Can a state truly have a "Best Geography?"
I gave it a shot and here's my abridged methodology!
Figuring out the “best geography” for any given geographic area really is an effort in futility. Let’s get that out of the way right now. The world is a such an incredibly complicated place and any perceived benefit could very well be a negative under different circumstances or with different perspectives.
So consider that as you read this methodology. While I’ve attempted to make a subjective list of the best geography by state, the reality is that there’s no such thing. Even by pulling and using data that is authoritative, how I’ve interpreted that data and how it was broken out can’t be subjective. There’s just no way.
The other thing I will mention is that, since you’re probably here because you watched the video, please take it easy. This video is meant for entertainment purposes. If your state didn’t rise to level you think it should be, or if another state you personally dislike was ranked higher than you think it ought to be, that’s fine. There’s nothing won or lost by being 1st or 50th in this list. It’s all just for fun.
With all that out of the way, here’s how I came to the conclusion I did.
For starters, each state could get a maximum of 15 total points, with 5 points being allotted to each bucket of data. Those buckets being:
Natural Features: Prominence, interesting features, and deviation from the baseline elevation
Human Habitation: Cities, water accessibility, and agricultural productivity
Climate: Comfortability of climate, and number of diverse climate types
Natural features included data sets from the National Parks Service (number of National Parks and National Monuments), U.S. Forest Service (forest coverage by state), U.S. Geological Survey (mountain ranges and mountain peaks) and U.S. Census Bureau (coastline). Here’s what I did with each dataset:
National Parks - Total square mileage of parks normalized by total area size of each state.
National Monuments - Simple count within each state.
Coastline - Total mileage of coastline (fresh or saltwater) normalized by total mileage of border.
Mountain ranges - Total square mileage of mountain ranges normalized by total square mileage of land and converted into a percentage.
Mountain peaks greater than 2,000 meters - Simple count normalized by area size.
Forest coverage - Total square mileage of forests normalized by total square mileage of land and converted in percentage.
Human habitation included data sets from the U.S. Census Bureau (metropolitan regions and population, rivers and lakes), and the U.S. Department of Agriculture (agricultural productivity by state).
Freshwater lakes - Total square mileage of freshwater lakes normalized by total area size
Rivers - Total mileage of rivers normalized by area size to give the amount of river miles per single square mile
Agricultural exports - Total agricultural exports in US dollars
Metropolitan areas - Number of metropolitan areas with more than 3 million people, number of metropolitan areas with more than 1 million people and number of metropolitan areas with more than 500,000 people
Climate included data from the Koppen Climate Classification which was further bucketed into temperate, dry, continental, polar, and tropical climate types. I also used the Environmental Protective Agencies Level III ecoregions dataset to determine when and where there were significant shifts in climate biomes.
Climate types: Percentage of total land area that is either continental, dry, polar, temperate, or tropical per the Koppen Climate Classification
Ecoregions: Number of ecoregions per state
Each sub-data set was given its own score of 1-5 and then each bucket was simple average of the each individual data score so, in theory, each state could get a maximum of 5 points from each bucket. This totaled at the end to produce a final score out of 15 points.
Limitations of data
What I’ve done here is definitely not perfect. I don’t claim that. If I had an academic grant of thousands of dollars to figure this out, maybe I would. But I had only a single GIS assistant to help me pull together all of the data. All that said, the data I’ve used has been manipulated in ways in order to get as equal access to data across the states as possible. Alaska and Hawaii data, in particular, were very challenging to get and, as such, substitute data that might not be as good was acquired.
Additionally, while this was a really fun project to undertake, due to time and budgetary constraints, some things had to be removed from considerations. This project did not include:
Renewable energy potential by state
Likelihood of natural disasters
Pollution and policies around pollution
And many more things that are all HIGHLY geographic and, were they put into the model we created as their own buckets, would likely have drastically changed the results.
I make no claim that this is a perfect methodology, but it did allow me to create a fun video in a way that was at least more objective than most. And I hope you had fun with it!
Interested in the data itself? Check it out here. I probably made a few errors. 😅
And of course you can watch the video here:
The spreadsheet was fascinating. Off the top: Although NY state has no national parks, it has the largest state park in the US, over 6 million acres. Today the Park is the largest publicly protected area in the contiguous United States, greater in size than Yellowstone, Everglades, Glacier, and Grand Canyon National Park combined.
The boundary of the Park encompasses approximately 6 million acres, nearly half of which belongs to all the people of New York State and is constitutionally protected to remain “forever wild” forest preserve. The remaining half of the Park is private land which includes towns, farms, timber lands, businesses, homes, and camps.
Will be spending more time with the spreadsheet.
Why does Hawai’i have the highest score for National Parks per 1,000 Square Miles yet receive one of the lowest National Park scores, a 1? Also the Big Island contains eight different climate zones alone, so Hawai’i definitely has some percentage of dry (most of its western coast) and polar climate (Mauna Kea and the like)