Category: Uncategorized

Greatby8: The easy way to find early childhood resources

Posted on Author Katie Hawkins

Throughout my semester of data visualization I worked with the partnership named Great by 8. Great by 8 “is a community collaborative focused on improving access to high quality services in the areas of early childhood education and health. Great by 8 is a combined Graustein Discovery Community Board and the Town of West Hartford’s School Readiness Council. [They] have a formal board comprised of parents, teachers, public school administrators, private program directors and other community members to ensure that all West Hartford children are Great by age 8.”

In my data visualization I used a polygon outline of West Hartford and found useful locations to place on the polygon layer. The useful locations are there in order to make finding resources, such as health care units, dental care, recreational facilities, day cares, clothing stores etc. for children 8 years and under, easy to find and more widely known.

Click here to access my data visualization.

Assignment 9

Posted on Author Raymond Li

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.

Assignment 9

Posted on Author Marissa Block

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.

How to Lie with Maps

Posted on Author Marissa Block

The first map below shows the four school zones of the Hartford school district, which are shaded based on the average percentage of CMT proficiency of the schools in the that zone in 2013. The averages range from 50.4 to 74.09, so in the first map the red shaded areas are 50.4-62.23 and the green shaded areas are 62.24-74.09. This splits the zones into either good or bad, which is obvious in the map. Additionally, the use of red also further highlights the negative performance of the school zones. In shading an entire school zone in red, such as Zone 2 (top right), viewers might react negatively towards all the schools in that zone, even though one point (Capital Prep) is green, meaning the percentage at proficient is 70% and above.

The second map shows the exact same data, but rather than dividing school zones into good versus bad, zones are shaded as a gradient, so identifying the worse performing school zones does not quite jump out at the viewer as much as the last map. Again, the range for the gradient is 50.4-74.09, and as the shading gets darker the percentage at proficient increases. In this map, the viewer can determine that Zone 1 (top left) seems to be the darkest shaded, and this is confirmed by the yellow and green points, which are schools with percentage at proficient of 51% and above. In terms of the other zones, it is not quite clear which are better performing. For example, it does not seem that Zone 2 (top right) and Zone 3 (bottom left) are any worse than Zone 4 (bottom right), whereas in the first map, Zone 2 and 3 were shaded red, while Zone 4 was green.

Assignment 8

Posted on Author Raymond Li

This sample map shows the percentage of minorities in the school districts. Using many gradients, I was able to create ranges that are very similar in color. Therefore, I was able to show that in the small area of CT, the percentage of minorities was similar throughout the towns/cities. If I wanted to show extreme differences, I would have used less gradients and ranges and had colors that contracted each other. In this map, There was a big range, but the colors representing each gradient was a similar color.
In the second map, I used data from IPC. I calculated the total number of injuries in CT and also added in population statistics. Using those stats, I was able to calculate the injuries per capita. Most cities/towns had a relatively low level of injuries. However, to make the map show an extreme difference, I used many gradients, but also chose different colors to represent each. For instance I used blue, orange, and red to show a greater contract between colors.
The third map shows the same data, but using the same color. Because the ratios are so low and similar, it looks like as if there is no difference in ratio of injuries in each town/city. However, this is not the case since the Hartford area as well as Cromwell, have the highest ratio, but it is not shown in the third map