Data For Good Projects
The team projects below answered the call for proposals and advanced to the Seed Grant Phase.
Access to public data is vital to transparency and accountability of governments. While some cities, such as New York, have taken major strides towards this goal, there is no unified framework for evaluating openness of data at a local level. We address this problem by scoring openness of city data based on several factors, including ease of access, amount of data, recency, whether the data are up to date, etc. Using this metric, we aim to analyze and compare cities including Berkeley, Oakland, Santa Rosa, San Mateo and Palo Alto. Our project should be a tool for citizens to hold their local officials accountable and demand better practices.
Fellow(s): Arun Ramamurthy, Winne Luo, Patrick Chao
Collaborators: Kana Mishra, Yiming Shi, Rohan Narain, Elliot Stahnke, Ash Mohan, Kazu Kogachi, Kenna Schoeler, Ashley Chien, Sidney Le, Abhinav Bhaskar, and Suhas Rao
In recent years, demand for California housing has skyrocketed. Housing availability, however, has failed to keep up, resulting in dramatic price increases and widespread housing insecurity. This issue is well-known and well-documented, yet state and local efforts have been ineffective at providing affordable and available housing to California citizens. The Statistics Undergraduate Student Association (SUSA) aims to tackle the housing crisis head-on by utilizing historical housing data in the public domain to analyze public policy directives. By creating machine learning models to predict the future housing situation in California in response to policy levers, we will directly quantify the effectiveness of various legislation and compare potential policy solutions. Another aspect of our work with an immediate and accessible public benefit involves the development of a web application to provide California residents with personalized housing suggestions.
Around the world, nearly 25% of communal water access points are non-functional. This challenge has been difficult to address at a global, national, or even local level due to a severe scarcity of data. Consequently, the Water Point Data Exchange (WPDx) has created a harmonized data exchange
standard for water point data and supported the development of a global repository. We aim to use the WPDx data to create a web-based portal that will help governments manage water access in their countries. Our goal is to create four tools that will enable governments to make evidence-based
decisions at the click of a button. Collectively, the tools should give government officials a comprehensive view of water access across their respective countries, thereby empowering them to efficiently allocate resources towards areas of greatest need.
Oakland has one of the highest crime rates in California, 153 crimes per 1,000 residents. In violent crime volume, Oakland is second only to Los Angeles, which has 10x the number of residents. In non-violent crime volume, Oakland is ranked fourth overall. We plan to address Oakland’s crime problem by creating an app that predicts crime, making citizens more crime-aware so that they will better understand their crime risk. Our app will also provide crime education to citizens, informing them of crime predictors. To facilitate community involvement in reducing crime, our app will also serve as a platform for community discussions. Our app will be open-source and the data inputs will be from publicly available data to encourage algorithmic fairness discussions and actions. We aim to reduce crime by promoting crime awareness, crime education, and community engagement.