I used Fusion Tables to create nodes that link pledge amounts with names. It’s not the most efficient way to visualize the data, but I really liked the nodes, so it’s what I used.
In this pie chart, I decided to illustrate the percentage of backers from different cities.
This pie chart illustrates total donation amounts by city to the Kickstarter project. I decided to visualize the data by doing a sum function in Google Sheets, exporting those numbers to another sheet, and then pop it in with a pie chart in Fusion Tables. The software automatically geocoded everything as well, which I found kind of odd.
Attached is a link for the fusion table I created for the data presented. I made a table showing the donation percentage for each donor. I wanted to utilize the maps, however I ran into some issues.
I thought it would be interesting to look directly at the University of Alabama. After much researching I was able to find the “Retention and Graduation Rate” for UA. This data is so important to so many people including the university, students, professors, futures students, parents and much more. This data helps to reflect the overall success of our university in regards to education. Here is the link: http://nces.ed.gov/collegenavigator/?q=University+of+Alabama&s=AL&id=100751#retgrad
- The first story idea that I would do is look at the overall retention and graduation rate between full-time students and part-time students. I believe that there is a story here about this because I know my parents always told me that “you’re less likely to go back to school if you leave and go into the work force or you are more likely to drop out of school if you do it part-time.” I would be curious to see why the graduation rate for full-time students is 87% and part-time is 60%.
- My second story idea would be to look at the overall graduation rate spilt up among race. Why is it that “American Indian or Alaskan Native” have the lowest graduation rate of 57%? That is almost saying that half of them that attend UA don’t graduate. That’s crazy to me.
- Thirdly, my final story would be about the amount of bachelors degrees given out based on a students major. I would look at the data from 2013-2014 (most recent data that they have) and try to decipher why more people graduate UA with a business degree then any other degree. Then I would probably try to reflect and see where the majority of UA’s educational money is going and into what programs.
I chose a data set from the US government on Federal Student loan data. This data is about the recipients and distribution of federal financial aid. Costs of higher education are steadily rising and in a society that has such an high emphasis on college education. There are so many opportunities for stories that can be found in this data.
- Explore the correlations between amount of education (thus amount of financial aid) and rate of success after graduation (measured by job placement rate and median starting salaries)
- Comparison between success of students who received federal aid versus those who received private aid.
- Comparison between the success of the students that come from impoverished communities versus those who come from lower-middle class communities.
I found a data set that shows the crime statistics in and around London’s borough of Brixton. Brixton is considered the most crime ridden borough in London and since my boyfriend works for the Metropolitan Police Service in that borough, I thought it would be interesting to see the different types of crimes that occur in the area. The data set shows how crime has fluctuated from November 2014 to November 2015 and is organized by area and crime.
- It would be interesting to see how many of the people are arrested for each crime and what races tend to be arrested more often in the different crimes. With everything going on in America with the police, it would be interesting to take that sort of angle with the Met.
- How many of these crimes required the special task forces that are trained to use firearms? The normal officer doesn’t carry a firearm, but sometimes they are called upon to help subdue a criminal.
- To build off of how many are arrested, it would be interesting to look at how many were actually convicted after their initial arrest.
I have always loved to travel, so I found a data set about Airbnb, which is a company that allows people to rent places to stay from local hosts all over the world. This particular data set is about Airbnb in NYC. It is an overview of how many hosts are in each borough of NYC, the variety of costs, how popular the Airbnb home is, etc. The links are listed below:
- What is the borough with the most Airbnb hosts and why? It could have interactive videos of the most popular Airbnb homes in that area. It could also have statistics about the area: crime rates, restaurants/coffee shops, schools/universities, museums, etc.
- What is the most popular weekends out of the year to book with Airbnb in NYC? Does it correspond with holidays? How does weather effect Airbnb travelers? It would be cool to get real accounts from people that have rented from Airbnb on busy weekends in the city and to share their experience along with the data.
- What kind of people usually rent out their homes to Airbnb? What sort of income do they have? Do they have more than one home? How much of the profit do they make? In-depth interviews with these people would be helpful.
I found a database through data.gov called “Average monthly residential energy usage by zipcode” by the City of Los Angeles. This particular set interested me for a few reasons. First, as a California native, I am aware of how eco-conscious most of the population can be; for example, it is not unusual for a family to own a Prius and solar panels. Second, I’m interested in the breakdown of the usage by zipcode. In Los Angeles, like many big cities, there is a vast range of socioeconomic classes which energy usage most likely correlates to (more money = more usage), however, I am eager to see if this is true. From this data I’ve thought of three possible story ideas:
- Traditionally, Los Angeles neighborhoods vary in cultures. Which cultures are most likely to use the most energy? Which are the least? Do these changes reflect certain characteristics of certain cultures/behavior patterns? Are certain households more likely to have more members than others? All these questions can be answered by cross referencing the data set with a map of the city. This can also be a unique culture piece. Interviewing members of each culture could also provide insight into a “day-in-the-life” type of piece.
- Similar to the idea of cultures influencing energy usage, socioeconomic class can also be examined as a factor for more or less energy usage. Los Angeles has a huge range of living situations (from multi-million dollar homes, to small apartments and two-bedroom houses). This difference in usage can also dive into a more human element. For example, how conscious of energy usage is someone in a two-bedroom home, who may be concerned about their monthly bill, as opposed to someone who lives in a multi-million dollar estate? Further, how dependent on energy are both of those people? Is there a dramatic difference, or, are they more similar then they seem?
- Size of the household can also be a factor in the amount of energy used. Areas with more single bedroom apartments are most likely areas of less energy usage. On the other hand, areas with sprawling homes, such as the 90210 (Beverly Hills zipcode) are more likely to have larger amounts of people living in the home. This can be unpacked further to identify areas with more small children versus areas that may have multi-generational family members living in one household. This can be interesting to compare ages of home occupants with the amount of energy used. Size of family and age comparisons would be a very interesting piece to put together and map in a visually appealing way.
I think it would be interesting to study the relationship between the median income per capita in each state, the per-pupil spending in each state, and the educational attainment in each state. Each combination tells a slightly different story.
1. Per-pupil spending data, when compared to educational attainment, might tell us if spending more on students increases their chances of graduating from college. This might not measure how effective educational expenses are as well as comparing per-pupil spending and average state SAT score, but it would at least show whether or not spending more increases their desire and ability to go to college.
2. Comparing per-pupil spending and median household income would show if rich states always invest more in education than poor states. It might also indicate if per-pupil spending has an effect on income, but this might be a stretch.
3. Finally, going along the same lines, if one compared median household income and educational attainment, the data might indicate to what extent higher education levels statewide lead to higher pay statewide.