Tag: Government-led

Government-led events are hosted by government employee or office.

The NYC Independent Budget Office (IBO) aims to enhance understanding of New York City’s budget, public policy, and economy through independent analysis. We leverage Open Data in numerous ways to achieve this goal. IBO researchers will present 3 separate projects:

– Brian Cain (Assistant Director) & Jordan Paige (Budget and Policy Analyst) modeled the cost and burden of potential stormwater fees in NYC using geographic data;

– Valerie Gudino (Budget and Policy Analyst) researched IBO police overtime trends, specifically the relationship between NYPD tenure and hours of OT usage of uniform NYPD personnel in fiscal year 2024 using tabular data; finally,

– Emma Gossett (Budget and Policy Analyst) compiled NYC Education Indicators using Department of Education student-level data that is now available in the aggregate as a public resource.

This event is perfect for anyone interested in data-driven policy analysis, or the specific areas of environmental, public safety, or education policy. IBO will present all three projects then take audience questions.

Open Data is rich, but can be overwhelming. Creating succinct, focused measures from Open Data allows New Yorkers quickly see how things are going and better understand complex issues.

The NYC Comptroller’s office recently released two new data dashboards that use Open Data resources to (1) track NYC Agency performance (https://comptroller.nyc.gov/services/for-the-public/measuring-nyc-government-performance/overview/) and (2) chart homelessness and eviction challenges facing New Yorkers (https://comptroller.nyc.gov/services/for-the-public/charting-homelessness-in-nyc/overview/).

This in-depth demonstration will:

  • Detail the information available on these two dashboards:
  • Measures of how key City agencies are delivering for New Yorkers
  • Details on the populations experiencing homelessness, facing eviction, and actions by the City to prevent eviction and move individuals out of shelter into permanent housing.
  • Show how we endeavored to tell full, meaningful stories of how City agencies are doing and the challenges New Yorkers face, relying only on public Open Data.
  • How to create relevant, informative measures from raw source data
  • How to combine and overlay data sources to add depth to a complex topic like housing and homelessness
  • Facing limitations of the data available
  • Give an overview of the technical tools to build dashboard views on Open Data sources
  • Querying Open Data data and metadata
  • Connecting data to PowerBI and custom tools

This session is a chance for researchers and Open Data users to get an in-depth understanding of the data available on these new dashboard products, learn how to design rich dashboards that contribute to understanding on complex policy issues, and get technical pointers for accessing Open Data.

In this session we will discuss the problems that Data Engineering at the Department of City Planning encountered managing datasets and introduce the open source tooling that we’ve built to manage metadata, generate documentation, enforce data quality, and automate distribution of data to platforms, with a focus specifically on NYC Open Data.

This talk is aimed primarily at those who have an interest in automating some or all of the above. We will walk through how we, at City Planning, catalog our dataset metadata; how that metadata is used to generate READMES, data dictionaries, and other metadata files; how to leverage metadata for automated QA; how to automate distribution of data to destinations like the Tyler/Socrata open data platform, databases, FTP servers, and data lakes; and finally how interested developers can make use of our framework and potentially contribute code of their own.

This presentation is part of the Open Data @ NYC Planning event series.

Click here to RSVP for virtual attendance.

Click the blue “Going” button below to RSVP for in-person attendance at the Department of City Planning’s offices (120 Broadway, New York, NY 10271).

As data analysts and engineers know, quality source data is crucial to sound analyses and healthy pipelines. It can also take a lot of time, effort, and resources to wrangle. Data Engineering at the Department of City Planning has written a new tool (python module/CLI) to manage data extraction and archival.

In this session, we will show potential users how they might simplify and automate extracting data from external sources like NYC Open Data and ArcGIS Online. We will touch on some of the built-in features of the tool as well as where we’re going: simple data cleaning, automatic geocoding, data validation, and more.

This presentation is part of the Open Data @ NYC Planning event series.

Click here to RSVP for virtual attendance.

Click the blue “Going” button below to RSVP for in-person attendance at the Department of City Planning’s offices (120 Broadway, New York, NY 10271).

NYC Planning has developed the Fast Tracker app to allow users to determine whether planned housing projects are eligible for the new citywide Green Fast Track rule. The rule streamlines housing production by allowing projects of a specific typology to simplify the environmental review process, while satisfying state and city environmental standards. The app integrates ESRI Experience Builder with Survey123 and Microsoft Power Automate so users can enter project criteria and determine eligibility under the rule. We will discuss developing the app in tandem with the rule, under a strict timeline and scope. This presentation will focus on how the app was developed, how data was collected, reviewed, and processed for inclusion in the app. We will also talk about how the app and data pipelines are maintained.

This presentation is part of the Open Data @ NYC Planning event series.

Click here to RSVP for virtual attendance.

Click the blue “Going” button below to RSVP for in-person attendance at the Department of City Planning’s offices (120 Broadway, New York, NY 10271).

The NYC TLC Factbook is a public-facing PowerBI dashboard that showcases charts, maps, and key metrics on various data and policies, including trip counts, electric vehicles, driver pay, utilization rates, and driver demographics. The underlying data for the dashboard includes publicly available information, such as trip data, as well as more sensitive data that is kept confidential to protect driver privacy. The Data Analytics team, part of TLC’s Policy Division, invites members of the public to an interactive presentation that offers an in-depth look at the analysis behind the dashboard, as well as how it helps evaluate and monitor critical policies

This presentation, hosted by the NYC Office of Management and Budget, will take participants through the journey of building a centralized data pipeline on a generic cloud platform to deliver accurate, consistent, and timely insights automatically! We will walk through step-by-step the entire lifecycle of data management, from raw data ingestion and cleaning to transforming it into a processed and standardized dataset that serves as the foundation for consistent and accurate reporting across the organization.

We will also focus on the general motivations behind the automation process and critical data decisions made during the cleaning process. And going from the general to the specific, we will show how we have set up a data workflow that allows us to run automatic reporting, both in the form of a dashboard and regular emails, using the City’s 311 data.  In addition, we will talk about cloud data storage, cloud computing, and other modern digital tools we used.

The Workforce Strategy Team in the Human Capital division of the Department of Citywide Administrative Services will present on the production of the Workforce Profile Report (WFPR). The WFPR is an annual summary of employee data reflecting the City of New York’s municipal workforce across 72 agencies. The team will give a broad overview of the collection of the roughly 4 million entries of city employee data across 10 years, the data processing, and the final production of our Workforce Profile Report Shiny App.

Presenting will be Ruchika Sah, Feba George, and Manny Merino from the Workforce Data Science team at the Department of Citywide Administrative Services. This event welcomes users at all levels of expertise. Anyone with an interest in large scale data projects, R programing or data processing is invited to attend. Familiarity with programing whether in R or another language such as Python is recommended. Participants will learn about the processes involved in taking this data into a final product and can ask questions involving the project or suggest improvements.

Brian Schroeder and Jonathan Campbell from the Department of Youth and Community Development’s (DYCD) Analytics team will present an introduction to DYCD and our Open Data offerings, along with a demonstration of data analysis made possible by relating three DYCD datasets: Department of Youth and Community Development (DYCD) Contracts, DYCD Program Sites, and Evaluation and Monitoring Reports for Program Sites. These tables can be used to analyze the geographic distribution of DYCD programming, and to review point-in-time monitoring of program sites. Datasets which fulfill public reporting requirements mandated by local or Open Data laws will also be reviewed. We welcome anyone who is interested to learn more DYCD data and/or DYCD programs to attend.

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.