Category: Google Charts
IPC Transition Plan
The data I received came from a database of Pediatric Trauma Patients from 2007-2012 which is owned by Connecticut Children’s Medical Center. All the data that I have are either in Microsoft Excel format or Google Spreadsheet. Because a lot of information is confidential in the original data or easily identifiable, the original data set cannot be shown publicly. However, as long as it is de-identified , such as removing specific columns of data (ie. age, city/residency) data may be displayed.
All my graphs and visualization can be accessed through my github site here. As for the actual data, all my formatted data is on a Google Drive folder that is easily accessible with the visualizations as well.
Although these are simple charts, the formatting of the data may be useful for the future just in case a data visualization expert would want to create more sophisticated charts. Although I do not have the training in coding that is required for more complex visualizations, I feel like the ones I have now create potential ideas for future visualizations. However, complex visualization may not be necessary because there is still so much that can be visualized from the data set. Just using Google Charts can be sufficient for creating more visualization that don’t require expertise in data visualization.
Transition Plan
Currently, all of the visualizations I have made are GitHub repositories that can be forked (shared) with others. The repositories are titled as follows: CAPTMap, CMTMap, CAPTProficiencyChart, CMTProficiencyChart, and CohortChart. After creating a GitHub account, these repositories can be easily accessed by searching for my username marissablock23.
The underlying data was provided by Achieve Hartford and the data is already public. I did not make many changes to the data that AchieveHartford provided me, so they should easily understand my spreadsheet, though I cleaned it up a bit. I saved the data which as Google Spreadsheets and then created the maps using Google Fusion Tables. The maps are also available publicly entitled CAPT_Proficient and CMT_Proficient.
Finally, in terms of updating the visualizations for the future, one does not need to have a significant amount of coding experience to update the maps and charts. However, enhancing the visualizations will require more time and effort. With limited coding experience, I could not include some of the more interactive features that I would have liked to.
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 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.