I chose to look at data visualizations of “Best City in Florida” from one of the data sets offered using the Google Fusion Tables tool. Based on 20 cities in Florida, the data analyzed several different points of quality of life. The different data points that determined quality of life is based on the factors of income, commute, job growth, physicians, murder rate, rape rate, gold, restaurants, housing, median age, recreation and literacy.
From this data you see 20 cities ranked on these scales, yet they do not have the name of the city so it is difficult to see trends in location within the state. I chose to look at a scatter plot of median age on y axis and job growth on x axis. From this I saw a fairly clustered upward trend that showed the older the average age of the people in the city; the more job growth there is. I find this interesting, as you would assume the younger populations would be in the cities that has a faster job growth rat. The data does not show population size of the cities, but from the visualization of job growth to average population age you can infer that the cities with the younger median age and higher rates of growth will be some of Florida’s biggest cities in the next couple of decades, as there will be more births and job opportunities in these areas from the younger couples.
Another visualization I made to analyze was to look at a Stacked Line graph to compare the safety of neighborhoods with housing price and income. The X-Axis shows the rape rate within the city and the y-axis is in dollars, comparing housing prices and average income in that city. Visually seeing the data was fairly reasonable as the areas with the lowest rape rate had some of the highest house prices, which is what I would expect But, there was one city that had one of the highest rape rates and also the most expensive housing and largest average income. I would assume that this city is one of the bigger cities in Florida as typically rape rates are higher in high density, urban areas.
Comparing the average income and house prices you to see a fairly similar shape in both their trajectories as typically cities with higher housing prices also have higher incomes to be able to afford the housing.
When I just looked at the data it seems that locations where there is a higher income, you would assume you are making more money but my visualization shows that there is a direct relation between housing and income; so even though you are making more, a higher percentage of income will be going towards rent.
Your blog post did a great job showing how different types of data visualizations are needed for comparing and showcasing different data types. I thought it was very interesting how your scatterplot showed that the higher median ages had a higher job growth rate very apparently because that wouldn’t be something I would have probably looked for in the original dataset like you said. Also, the different colors used to show the relationship between income and housing prices was effective in showing the direct relationship quickly!