For IPC, there are many types of charts and maps that are useful in displaying data. So far I have been moving along with organizing my data. In this map, it shows the number of patients in each residential city/town in 2007. As I progress with the data, I hope to add more information, such as frequency of injury in each city.
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Sample map 2
Sample Post: Maps
Assignment 3
After meeting with Erin Kemple last week we decided at our first plan would be that I read through the Connecticut Analysis of Impediments to Fair Housing Choice, which is a report hat is still in the process of being completed. This document is over 200 pages and includes a lot of information so while going through it I made a list of data that I think we should make available using visualizations. Erin Kemple is also going to go make her own list so that at our next meeting we can compare and decide together what information we think is significant to use and then we will have a better idea about how exactly to display he selected data. Although there is still much to be decided on, I have come up with some ideas for design choices, with some guidance from Erin about where I can look for some inspiration.
Since a significant portion of the report addresses racial segregation statewide in Connecticut, I have been thinking about and researching different ways racial segregation has already been shown using maps. The Atlantic Magazine has a very interesting map of racial segregation throughout the United States that uses individual dots to display population density by race in different parts of the country, as soon below.
A map similar to this would be useful for Connecticut Fair Housing, but only if other variables can be added that are related to impediments to housing opportunities that would allow viewers to have a better understanding of what groups of people are being affected by certain housing practices and policies, mostly focusing on people of color, people with lawful sources of income other than employment, people with disabilities, single-parent households and LGBT people.
I also came up with another idea that I thought of by looking at a Comprehensive Opportunity map on the Al Jazeera America website as well as a map displayed in the Impediments to Fair Housing Choices report of how two programs, the federal Housing Choice Section 8 Voucher program (HCV) and the State Rental Assistance Payment program (RAP) have statewide patterns of that their participants are living in areas that are disproportionately low income and have a significant population of people of color. This means that these programs are handing out vouchers to their participants (who are disproportionality made up of people of color, those with disabilities, single-parent households) in low income areas, which most likely means that there are little opportunities in these areas. The map of the Comprehensive Opportunity Index for the Baltimore Region shows different sections of various levels of opportunity in terms of jobs, safe neighborhoods, and good schools. While I do not know if it is possible to obtain this information about opportunity index scores throughout Connecticut, it could be significant if I am able to get data about something such as where good schools are located and created a map that compares this data with where housing voucher participants are living.
Plans for Pediatric Trauma Registry
Over the past two weeks, I have been going to the Injury Prevention Center quite frequently and working with Garry on the data that he gave me. He has given me a published paper on how the IPV has categorized and analyzed the data; therefore I have been using this paper as a guide. The data that has been given to me contains 5 years (2007-2012) of all pediatric traumas that have occurred. The information is extensive and contains information such as age, gender, ethnicity, time, place, injury type, and etc. Because this data base is owned by Connecticut Children’s Medical Center, the data itself is private and cannot go out into the public unless it is shown as a visualization.
The first priority is to organize the data as shown in Using Trauma Registry Data to Guide Injury Prevention Program Activities. By grouping information together such as age and injury severity, we can better see the correlation between different age groups and the result of the injury. By the time I am finished I will hope to have the trauma patients’ demographics, the cause of injury by mean age, temporal characteristics, injury severity, injury location, injury intent and mechanism, and injury location.
Next, as we move along in the class, I will then start making charts that will showcase the information that is interactive but also easy for the public to understand. For instance, I would group together injury types and display which types of injuries are more prevalent, and add other information such as age groups most affected. Here is an example of an interactive chart that could be helpful: Health Chart
As well as injury information, creating maps would also be helpful in looking at cities and locations where injuries often occur. The authors of the Trauma paper, used ArcGIS to map cities in Connecticut, but it would also be beneficial for the map to be interactive as well. Here would be an example: Health Map