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- Responsibilities of each member Lin Thit Myat Hsu (Anyone else can help me figure out who did what it’d be perfect)
- 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
Application Architecture and Tech stack
By the tim
Project Summary
Major achievements
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The website’s map’s ability to highlight certain information also makes it easier for users to gather and display information in a quick and efficient manner compared to other websites, and since it’s all accessible on one page, switching between different maps is quick and easy. The fact that each of the areas on the map can be interacted to display as more information, as well as the graphs, makes our website a potent source of information at any given time.
Use cases:
The target user would be policy makers who are working in local authority in USA.
Our website could deliver visualisation graphs which are related to the analysis of pandemic risk level among all counties in USA and medical resources distribution.
There are four types of visualisation graphs provided to target user:
a. Risk Level.
b. Vaccinations.
c. Staffed All Beds [Per 1000 Adults (20+)].
d. Staffed ICU Beds [Per 1000 Adults (20+)].
e. Licensed All Beds [Per 1000 Adults (20+)].
When a policy maker checks the severity of one specific county, there is one customized colour bar with discrete number as indicator shown below the risk level graph. It can use colour to differentiate the severity among all counties.
When a policy maker hovers on the specific county, there is one small black box pops up. It describes the risk level of that county in a simplified way.
When a policy maker clicks one specific county on the graph, there is one side bar on the right-hand side pops up, it contains three parts, first part above is including county name and risk level latest update date. Second part middle includes a status bar that shows the severity of that county based on the risk level. As well as infection rate and other related information. Third part at the bottom exists one line graph which records the change of the number of epidemic cases in past few days. This detailed information can bring a clear overview of the county during pandemic which gives policy maker makes appropriate decision.
When a policy maker checks the vaccinated rate of one specific county, the map type would be switched to Vaccinations graph. There is one customized colour bar from light colour to dark colour to indicate how large the vaccinated rate is. It uses colour to have a clear comparison of vaccinated rate between adjacent counties.
When a policy maker checks how many empty beds left, the map type would be switched to Staffed Beds graph. There is one customized colour bar from light colour to dark colour to indicate how many beds left in each county. It could make it possible for distributing medical power between counties because the complete data analysis of staffed beds and risk level provided in the visualisation. The policy makers can compare the risk level of the county they live in with their adjacent counties, as well as comparing the staffed beds left between them. Thus, it could show the possibility to borrow some beds from one county to another which dealing with the insufficient supply of beds for that county.
Responsibilities of Each Member
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