Data For Good Competition 2018 – Projects
The team projects below answered the call for proposals and advanced to the Seed Grant Phase.
Here are the final results of the competition.
Data for Good Competition 2018 – Projects
Accountability in the Information Age: A Study of Data Transparency in Local Governments
Fellow(s): Akhil Jalan, Ameet Rahane, Aura Barrera, Clara de Martel
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.
Evaluating and Advising Public Policy Directives for the California Housing Crisis
Runner-Up – Data for Good Competition 2018
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 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 a more representative version of the housing affordability index, we provide a tool to more accurately quantify the housing situation. Building on the index and existing housing research, we developed a set of policy directives that act as a pipeline for legislators to make and evaluate effective housing policies. Lastly, we developed a web application to provide California residents with personalized housing suggestions based on inputted priorities.
Improving Access to Water in Developing Nations
First Place – Data for Good Competition 2018
Fellow(s): Alex Jamar, Chandler McCann, Pranay Suri, Dan Watson
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 Crime Prediction App
Fellow(s): Raymond Lee, Jeffrey Hsu, Ramya Balasubramaniam, Christina Papadimitriou, Vincent Chu, Chase Inguva
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. Most residents don’t know crime statistics, and receive their crime information mainly through word of mouth, internet message boards, and news reports–sources that provide a limited view of crime. We aim to address Oakland’s crime problem by creating an app that predicts crime, making citizens more crime-aware. Crime-aware residents will have a more holistic view of crime and will be able to have more nuanced crime discussions, leading to impactful crime-reducing actions. Our app is open-source and the data inputs are from publicly available data to encourage algorithmic fairness discussions and actions.