Problem Brainstorming

All four of us working on the project connected via Facebook before the event. Some of us are mutual friends and some of us were meeting each other for the first time. After getting to know everybody, we found out that we've all had horrible experiences with Seattle's homelessness problem. After doing some research, we found out that the homeless population in Seattle and King County is the third-largest in the country by numbers. Therefore, we narrowed down our scope to help solve the issue of homelessness in the Seattle area. 

However, homelessness is a really broad topic. If our solution is to design an app to help solve homelessness, how are we going to achieve it? After some online research, we found out that most people who are homeless because of the high rent in certain areas, and Seattle’s rent rate has always been high. We then focused on finding affordable or even free housing options as a solution to homelessness. As Airbnb slowly replacing a lot of the expensive hotels, we thought of the idea of giving hosts an option to help homeless people by offering vacant Airbnbs as a home for them.

Data Analysis​

We used RStudio to analyze some online datasets. The two main analyses include Airbnb analysis and homelessness analysis. For the Airbnb analysis, we used the datasets to answer questions such as ​

• In what neighborhood are most Airbnb located?

• What is the most expensive Airbnb?

• In what neighborhoods are Airbnbs the most expensive (within a $200 range of the max)?

• What is the minimum price for an Airbnb?

• In what neighborhoods are Airbnbs the least expensive?

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For the Homelessness analysis, we used datasets to answer the following questions

• What is the homeless rate in Seattle as of 2019?

• Has the homeless rate in Seattle increased or decreased over the past few years?

• What is the demographic of homelessness in the Seattle area?

• Currently, where do homeless people usually stay?

Using this data analysis, we drew a heat map that indicates Airbnb listings in the Seattle area (colored dots) and the areas with the highest amount of homeless people (green bubbles).

Define Requirements​

After analyzing the datasets and getting a better grasp of the scope of the issue, we started on the design of our app. We listed a few requirements of the app that we want to include.

• Tell our story and the purpose of making this app

• Able to provide information about the nearest Airbnb for a homeless person to locate

• Able to give directions from the current location to the selected Airbnb

• Able to choose the check-in/check-out date, number of guests, and food options when booking an Airbnb

User Journeys and High-Fidelity Prototype​

While we only had a limited amount of time, I started the design of the app on Figma and checked in with my teammates to make sure everyone is on the same page with the general idea of the app. The name and the logo of our app represent our brand loud and clear. We strive for finding homes for homeless people and make the process easier for them. 

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We listed some user journeys for the app so people can resonate with it more.

1. Logging in

2. Creating an account

3. Booking for Cozy Blue House and getting directions to the Airbnb

4. Eliminating some vacant Airbnbs using the filter function

5. Learning more information about our purpose and our main goal

Reflection​

What went well:

• Work responsibilities were clear, even though we only had two days. 

• We successfully narrowed down a really broad topic, and the app solution is realistic. 

• During the Corona-virus pandemic, similar events happened where Airbnb hosts helped provide housing to 100,000 COVID-19 Responders. Airbnb’s inclination to solving worldwide issues is high, and homelessness could be one of them.

Opportunities for improvements:

• Time management could be better. Some of us should start working on creating wireframes for the app starting from the beginning. 

• More functions could be added to the app. 

• Ask for feedback from the Hackathon judges.

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