This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
Description: We were performing exploratory data analysis and wanted to tell a story that involved being able to play with some of the mapping functionality.
Data Source: The US Department of Agriculture - Economic Research Service publishes county-level data
Our Process: In the end, we decided to combine the county level data for poverty and education to come up with maps to display the counties with the best and worst proportions for the percentage of adults who have college degrees, and those who are in poverty.
Results: The top 25 counties with a large percentage of college-educated adults and also high marks of adult poverty were heavily concentrated in the midwest, particularly western Nebraska. The bottom 25 counties were primarily in the south-east, particularly Georgia, Tennessee, and the Carolinas.
Questions/Observations: The data includes detailed splits for ages 0-4, 5-17, and 0-17. However, there is no corresponding breakouts for seniors -- this seems like a major gap in the data where we provide high visibility into youth poverty, but are entirely blind to elderly in the official government data sets.
Using the World Happiness report, we placed countries in a scattered chart with life expectancy and happiness level in the X and Y axis.
Then, using a second data set, we created bubbles for each country, where the size of the bubble represented the meat consumption.
The workflow output has two charts: one with total consumption per country, and one with average consumption per person.
Based on the size and location of the bubbles, we could present data to show that higher meat consumption results in longer life expectancy and higher happiness levels.
The debate will remain on whether increasing meat consumption increases happiness and longevity, or if happy long-living people just happen to eat more meat. In any case, the resulting graph is an asset for meat lovers who can now back up their meat eating habits with actual data. Thank you Alteryx!
Team 3 | 05/13/2021 | @andrew_mendoza_21@AmandaG@alex_moran -Project Description - Our team worked on a weekly challenge that we found in the Community! The challenge we picked with analyzing popular baby names by year. -Data Sources used: We used the names.yxdb file from the Community Weekly Challenge. -Files for your workflow: Our workflow outputs a Tableau .hyper file which we used to build a dashboard.
We wanted to analyze data on Colorado 14ers and determine what factors contributed to average visitors at a particular 14er peak. Since most of our group is in Colorado, we also wanted to find the peaks with the least traffic so they could go explore. We looked at distance from Denver and route difficulty as two fields of interest. We determined that distance from Denver had the highest correlation to average number of visitors, so we used the average visitors as the theme and color coded it on the map. It did in fact show that the further from Denver you get, the fewer the visitors.