2017 Urban Science Intensive
1. The Quantified Community: Pulse of the City
CUSP Students: Trang Dam, Aaron D’Souza, Benjamin Miller, Zhaohong Niu, and Chunqing Xu
CUSP Mentors: Constantine Kontokosta, Martin Traunmueller, Nick Johnson, and Yuan Lai
USI Sponsor: New York Downtown Alliance
Internet has become one of the indispensable components in people’s lives. Being able to have access to wifi, especially to public wi-fi network, does not only fulfill individual’s need to “connect”, but also shed light into the research of urban dynamics.The rapid growth of public wi-fi networks have expanded the public space to fulfill urban residents’ needs of both leisure and work. High aggregated level of population are often seen in areas where public wi-fi is provided. Therefore, understanding urban dynamics through the analysis of wifi connection could be a novel and promising way for urban planners.
Understanding urban dynamics is essential because urban decision makers, including business improvement districts (BIDs) like New York Downtown Alliance, transportation policy makers, and law enforcement officials, need to know how many people are in a city at a given time. Transportation policy makers need to know how many people there are and how best to move them throughout the city. Law enforcement officials need to know how many people are in an area to prevent crime and prepare emergency response plans. Our client, Downtown Alliance, wants to know how many people there are at a given time in order to improve their services of public wifi and events in Lower Manhattan area. Following client’s directions and thinking as urban planners, we raised our question: to what extent does the Wi-Fi connections data in Lower Manhattan correlate with population and other city services (detect pulse of the city)? Furthermore, can this relationship be used to inform business and policy decision making?
This project aims to understand the “pulse of city” through space and time by using wi-fi counts data as a proxy. Pulse of the city, by our definition, is the regular fluctuations in population and demand for services. Our topic started with wifi counts data at hand provided by clients in order to better help city development and urbanization. To address the problem, we defined the term “pulse of the city” and built our analytical model based on its definition.
2. SQUID-Bike, Street Quality Identification for Citywide Bike Lane Infrastructure
CUSP Students: Sichen Tang, Geoff Perrin, Nicola Macchitella, and Felipe Diego Gonzalez
CUSP Mentor: Varun Adibhatla
USI Sponsor: ARGO Labs
SQUID-Bike is a project to measure, in a standardized manner, the general condition of citywide bike lane infrastructure by integrating digital street imagery and ride quality data using open source technology.
SQUID-Bike enables cities answer a simple question in a cost-effective manner “Which bike lanes are worse than others in a city?“. SQUID-bike empowers cities to be proactive about bike lane maintenance by adopting digital surveys of all bike lanes in a city.
Upon frequent use, longitudinal data from SQUID-bike will allow city agencies observe bike lane degradation over time. and could be used to power an anticipatory maintenance program and avoid the huge financial and political costs of deferred maintenance.
3. WiFind: Analyzing Wifi Density Around NYCHA Housing Projects
CUSP Students: Christian Rosado, Dongjie Fan, Jie Zhou, Kai Qu, Xiaomeng Dong
CUSP Mentor: Charlie Mydlarz and Justin Salamon
USI Sponsor: NYU CUSP
The team last year developed an android application called WiFind as a practice in urban sensing, and visualized Wi-Fi signals both on the mobile and the website. This year we collected new data and conducted the analysis with NYC open data.
Our projects is motivated to examine the fairness of Wi-Fi accessibility across the New York City, especially focusing on comparisons of open Wi-Fi density around public housing projects and their adjacent housing projects. Our team would like to find out whether the reality of Wi-Fi access density in neighborhoods with different income levels is also different.
There are several limitations on our projects. We have limited access to the buildings, the population getting access to Wi-Fi is uncertain, and the sample size is small in the analysis.
4. The Urban Observatory: First Empirical Quantification of the Rebound Effect
CUSP Students: Daynan Crull, Akshay Penmatcha, Anastasia Shegay, Priyanshi Singh
CUSP Mentor: Gregory Dobler
USI Sponsor: NYU CUSP
The project aims to carry out the first empirical quantification of the rebound effect–a reduction in expected gains from energy efficient technologies because of increased consumption. The approach is to collect raw data on the use of lighting technologies through remote sensing, leveraging CUSP UO instrumentation for hyperspectral and broadband imaging, apply image processing techniques to identify light sources, classify technology types, and measure durations of use by extracting on/off transitions. These light sources will be integrated with available records data (3-dimensional models of the observed urban landscape, land use data, and socioeconomic and demographic survey data) to characterize the observed population. Efficient and conventional lighting technologies will be compared in terms of durations of use in order to interpret the presence of a rebound effect.