2017 Urban Science Intensive
Operations & Policy

 



1. Large-Scale Analysis of Water Efficiency in California

CUSP Students: Yue Cai, Kevin Han, Fernando Melchor, and Ian Stuart

CUSP Mentor: Brendan Reilly

USI Sponsor: California Data Collaborative (CaDC), ARGO Labs, and Moulton Niguel Water District (MNWD) 

Our project intends to help CaDC and its partners accurately analyze and predict customer demand so that more informed and targeted conservation policies can be implemented. Automation and scalability are key, as CaDC is also looking to extend its data management and analysis tools to water districts throughout the region.

Our data mainly comes from two sources: CaDC (for water use information) and public APIs (such as Google, Yelp, Mapzen, and Foursquare, for business establishment information). In terms of our analytical method, we are proceeding through 4 phases: Data Acquisition and Cleaning, Data Integration, NLP Classification, and Iterative Approach to Classification and Benchmarking.

Our project has two primary objectives: 1. Develop a reliable, scalable software tool that automates the classification of water customers according to North American Industry Classification System (NAICS) standards. These classifications for customers in the commercial, industrial, and institutional (CII) spaces will be more granular than the data currently available from the water district. 2. Use the customer type classifications described above to model water usage for the district(s) being studied, and develop a benchmarking system whereby a CII customer’s usage patterns can be accurately evaluated against its peers. These tools can be used to predict water usage, detect anomalous users, and guide targeted policy interventions. Ideally, this model development and benchmarking process would be exportable to any water district.


2. The “Energy Snapshot”: Driving Behavior Change Through Energy Data Analytics

CUSP Students: Xianbo Gao, Enrique Sanz Gonzalez, Victor Sette Gripp, and Peng Jia

CUSP Mentors: Constantine Kontokosta, Bartosz Bonczak and Sokratis Papadapoulos

USI Sponsor: NYC Mayor’s Office of Sustainability and NYC Department of Buildings

In the “80 x 50” plan New York City has taken the challenge of reducing 80% of its carbon emissions, based on 2005 levels, by 2050. Since more than 70% of the city’s emissions come from energy used in buildings, in order to meet that target, it is essential to understand energy consumption patterns in the city’s buildings and identify opportunities to improve energy efficiency. As initial steps, the city passed two laws aimed at collecting granular and detailed data about the energy usage in some of its most significant emission sources: large buildings. These two laws are Local Law 84 (LL84) and Local Law 87 (LL87).In this context, this project is being sponsored by the NYC Mayor’s Office of Sustainability (MOS) and has two main intended outcomes. The first is to design a performance metric for energy efficiency, on the same lines of the Environmental Protection Agency Energy Star Score, but specific to the New York City building market. This developed metric will take into account specific features of different buildings’ typologies and offer insights about the reasons behind the performance of each building in addition to possible actions to improve that performance. Secondly, this project has the goal of defining a methodology to identify peer groups within the NYC buildings (included in LL84). The performance metric will then be evaluated only within peer groups. Both the performance metric and the peer group will be part of the Energy Snapshot, a building specific score card that will be sent to each building owner (or manager) as a way for the city to give back to them, in a more interpretable and actionable way, the information that they provide when complying with LL84.

The underlying motivation behind this project is that simply by better informing the building owners it is possible (and expected) that there will be some behavioral changes towards a more efficient use of energy. That alone, i.e. sending a clear and informative Energy Snapshot to each of the building owners, may be found in the future to be an effective policy to significantly contribute for a more energy efficient and less carbon intensive NYC.


3. City of New Orleans Emergency Medical Services Resource Optimization

CUSP Students: Alexis Soto-Colorado, Connor Chen, Adriano Yoshino, Matt Sloane

CUSP Mentor: Martin Traunmueller, Boyeong Hong, Constantine E. Kontokosta

USI Sponsor: City of New Orleans, Office of Performance and Accountability

Since 2010, requests for emergency medical services (EMS) within the City of New Orleans have steadily increased while the resource capacity (i.e, ambulances and associated staff) of the New Orleans Emergency Medical Services (NOEMS) has not increased to meet this rising demand. This dynamic has resulted in a declining quality of service on the part of NOEMS, including increased wall times, use of mutual aid, the failure of NEOMS to respond to high priority calls in response times that are consistent with national goals and averages.

With this resource inadequacy and associated service shortcomings in mind, the City of New Orleans Office of Performance and Accountability (NOOPA) has requested a data drive analysis of how to optimize the scheduling of current NOEMS ambulance resources in order to maximize their effectiveness in responding to EMS requests throughout the City of New Orleans. Further, NOOPA has also requested that the optimization also allow for the consideration of hypothetical additional NOEMS ambulance resources in order to measure how additional ambulances would affect NOEMS’s ability to respond to EMS requests.

The basic framework of the optimization analysis is twofold. Firstly, a prediction model based on various data that will “predict” the location and time of future EMS incidents / requests. Secondly, an EMS resource optimization model will be developed that incorporates this prediction model to best identify the likelihood of the inability of NEOMS to respond to an incident.


4. Municipal Performance Management for Small Cities

CUSP Students: Adrian Dahlin, Danny Fay, Maisha Lopa, Jonathan Pichot, Chris Streich

CUSP Mentor: Neil Kleiman

USI Sponsor: National Resource Network

There are 825 US cities with populations between 40,000 and 400,000. They are not the cities with the biggest economies, latest technology, most available data, or strongest tourism, and they’re mostly not getting large smart cities investments. They are former industrial towns, suburbs of larger cities, population centers in otherwise rural regions, and capitals of smaller states. Most of them have tight budgets, limited capacity, and few if any dedicated data analysts, but they still have many of the same needs and challenges of bigger cities. They have school systems to run, infrastructure to maintain, fires to put out, crime to fight, workforces to manage, IT systems to update, and economies to reform. All of these efforts can be improved with good data, smart analytics, and effective performance reporting. A big city like New York has data scientists in every major department. But smaller city governments consist of department heads and the people on the ground doing the daily work; they don’t have mid-level professionals who can do the analytical work needed to help an organization run better.

This project has centered around consulting for two small cities: New Bedford, MA, and Cleveland Heights, OH. In New Bedford we were asked to analyze code violations, crime, and fire department dispatches to see if relationships between these data might help the city reduce all three of these phenomena. In Cleveland Heights we built two tools that the city will use moving forward. First, we built a “building intelligence” database that gathers all available data about properties in the city and prints out reports that can be used by building inspectors and other City personnel who visit homes. Second, we built a “neighborhood score” tool that aggregates buildings data and other information and visualizes it with an online map that will help the city identify struggling neighborhoods and allocate services accordingly.

This work matters, because local government affects people’s lives. This is the level of government that determines the safety of neighborhoods, the condition of streets, the quality of schools, and development rights of properties. It’s also the scale at which individuals can have the greatest impact, which makes performance management an issue that any civically engaged person should care about.