This chart shows the number of students and staff currently quarantining according to Anne Arundel County Public Schools (AACPS) COVID-19 data dashboard. The dashboard is currently being updated daily, but I have not been diligent about collecting the data daily; therefore there is data missing from some days. So far, according to the data I recorded, the number of students quarantining peaked at 1,756 on September 29, 2021. On that date, there were 40 staff members quarantining, which was also a high point.
Below I have copied what AACPS says about who is included in the number of students and staff currently quarantining.
The number of current quarantine cases above includes those who are COVID-19 positive through confirmed testing, those who have been designated as having COVID-like symptoms, and those who are probable cases as defined below on this page. It also includes those who have been determined to be close contacts of those persons and who are not fully vaccinated. The data includes cases traced in schools as well as those involving AACPS students that are traced in the community and have no immediate impact on schools.
I like that datawrapper provides several options for displaying the date at the bottom of a chart. Because I have taken foreign languages and lived abroad, I have used both the day/month/year date formate and the month/day/year formate. I find it sort of confusing switching between the format conventions, so I'm happy that there is an option that eliminates that potential confusion by using the month abbreviations as such "Sep" or "Oct" with the date number above. I find this date format easier to read.
I used the straight number of students and staff currently quarantining for this chart because I was interested in the actual numbers. I could also show the percentage of students and staff currently quarantining. I would need to find an accurate count of students and staff for the current school year to make that chart.
I have been testing the features of the Datawrapper data visualization tool. I started with maps, and still have mapping features to explore, but today I decided to try making a chart. In particular a data range chart. Like my other experiences with Datawrapper the range chart was easy to make. I just put the data into four columns, changed the percent data to just the number without the percent sign, saved the Excel workbook, and uploaded it to Datawrapper. With a few clicks and a bit of typing, I made the chart below which shows college enrollment for high school graduates.
Public Policy Questions about the CHart
Statewide 78% of public high school students enrolled in college either full-time or part-time as a degree-seeking or non-degree seeking at any point after high school graduation. The statewide average hides the variability in college enrollment in the State both by county and by family income. Only 44% of low-income high school graduates from Kent County enrolled in college, while 93% of non-low-income high school graduates from Howard County enrolled in a college. That is a difference of 49 percentage points!
All counties also have gaps in college enrollment between low-income and non-low-income high school graduates. In particular, Carroll and Queen Anne's counties have the largest gaps, 31 and 29 percentage points respectively. Talbot Couty stands out as having the smallest gap in college enrollment between low- and non-low-income high school graduates, 4 percentage points.
What I don't know from this data is what is a good level of college enrollment. Many studies have found that earning a bachelor's degree pays off financially for most people, but there are other pathways to financial and life fulfillment. This data captures some "non-traditional" education pathways such as certificate programs, so some "trades" are captured, but not all. Although I do not know what the college enrollment rate should be, I think that high school graduates should have equal opportunities to enroll in college. The huge variability in college enrollment rates might point to underlying factors that prevent some students from enrolling in college.
This data also does not tell me why high school graduates choose to enroll or not enroll in college. However, given that low-income students enrolled at a lower rate for all counties, money is likely a major factor. Other factors may be the distance to an affordable college, high school preparation, or community expectations.