TODO:
Links to final source code (github repo) Mashira Farid
Use cases/ requirements of API (Focus on what has been achieved)Zifan Wei
The system design and Implementation
- update from feedback Avijit Prasad
- Final software architecture (diagram) Tingzhuang Zhou
- tech stack we using (any changes) Tingzhuang Zhou
- other teams API using (used team minions' API but discard finally- justify this) Avijit Prasad
- existing API using Avijit Prasad
- Summary of Key benefits/achievements of project relating to design/implementation Lin Thit Myat Hsu
Team organization and conclusion/appraisal of your work
- Responsibilities of each member Lin Thit Myat Hsu
- How did the project go Mashira Farid
- Major achievements in project
- Issues/problems encountered
- What kind of skills you wish you had before the workshop (this way we can try including them in other courses)
- Would you do it any differently now? • I.e. tools, different technology, time management, etc
Project Summary
Major achievements
Choropleth map
One of the main features of our final website is the interactive choropleth map of each county in each state in the US. It displays different types of data, such as covid risk level, the ratio of the population vaccinated, and the number of staffed and licensed beds, including ICU beds. The user can select which data they wish to view via a dropdown menu, and updates the map and colour scale legend accordingly. Users can zoom in and out, hover over a county to get its name and the value of the data being shown, as well as click on the county to get more information. By having this map, policymakers can coordinate with other counties and plan resource allocation more efficiently.
Charts and county info
Another feature of our final website is the ability to view charts regarding the number of covid cases in a certain county over time. When the user clicks on a county on the choropleth map, some information regarding the county, such as the covid community level, infection and vaccination rates, and the number of hospital beds available, are shown on the right side. A line chart showing the number of covid cases in the county is also shown. By having this information easily available, policymakers will find it easier to make planning decisions.
Preprocessing data
Many of the features of our website, such as the choropleth map and charts, require taking data from multiple sources, putting them together, and mapping them to the correct counties. Some values, such as the covid community risk level, also need to be calculated by taking data from multiple sources. In order to preprocess all this data, one of our team members created a Jupyter notebook, which when run, merges data taken from CovidActNow and CovidCareMap, filters out only the data required, calculates the risk level for each county, then maps all this data to their corresponding counties via a unique FIPS code. The result is outputted as a JSON file, which can then be easily used by different components in our website.
Problems encountered
Backend
took long time to understand what exactly was needed from API
API datasource wasnt very cooperative, hard to scrape from
Frontend
took long time to decide on target user, had to go through many iterations
Skills we wish we had beforehand
How to scrape data
What we would do differently
stricter deadlines
add more features
get API properly working
more defined roles for each member, instead of randomly assigning tasks
spend more time narrowing down target user, instead of focusing on features to offer