2022 Capstone Projects
CUSP received its highest number of proposals ever for the 2022 cycle of the Capstone Program. Thanks to our sponsors, our graduate students have the opportunity to have an impact on critical urban issues while continuing to develop their data science skills and honing their public policy expertise. Projects have been grouped into four categories: disaster resilience and climate change, fairness and inclusivity, health and wellbeing, and modern civil and communications infrastructure. To view the full list of 2022 projects, see below. Stay tuned throughout the spring and summer semesters to hear more about how our 2022 Capstone projects are progressing!
Disaster Resilience and Climate Change
A New Dataset to Develop Smart Assistants for Specialized Training with Augmented Reality
Emergency response personnel (i.e. firefighters, medical personnel, and utility workers) require specialized training to act in time- and precision-sensitive tasks. Comprehensive training requires time, practice, and continuous guidance from a professional and experienced trainer able to predict and correct the trainee’s actions. The trainer-to-trainee ratio currently limits the amount of individuals who are trained at a time. Ideally, such training could be carried out by an automatic and smart agent using augmented reality devices like the Hololens. In this project, we aim to develop a system for guided monitoring of a person’s actions as they learn a specialized task.
A Tale of Two Cities: Assessing the state of the thermal environment for New York and Athens
- Constantinos Cartalis, Professor, National and Kapodistrian University of Athens (NKUA)
- Anastasios Polydoros, Ph.D. Candidate, National and Kapodistrian University of Athens (NKUA)
Mitigation plans to counteract overheating in urban areas need to be based on a thorough knowledge of the state of the thermal environment, most importantly on the presence of areas which consistently demonstrate higher or lower urban land surface temperatures (hereinafter referred to as “hot spots” or “cold spots”, respectively).
This is because Land Surface Temperature (LST) is a controlling factor of energy exchange between the surface and the atmosphere, and thus a cause of meteorological and climatic variation. Such exchange is through latent and sensible heat as well as the emission of radiation at the thermal infrared part of the spectrum.
As a matter of fact, as urban areas are covered with buildings and pavements; as a result moist soil and vegetation are being replaced with cement and asphalt. These materials have high thermal mass and tend to absorb more solar radiation than the surfaces found in rural areas, with the result being higher land surface temperatures. Additionally these surfaces are impermeable and tend to dry more quickly after precipitation, reducing evaporation, which has a cooling effect in green areas.
Creating a High Performance Construction Project Database To Accelerate Building Decarbonization and Resilience in NYC
- Marianna Koval, Director, Invest NYC SDG, NYU Stern Center for Sustainable Business
The power of experience curves in technology (known as Wright’s Law, Swanson’s Law, or “learning by doing”) has made clean energy technologies less expensive than fossil fuel-generated energy, driving exponential growth in clean energy deployment globally. Can this power of learning also be harnessed for the technology of low carbon building to make Passive House construction less costly than traditional methods? This project, a partnership between Invest NYC SDG, Passive House Accelerator, and Source 2050 will (1) study how “experience curves” apply to Passive House design and construction, and (2) create a global project database to accelerate those experience curves.
Data-Life: Exploring Post-Covid Scenarios Through Data Science
The project will combine quantitative data coming from open data, social media, and other sources, together with ad-hoc analysis, crowdsourcing, and gamification practices, to collect data and understand the perception of people about the present and future of the pandemic. It will look into the post-pandemic future, trying to understand how things are going to change and how people may react to different alternative policies and decisions at different levels.
Developing an AI-based Image Classifier for School Infrastructure Baseline Data Collection in Large Scale Disaster Risk Analysis
This project will develop a risk-informed classification system to support AI computer vision algorithms for assessing seismic vulnerabilities in schools. The project will be led by the World Bank’s Global Program for Safer Schools (GPSS), the NYU Disaster Risk Analysis Lab, and the NYU AI4CE. The project’s main goal is to develop a simplified vulnerability classification system based on existing detailed taxonomy from the Global Library of School Infrastructure (GLOSI: https://gpss.worldbank.org/en/glosi/overview), to support AI-based computer vision tools to reduce structural vulnerability data collection time and costs in large building portfolios. We envision that this simplified classification will enable more reliable AI computer vision tools to empower communities to be engaged in governments’ disaster risk management efforts more easily, make risk analysis more accessible and informed by up-to-date baseline information worldwide and guide large-scale school safety and resilience investments more efficiently.
Emergency Response after Earthquakes: Assessing Risk and Guiding Coordination in Hospital Systems
- Luis Ceferino, Assistant Professor, NYU Disaster Risk Analysis Lab (DRAL)
- World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR)
- Earthquake Engineering Research Institute’s Public Health Working Group
This project will assess the earthquake risk of hospitals and their ability to sustain operations after future large earthquakes. The project will be led by the NYU Disaster Risk Analysis Lab, the World Bank’s Disaster Risk Management Division, and the Earthquake Engineering Research Institute (EERI)’s Public Health Working Group. The project’s main goal is to apply robust disaster risk analysis techniques on hospital datasets to better understand post-disaster hospital capacity. The project will investigate new risk metrics relevant to inform practical risk mitigation policy implementation and emergency planning, e.g., mobilizing patients from neighborhoods with little hospital capacity to high hospital capacity. The goal is to inform communities on how to mitigate not only potential economic losses, as currently done in practice, but also potential functional and societal impacts.
FloodNet - Computer Vision for Urban Street Flood Detection
In NYC, sea level rise has led to a dramatic increase in flood risk, particularly in low-lying and coastal neighborhoods. Urban flood water can impede pedestrian and vehicle mobility, and also can contain a diverse array of contaminants, including fuels, raw sewage, and industrial/household chemicals. For this capstone project, the team will train, test and deploy computer vision (CV) and deep learning (DL) models for the detection of street flood events. Existing labelled datasets will be used for training. In addition, an unlabelled NYC street image dataset will be provided for labelling and training of a NYC centric model.
Hardening New York City’s Interdependent Water and Energy Infrastructures Against Climate Change and Cyberattacks
Extreme events stress New York City’s (NYC’s) interdependent water and energy infrastructures; impact human livelihood; and can disrupt local ecosystems. The dependence of water and wastewater operations on power implies that a blackout, coupled with backup system components’ failures, can force the discharge of untreated wastewater into NYC’s waterways, and result in a public health emergency. Data-driven and optimization techniques can leverage publicly available data to reveal vulnerabilities in electricity, water, and wastewater infrastructures. Our analysis can aid policy design against natural hazards and cyberattacks, and thus inform the modernization of interdependent urban water and electricity infrastructures.
Modernizing Organics “Collection” for Managing the City’s Municipal Solid Waste and Achieving Zero Waste Goals
This capstone will develop a data visualization tool to illustrate the lifecycle costs and benefits of leveraging late 20th century technology to solve a 21st century problem—the need to achieve zero waste and reduce CO2 emissions– as compared to the current use of 19th century (and earlier) technology.
Virtual and Augmented Reality for Community Preparedness to Disasters
- Qi Sun, Assistant Professor, NYU Immersive Computing Lab
- Luis Ceferino, Assistant Professor, NYU Disaster Risk Analysis Lab (DRAL)
- World Bank’s Global Program for Safer Schools (GPSS)
This project will create physically realistic virtual and augmented reality (VR and AR) environments that represent extreme events such as wildfires, floods, landslides, winds, and earthquakes affecting our communities. The project will use these environments to show how resilient infrastructure and response preparedness in a disaster can significantly reduce the probability of physical and human losses. The virtual environment will then be deployed to VR/AR devices for egocentric and immersive viewing. The project will be led by the NYU Immersive Computing Lab, NYU Disaster Risk Analysis Lab, and the World Bank’s Global Program for Safer Schools (GPSS). The project’s main goal is to raise awareness and prepare our communities to respond to extreme events using immersive realities to enhance the effectiveness of drills, such as evacuating during floods.
Fairness and Inclusivity
Assessing Patterns of Prosecution Decisions by Assistant District Attorneys
- Daniel Neill, Associate Professor of Computer Science, Public Service, and Urban Analytics, NYU Machine Learning for Good Lab, NYU Public Safety Lab
- Robert P. Constantino, Jr., Chief of Innovation & Strategy Unit, Office of Suffolk County District Attorney Rachael Rollins
A key decision in criminal justice proceedings is whether the assistant district attorney decides to prosecute a case. For non-violent misdemeanors, this decision involves considerable discretion, and has substantial impacts on incarceration and reoffending, as well as broader impacts on racial equity. Particularly for first-time offenders, nonprosecution of a nonviolent misdemeanor leads to large reductions in new criminal complaints, suggesting potential interventions to increase prosecutors’ leniency. This project will extend and integrate a prototype visualization tool which compares ADAs to their peers in terms of leniency and return rates, incorporating new machine learning methods to assess systematic patterns of overprosecution.
City of Bogotá: Data Driven Door-to-Door Care
The Office of Women’s Affairs is looking to make its Care System innovative in its objectives and how it uses data. The Care System is an initiative to reach female caregivers living in dire conditions. It brings services directly to those who often cannot leave their homes because of their domestic workload. Primary caregivers receive certified skillset training, well-being activities, and become part of community-building networks with professional facilitation. Others receive care and services to develop their autonomy. Importantly, the initiative is delivered to those in need and provides evidence on the value of redistributing care for closing gender gaps and economic recovery. That’s where data comes in.
Community Economic Recovery Tool
The COVID-19 pandemic has only exposed the existing health, economic, and social challenges within the county, and have highlighted the need to be prepared for such events in the future. Governments of all levels are tasked with easing the burden that citizens and local businesses face. To address this challenge, we propose to create an “Economic Recovery” tool that offers real-time strategies to community leaders recovering from COVID-19 shocks and can be used post the recovery to identify other underrepresented communities.
Measuring Geographic Distribution and Predictors of Variation of Over-Policing Across NYC
Mass incarceration is a well-recognized public health issue and driver of racial inequities. However, focusing on arrests and subsequent incarceration underestimates the totality of police-community member interactions, and risks obfuscating the full magnitude of disproportionate policing within a city. This project will use publicly available NYPD policing data on multiple endpoints of policing (e.g., arrests, desk appearance tickets, criminal summons) to construct a geographic visualization tool to assess the burden of policing across neighborhoods and time in NYC. Through data linkages with the American Community Survey, this tool will allow for the investigation of predictors of over policing within NYC.
Repairing Dallas: Leveraging data to improve housing quality
Substandard homes severely impact resident wellbeing: deficient housing quality is associated with asthma and respiratory illness, lead poisoning, accidental injury, anxiety and depression, and poor academic outcomes. Data on housing quality is limited to MSA-level estimates of housing adequacy and subjective assessments by the local appraisal district, so it’s difficult for housing advocates to understand where housing quality issues are most acute and how to direct resources for repair. The project purpose is twofold: (1) identify neighborhoods in Dallas where there is poor housing quality and (2) develop a sampling and surveying approach to collect granular data within high-repair neighborhoods.
Simulating Interactions with Visually Impaired
Urban environments represent particularly dire challenges for the mobility of the visually impaired, who must travel complex routes in often crowded and noisy conditions with limited to no assistance. To help visually impaired regain their independence, they are offered orientation and mobility training (O&M). However, O&M training represent a risk to the visually impaired, as it exposes them to dangerous situations and falls. We seek to overcome this issue by simulating O&M training in virtual and augmented reality (VR/AR), in which trainers and trainees interact within a safe and controlled environment that simulates part of a city.
Trustworthy AI for Human Machine Interface
- S. Farokh Atashzar, Assistant Professor, Medical Robotics and Intelligent Interactive Technologies Lab (MERIIT @ NYU)
- Jackie Libby, Smart Cities Postdoctoral Associate, NYU CUSP
We exist in a world with advanced AI, yet there is a lack of AI for assisting disabled populations who cannot achieve basic manipulability functions. This project will be to develop trustworthy Machine Learning models to to address existing problems of human-machine interfaces.
Understanding Public Opinion About the Police in New York City
Defunding the police is a polarizing topic that is on the rise in the United States. Public opinion is generally divided due to controversial recent events that have involved law enforcement officers (LEOs). However, how we perceive or not violence around us likely contributes to our own assessment of LEOs’ necessity. In this project, we seek to carry out an analysis about the interplay between these two factors to study if violent incidents, whether from LEOs or criminals, shape the opinion of New York City (NYC) inhabitants.
Health and Wellbeing
Challenges and Solutions for Walking with Assistive Wearable Robots in Urban Environments
The goal of this capstone project is to identify challenges for the locomotion of persons with lower-limb disabilities who use wearable robots (powered exoskeletons, prosthetics, etc.) for gait assistance. In particular, this capstone will focus on those related to urban environments, and possible solutions to overcome those obstacles. One of the reasons that wearable robots are not commonly used is the discrepancy between the challenging environment in real world as compared to well-controlled laboratory settings. In this capstone project, these under-explored aspects will be investigated.
Mapping Agricultural Production in NYC (M.A.P. NYC)
- Wythe Marschall, Senior Research Project Manager, Food and Health, Invest NYC SDG (an initiative of the NYU Stern Center for Sustainable Business)
To support an expanded, more just, and self-sustaining urban agriculture sector, Mapping Agricultural Production in NYC (M.A.P. NYC) is using data science to conduct research into NYC’s current food production and distribution. Having created the M.A.P. NYC platform in 2021, the project team is seeking CUSP students to employ this tool to research gaps and opportunities in the local food landscape, analyzing links between urban agriculture and food security status, health outcomes, and land use. Specifically, we seek to set a baseline for agricultural production as a policy recommendation to the new Office of Urban Agriculture.
Shape Estimation and Data-driven Intelligent Control of Soft Robotic Upper-limb Exoskeletons for In-home Telerehabilitation
- Jackie Libby, Smart Cities Postdoctoral Associate, NYU CUSP
- S. Farokh Atashzar, Assistant Professor, Medical Robotics and Intelligent Interactive Technologies Lab (MERIIT @ NYU)
In our aging society, neuromuscular disorders like stroke are becoming more prevalent. With that comes an increasing need for labor-intensive physical therapy, which is prohibitively expensive for many patients in need, resulting in long-term paralysis from lack of appropriate care. Soft robotic exoskeletons can deliver safe, in-home, and quantifiable teletherapy for these patients. We are building a soft exoskeleton to control the hand, wrist, and elbow. We are fabricating, sensorizing, and controlling soft modular actuators. Shape estimation and control of soft robots is nontrivial. In this project, CUSP students will work with us to fabricate soft robots and train machine learning models for shape estimation and data-driven control.
Modern Civil and Communications Infrastructures
Addressing Complexity of Urban Networks with Deep Learning
Over the recent years, Graph Neural Networks (GNNs) have become increasingly popular in supplementing traditional network analytic techniques. The capstone project will seek proof-of-concept applications on the GNNs and the Hierarchical GNNs in particular to diverse cases of urban network analytics ranging from urban mobility and transportation networks, social media analytics, social networks, urban infrastructure, environmental sensing and beyond.
Airport Departing Passenger Profile Curve at EWR Terminal B: Understanding passengers’ journey through PANYNJ airports
This project aims to create a prototype of a departing passenger profile curve to help EWR Terminal B proactively manage its terminal frontage, baggage/check-in, and TSA queues. Using data from various stages in a passenger’s journey at our airport terminal, we hope to estimate when and where passengers will be throughout their airport journey. The model should consider industry knowledge of passenger dwell times and other passenger preferences. Understanding how passengers interact with our terminal will allow EWR Terminal B management to highlight pain points in their journey and plan for future improvements in design or technology.
Audio-Visual Vehicle Localization for Urban Traffic Monitoring
Monitoring road traffic is key to ensuring user safety and smooth operation. Increasing traffic volumes impact the stress level of commuters and increase noise levels in communities, leading to health problems. Local authorities need reliable monitoring systems to create policies to help mitigate this. Ideally, automatic monitoring systems should be able not only to count vehicles but also to detect the type of vehicle (e.g. car, truck). In this project we aim to develop a system for classification of vehicles that delivers audio-visual data for the robust localization of vehicles in the wild.
Behavior Modeling Using Multi-Modal Mobility Data
Develop and demonstrate methods for analyzing patterns found in mobility data, such as the aggregate tracks of vehicle populations. An expanding collection of research indicates that information about the movements of populations (i.e., syntactic trajectories) provide informative patterns. This project will explore how multiple sets of data can be jointly analyzed.
Building Accessible City by Self-Supervised Visual Place Recognition
Visual Place Recognition (VPR), which aims to identify previously visited places during navigation. In the context of SLAM, VPR is a key component in relocalization when the tracking is lost. Existing learning-based visual place recognition methods are generally supervised and require extra sensors (GPS or wheel encoder) to provide ground truth location labels. Differently, we want to design a self-supervised method for visual place recognition which can smoothly recognize the visited locations in a single scene environment without any ground truth labels. The method should be able to handle a variety of input modalities, including point clouds and RGB pictures.
Creating a NYC Business Survivability Predictor
COVID-19 disproportionately impacted on small business. Yet socio-economic conditions alone are insufficient to reliably predict their closures/survivability. In NYC small business disproportionately contribute to new job creation. Thus understanding spatio-socio-economic factors related to business survivability can minimize risk for prospective business owners, landlords, and lenders. This project considers 16 NYC neighborhoods via open–access data and uses the COVID-19 2020 PAUSE order as a stressor to test small business resiliency. The project is a follow on from a 2021 pilot study that considered only restaurants and food related retailer in only a handful of communities.
Data-Driven Agent-Based Modeling of Fake News Dynamics over Online Social Networks
The spread of misinformation through social media has led to significant issues in sectors like public health and political discourse. We leverage Twitter data to create misinformation models using agent-based models. We aim to understand the spreading pattern of misinformation and the human response to it. This research will also create intervention mechanisms that will combat the spreading of fake news and its impact on the population.
Democratizing New York City’s Urban Development Processes
New York City’s current planning process is a jumble of information on the websites of various community boards in different boroughs. There is no unified source of truth that various stakeholders like developers, city planners or concerned citizens can access this information through. Through this project we’d like to create a unified dynamic map for New York City highlighting city planning projects in flight along with citizen comments and concerns on them.
Learning Efficient Multi-Agent Robotic Navigation for Exploration and Mapping
This project involves formalizing both theoretically and experimentally a distributed multi-robot or swarm navigation and exploration problems leveraging Graph Neural Network (GNN) architectures within the context of reinforcement learning policies. This approach directly operates on the graph structure with the potential to enable resolution of the multi-robot navigation problem, by leveraging the distributed representation functionality of GNN methods, thus potentially enabling prospective scalability for large swarms, comprised hundreds of agents.
Low-Power Computer-Vision Counting Research
Many City agencies are involved in the use, planning, and design of public space but good data on pedestrian flows can be hard to come by. Manual counts take coordination and planning as well as staff costs. Computer-vision (CV) counting technologies are being tested in the city now but it is already clear that the infrastructure requirements (tapping into electricity and mounting to light poles) will be a limiting factor in using this technology more broadly and particularly for shorter-term studies where the infrastructure investment is not worth the time and effort. A low-cost, battery-powered CV sensor can help fill the data gap and allow agencies to utilize privacy-protected automated counts in short-term deployments with minimal infrastructure requirements.
Neighborhood Spatial Characteristics as Proxies for Socio-Economic and Quality of Life Issues
The provision of governmental services (e.g. public transport access, well-maintained streets) are widely recognized to have socio-economic impacts on the surrounding community. What is less understood as how the spatial characteristics of a community’s built infrastructure and distribution of those characteristics (building heights, sidewalk widths) may correlate with the socio-economic characteristics, social vulnerability, COVID-19 vulnerability, and quality of life at a neighborhood level. This project strives to establish a robust set of spatial metrics to investigate the relevance of spatial attributes as predictors for community well-being.
Rapid Detection of Power Outages with Time-Dependent Proximal Remote Sensing of City Lights
In this capstone project we propose to use imaging data collected from CUSP’s “Urban Observatory” (UO) facility to build a data processing pipeline and application that can detect and geolocate power outages and restoration in near real-time. The UO’s historical imaging data set includes visible wavelength images of Manhattan at a cadence of roughly 10 seconds per image. An analysis of the lighting variability patterns in these images will be used to identify clusters of lights synchronously turning “off” in the images and with photogrammetric techniques, the geospatial location of those lights will be determined.
RealCity3D: A Large-Scale Georeferenced 3D Shape Dataset of Real-world Cities
Existing 3D shape datasets in the research community are generally limited to objects or scenes at the home level. City-level shape datasets are rare due to the difficulty in data collection and processing. However, such datasets uniquely present a new type of 3D data with a high variance in geometric complexity and spatial layout styles, such as residential/historical/commercial buildings and skyscrapers. This work focuses on collecting such data, and proposes city generation as new tasks for data-driven content generation. In addition, a proposing new city-level generation models is also included in this project.
Study of Indoor Spaces Occupancy and Its Correlation with the Performance of HVAC System
Buildings consume around 40% of total US energy use, while heating, ventilation, and air conditioning (HVAC) systems account for 74% of building energy consumption. Current HVAC systems often rely on a fixed schedule, which typically results in conditioning of indoor spaces unnecessarily, without knowing the actual flow of the users. In this project, we directly measure the occupancy of university indoor spaces using a distributed sensor network. We then investigate correlation between the performance of the HVAC system and actual occupancy of these spaces to provide insights into building use patterns for adaptive control strategies of the HVAC system.
The Electric Commute: Envisioning 100% Electrified Mobility in New York City (TEC-NYC)
Every day, almost two million persons enter and leave the central business district of Manhattan using light-duty vehicles such as cars, taxis, vans, or trucks. Currently, around 1% of these vehicles are electric. This project aims to quantify the ramifications of a 100% electric commute in New York City. We will create a model that translates NYC’s transportation needs into electric charging demand, including emerging mobility trends (e.g., electric scooters) and remote work patterns. Interactive visualizations produced by the model will allow citizens, urban planners, and politicians to analyze the impact of mobility electrification and their policy decisions.
V2X-Sim - Collaborative Perception for Self-Driving in Urban Scenes
Vehicle-to-everything (V2X), which refers to collaboration between a vehicle and any entity in its vicinity via communication, might significantly increase perception in self-driving systems. Due to a lack of publicly available V2X datasets, collaborative perception has not progressed as quickly as single-agent perception. For this capstone project, we present V2X-Sim, the first public synthetic collaborative perception dataset in urban driving scenarios. The team will train, test and deploy computer vision (CV) and deep learning (DL) models for collaborative perception on V2X-Sim dataset.