Researchers Turn Data into Dynamic Demographics
Aside from showing off how their travel, culinary and nightlife habits, users of the geolocated “check-in” service Foursquare could shed light on the character of a particular city and its neighborhoods.
Researchers at Carnegie Mellon University’s School of Computer Science say that instead of relying on stagnant, unyielding census and neighborhood zoning data to take the temperature of a given community, Foursquare checkin data can provide the much –needed layer of dynamic city life.
The researchers have developed developed an algorithm that takes the check-ins generated when foursquare members visit participating businesses or venues, and clusters them based on a combination of the location of the venues and the groups of people who most often visit them. This information is then mapped to reveal a city’s Livehoods, a term coined by the SCS researchers.
All of the Livehoods analysis is based on foursquare check-ins that users have shared publicly via social networks such as Twitter. This dataset of 18 million check-ins includes user ID, time, latitude and longitude, and the name and category of the venue for each check-in.
“Our goal is to understand how cities work through the lens of social media,” said Justin Cranshaw, a Ph.D. student in SCS’s Institute for Software Research.
The researchers analyzed data from foursquare, but the same computational techniques could be applied to several other databases of location information. The researchers are exploring applications to city planning, transportation and real estate development. Livehoods also could be useful for businesses developing marketing campaigns or for public health officials tracking the spread of disease.
For now, however, it’s being used to get a grip in the cultural and even class distinctions present in a community. For instance, in their study of Carnegie Mellon’s home in Pittsburgh, the researchers found that the Livehoods they identified sometimes spilled over existing neighborhood boundaries, or identified several communities within a neighborhood. The Pittsburgh analysis was based on 42,787 check-ins by 3,840 users at 5,349 venues.
For instance, “they found that the upscale neighborhood of Shadyside actually had two demographically distinct Livehoods — an older, staid community to the west and a younger, “indie” community to the east. Moreover, the younger Livehood spilled over into East Liberty, a neighborhood that long suffered from decay but recently has seen some upscale development.”
And how does this match up to the class and cultural viewpoints of a human observer? Right on… “That makes sense to me,” observed a 24-year-old resident of eastern Shadyside, one of 27 Pittsburgh residents who were interviewed by researchers to validate the findings. “I think at one point it was more walled off and this was poor (East Liberty) and this was wealthy (Shadyside) and now there are nice places in East Liberty and there’s some more diversity in this area (eastern Shadyside).”
Speaking of class divides, the limitations of the research shine through as a viable point of study themselves. Foursquare users tend to be young, urban professionals with smartphones. Consequently, areas of cities with older, poorer populations are nearly blank in the Livehoods maps—an indication of the class makeup—something potentially valuable when seeking new dwellings or pricing real estate, for instance.
Maps for New York (first map above), San Francisco (just above) and Pittsburgh are available on the project website, http://livehoods.org/. The team has added voting for the next city to be “checked.”
November 26, 2014
November 25, 2014
November 24, 2014
- NTT Comware Using MapR Distribution for Hadoop to Power SmartCloud
- Splunk Announces Third Quarter 2015 Financial Results
November 20, 2014
- Datawatch Reveals Fourth Quarter 2014 Financial Results
- WANdisco Announces Integration with Cloudera Manager and Ambari
- Progress Partners with DataStax to Provide Data Integration for Cassandra
November 19, 2014
- Splice Machine Hadoop RDBMS Now Available
- Teradata and MapR Expand Partnership
- Cray Adds Cloudera Enterprise to Urika-XA System
- Qubole’s Big Data as a Service Now Available on Microsoft Azure Cloud
November 18, 2014
- Seagate Unveils Apache Hadoop on Lustre Connector
- HP Vertica for SQL on Hadoop Now Available
- Glassbeam Introduces New Version of SCALAR Tightly Integrated with Spark
- GoGrid Announces New Partnership with Cloudera
- DDN Announces Latest Generation of EXAScaler
- Context Relevant Wins Cloudera 5 Certification
- Nimbix Introduces Extension of JARVICE to Support Hybrid Cloud Implementations
- Oracle Advances Data Integration Portfolio
Most Read Features
- Spark Just Passed Hadoop in Popularity on the Web–Here’s Why
- Congratulations Hadoop, You Made It–Now Disappear
- Apache Spark: 3 Real-World Use Cases
- ‘What Is Big Data’ Question Finally Settled?
- Graph Analytics Poised to Solve Tough Big Data Problems
- Today’s Baseball Analytics Make Moneyball Look Like Child’s Play
- How Big Data Analytics Is Shining a Light on Anonymous Web Traffic
- Plotting a Big Data Career Change
- Why Twitter Is the Low-Hanging Fruit of Social Analytics
- Businesses Are Going About Data Science Wrong–Here’s How To Get It Right
- More Features…
Most Read News In Brief
- Six Big Name Schools with Big Data Programs
- Survey: Mega-Vendors Still Dominate Database Management Market
- Hadoop on a Raspberry Pi
- Google Targets Big Genome Data
- In-Memory Computing Goes Open Source
- Tesco’s Collapse is a Cautionary Tale for Big Data
- Hadoop and NoSQL Now Data Warehouse-Worthy: Gartner
- ‘Decision Science’ Meets Business Intelligence
- Machine Learning Gets a Boost from Google
- Teradata Has Hadoop Covered with MapR Partnership
- More News In Brief…
Most Read This Just In
- NY Mets to Utilize SAS Analytics
- Platfora Releases New Version of Big Data Analytics Platform
- Cloudera Announces Formation of Cloudera Labs
- Teradata Announces New QueryGrid Data Fabric Capabilities
- Syncsort Combines Test Drive with MapR Sandbox
- Anil Gadre Joins MapR as SVP of Product Management
- MapR Teams Up with MongoDB
- NVIDIA Unveils Tesla K80 Dual-GPU Accelerator Designed for Data Analytics
- Teradata and MapR Expand Partnership
- Platfora Introduces Analytics Offering for IoT
- More This Just In…