Category: xSpr14 Assignments
IPC – Pediatric Injury Trends (2007-2012)
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.
INJURY DEMOGRAPHICS
The following graphs show a few demographics on how injuries affect different ethnicity, age, gender, and locations in Connecticut. In terms of ethnicity, people of white descent make up the majority of injury patients who are serviced at Connecticut’s Children Medical Center, followed by people who did not give a specific ethnicity. Followed by those groups are Hispanics, Blacks, and then Asians.
For age groups, interestingly, the majority of male patients are aged 5-9, but there is a decrease after the age of 9. For females however, there is an increase of injury patients during early and late adolescence. Overall however, males suffer from injuries more than females.
The next graph shows where the majority of injuries occur. There are more injuries that occur at home more than any other location. The next location where injuries occur the most are in recreational areas, such as parks. These injuries may include sport injuries, or anything involving physical activity.
This next graph focuses on the Injuries based on Mean age. This graph represents the most common injuries per mean age group. For instance as the title suggests, falls are the most common injury that patients come to the hospital for. Also, falls are more likely to happen to young children.
The next map shows the distribution of patients serviced at CCMC based on the patient’s home town. The majority of patients come from the immediate Hartford area, which is not surprising because CCMC is located in Hartford.
The following graphs represent injuries over time. In the first line graph, we can see that during the day, patients come in the hospital more frequently at night than the day. This does not mean that the patients had their injuries occur during the night, but it is possible that the majority of injuries do.
INJURY BASED ON TIME
During the day of the week Saturdays have the most injuries.
There are spikes in injuries during the spring and summer time
The next chart shows the trends of injury that occur over the years of 2007-2012. One trend to look out for is that sports injuries seem to happen early on in the year, whereas Falls increase during the months of May – August. This trend seems to be consistent for all years from 2007-2012. The last graph sums up all the patients from 2007-2012 into on graph to show the trend from five years.
Instructions for Motion Graph
In order to use this graph, you must change the X Axis to “Time” and the Y Axis to “Number”. Also on the right hand side, the user may change the size of the bubbles based on “Total number of Patients” to distinguish each bubble as it moves. The user can then press play to see patterns. Also the user may see changes through a bar graph or a line graph using the options on the top right.
Falls Increase During the Spring/Summer
2007
2008
2009
2010
2011
2012
2007-2012
***Disregard the year, as it is only there for technical purposes only.
Visualizing Data for Park River Watershed
Water quality and quantity is measurable in our bodies, rivers and built environments. We depend on this natural product to sustain us and keep communities flowing and regulated. Every being on this planet depends upon water. Yet, we still face challenges convincing our neighbors and peers to work together and give people the tools to make differences that improve our one and only shared natural earth.
In order to develop a successful watershed stewardship, we need to identify interested groups, such as schools, home owners, park friends & groups or. . . municipalities, etc., who live on or around the river. Many people don’t realize that their own back yard contributes to the health of a stream system. For example, lawn fertilizers or salt deposits washed from rainstorms can quickly build up in our reservoirs or drainage systems. The Park River Watershed can help orient these interested groups towards annual activities that improve water quality and gather information from groups, so as to aggregate site specific data.
Collected data can then be forwarded to state and federal environmental programs or organizations, government agencies as well as nonprofits that work on the larger water bodies such as Connecticut River Watershed Council, and Save the Sound, which is the long island sound coastal environmental nonprofit group.
Utilizing the GitHub tool, I was able to play around with the town boundary and watershed boundary lines. GitHub allows one to turn on or off the different types of polygon layers present. I also have divided the SmartChoices Schools into three different types of schools: Interdistrict, district, and Pre-K centers. You can learn more about what SmartChoices is here.
This interactive data visualization helps parents around the Hartford area to view specific schools they are interested in for their children. It my also be helpful for teachers or people interested in the educational system to help them discover these specific schools that are in or around the Park River Watershed. Please see this link to my map here.
Below are the descriptions and color identifications for each school:
Yellow/1) Water or wetland adjacency: school grounds are within walking distance of a river, brook, or pond. This increases opportunity for water quality monitoring, environmental research and stream stewardship.
Green/2) Environmental goals: School curriculum prioritizes natural sciences, which increases potential for local environmental research and stewardship.
Purple/3) A nature trail, park, or open space within walking distances indicates opportunities for classroom or extra curricula site specific. environmental activities.
Pink/4) Science goals could include topics such as medicine or technology that is not directly related to the environment.
Red/5) STEM schools are schools that concentrate on Science, Technology, Engineering , and Math.
Data is constantly changing especially when incorporating new and existing school information. But I hope this was a small step into teaching others about how important a watershed does affect us and our community. Thanks for checking my page out!
Greatby8: The easy way to find early childhood resources
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.
Educational Studies Visualizations. Income and Educational Achievement
The following visualizations all can be categorized as visualizations which examine the factors of educational attainment. For these specifically, we will look namely at the influence of income on scholastic achievement
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.
The 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’s important to note that this does not show complete causation. Other factors often play roles in trends like these, and one way to show the significance of a specific factor is to discount the specific significance of others.
In light of this fact, this following visualization explores the impact of geography on test scores, and examines what impact, if any it would have on the first trend we examined.
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 areas of poverty and thus low test scores are more prevalent near the big city, but this information would not be sufficiently backed with our given data.
Now it’s important to recognize that though the data presented above is specific, plentiful, and convincing, it only deals with a confined area of Connecticut. Who’s to say that these trends wouldn’t look completely different elsewhere in the nation?
This final visualization looks at a more widespread measure of scholastic aptitude, which is the SAT.
This visualization shows a similar trend, with all three areas of the SAT having the top socioeconomic group score more than 100 points higher than the bottom socioeconomic group.
The above visualizations have given an inside look as to the ability data visualizations have, and how they can be an enhancement to any presentation.
I hope you all enjoyed this!