NYC School of Data 2020

• smooshr: A tool for generating and sharing useful taxonomies derived from open data.
• Newerhoods : A Data Clinic project that aggregates NYC Open Data at the tract-level and uses Machine Learning techniques to re-imagine neighborhood boundaries

NYC Planning’s Data Engineering team is transforming the way we think about Open Data in government by opening up the processes behind making public datasets.  As data engineers we develop new data products and modernize the creation of existing datasets, such as PLUTO, which is NYC’s definitive tax lot dataset that contains over 800k rows and 87 columns capturing lot level, building level, and geospatial attributes sourced from a dozen input data sources.  During this talk we’ll show how we re-engineered PLUTO and made sure that the data matched previous versions, discuss why it is important to us that the code to build PLUTO is available on GitHub, and describe where we’re going next, as an example of the type of work that we do.

Though, we’re not just excited about the data products we build, we’re equally passionate about how we build them.  In the later portion of the talk we’ll do a deep dive into a couple of the core technologies we use that enable us to iteratively integrate improvements, distribute our maintenance responsibilities, and generate products efficiently.

• Exploring and visualizing Open Data to create a data collection project;
• How to quickly and accurately collect data in the field with logic-based smart forms;
• How to conduct real-time survey reporting through dynamic dashboards;
• And how to support consistent and compelling constituent engagement through modern, mobile-responsive storytelling apps.

• Anyone in the community submits a vacant storefront (with photo)
• That location gets a unique URL, which we print out a QR code sticker for
• We post the sticker near the vacant storefront
• People scan the QR code and submit/vote on what they wish would open there

• Exploring and visualizing Open Data to create a data collection project;
• How to quickly and accurately collect data in the field with logic-based smart forms;
• How to conduct real-time survey reporting through dynamic dashboards;
• And how to support consistent and compelling constituent engagement through modern, mobile-responsive storytelling apps.

• CS and the City, in collaboration with the NYC DOE and BetaNYC: In this course, students to see how they can use front-end tools like React.js to present their findings about the city they live in and advocate for change based on the conclusions they draw. Bayside teacher Renne Castro will present some student artifacts.
• Data Science 1, in collaboration with Women in Tech New York (WiTNY) and Google: In this course, students use Python, Pandas, SciKit, and Keras to do exploratory data analysis, predictive analysis, create visualizations, and train some basic machine learning models. Students Yessenia Guerrero and Sundus Khan will present their final project from this two-week course. 

NYC Private Housing Maintenance Problems - a Data Dashboard app

As an NYC tenant myself, I wanted to better understand the overall conditions of NYC private buildings, namely what type of maintenance problems people have to deal with.
Through the NYC Open Data portal, I accessed the main database of this study: 311 HPD Complaints (HPD, Dep. of Housing Preservation and Development is the agency that regulates housing conditions), where it is shown a list of tenant complaints about poor housing conditions on private multi-dwelling buildings. It includes problems like heating, electric, heat/hot water, unsanitary conditions, etc. Using this data, I applied statistics, GIS and R coding magic and I ended up learning insights about: 

1. The proportion and type of maintenance problems that private tenants face, currently and historically, as well as emerging and ongoing problems.

2. Where are the complaint hotspots, what neighborhoods are the worst vs. best? And who is most affected by the overall NYC's bad housing conditions?
Check out the study and the data dashboard here: NYC Housing Problems NYC

• the promotion of civic data literacy
• the preservation & deposit of datasets into libraries
• the role of libraries in data privacy & the privacy implications of big data in a library context 
• the use of library collections as data & the digitization and transformation of collections to make historical data that accessible for computational analysis

• How might large city governments help smaller local governments?
• Where are there good opportunities for regional and statewide collaboration?
• Are there ways in which small governments may have advantages in terms of nimbleness, speed of execution, and ability to experiment?
• How can smaller places develop a civic-tech community?

• What’s in the raw voter file
  
• The process for correcting some of the many misspelled street names and other idiosyncrasies of the Brooklyn voter file
  
• Ordering the sheets in an approximation of how a canvasser might walk the streets
  
• Highlighting recently active voters for each district
  
• Creating maps of each election district using shapefiles from NYC open data
  
• Creating the file structure and writing out the maps and walk sheets formatted for print and digital
  
• Making the ED walk sheets accessible to the correct canvasser