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.”
July 26, 2016
- Impetus Technologies Announces Cloud-Based Trial of its Analytics Platform
- MapR Converged Data Platform Utilized by RiskIQ
- TrendMiner Receives Additional $1.1M in Funding
- Research and Markets Adds New Hadoop Market Report to Offering
- DataStax to Host Cassandra Summit 2016
July 25, 2016
- The ASF Announces Apache Kudu as a Top-Level Project
- Teradata Purchases Big Data Partnership
- XSEDE Project Successfully Tests Scheduled Networking of Big Data
July 21, 2016
- Datawatch Reports Third Quarter Fiscal 2016 Financial Results
- Cloudera Enterprise 5.8 and Navigator Optimizer Now Available
- The Scripps Research Institute Leverages DDN Storage
- Hazelcast and Heimdall Data Launch Intelligent SQL Optimization Solution
July 20, 2016
- TIBCO Mashery Enterprise Introduced
- Oregon State University’s New Data Analytics Programs Aim to Address National Shortage
- Deloitte Advisory Cyber Risk Services and Cray Introduce Cyber Reconnaissance and Analytics
- Snowflake Reports New Product Innovations and Strategic Partnerships
July 19, 2016
- Zettaset Big Data Encryption Solution Achieves Certification With MapR Converged Data Platform
- MapR Risk Management Quick Start Solution for Financial Services Now Available
- Global Corporation Selects Attunity Replicate for Hadoop
- NICE Actimize Signs Agreement With Tableau and Launches Visual Analytics Solution
Most Read Features
- 9 Must-Have Skills to Land Top Big Data Jobs in 2015
- Concord Claims 10x Performance Edge on Spark Streaming
- Solr or Elasticsearch–That Is the Question
- 9 Ways Retailers Are Using Big Data and Hadoop
- Spark Streaming: What Is It and Who’s Using It?
- Three NoSQL Databases You’ve Never Heard Of
- Which Type of SSD is Best: SATA, SAS, or PCIe?
- How Uber Uses Spark and Hadoop to Optimize Customer Experience
- Skip the Ph.D and Learn Spark, Data Science Salary Survey Says
- MongoDB Struts Its NoSQL Stuff in NYC
- More Features…
Most Read News In Brief
- Six Big Name Schools with Big Data Programs
- Investments in Fast Data Analytics Surge
- Why Gartner Dropped Big Data Off the Hype Curve
- Companies Struggle to Find an ROI on Analytics
- Report: Machine Learning Driving AI
- Crunchy Data Container Suite Packages PostgresSQL
- Melania Trump and the Anti-Plagiarism Algorithm
- Doubts Mount About Data Payoffs
- The Rise and Fall of Qlik
- Big Data Career Notes
- More News In Brief…
Most Read This Just In
- H2O.ai Unveils Sparkling Water 2.0
- Continuum Analytics Releases Anaconda Mosaic
- TransUnion Powers New Prama Self-Service Analytics Platform With MapR
- BI Leaders Join With Teradata to Enhance Presto for the Enterprise
- Qualys Introduces App for Splunk Enterprise
- Qubole and WANdisco Launch Cloudera Migration Program
- Splice Machine’s New Open-Source RDBMS Sandbox Goes Live on AWS
- IMS Health Chooses Cloudera Enterprise to Support Big Data Factory for Life Sciences and Healthcare
- Bigstep Launches Bare-Metal Cloud for Big Data in the U.S.
- New Report Says Big Data Market Investments to be Worth $72 Billion by 2020
- More This Just In…
September 8 - September 9Boston MA United States
September 19 - September 20Malaysia
September 19New York NY United States
September 26 - September 29New York United States
September 26 - September 27New York United States