Next Steps for Newly Admitted CUSP Students

Time: 9 – 10 AM EST

Led by Peter Huu Tran, Assistant Director of Academic Administration and Student Engagement at CUSP, this interactive workshop is designed for you to explore your sense of purpose and how it aligns with urban science. Through guided reflection and discussion, you'll articulate your motivations, refine your understanding of urban science, and craft a personal mantra to help center your class selection and career aspirations.

Learning objectives include:

1. Connect Reflection to Purpose: Define your purpose and how it connects to your academic and career goals.
   
2. Deepen your Understanding of Urban Science: Explore personal interpretations of urban science and its role in shaping cities and communities.
   
3. Create a Personal Mantra: Develop a guiding statement to inform your class choices, research interests, and career direction.

Time: 9 – 10 AM EST

Led by Ziwei Zhang (NYU CUSP ’25), this hands-on workshop introduces participants to the ArcGIS Online Map Viewer, focusing on how to visualize and analyze spatial data for real-world applications. Using NYC eviction data and U.S. Census Bureau income statistics, you'll create thematic maps, explore spatial relationships, and gain insights into how GIS can be used for social analysis. No prior GIS experience is required.

Learning objectives include:

1. Understand Basic GIS Concepts: Learn the fundamentals of Geographic Information Systems (GIS) and how ArcGIS Online facilitates spatial analysis and visualization.
   
2. Create and Customize Thematic Maps: Use ArcGIS Online’s tools to add, style, and analyze datasets, including NYC eviction data and census tract income layers.
   
3. Perform Spatial Analysis: Apply techniques like data aggregation and filtering to uncover patterns in socioeconomic data, improving decision-making and storytelling with maps.

Time: 9 – 10 AM EST

Led by Nissim Ram (NYU CUSP ’26), this workshop will guide you through the process of creating compelling graphical visualizations with just a few lines of Python using powerful plotting libraries like Seaborn and Matplotlib! Learn to convey critical insights through heatmaps—whether analyzing trends, mapping geographic data, or uncovering patterns in large datasets. This hands-on course lets you follow along in real-time, ensuring you leave with a polished heatmap and practical skills to apply immediately.

Learning objectives include:

1. Understand the Fundamentals of Heatmaps and Geographic Heatmaps: Learn how both traditional and spatial heatmaps work, and when to use each.
   
2. Create and Customize Heatmaps using Python: Use libraries like Seaborn, Matplotlib, and Plotly to generate standard and geographical heatmaps.
   
3. Apply Heatmaps to Real-world Spatial Data: Visualize geographic trends, such as population density or temperature variations.

Time: 9:30 – 10:30 AM EST

Led by Rishabh Chauhan, Industry Assistant Professor at CUSP, this workshop will offer hands-on experience in data visualization using Python, exploring libraries like Matplotlib, Seaborn, and Plotly to create meaningful visualizations from real datasets. This session will cover the best practices and identify the potential pitfalls, as well as the crucial role of data visualization in urban science and policy-making.

Time: 11 – 12:00 PM EST

Led by Tekle Kahsay, Visiting Associate Professor of Environmental Planning at CUSP, "The Need For Municipal Solid Waste (MSW) Data Characterization: A Pilot Project Experience" workshop will discuss the purpose of analyzing segregated municipal solid waste (MSW) data, highlighting its environmental and economic importance based on a practical pilot project carried out in Addis Ababa and offering the perspective of an emerging megacity in a developing country. The experience details a demonstrated change from a burden of dumping into an important income-generating source of employment. The project involved a systematic separation of solid waste into four categories: metals, plastic and papers, glass and related, and organic. All three categories were sold directly to recyclers while the organic waste was composted and used as an organic fertilizer.

Learning objectives include:

1. Understand the nature of MSW data and its different categories.
   
2. Perceive how segregated MSW data was converted from a burden of dumping into a resource that created opportunities for both employment and income—in addition to environmental benefits. By introducing a separation system involving four categories (metal, glass, paper and plastic, and organic waste), each was transformed into an income-generating opportunity. Waste segregation training was offered by experts to both households and to those who collect separated waste and sell it to reusers and recyclers.

Time: 9 – 10 AM EST

Led by Anton Rozhkov, Industry Assistant Professor and Director of the M.S. in Applied Urban Science and Informatics program at CUSP, this hands-on workshop will show you how to transform real NYC Open Data into interactive, insightful maps using an easy-to-use online mapping tool. Whether you’re a beginner or have some experience with data visualization, this session will guide you through the process of finding, analyzing, and mapping public data to uncover trends and tell compelling narratives. We’ll start by exploring NYC’s vast open data resources, covering everything from transit patterns to environmental data. Then, using a user-friendly mapping platform, you’ll learn how to visualize this information to communicate key insights. By the end of the workshop, you’ll have built a simple map that tells a story. Unlock the power of storytelling through maps!

Learning objectives include:

1. Discover and Access NYC Open Data: Learn how to find and interpret publicly available datasets to uncover meaningful insights.
   
2. Create Interactive Maps: Use an online mapping tool to visualize data and craft engaging, informative maps.
   
3. Tell a Story with Data: Develop a clear narrative using mapped data to highlight trends, patterns, or issues in NYC.

Time: 8 – 9 AM EST

Led by Manny Patole, Industry Assistant Professor at CUSP, this interactive class invites you to explore how Design Thinking and Community Science can be used to co-create projects that reflect community needs and aspirations. Through hands-on exercises and case studies, you'll learn practical strategies to collaborate as equal partners, ensuring that projects are inclusive, actionable, and sustainable.

A key activity will involve creating an empathy map, helping participants understand what different community partners think, feel, say, and do when given the opportunity to share. This exercise fosters deeper insight into community perspectives, enhancing the design and implementation of impactful projects.

Whether you're a policymaker, researcher, nonprofit leader, or grassroots organizer, this session will provide valuable tools to prototype, test, and refine ideas with communities—not for them. Join us to learn how to turn shared knowledge into meaningful change.

Learning objectives include:

1. Apply Design Thinking for Collaborative Problem-Solving: Learn how to use human-centered design principles to co-develop, test, and refine community projects that address real needs through an iterative process.
   
2. Utilize Community Science to Integrate Local Knowledge: Understand how to engage community members as community scientists, leveraging their lived experiences and expertise to shape data-driven, community-led solutions.
   
3. Develop Empathy-Driven Insights for Inclusive Project Design: Use empathy mapping to analyze what community members think, feel, say, and do, ensuring that project development is responsive, inclusive, and reflective of diverse perspectives.

Time: 9 – 10 AM EST

Led by Hanxue Wei, Industry Assistant Professor at CUSP, this workshop explores how the intersection of urban data science and public health offers opportunities to design cities that support mental well-being and how spatial data, environmental metrics, and computational modeling can be leveraged to analyze the relationship between urban environments and mental health outcomes. You'll engage in team-based activities to: (1) examine real-world case studies on how urban factors may impact mental health, and (2) propose data-driven strategies for evidence-based urban interventions.

Learning objectives include:

1. Learn how urban data science can be used to analyze the relationship between urban environments and urban health.
   
2. Work with real-world case studies to propose data collection strategies, modeling approaches, and potential urban interventions to improve mental health outcomes.