Category: xSpr14 Assignments
Visualizing Achievement in Hartford Schools
Cohort data follows the same group of students from year to year in order to track their achievement gains and/or losses. This data tracks a group of students beginning in Grade 3 in 2007 until Grade 8 in 2012. Looking at the cohort of all Hartford students, the percentage at proficiency increases from Grade 5 to 6, but then gradually declines in Grade 7 and 8. In looking at all the schools, it seems as if the general trend is that percentage at proficiency peaks around Grade 6.
Another important takeaway from the graph is comparing magnet school performance to Hartford overall, and comparing Hartford residents of those magnet schools to the magnet school overall. In general, the magnet schools tend to be above the Hartford average. When comparing Hartford resident magnet school students to the rest of the magnet students at that school, a lower percentage of Hartford residents tend to meet proficiency. However, when comparing Hartford resident students of magnet schools to students of district schools, a higher percentage of magnet school students tend to meet proficiency.
The maps below offer a spatial distribution of the schools in Hartford by school zones. The schools are coded (red, yellow, or green) based on the percentage who met proficiency in 2013. The maps aim to answer the question, “Do certain zones have better school options than others?” The map of schools based on CMT proficiency seems to indicate that Zone 1 (top left) has the most school’s with higher achieving students. The CAPT is more difficult to interpret because it contains a small number of schools. The CAPT is taken during 10th grade, so the map only shows high schools. Based on the map, there does not appear to be very much middle ground between performance levels. Furthermore, in viewing the graph that tracks the progression of proficiency from 2007-2013, all the magnet high schools are above the Hartford average for every year between 2007-2013. All the district high schools are below the Hartford average, with the exception of Bulkeley in 2009 and 2010.
Protected: Data Visualization of Lottery Project for the CT Mirror
Assignment 9 Writing about data
The question has often been asked, what outside factors influence educational proficiency. The following visualizations and facts presented take a look at what some of these factors may potentially be.
This first visualization shows the most striking trend which has long been discussed and debated. This is the clearly strong correlation between income, and educational attainment.
This correlation coefficient, which is about .8 in this case, is close to a perfect correlation of 1. This means that the data is very nearly fit to a line with a constant slope, deviating very little from the trend of income to test scores. While this graph is very telling, it isn’t an end-all to the education debate.
The following visualization explores the impact of geography on test scores.
Seeing as this data is less concrete, it is nearly difficult to unequivocally estimate fixed trends. This being said, to the naked eye, at least in the instance of the Greater Hartford Area, there appears to be little to no correlation between geography and scholastic achievement. The case could be made that the areas of poverty and thus low test scores are more prevalent in the city, but this information would not be sufficiently backed with our given data.
We are able to see the tip of the iceberg with these above visualizations, but what lies in the ocean is a whole other story.
Assignment 9
Through popular notions of accidents and media interpretation of injuries, many people have a perception that mostly all injuries are deemed to be “accidents” and are therefore part of everyday life. However, what people don’t notice is that many of these injuries are preventable if certain regulations, laws, and practices are implemented. For instance, seat belt safety and the widening of roads have decreased the amount of motor vehicle crash and lessened the severity of motor vehicle related injuries.
Many people also don’t know the trends that occur or the statistics that relate to injury. A lot of this information had been mainly for academic or medical uses only, but not for the public. The public’s knowledge on injury is mainly grounded on the media and it’s representation of injuries. Using a Pediatric Trauma Database from 2007-2012 and a paper on Pediatric Trauma, I have compiled a plethora of graphs and charts that represent injuries, not as accidents, but as something that can be predictable and hopefully preventable.
In these 5 graphs, they represent the number of injuries that occur during a whole year span, starting from 2007 to 2012 respectively. I would like to use a program or site like jsfiddle.net to group these graphs together. According to these charts, injuries are very frequent, but in many cases, injuries are more common in the summer time than any other time of the year.
The pie chart below show the percentage and number of injuries that occur to different ethnicities. The first represents males, while the second represents females. The last pie chart shows injuries based on location.
In terms of the time of day injuries occur, this line graph shows that there is a clear trend of when injuries are most likely to happen. The data shows, when patients were brought to the hospitals. Around the hours of 9PM, 10PM, and 12AM are when there is a huge spike of accidents that occur. Nighttime injuries are the most frequent.
This bar graph shows the number of injuries that occur during the days of the week from 2007-2012. From this chart, during the weekdays, the injuries are fairly consistent, but as soon as the weekend rolls around, there are far more injuries, especially on Saturdays.
This map shows the number of injures that occur in each town/city in CT based on Injuries per capita. The map also shows the number of injuries that occur in each city.