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Big datasets. Small moments.

A second grade teacher once invited me and my fellow geography graduate students to teach maps to her students. She told us she had shown them Google Earth in class and things had quickly gotten out of control. The students all immediately zoomed in, intuitively finding their own houses and gleefully sharing with their friends. Sensing the privacy and inequality issues this presented, the teacher shut it down, with the promise of a real map lesson from some alleged "experts,"  AKA graduate students. 


Something stuck with me about this amusing anecdote. What did you do when you first opened Google Earth? You zoomed in on your house, I bet, didn't you? Or some place you love, or dreamed of visiting. When humans get a glimpse of the big picture, we have the drive to zoom in and see where we fit into it. It's how we make the scale more relatable and familiar. Even second graders have this innate instinct to try and understand how we fit into the greater whole. And then, when we zoom back out again, the bigger picture makes a whole lot more sense. 

Small Town Big Data is a data blog. Kinda. It's a kinda data blog that zooms in on the small moments in big datasets.   

There will be no p-values here. There will be no Bayesian predictive models. I might leave off a scale bar and my code is ugly. But there's space for data exploration that isn't scientific. One that's more observational. Flexible. Intuitive.


Small Town Big Data inhabits this space. We live in an age of gluttonous data production. Some of it's used to save the world; most of it's used to sell us stuff we don't need. But the rest? Those little bits and pieces of data passed over by science for not being novel enough, robust enough, interesting enough? Those are the bits and piece I love. The ones that remind us of the way things are, used to be and might be. That tell the tiny, familiar vignettes of daily life. That describe snow piling up on a mountain, how a river bends, when bears get into trash cans, how we die, what we see and what we don't. It's like having a sixth sense: a crack in the matrix where you can see some of the wobbly source code that explains all of the little things that make up the big things.   

Data naturalism.

So, what exactly should we call this?

Well, it's not data science, due to the aforementioned lack of scientific method and rigor. And it's not really data journalism either. When I think data journalism, I think of elections, pandemics; newsworthy, headline-grabbing revelations and polished interactive charts made with proprietary fonts. 

My work has been deeply inspired by the data humanism movement pioneered by Giorgia Lupi, whose Ted Talk, "How We Can Find Ourselves in Data," will make you want to quit your job this very minute and start writing gorgeous data viz postcards to your bestie (see Dear Data, her stunning project with fellow designer Stefanie Posavec). Lupi writes: 

"We are ready to question the impersonality of a merely technical approach to data and to begin designing ways to connect numbers to what they really stand for: knowledge, behaviors, people."


While inspired by the data humanism movement, my perspective propounds to change those final words to: "knowledge, nature, experience."  So with a grateful nod to Lupi and Posavec, and for lack of a better term at the moment, I've settled on: 

What to expect

In these pages, I'll take you through dimensions of small town life through the lens of national and global publicly available datasets. There will be some rudimentary analysis and data exploration, plus visualizations that spark insight and inquiry into ourselves and our relationship to the natural world.

A secondary goal is to give you a peek under the hood. If you want to play along with me, posts will often be accompanied by tutorials containing some introductory geospatial basics and data science fundamentals so that you, too, can develop a time-sink of a side hobby whose perks includes late nights on StackOverflow and utter dereliction of your actual responsibilities.


Each post will:

Share a visualization or brief analysis illuminating an aspect of lived experience or natural phenomena in a small town.


Introduce an open dataset.


Where possible, include a reproducible tutorial, available on Github, so you can follow along and explore the data on your own.


Meet the editor.

Hi. I'm Nikki Inglis. I've called Steamboat Springs home since 2009. You may know me from my days as a reporter at the Steamboat Pilot & Today, where I went by my pen name, Nicole.

I have a master's in environmental science from Cal State Monterey Bay, and a PhD in geospatial analytics from North Carolina State University. The Yampa Valley will always be my soul home, but I'm not always physically there. In my spare time - which doesn't exist, this is me just procrastinating - I started tinkering with the datasets from my coursework and research and zooming in on Steamboat. I found it strangely relaxing. Soon I found I had folders and folders of code, visualizations and satellite imagery and nowhere to share it that made sense. This blog is a way for me to stay connected to my hometown, no matter where my path takes me.


** While I'm navigating the end of my PhD and the start of a new job, new posts will be on hold. Stay tuned for new posts coming Spring 2022**   

I'm no expert. 

In anything, really. I'm a novice data scientist, researcher and programmer. I'm new to data visualization. I'm an out-of-practice journalist. I'm exploring, learning and growing as I figure out new ways to tell stories that reflect the essence of intangible experience. At the very least, I hope you find a little bit of your own curiosities reflected here. I also hope my work can help connect you with the tools to get you stoked about data science, open data and the possibilities at our fingertips when we sit behind our shiny screens. 


This is a shared experience. Please overwhelm me with corrections, call outs, suggestions for datasets or topics, and of course, contributions of your own. Contributions should be anchored by a visualization piece, introduce a dataset, tell a compelling narrative of small town life and, if possible, include an open source, reproducible tutorial. 

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