Ideas List for Google Summer of Code from the Mayor's Office of New Urban Mechanics (City of Boston)
Below, you will find a list of ideas we have for a Google Summer of Code student for the summer of 2021. Thank you for your consideration!
The City of Boston is working with FEMA and regional partners in Greater Boston on a plan to improve disaster preparedness and regional collaboration. An important piece of this plan involves post-disaster housing. How can we find places suitable for short-term housing after a disaster?
Boston’s Office of Emergency Management believes that geospatial analysis is key to this effort. Using open-source geospatial libraries such as GeoPandas and Jupyter notebooks, a Google Summer of Code student will be asked to build a tool to help City planners identify areas in Boston that may be a good fit for post-disaster housing. Finding these areas requires several layers of analysis, including mapping the boundary of a flood zones, proximity to other highly populated residential areas, and a City’s zoning code (how cities regulate land use).
We give this project a medium level difficulty.
This project will require intermediate experience with Python and GIS/geospatial data analysis packages e.g., GeoPandas. The student will be expected to use the Jupyter Notebook app to document their work over the summer.
The mentors for the project will include the GIS Analyst and Project Director from Boston’s Office of Emergency Management and a Program Director from the Mayor’s Office of Urban Mechanics (a previous Google Summer of Code mentor at Code for America).
The City of Boston's Parks Department maintains a comprehensive data set on trees in Boston. However, it's a manual and laborious process to get the data (such as conducting site visits for tree counts) on a regular basis. We would like to explore the use of satellite and aerial imagery to get a regularly updated census of trees across the city. We would also like to look at the feasibility of determining tree health and the variety of tree species across the city.
The ideal outcome would be 1) the creation of a new, accurate machine learning model focused on urban tree canopies, 2) the creation of a simple web interface for the Parks Department to upload new aerial imagery for analysis, and 3) the ability to the Parks Department to generate a list of statistics on tree counts to ensure that it continues to plants trees in an equitable manner across the city.
We give this project a medium level of difficulty.
This project will require intermediate experience with Python, machine learning (in particular TensorFlow and training models with imagery), interacting with RESTful APIs, and limited experience with JavaScript, HTML, and CSS.
The core code base for the project is Deep Forest, a machine learning library for tree crown detection. The mentors for the project will include two Program Directors at the Mayor's Office of Urban Mechanics, including one who served as a Google Summer of Code mentor at Code for America in 2011.
In 2010, our office launched the City of Boston's 311 app (one of the first in the nation). The app allows residents to report an array on non-emergency issues (such as potholes) with their smartphones. Historically, the app has only been offered in English, and we have done some of the preliminary work to provide it in other languages. This is a very important issue to address, since up to 33% of the city does not speak English.
Inspired by the City of San José, we would like to build an API-accessible service that improves the translation of text from residents reporting issues through the 311 app. The translation service would be based on a custom, trained model using vocabulary frequently associated with City services.
The ideal outcome would be having an API endpoint that would accept text from a 311 request and return translated text that could be easily understood by our City operations teams. We would also like to be able to benchmark the progress of this translation service against more general translation services.
We give this project a medium to hard level of difficulty.
This project will require intermediate experience with machine learning, building and training models with text classification, natural language processing, and Python. It will also require intermediate experience with building web service APIs with with a web framework like Flask, Django etc.
The mentors for the project will include two Program Directors at the Mayor's Office of Urban Mechanics, including one who served as a Google Summer of Code mentor at Code for America in 2011.
The Climate Planning team in the City of Boston's Environment Department is interested in building a lightweight web service API that provides data on the solar energy potential of building roofs throughout the city. The API should also provide data on how alternative energy sources could reduce a particular building's emissions footprint. Finally, we would like to build a simple web interface for a building owner to search for their property to view data on the solar potential of their roof and the impact on the emissions associated with their building's energy use.
We give this project a medium level of difficulty.
This project will require intermediate experience with data analysis packages, GIS, and building web service APIs with either Python or Ruby (we have had good experiences wih Flask for this task).
The mentors for the project will include two Program Directors at the Mayor's Office of Urban Mechanics, including one who served as a Google Summer of Code mentor at Code for America in 2011.