Inspiration
Our inspiration for this project was the large economic deficits in communities along with the increasing crime rate in many California cities. One story that really inspired this project was how one of our teammate's parents had a friend who taught in Compton. In Compton, they struggle highly with city crime and gun violence, so much so that teachers can't stay a minute after school and are constantly hiding after hearing the sound of gunshots. This new fear aspect is not explored by many students or even adults in Irvine, showing the need for a change in cities lying in California. One key feature we noticed in California was a growing distance between the rich and the poor, along with the progressive decrease in the economy of many large metropolis cities. This inspired us to create a project that focuses on decreasing these large distances between the highest and lowest percentile while making these changes sustainable. Thus, these changes would allow for safety to all citizens and help preserve the image of the nation, that others have put great efforts to attain, while allowing it to last for many years without new problems arising.
What it does
Essentially it works as a city simulator. We uploaded a map of San Jose, a city facing high economic problems, and highlighted certain "zones" that had issues regarding poverty, housing, etc. The budget is also shown, for both each individual sector of problem(ex. transportation, housing, poverty), as well as overall city budget. You can click on a zone, to move it, and see how that affects the budget, as well as double click to see our predictions on how putting money into that sector in that area could positively or negatively affect that city.
How we built it
We built this using the Unity Engine, a whole lot of data, and of course our blood, sweat and tears. Essentially, we researched heavily into the city of San Jose, finding maps, data regarding employment rates, salary rates, and so on. We then combined this data into Unity, by creating a basic map in which you could look at a physical representative version of all of this data, and created an algorithm for determining the growth of a sector, based on both past research and current plans, to make it all data-driven and completely reliable.
Challenges we ran into
There were multiple challenges we ran into. The first was accessing the needed data for location. Are project focuses on parts of the city but the city allocates the budgets into services. This means instead of finding the amount of money given to a city we had to find how much money is allocated to a services and what parts of the city are affected by this. We then had to do proportions to figure out the budget for a sector of the community in a specific service. Another issues we came into was extrapolating data as you need to first predict vacancy rates and how this affects each piece of revenue along with how this increases many issues that we are looking to face. Along with this we had to also consider compensation and the increase in minimum wage and how this cannot be change while inflation will continue to rise, changing the budget. Along with this San Jose is currently doing tax exemption which further lower the current revenue of the city but will increase it over time, meaning that is needs to be tracked and predicted to how much money this can give back to the city and the rate as well as how much these businesses will need to pay after the tax exemption is revoked. Lastly, with large funding from Covid 19 and many complications it future complimented the correlation between spending and revenue along with how we can predict the future budget as there is a very low chance of another pandemic which would drastically increase the cities funding in a short period of time.
That's not even all of it! Those were primarily the research challenges, but we had many coding challenges as well, with the primary one being how to reduce CPU usage. Because our project relied on having objects instantiated at runtime, this caused a huge lag at the beginning, as well as a long compiling time, at one point reaching up to two hours before I force quit the application. We solved this by getting an AWS server with a huge CPU and memory, which allowed it to run at a significantly faster speed, as well as helping with ensuring our program could be run across multiple platforms. Furthermore, we had a conversion issue with transforming the maps and data into csv files that the computer could read, and then turn into the coordinates and information displayed at runtime. This was solved by having an ai summarize the documents, and then hand typing all of the necessary information into a document.
Accomplishments that we're proud of
We are very proud of our consistency in our project and our output for it being our first hackathon. In congruence we are proud of the very complex idea we took on that is directly related to constant affairs and the economic/mathematical intensity this project included. We believe this project is something that could be implemented and make a large difference present day while directly coinciding with our inspiration and how we may be able to fix this.
What we learned
We were introduced to many more current economic and safety problems that we had not yet been aware of. We were also able to connect these economic issues and their variables to correlations and calculate predictions for a city in which we had never put into practice for real world problems and we were introduced to real life applications.
We also learned some tech tricks that we could use in future projects when we got stuck. For example, we learned that AI can be a very powerful tool, as we used it to help us transform the pdfs into csv files. Another example would the AWS, we learned that we could host servers online, with different capabilities, extra CPU, or extra GPU, to figure out what was causing issues with the program(using it to pinpoint whether it was a computer issue or a coding issue).
What's next for No Way Not Jose!
No Way Not Jose can be expanded on to go more into depth into exact services and include a visual aspect that can allow one to see the actual progressions of their changes through graphing. This project helps allow sustainability to become a reality and can be replicated to other cities by focusing on their geography and pasty revenue. Not only does this lead to sustainability for a city but a state or nation in general which is one step closer to preserving the nation others have fought and strived to create. One goal is even to adopt this to other cities such as Compton, so as the economy and budgeting changes combined with services (gifts for guns) teachers, students, and just every day citizens do not need to be afraid of their lives on a every day basis nor is there such a large range between the lower and upper class.
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