that likes to inflate their travel time.
  • Wishful thinking as well as excited announcements of moving abounded. I found many variations of “Wishing I were in X”, “It should be X,” “X, one day…”, and “Y, but very soon to be X,” the last one often punctuated with exclamation points and smileys.
  • The location field can also be a place for clever little jokes (“behind U,” “near your ear”, “where ur not”) or possibly, flirtations?? (“ur place,” “in ya mouth”)

Finished product: a clean and unambiguous list of top location field entries for each city we were crawling. From here, I use these lists to geo-code millions of tweets to my selected cities if the user location field matches up with an entry on the list.

Find this topic (location fields in Twitter) interesting? Many more Bieber-isms and much more in-depth research on user location fields in the following research paper by Hecht, Hong, Suh, and Chi: Tweets from Justin Bieber’s Heart [pdf]