Tag: Artificial Intelligence

Únete a este evento para explorar el ecosistema editorial en español de Nueva York a través de una perspectiva de datos. Esta sesión interactiva presentará los hallazgos iniciales de nuestro mapeo de editoriales, librerías, bibliotecas y autores que trabajan en español, destacando tanto los datos existentes como las brechas críticas de información. Dirigida por Juan Pablo Marín Díaz (Datasketch) y Viviana Castiblanco (editora), examinaremos cómo una mejor recolección y compartición de datos podría fortalecer las conexiones entre autores, editores y lectores en la comunidad literaria hispana de NYC.
La sesión combinará visualización de datos con conocimientos de la industria, presentando perspectivas de editores, libreros y autores en español basados en NYC. Los participantes tendrán la oportunidad de contribuir a identificar áreas prioritarias para futuras recolecciones de datos y discutir enfoques colaborativos para construir conjuntos de datos abiertos más completos sobre la publicación en español en NYC.
Este evento es ideal para editores, libreros, bibliotecarios, autores y cualquier persona interesada en la literatura en español o datos culturales. Ya sea un profesional editorial que toma decisiones con visibilidad limitada, un entusiasta de los datos interesado en aplicaciones culturales, o un miembro de la comunidad involucrado en la literatura en español, únase a nosotros para ayudar a mapear y fortalecer este ecosistema cultural vital.

This event will be held in Spanish. Attendance is limited, and registration is required to confirm your place. Only registered guests will be admitted.

COVID-19 changed people’s lifestyles all over the world. This event will focus on analyzing resident housing property sales data in New York City from 2019 to 2023, before, in, and after COVID periods. By examining trends in sale prices, property characteristics, and neighborhood differences, this analysis aims to uncover key insights into the residential real estate market. Furthermore, machine learning techniques will be applied to predict property values and classify neighborhoods based on various factors such as location and building type. An end-to-end data pipeline process will be demonstrated in this talk (data collection, wrangling, visualization, feature engineering, machine learning modeling) via the python notebook.

Reflective roof colors reduce roof temperatures, internal building temperatures, the Urban Heat Island Effect , and carbon emissions; improve air quality; and extend the lifespan of rooftops and HVAC equipment. This project, conducted by John Hocknell, a student at Hunter College, uses machine learning to detect these cool and warm rooftops, and use that information to assess potential energy savings.

This work is inspired by the NYC CoolRoofs Initiative to reduce NYC’s carbon footprint by painting rooftops white.

Graphic with bright orange background and white text that reads School of Data.

NYC School of Data is a community conference that demystifies the policies and practices around open data, technology, and service design. This year’s conference helps conclude NYC Open Data Week and features 30+ sessions organized by NYC’s civic technology, data, and design community! Our conversations and workshops will feed your mind and inspire you to improve your neighborhood.

To attend, you need to purchase tickets. The venue is accessible, and the content is all-ages friendly! If you have accessibility questions or needs, please email us at schoolofdata@beta.nyc.

Thank you to Reinvent Albany and Esri for helping to cover conference costs and making it possible to meet in 2025.

And If you can’t join us in person, tune into the main stage live stream provided by the Internet Society New York Chapter. Follow the conversation #nycsodata on Bluesky.

Purchase your tickets here.

Join us as we share lessons learned from applying GenAI and Natural Language Processing (NLP) to alternative data sources! We’ll walk through a project where we used Public Pulse Mining to evaluate how the public engages with the General Services Administration’s construction projects and better understand local stakeholder priorities and perceptions.

Then, we’ll dive into an interactive prompt engineering exercise using our master prompt templates for structuring unstructured data. You’ll gain practical takeaways on using AI for public engagement, including how to extract insights from free-text datasets like NYC public meeting YouTube transcripts, 311 feedback, and consumer complaints.

This session is open to all audiences, regardless of technical background. We’ll also share open-source tools and scripts on GitHub so you can apply these methods to your own datasets!

  • How confidently can we predict the impacts of zoning change on housing supply?
  • Can we use AI to create novel datasets that may allow us to better understand housing phenomena?
  • What would it take to model a reality in which we build 1 million housing units?

These were some of the questions that led Janita Chalam, an independent researcher with a background in software engineering and machine learning, to begin their research journey into discovering how open data, statistical modeling, and AI can help us tackle the housing affordability crisis.

This presentation will walk through what Janita has learned about the variables at play in NYC’s housing landscape and present a statistical analysis of the Bloomberg-era upzonings as a case study in examining the frictions to building more housing in NYC.

Finally, Janita will propose some ideas for what kind of data and methodologies we might need in order to make bolder claims about what it takes to get us out of the housing crisis. By the end of this talk, we will hopefully have a better understanding of the role that data and empiricism can and should play in our conversations about housing policy.

This talk is for anyone interested in housing affordability and will not require any expertise in the technologies mentioned.

Sandwiches are my passion. When the New York Times unveiled its list of 57 sandwiches that define New York City, I wanted to try them all. The problem: the NYT list only provides sandwich names and restaurant addresses. Determining if I’m near an iconic sandwich requires scrolling, reading, and flipping between the list and Google Maps. The solution: I need a sandwich map!

In this workshop, rather than just traditional coding, we’ll use a large language model (LLM) as a pair programming partner to help us tackle challenges, offer suggestions, and streamline the development process. By the end, you’ll know how to combine basic Python coding with web scraping, Google Maps, and GitHub Pages.