Classifying The Sounds of NYC:
The CUSP Sound Project aims to generate a large scale sound map of New York City, with the goal of analyzing and understanding the city’s soundscapes automatically using a sensor network.
To accomplish this goal, we require a large amount of annotated urban field recordings! Our goal this hackfest is to build a crowdsourcing project to turn the annotation (labeling) process into a citizen science endeavour!
During the hackfest we’ll be working on the first iteration of such an interface, allowing users to listen to field recordings, and label the start and end times of different urban sound sources (sirens, car horns, etc.)
We also want to find a way to engage the participants so that they are motivated to become sound labeling experts.
OpenNYC is a citizen science project that uses the knowledge of the crowd to better assess the demand for open data in NYC. In partnership with the Department of Information Technology and Telecommunications (DoITT), OpenNYC aims to determine basic characteristics of NYC open data portal users.
Most of the urban dwellers use restrooms that spread out over a large geographic scope in the city without noticing it. The patterns and types of germs adhering to the surfaces of toilets vary as well. The idea is to encourage volunteers take pictures of the toilets with locations recording while sampling germs on the surfaces of the seats under the standard procedure and send them back to the team for testing. The team will then post the photos with the results of bacteria testing, volunteers can therefore track and map their toilet footprint and detect the sanitation level of the facilities and environments of the bathrooms they’ve been using.
The testing results may contribute to infectious disease and bio studies, and also the public hygiene management. It will also help people have a better understanding of the reality of their daily behavior.
Building a platform that functions only in spatial proximity (within a specific radius) where active citizens provide real-time personal data on consumption issues (water, food, energy consumption or waste generation) that is processed and accessed only as collective data.
Zoner communicates transparency to all the regulatory restrictions on your property so that you can get on to the important decisions of your business. Zoner is fast, accurate, and flexible according to the specificities of your site. Zoner frees your time removing uncertainties so you can apply your valuable energy and time to making the most out of your business. Zoner does all the heavy lifting of figuring out the technical issues of zoning envelopes so your feasibility studies become one step closer to the real thing. Our team of architects, engineers, and computer scientists have brought you a product that will satisfy banks, tenants, architects, engineers, and most all regulatory agencies. Zoner gives you what you need fast with competitive pricing so you can get on to what you do best.
The Ecan provides tools like gloves, bags and grippers to make cleaning up the city fun and rewarding. It interfaces with www.emrals.com as well so you earn Emrals, a new cryptocurrency created for civic good. The can uses a touch screen, solar panels and interfaces with the app on your phone to access the internet and keep score. A sonic sensor and weight level show you the effectiveness of the cleanup.
Finding an apartment or a room in a big city can be a quite complicated task. Apartment hunting is a market with big gaps of information for the people that are interested in them. Pictures and descriptions only tell u small part of the story. What if you could access the noise level of an apartment you are interested in?
Noise is clearly a huge quality-of-life issue in urban settings. It directly impacts the health of a city’s population, correlates with urban problems and affects real estate values. Currently at CUSP - Center for Urban Science and Progress the Noise Project is taking the lead in generating big data about noise.
We believe that a crowdsourcing app could provide a platform where people would share their apartments’ noise levels, especially those who are most affect. For example, 80% of the complainings that are presented to 311 are about noise! People do care!
We envision that in the future, if we introduce an easy and accessible way of measuring noise levels in apartments across NYC, consumers can take more educated decisions about where to live and probably this will affect prices in the real state business. It’s all about cooperation among masses to improve the market information.
Every city has its unique visions, which could either be the beautiful sights of the city, or some focused urban issues. The “ City Vision” project aims to build a platform for citizens sharing pictures that are the most representative visions of their cities. An image of the environment of the city, the features of the city, what people love / hate about the city can be mapped.
This can help in providing focus groups to work on the relevant issues & re-branding the image of the city. Thus, government will plan their policies aiming to mitigate the precise issues & predicaments that the people in their city want them to address & not the other way around.
The platform allows people to:
Then, we are able to analyze the information of these pictures and figure out the most representative features that people are concerned about by using data mining techniques. By doing this, a better vision of the issues in the city can be mapped.
We propose using geo-tagged tweets to create a mood map, whether localized or international. Most mood maps that use tweets are based on machine understandings of language. Our proposal is to use crowdsourcing, where volunteers would get to read anonymous tweets and choose the mood they convey. We believe this would be a far better approach than machine-based understandings of mood since the subtleties of context and subtext in human language can much more accurately be understood by humans than by machines depending on certain connotations of certain words.
Problem: Stale youth voter data
Solution: A multilingual network to provide current youth voter metrics & engagement