Donald Davis | Mining Yelp Reviews to Measure Segregation in New York City
Until they were dismantled in the mid-1960s, the segregationist Jim Crow laws in the southern United States severely limited social interactions among ethnic groups. Despite the Civil Rights Act and later reforms, the U.S. remains deeply segregated, even in northern cities like New York. While standard measures of segregation exist for residences, jobs, and schools, we currently have no way of measuring how segregated common public activities like going to restaurants is. By studying five years of Yelp reviews in New York City, my colleagues and I provide the first estimate of diversity in city restaurants. Early results suggest that dining patterns are also segregated, though not as markedly as in housing.
Xiaofan (Fred) Jiang | Smart Systems for Monitoring Air Pollution and Personal Energy Use
Analyzing observations of the physical world can be a messy process. But the rise of sensors to measure air quality, ocean temperatures and any number of other changes is allowing us to study our environment and actions like never before. I will discuss two projects that use intelligent sensor systems to map the environment. In one, my colleagues and I combined inexpensive, custom-built Internet-connected sensors with cloud-based data analysis to measure and infer air-quality at city scales. In a second project, here at Columbia, my lab is combining building energy-use monitoring with location data to estimate an individual’s energy footprint to provide real-time feedback to cut energy use.
Desmond Patton | Preventing Gang Violence through Social Media Analysis
Social media is often an extension of the street for gang-involved youth. They may taunt rival gang members, downplay shootings and brag about fights and drug deals. Sometimes the tough talk turns into real violence. To be able to intervene, social workers need to understand how likely a specific post on Twitter may lead to violence. To do so requires deciphering the coded language and culture of gang-involved youth. I have recently collaborated with social science researchers and data scientists to analyze Twitter posts by Chicago gang members. Our goal is to combine observations with natural language processing tools to detect and decode high-risk language. I will discuss our process and early results.
