Data Dives

1. Are buildings' energy efficiency performance affected by certain features, or any other exogenous environment factors?
   For example, how do factors like the age of housing stock, building materials, roofs, and construction styles affect energy efficiency ratings in different boroughs? What policy initiatives have different London Boroughs introduced to improve the energy efficiency of domestic properties? Are there disparities in energy efficiency ratings that could exacerbate fuel poverty in certain boroughs? To what extent can innovations in construction, such as green building materials and energy-efficient design, be used to improve the energy efficiency of homes in different boroughs.
   
2. Are urban parks or trees really playing a role promoting citizens' physical or mental health?
   For example, how do urban parks improve environmental sustainability and contribute to biodiversity in cities? What is the impact of urban parks on improving air quality, and how does this benefit respiratory health? How do green spaces in urban areas help reduce stress, anxiety, and depression?
   
3. How the climate risks (e.g., flood, heatwave) affect urban liveability? Could greenspace (e.g., parks) or trees help out?
   For example, how do parks manage stormwater and help mitigate the effects of urban flooding? How do parks help reduce urban heat island effects, and how might this contribute to better health outcomes in heat waves? How can parks be integrated into urban planning to create more liveable, sustainable, and healthy cities? Can trees help London adapt to extreme weather events, such as heatwaves, floods, and heavy rainfall? What role do trees play in reducing surface runoff and preventing urban flooding? What economic benefits do trees bring to local businesses/retails in terms of attracting customers and increasing sales?
   
4. How can urban transportation modes better support sustainable economy development in metropolitan cities?
   For example, how will the air quality alongside the road network improve towards 2030 sustainability goals (i.e., PM2.5, PM10, NOx), with predicted future mobility scenarios? How will it then pose influences on local banks and retails site selections?
   
5. How does urban waste management contribute to cities' sustainability goals towards 2030 and 2050?
   For example, what role do waste-to-energy (WTE) facilities play in managing urban waste, and how effective are they in reducing landfill use? How effective is urban recycling in reducing waste sent to landfills, and what barriers exist to improving recycling rates among boroughs? What roles do smart waste management technologies, such as IoT sensors and data analytics, play in optimizing waste collection and disposal in cities at a local scale?

For each of the five challenges below, the tasks are to:

1. Wrangle and understand the data structure.
   
2. Analyze the data and find key messages.
   
3. Design a clear compelling data presentation for an end-user.

Participants may vary the context and secondary datasets used to address questions that most interest you as a group, in consultation with the mentors and using research in the literature. Across all questions, groups will be asked to produce the technical outcome and an outcome suitable for communicating to the public. The public communication element may be visual or text, or both, but should assume little or no mathematical knowledge.

Dr. Yijing Li, Acting Director of CUSP London King’s College London (KCL)
Dr. Peter Baudains, CUSP London Research Software Engineer (KCL)
The Crown Estate
Professor Tyler Woebkenberg (M.S. in Applied Urban Science and Informatics Alum, Class of 2017)

The outline below reflects the schedule for New York City-based participants from CUSP at NYU Tandon. All programming will take place on the 13th Floor Learning Commons and a final showcase on Monday, February 23, 2025 will be open to the public.

Tuesday, February 18, 2025

• 7:45 – 11 AM ET
  NYC-based students join remotely for:
  • Talks from CUSP London, Academics and Partners
  • Teams and facilitator introductions
  • Warm-ups, teammate introductions
  • Team Brainstorming, and individual assignments allocated within each team

Wednesday, February 19, 2025

• 8 – 11 AM ET
  NYC-based student teams report on descriptive statistics, working independently of London-based teams.

Thursday, February 20, 2025

• 9 – 11 AM EST
  NYC-based students teams report on data insights, working independently of London-based teams.

Friday, February 21, 2025

• 7:30 – 11 AM EST
  All participating groups deliver their final presentations, and winners are announced.

The Fall 2024 Local Data Dive took place on Saturday, October 19, 2024 and included the participation of four projects sponsors: the International Center for Community Land Trusts; the New York State Office of Parks, Recreation and Historic Preservation; The Municipal Art Society; and Town+Gown at the NYC Department of Design and Construction.

The winners included:

1. FIRST PRIZE: Shu Yang, Ruolin Wu, Yutong Wu, Yuqi Wen, and Sabrina Fang designed a chatbot interface for the Community Land Trust State Law Database Project, a database of state statutes in the U.S. that relate to community land trusts. Sponsored by the International Center for Community Land Trusts, this interface includes an AI chatbot alongside map-based and keyword-driven search options.
2. FIRST PRIZE: Ketan Sinha, Irene Huang, and Kamili Afra initiated the development of a GIS layer for all grant-funded properties around New York State. Sponsored by the New York State Office of Parks, Recreation and Historic Preservation, this project addressed the challenge that New York State faces in determining where grant-funded properties are located due to incomplete records, name changes to parks and sites, and decentralized storage of paper records.
3. FINALIST: Nissim Ram, Abhishek Kumar, Anne Driscoll, and Daniela Benitez proposed a user portal for locating nearby open spaces. Sponsored by the Municipal Art Society, the project proposed inputting datasets related to New York City’s public spaces into a Large Language Model to avoid complicated merging situations.

Project Sponsors and Challenges:

1. New York State Office of Parks, Recreation and Historic Preservation
   The New York State Office of Parks, Recreation, and Historic Preservation has awarded thousands of grants for open space, trails, and park development. The information related to these grants have been stored in an Access database, and records related to each grant project, such as paper maps and deeds, have been stored in paper files around the state. Some records are incomplete. The State has a responsibility to manage these grant-funded properties to ensure public access to open space and recreation. However, it is difficult for the State to determine where all of these properties are due to incomplete records, name changes to parks and sites, and decentralized storage of paper records.
   
2. The Municipal Art Society
   New York City contains many different types of publicly accessible open spaces, from large parks and natural areas to neighborhood playgrounds, community gardens, and plazas. It is widely recognized that some neighborhoods are endowed with a range of well-maintained open spaces while others are not. Recent research from organizations like New Yorkers for Parks and The Trust for Public Land underscores the disparities. The challenge is that when it comes to fully analyzing this important amenity, there is no single dataset or web tool that sufficiently aggregates all types of publicly accessible open space and allows researchers and advocates to more accurately evaluate quality and distribution. Information is not centrally located and resources are published separately by a multitude of governmental agencies. There are also inconsistencies in how different entities define “open space.” Together, these issues lead to an incomplete and inconsistent picture of open space in New York City and uninformed policy decisions about where and how to invest in it.

   3. International Center for Community Land Trusts
   As part of nternational Center for Community Land Trusts' Legal Collaborative project, the organization has been developing The CLT State Law Database Project, a database of state statutes in the US that relate to community land trusts. An an early beta version of this database can be viewed here.

   The preiliminary development road consists of:

   • Beta Version (current): spreadsheet data in Airtable (table and card views)
   • Version 1.0: expanded to include research from Vermont and NYU law schools (which will be completed by December 2024), with a chatbot front-end that solely references this data source. If we are able to get a little fancier, we could also include a map-based interface to make it easier to zoom in on specific states.
   • Version 2.0: chatbot front-end that expands data sources to include publicly available datasets for state and municipal laws, curated scholarly articles, and model statute/ordinance language provided by subject matter experts.
   • Version 3.0: expanded to include international datasets and accessibility for multiple languages.
     
3. Town and Gown at the NYC Department of Design and Construction
   T+G’s Utilidor Working Group Town+Gown Working Groups (nyc.gov) has supported various analyses, including life cycle cost benefit analysis modeling of implementing utilidors (or multi-utility tunnels) and analysis and modeling to identify opportunities for implementing utilidors as one innovative subsurface design solution to support changes to current practice of direct burial of subsurface utility distribution infrastructure.

   Capital projects to repair and/or upgrade public and private utility aging subsurface distribution infrastructure, known as state of good repair (SOGR) projects, compete with other utility capital needs. They are also buried under the street requiring excavation to conduct SOGR activities, which make SOGR expensive and thus done less than at optimal levels. Traditional key performance indicators (KPIs) for aging subsurface infrastructure lead to practices that extend their useful lives within targeted maximum failure rates.

   This infrastructure includes electricity, gas, steam, telecommunications, water and sewer distribution pipes. It is vulnerable to aging, extreme weather and climate change. Aging distribution infrastructure is a hazard like natural and manmade hazards but is not included in hazard analysis. Traditional KPIs lead to institutional practices to extend assets’ useful lives within targeted maximum failure rates, which expect infrastructure failures and do not include consideration of the impacts on communities where failures occur.

   There is little reliable subsurface asset locational and condition data at the utilities, much less publicly available. Surface data observations, however, can suggest subsurface infrastructure vulnerability from aging subsurface infrastructure and has, in smaller research projects demonstrating proof of concepts, been deployed via mapping tools to analyze subsurface vulnerabilities as well as to analyze the impacts of failures in the neighborhoods they occur. This project would expand on all prior smaller projects and apply them to create a city-wide mapping analysis tool to suggest areas of interest for potential utilidor implementation and assess the impact of failures in communities.

The 2024 Global Data Dive was hosted by CUSP London between February 20-23, 2024. Students from CUSP at NYU Tandon participated virtually from New York City on the challenge which considered urban data variables in the domains of transport, safety, air quality, and health in London, Newcastle, and New York City. Students from King’s College London, University College London, the University of Glasgow, Imperial College London, NYU, and NYU Abu Dhabi participated and worked with partners including TfL, the Department of Transport, Westminster City Council, Newham Council, London Ambulance Service, AccuCities, WSP and the Bicycle Association of Great Britain.

Data Dive Challenges:

• Analysis of spatial, temporal, and spatiotemporal correlations between congestion, safety, air quality, or health
• Longitudinal analysis of urban variables
• Inter-city and inter-continental comparisons

Jury

• Nick Holliman, Director of CUSP London, King’s College London
• Rita Borgo, Reader in Data Visualisation, King’s College London
• Hannah Bougdah, Senior Insights and Reporting Manager, Safety Health and the Environment, Transport for London (TfL)
• Peter Baudains, CUSP London Research Software Engineer, King’s College London

Five participating CUSP student teams included:

• Iman Cumberbatch, Amari Garrett, Emanuel Simmons, Sneha Tirchy Shekar, Yushan Li, Tanisha Dighe, and Connor Xu
• Akanksha Patil, Godfried Junio, Ismail Rashad, Koyel Das, Swati Sharma, Vinayak Saxena, Zheyan Chen
• Xueliang Yang, Feiyang Ren, Jiayi Weng, Weilai Xu, Franklin Tang, Xiaoyue Zhang
• Amy Xian, Qingyuan Feng, Mingchuan Ma, Linuode Ye, Siyu Miao, Zihan Xu
• Madison Buchholz, Soohyun Bae, Jiyun Cho, Irene Huang, Yanchi Jin, Junyi Li, Yuan Zhou

The Spring 2024 Local Data Dive was hosted by CUSP at NYU Tandon from April 15-19, with a final public presentation on Friday, April 19th. Student teams were encouraged to adopt a multidisciplinary approach, drawing from fields such as urban planning, public health, environmental studies, sociology, and psychology to address the factors influencing health in urban settings in a comprehensive manner. Key topics included the role of social, economic, and racial factors in determining health outcomes. From understanding the roots of health disparities to exploring the intersection of poverty, race, and health, participants delved into various critical urban health issues.

Data Dive Challenge

By considering mental, physical, and environmental dimensions, student teams were asked to explore complex interactions between urban living conditions and health outcomes. Participants could select up to two of the following dimensions to guide their analysis:

• Mental Health: Examine urban factors impacting mental health, including stressors associated with city living, access to mental health services, community support, and the effects of urban isolation.
• Physical Safety and Health: Investigate how urban design, policing policies, community programs, and infrastructure contribute to or detract from the physical safety of residents, examining aspects like crime rates and public space design.
• Environmental Impact on Health: Analyze the effects of urban ecological factors (e.g., air and water quality, green spaces, noise pollution) on physical health and explore the theme of environmental justice.

Potential Research Areas and Deliverables

• In-Depth Disparity Analysis: Explore the relationship between socioeconomic factors, race, and health outcomes.
• Neighborhood Comparative Analysis: Compare health metrics across different areas to identify location-specific health influences.
• Community Engagement Initiatives: Develop educational materials or programs to raise awareness of health disparities.
• Decision-Making Enhancement: Leverage data-driven insights to inform health policy and planning.
• Social Determinant Impact Studies: Examine how housing and education affect health disparities.
• GIS Hotspot Identification: Use GIS to pinpoint areas with significant health disparities.
• Interactive Visualizations: Create dynamic dashboards or story maps to illustrate health disparity data.
• Cross-Disciplinary Collaboration Projects: Foster interdisciplinary approaches to address health disparities.
• Health Disparity Mapping: Use GIS tools to map health disparities visually.
• Network Analysis Projects: Investigate complex relationships among factors contributing to health disparities.
• Policy Effectiveness Evaluations: Assess the impact of health policies and interventions.
• Predictive Analytics and Modeling: Implement advanced analytics to predict future health trends.
• Resource Planning Analysis: Guide the strategic allocation of health intervention resources.
• Spatial Health Data Visualization: Use spatial analysis to reveal patterns in health disparities.
• Time-Series Health Trend Analysis: Identify long-term impacts and changes in health disparities.
• Health Metric Tracking: Monitor the effectiveness of health interventions over time.

The 2023 Global Data Dive was hosted by CUSP London between February 21-24, 2023. Students from CUSP at NYU Tandon participated virtually from New York City on the challenge. For the event, student teams and mentors from King’s College London, University College London, Glasgow University, CUSP at NYU Tandon, and Curtin University worked with external partners including AccuCities 3D City Models, NHS NE London ICB, Ansell & Bailey Architects, Helix Data Innovation, and WSP.

The Best Overall Prize was awarded to a student team from CUSP at NYU Tandon that included Yue Yang, Lantian Zhang, Yifan Zhang, Junru Song, and Xiaohe Yin.

Data Dive Challenges:

• Clustering: Do common mental health problems like depression and anxiety cluster in particular geographical areas across the UK?
• Prevalence: Are serious mental health problems such as psychosis-schizophrenia and bipolar affective disorders more prevalent in urban built areas?
• Physical Environment: What physical features of the built environment (i.e. green space, air pollution levels, street intersections, 'neighborhood walkability', etc.) are associated with common mental health conditions and/or serious mental health conditions?
• Social Environment: What social features of the environment (population density, social disorganization, etc.) are associated with common mental health conditions and/or serious mental health conditions)?
• Correlation: Do mental health issues correlate with known physical health issues?

Two participating CUSP student teams included:

• "Walking the city: Analysis of NYC’s built environment on mental health" by Akshay Shetty, Shantanu Anikhindi, Ajayrangan Kasturirangan, Marcia Lucia Cornejo, Vickram Peter, Sharvari Deshpande, and Anshika Gupta. View the presentation here and the GitHub repository here.
• "Does the distance to subway stations affect a person's mental health?" by Yue Yang, Lantian Zhang, Yifan Zhang, Junru Song, and Xiaohe Yin. View the presentation here.

The 2022 Global Data Dive was hosted by CUSP London between April 4-7, and included student teams from King’s College London, University of Warwick, University College London, Newcastle University, and CUSP at New York University. Experts from the transport planning and mobility engineering consultancy Systematica mentored New York City-based students throughout the event.

Data Dive Challenges:

• Participation: How could information derived from reoccurring residents surveys reflect localized “inclusive participation” in questions of urban wellbeing?
• Prosperity: Is it possible to derive forensic estimations on the economic performance for future years, using London’s Economics COVID-19 Labor Market Analysis and related datasets, to make further recommendations?
• Planet: London Local authorities have developed Reduction and Recycling Plans (RRPs); each borough also worked out the respective Waste Reduction and Recycling Plan, to contribute to the Mayor’s London-wide targets of cutting food waste by 50% per person and achieving 65% municipal waste recycling by 2030. How could the optimal model(s) be suggested among boroughs based on their local features and household waste recycling data?
• Place: How can the built environment, especially street greenery, exert positive effects on residents’ health and well-being? Are there any applicable measures to improve the environment further towards better influences on local health?
• Peace: Can we conduct enhanced analytics into the topic of hate crime, including both historic trends as well as the future; diving into some boroughs to reveal new insights; and exploring the links between recorded hate crime and potential reasons?

The 2021 Global Data Dive was hosted by CUSP London between March 15-19, and included student teams from King’s College London, CUSP at New York University, University of Warwick and University College London, University of Leeds, University of Glasgow.

Data Dive Challenges:

• What is the potential for micro-mobility and e-bikes to transform urban mobility in UK cities?
• How has Covid-19 impacted rail demand differently across different areas of London, and how might demand evolve in the future?
• Will future commuter travel in London be more or less active?
• What transport-related initiatives have international cities taken to make themselves ready for the future of urban mobility, and how do we compare them?
• How has cycling-relevant street infrastructure changed in London and what might this suggest for future support for cycling in the city in future?
• What are the best ways to improve air quality in London through transport changes?
• How has London’s driving behavior changed from the past to post-Covid?
• How is micro-mobility usage changing over time in different UK towns?

Jury

• Dr. Simon Miles, Director of CUSP London King’s College London
• Professor Mark Kleinman, Professor of Policy
• Thomas Stone and Howard Wong, Representatives from Transport for London