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[Project] The 20-Year Ups and Downs of the US National Park #175

Open christina10211 opened 6 years ago

christina10211 commented 6 years ago

Please complete all of the following sections, or the ghost of Joseph Pulitzer will spookily dance around your issue! A completed version of this template can be found at https://github.com/jsoma/data-studio-projects/issues/1

Pitch

National Park is one of the best traveling ideas in the US. While people enjoy their escape from the crowded world, their overnight stays choices at the park becomes an interesting topic to me. According to my dataset from the US National Park Services, the following are some main overnight stays (recreational stay) options available:

Summary

There are three main parts of my project:

Details

Possible headline(s): How Overnight Stay Decisions at US National Parks Changed Over Time?

Data set(s): https://irma.nps.gov/Stats/Reports/National

Code repository: https://github.com/christina10211/Data-Studio/tree/master/code/02-Overnight_Stay_Decisions_at_National_Parks

Possible problems/fears/questions: technical issue: I am having trouble changing numbers of ticks on my graph. I've tried the ax.set_xticks(listofticks,minor=True) method but it didn't work...

conceptual one: How to further analyze my dataset and get more helpful insights (popularity of stay options by states?)

Work so far

graph_visits

graph_overnights_option

Checklist

This checklist must be completed before you submit your draft.

christina10211 commented 6 years ago

I just realized that my dataset on the national parks includes more than just the actual parks, it also includes national monuments, historic sites, battlefields and many other categories...I guess I need to cleanup my data a little bit and redo everything from the beginning.

Also, I was thinking about broadening my angle here. I feel that i can't do much about the overnight stay option and honestly why do people even care about this pretty simple observation...I found a fivethirtyeight article on national parks and there is a part where the author tries to explain what cause the surge of visitations:

What explains this burst in popularity of the national parks?

“You mean besides the price of gas?” said Jeff Olson, a National Park Service spokesman. With gas prices below $3 per gallon last year, visitation surged. “When the price of gas goes up, visitation stutters. Then visitors get used to the price of gas, and visitation returns,” he said.

Pam Ziesler, the program coordinator for the NPS’s visitor use statistics, voiced another theory: “Visitors are saying it has to do with good weather. We had a beautiful spring and fall last year.” Weather can greatly affect visitation, particularly in the shoulder seasons (early spring and late fall). “But we have no firm, data-driven evidence” explaining the growing popularity of the parks, she said.'

Here the author quoted two people to bring in two possible explanations: oil price and weather. I was thinking maybe I can dig deeper in these two aspects, find some related data, do a regression analysis(if i have TIME to learn how to do that with python), and graph the correlation to see if the two statements made by the two spokespersons above makes sense data-wise.

christina10211 commented 6 years ago

I've also made some changes to my charts ( I was wondering if the formats and styles can deliver the message clearly). I tried to label them a little bit after Bui's session on annotation, and i am not sure if my annotation actually works...

I played around with designs of my lodging options chart in illustrator a little bit, and here are my experimental results:

overnights

Another chart I made:

visits

jsoma commented 6 years ago
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angelareplica commented 6 years ago

Omg these look so good! I love the color scheme and the way you used color in the text. A couple of small notes: -The annotated "small spike" during the financial crisis doesn't look like a spike to me-- it looks like a small dip? -I also don't think the red dot is necessary, since the line you drew makes it clear where to focus attention. -I wonder if the dip in 2013 is from the government shutdown. If so, that would be interesting to annotate. Overall, looks awesome!

vpenney commented 6 years ago

I agree! The colors look great and I like how you put the color legend in the text. I wonder if visitation rose starting in 2013/ 2014 because the economy was improving? It may also help to note if that last graph is total visitation, including day passes, or overnight visitation. Nice work!

christina10211 commented 6 years ago

Update

Your project content: images/words/etc

popular_month-01

popularity_changes-01

gas_visits

Any changes in direction or topic?

I used a new dataset, which is the monthly visits of the national parks for the past 20 years and I'd like to see if there's a pattern in the visits. I also did an analysis on the popularity change of NP (The graph took me a while to work on in the illustrator, and the point i want to make through this graph is to show which parks rise in ranking and which parks used to be popular are becoming less and less popular.

Problems/Questions

I'm still working on the gas price vs visits part since I am not sure how to apply regression models to this graph and I don't know if this graph mean anything to readers...

I'd also love some opinions on my other two new graphs （do they make any sense? are they meaningful?）

Checklist

xeophin commented 6 years ago

Hi!

christina10211 commented 6 years ago

Final

This is my final draft of this project. I've put all the text ( introduction, workflow, graph explanations, challenges, takeaways) in my website. It's live now! How exciting!!!!!! 😆

Project visuals/text

My main challenge when dealing with this dataset is how to visualize a pretty flat data trend. Line charts on the number of people for each stay options didn't work since the graph is composed of 5 pretty flat lines, except for the tent campers and concessioner lodging, which are the top 2 options, and they've been competing for people's top stay preference for basically 20 years.I also tried to plotted the percentage change of each year but the graph just doesn't make much sense. I finally decide to go with the treemap that just shows the which are the most popular stay options in 2017.

np_size_a_treemap

Headline: The 20 Years Ups and Downs of US National Parks

Published website version: https://christina10211.github.io/

Code repository: https://github.com/christina10211/Data-Studio/tree/master/code/02-Overnight_Stay_Decisions_at_National_Parks

Final data set(s): https://irma.nps.gov/Stats/Reports/National

What did you find to be the most difficult part of this project?

  1. To find the scope of research.
  2. Give up graphing something if you just can't find any interesting patterns. Text sometimes is better than a boring map.

Are you satisfied with what you produced? Is there anything you would like to change or improve?

Checklist

troboukis commented 6 years ago

I really like your graphs! But really hate the last one, the area one. Maybe bubble graph? But the rest are so good!

tsp2123 commented 6 years ago

I'm gonna have to disagree with Thanasis. I really liked the last one. It tells you what you need and I like your colour pallet. My one criticism is there seems like there's a lot of annotation going on in your second graph. I wonder if you could remove the arrows tracking from off-peak to peak seasons as the graph already shows that. I would also rather put the text in those annotations either in your body text or the dek of the graph.

nickospi commented 6 years ago

great stuff. well done!

sarahslo commented 6 years ago

lots of good information, well done. I think you are at the 'remove to improve' stage. in the first chart, you don't need to include the years in the text annotation as you are pointing to year. keep your annotations short, is Cooler wetter summer, better economy helps boost visitors (altho rain doesn't really help w. tourism)

on the average national parks by month, why not use the color you have for the dotted lines and put those colors in the bars? add a key and that removes one layer of annotation and allows us to see the data better. when you have so much type on a chart you begin to obscure the data.

i like what you have done with the third chart. i don't think the regions are adding much tho, there is no pattern to them and we have to work to discern what the point is. if you simplify the bar color and make them either, Gained or lost visitors and tie them to the charting in the middle we'll get a story.

Finally as to the treemap, i don't know what a 'concessioner lodging/camping' means. could you tie those colors together, make them both shades of the same color? put the squares in proximity to each other so they look like one square, because they relate somehow but they need to be translated from the jargon into something we understand.

speaking of which, what is a 'shoulder' season?

christina10211 commented 6 years ago

Thank you so much for the comments guys! These are really helpful.

Hi Sarah! Thanks for the detailed feedback, so to answer your question, I learnt about the shoulder season from some stories about national parks online, so it's like a time period in between peak and off-peak season, like early fall, late spring or even early winter when the parks are not that crowded but the weather is still good for travelling and camping. Regarding the concessioner lodging/camping, sorry about the confusion...i thought I put a definition somewhere but i didn't...In a nutshell, some people get permits from the government to operate private campground/lodging places for tourists in national parks, and tourists who stay in those places are counted towards concessioner camping/lodging. Since that's the term they used in the dataset, I don't know if i should change its name, but I will definitely make a note somewhere in the chart. Thank again for the comment!