Siena:

Toxic Earth is a three-dimensional data visualization that displays the spread of air pollution worldwide. According to National Geographic nearly 2.5 million people die worldwide each year from the effects of air pollution and Toxic Earth aims to spread awareness of the severity of air pollution. Most maps of air pollution are two-dimensional making them difficult to read and this project proves that data visualizations in three dimensions are more advantageous than those in two.

Toxic Earth was made for people unaware of how widespread air pollution is. The data is collected from a worldwide air quality index and then used in P5js to generate a greyscale map representing the intensity of the pollution. Then in Unreal Engine, the map is used to displace the geometry of a sphere. This effect uses the displacement of points on the textured surface to create a depth and relief effect on the geometry. Essentially the map generated dictates what certain points should be pulled on the Earth. The final visualization is similar to a bar graph but the pillars derive from the surface of the Earth. The pillars vary in height based on the intensity of air pollution in their areas. Toxic Earth is meant to inspire people to take extra steps to reduce air pollution while exercising a new way to visualize data.

Lily:

Toxic Earth is a data visualization that helps communicate the level of air pollution in different parts of the world. The data visualization also includes the air quality index from years prior. This data is very important because there are some conditions where the air is so hazardous that people should not even be outside. Hazardous air can cause heart problems, respiratory issues and aggravate asthma. 

Air quality has an impact all over the world. It is important for anyone with health conditions to be aware of, or people who are just interested in the quality of the air where they might be located. Toxic Earth works by the code created behind it that has a different colored dot corresponding to the quality of air pollution, and that dot is located on the country that lines up with the quality of the air. The dot's colors are any shade from black to white. The lighter the dot is, the higher the air pollution is. Once this code was made, it was transferred to Unreal Engine onto a globe. There are parts of the globe sticking up, and that is representing poor air quality in that location. Someone can interact with this data visualization by spinning the globe around and seeing where the pointiest part of it is, to determine the air quality index.

Occupancy is a mobile application for MBTA subway commuters. The pandemic highlighted the need for crowd density estimates. By projecting data to the public, people can decide if waiting for the next train car is safer than boarding the one that has just arrived. Occupancy also uses the MBTA’s JSON file, so arrival times and location are also displayed for convenience.

Measurements collected from weighted tracks, air sensors, and cameras are sent to the MBTA’s JSON file and used to display accurate and precise data in real-time. Occupancy shows the number of people per car, per train, allowing commuters to assess the risk of infection.

Max:

Occupancy is an app that is made to display common data and statistics from the Redline. The goal of the app is to have people understand when and where any train is and how full each car is. It allows the user to figure out the logistics of any journey on the trains. Using various visuals and constant updating information the user will be able to have an easy time navigating through the UI. 

The T can be a very crowded place during certain times of the day and having an app such as Occupancy will help spread out the traffic and make riding the T more pleasant. The app will have a map of Boston with a map of the Redline overlaid. The map will have each train shown on the map, clicking on a train will open up a sub menu with more information about it. Ontop of making an app with the existing statistics we will be adding passenger counting to each car. The hope of Occupancy is to make the subways less crowded by spreading out people on different trains.

https://editor.p5js.org/ChristopherV/sketches/Fy00GHboR

 The Interactive Bike Lane and Bike Crash Map was created to help bikers in the City of Cambridge feel safer by knowing where bike crashes have happened and where there are bike lanes. Cambridge is a city that has prioritized creating new bike lanes, both to encourage more people to utilize biking as a means of transportation, and to protect riders who choose biking over driving or walking. In spite of increased bike lanes, there are bikers who still feel uncomfortable biking on certain types of bike lanes or in areas where crashes have previously occurred. The interactive map enables users to have more information that will help them to make informed choices as they plan their bike routes in the city of Cambridge. This map utilizes MapBox to take the spherical surface of the earth and flatten it into a flat image that can be read on a screen (using latitude and longitude as coordinates). It also utilizes the coding program p5.js (Java Script) to take the latitude and longitude points and plot them on the map in the correct locations as well as for those locations to be selected and deselected.  

This interactive map can be utilized by any person who bikes in Cambridge, but is especially useful for bikers who are hesitant to bike in Cambridge because of the danger of getting run over by cars or even buses. Bikers can use this map to plan a route where it will be safer for them to bike by selecting on the map which types of bike lanes they would like to use. For example, a user can select “Separated Bike Lanes” from a list of bike lane types and the interactive map will highlight those lanes in a corresponding color. Bikers can also use a sliding bar that goes from 2015-2022 which will allow them to analyze a time period in which bike crashes have previously occurred and use that data to assess potential safety. Selecting the preferred type of bike lane and comparing that data with data about accidents in the path will allow the user to determine which paths will give them the greatest safety according to the parameters that they set.

Eli:

Community Solar Review is a device designed to show government officials of large towns and small cities how solar panels can be installed into their communities. It is easy to access, quick to use, and displays accurate reliable data. Sliders along with text indicate what variables (one house power usage in watts, gigawatts generated yearly, number of houses, high capacity solar panel space in square miles, and overall population) are being taken into account. The code will then generate statistics on a theoretical solar panel plan based on the user’s input. Due to the simplicity of the device, it is efficient and takes no time to use. The many variables in the code make the tool adaptable to different communities and their needs in terms of solar panel installation.  

Community Solar Review uses several data inputs including the total number of homes, the area's population, the average power use of a house, the number of GigaWatts generated yearly, and the amount of space available for solar panels. Collecting this data allows the code to relay the information back to the users. The users can then adjust the sliders to find a solar energy plan fit for their community. 

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Lane:

Community Solar Review is a tool for government officials of large towns and small cities to design an initiative to invest in solar power in their communities. It works with data inputs that include the average power use of a house, the number of GigaWatts generated yearly, the amount of space available for solar panels, the area's population, and the number of homes. By taking this information, the program can tell the user how many houses can be powered, the percentage of GW used, the portion of available space devoted to panels, and the population per house. From there, a user can adjust the sliders to achieve the best capabilities for their community and use the results to plan a renewable energy effort.

It is designed to be easy, efficient, fast, and adaptable. It is easy to use, basic sliders and text outputs mean that any individual can get accurate data quickly. It is highly efficient and takes the complicated task of working with the code on the backend out of a user's hands. Because of the simplicity, it can be done within minutes after data is gathered. Every value within the code is configurable and capable of adapting to suit a community's needs. The long-term benefits of solar panels on a communities economy, environment, and health are not to be overlooked in favor of short-term profit.

https://editor.p5js.org/kishp24@bcdschool.org/full/otq1Vlbjz

The Red Line Simulator is a game that puts the player in charge of expanding the northern section of the MBTA Red Line. For each in-game year, the player is able to place down one line. From this new line, they get additional profit each year. If a player puts a station down in a high-population area their profit will be higher compared to a station in a low-population area. The goal of the game is to reach the year 2032 and have enough money. If the player does not have the required amount of money by 2032 they lose, if they do have the required amount they win. The game was made to let people create their own ideas for how the Red Line can be improved. The Red Line is plagued with problems so it is interesting to see how different people would change it

Occupancy is a mobile application for MBTA subway commuters. The pandemic highlighted the need for crowd density estimates. By projecting train car population data to the public, people can decide if waiting for the next train car is safer than boarding the one that has just arrived. Occupancy also uses the MBTA’s JSON file, so arrival times and location are also displayed for convenience.