Assignment 9 Writing about data

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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

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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 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.

MoveUP! Writing about Data

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Demographics can play a large role in literacy, and what MoveUP! wants to try to figure out is what types of demographics are making an effect on their students. If certain factors make a large enough impact, it could potentially be something that MoveUP! would want to take into consideration when they advertise their adult literacy programs.

This graph shows the median household income per each census tract in Hartford County. It is evident that the western part of Hartford county has a higher median income, whereas the actual city of Hartford seems to have the lowest. When you click on the tracts, though, you are able to see that many more people are attending adult literacy programs in the tracts that are in the city of Hartford in comparison to those that are in towns that have higher median incomes such as West Hartford.

Building upon the first map of income, this map shows the distribution of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. This map essentially shows the inverse of what the previous map showed: it accentuates the lowest income areas, which is, again, primarily the city of Hartford.

Another demographic that isn’t related to income that is important to look at, particularly in relation to literacy is employment. There is typically a strong correlation between employability and literacy skills, therefore it is important to look at what kinds of match ups there are in terms of unemployment. What is essential to note about what is considered to be “unemployed” in this particular map and data set is that those individuals are part of the labor force. There are also individuals included in the data set that are under the category of “not in labor force”. It is interesting to note, however, that the highest rate of unemployment isn’t necessarily in the same tracts as the lowest income.

Assignment 9

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As a result of school choice programs, Hartford students are not limited to simply attending their neighborhood school. Students can apply to attend any district school within the HPS system, or participate in interdistrict choice, which includes magnets, charters, or district schools in the Hartford metropolitan region. Since Achieve Hartford! focuses on education in the HPS system, the former choice program will be the most important.

In conjunction with Achieve Hartford’s report detailing the results of the Connecticut Mastery Test (CMT), the following visualizations enable viewers to grasp the most salient results by amalgamating the series of achievement scores into one place. Though most of the schools are HPS district schools, there are a few magnets and one charter (Achievement First), which are all located within the boundaries of Hartford. It is clear that achievement, based on the percentage of students who met proficiency (3 and above) on the CMT has steadily increased since 2007, though in the last two years (2012 and 2013) it has leveled off. Now, if a Hartford parent would like to send his or her child to a different school within Hartford, this chart will facilitate making an informed decision. The parent can choose any number of schools to compare with each other, or with the Hartford average. As seen in the chart, the magnet schools tend to perform above the Hartford average. Magnets typically have specialized curricula, and they are designed to draw students from across districts. High achievement may stem from the peer effect, so lower-income Hartford students are benefiting from learning side-by-side with higher-income suburban students.

The performance of district schools is more difficult to generalize and there is no clear pattern. On the whole, most district schools do not seem to exhibit a steady increase in scores, but rather fluctuate from year to year.

If parents are unfamiliar with the many school options within Hartford, a map is more relevant to helping them make an informed decision. This map incorporates the achievement data from the graph, but displays it spatially. Thus, parents can easily see the distribution of schools based on achievement throughout Hartford, and type in their address to identify schools nearby. When the data are displayed spatially, it seems that Zone 1 (top right) has the best school options, several of which are magnets.