2014 CApstone projects
Economic Map of New York City
CUSP Students: Andrea Kanner, Awais Malik, Kara Leary
The goal of our project was to develop an economic profile of New York City. This project was part of a larger effort at CUSP to provide New York City agencies with analysis that facilitates better understanding of the City’s economic condition. The success of this project required integration of the Primary Land Use Tax Lot Output (PLUTO)data with other sources such as the U.S. Census and American Community Survey (ACS),as well as data from relevant New York City Agencies including the Departments of Finance, and Parks and Recreation. Our group created an interactive visualization tool, which could assist City agencies in achieving greater understanding of the key drivers of economic prosperity in the City. The initial stages of the project involved building the base layers of a comprehensive platform for analysis and visualizations of economic data through data cleaning and integration. The analysis stage began with simple geo-spatial correlations, and progressed towards area classification and correspondence, with the primary objective being to identify “main street” metrics and spatial patterns of equity. Our group also engaged in research on the analysis aspects of the project: summary statistics, metric correspondences, and neighborhood feature vectors for classification. Throughout the process, we were also involved in visualizations including simple plotting, interpretation, and mapmaking. Our final step was to create an interactive web-based visualization tool, which highlights some of the economic drivers the govern New York City. Future steps include enabling the identification of “like” locations based on demo-graphic and economic parameters of interest. Once the economic map is complete, an example of its application would be to evaluate the effect of proximity to parks on real estate market value and business turnover. Similarly, one could also analyze the economic effects of closeness to a Business Improvement District (BID) on a particular neighbor-hood, or the factors that make a BID more or less successful. We hope our product will assist City agencies in achieving a better understanding of the economic needs and assessments of New York City.
911 Data Project and Taxi Data Project
CUSP Students: Alex-Chohlas Wood, Aliya Merali, Warren Reed, Gang Zhao
CUSP Mentor: Dr. Theo Damoulas
The 911 Data Project was developed through a partnership between CUSP and the NYPD as a pilot project to launch collaboration between the two organizations. CUSP was given access to two years of 911 call center data, including information about the call types, times, and locations. The primary application discussed in the creation of the CUSP/NYPD partnership focused on the issue of improving current methods of allocation for the limited police resources. Using 911 call history and other data sources, the 911 Data Project team developed a predictive model and other tools to provide decision makers with more detailed information for resources distribution.
Impacts of Urban Land Use
CUSP Students: Katherine Elliott, Winnie Kaaria, and Amir Beshay
CUSP Mentor: Dr. Constantine Kontokosta
Environmental Impact Statements (EIS’s) were first enacted by the National Environmental Policy Act (NEPA), passed by the U.S Congress in 1969, and signed into law in 1970. Public and private projects in New York City involving city discretionary actions are required to follow a detailed environmental review process as outlined by the City Environmental Quality Review (CEQR). CEQR serves as New York City’s implementation of the 1975 State Environmental Quality Review Act (SEQRA). A project is subject for environmental review if requiring discretionary approvals or permits from a City agency, City funding, or if directly undertaken by a City agency. Phase 1 of the environmental review process is an Environmental Assessment (EAS), if following the EAS any significant impacts are identified the applicant is required to submit a detailed statement of the adverse unavoidable impacts if the proposal is undertaken, alternatives to the proposed action, short-term and long- term environmental gains, and the extent to which the proposed action may cause any loss of resources (including labor, materials, natural and cultural resource loss, etc.)
Applicants in submitting these statements often include data irrelevant to the project or write ‘telephone book’ statements with details undifferentiated in importance. Since the 1970’s, EIS’s are regarded from a legal standpoint as largely procedural with little scientific backing to the estimated environmental impacts. Practitioners involved in land use development and planning find the environmental review process to be lengthy and expensive. Consequently, there is no method testing if any variance exists between anticipated and actual impacts of discretionary actions. Discrepancies in the accuracy of impacts lead to inadequate mitigation and unsustainable building design. The goal of this study is to create a database that allows for easy documentation of the projected and actual impacts of proposed projects in New York City identifying any discrepancies where possible and creating a system to quality control impact- related data. Additionally, to allow for increased public engagement in the review process, an interactive map was constructed from the database (Appendix A). Policy recommendations outline methods to streamline the review process and improve the accuracy and content within in EIS’s.
Odometry-Based Trajectory Identification
CUSP Students: Ravi Shroff, Yilong Zha
CUSP Mentor: Prof. Manuela Veloso
In this report we examine the identification of walking trajectories of people equipped with mobile phone-based odometry sensors. Specifically, we build on prior work and implement a “snapping” algorithm to reconstruct human paths traversed in real indoor environments, given existing maps of those environments. This algorithm searches for the most plausible paths using the map and accelerometer and gyroscope data collected from a phone in pocket or hand. We also analyze two approaches to process the raw data, threshold-based and template-based step matching. Although we are particularly interested in indoor settings where GPS utility is limited, the algorithm we are investigating can also be used in certain outdoor situations at possibly higher resolution than GPS.