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Assignment 5 (IPC Update)
From last week, I have compiled and organized my data so that I was able to create visualizations. So far I have managed to incorporate, age and ethnicity. From these graphs we can see that the majority of injuries occur for males up until they are around age 5-9, however, females are more likely to get injured after 10 years old.
In this graph I compiled a list of patients based on their ethnicity. From the data, whites and males make up most of the injury patients.
Here I created a time series graph that shows the number of patients each year by ethnicity.
I will also be making maps. Although this is a incomplete map, it is a sample that of what I will make when I get the data. For each city, I will include the total population as well as the number of patients in that area from 2007-2012.
Assignment 4: Google Fusion Tables
The map below displays an estimate that the growing minority population will continue to be concentrated in urban areas, which will reinforce current levels of segregation in Connecticut. I used the Connecticut town boundaries, Census 2010 from MAGIC UConn Libraries and then I obtained the Increase in Minority Population (2010-2030) from the Analysis of Impediments to Fair Housing Report that Erin Kemple had given to me. In this report there is a static map impeded onto a PDF, which had been made using ArcGIS. I have not yet received the ArcGIS maps and shapefiles for these maps so for this assignment I had to manually add the increase in minority data manually by adding another column in Google Fusion Tables through looking at the pre-made map in the report. So for the map below, if you click on a certain town you will not get the exact data, since I have not received it yet. I also am unsure of how to get rid of the red shading at the bottom of the map.
Protected: Data Visualization for the CT Mirror
Experimenting with Google Fusion Tables
The data visualization below incorporates two sets of data: the geographical location of the schools operated by the Hartford Public School system and the achievement characteristics of each school, that is the percentage of students who met the proficient level on state tests in each school. As the legend suggests, red schools are the worst performing while green schools are the highest performing. The schools are divided based on data from 2013, but by clicking on the school, the viewer can observe the percentage of students at proficient from 2007-2013. This map easily lets viewers identify the locations of high performing schools and if participating in the Open Choice program, parents can gauge the distance from their home to their preferred school choice. Of course, this use will further be enhanced once I am able to include a searchable feature, which I provided a link for in my last post.
However, data can be misleading, and as Jack commented in my previous post, achievement data is not directly linked to the students which reside in that area because of the Open Choice program, which allows students to apply to attend any one of the HPS-run schools. Furthermore, the map does not fully highlight the progress from 2007-2013, and so one could assume that low-performing schools have always been low-performing schools, without clicking on each data point. For example, in 2007 36.4% of students at ML King met proficient. The school has steadily improved, and in 2013 57.2% of students met proficient. If this progress was more easily visible, a viewer might conclude that ML King has found a successful method to improve achievement, and further improvement is likely. To highlight progress, an interactive chart might best complement the map, or even a time slide feature in which viewers can select the year, and the achievement data will adjust accordingly.
This second map is very similar to the one above, but shows CAPT scores, which is the state test taken by students in Grade 10. As a result, the markers only represent high schools.