Symbolism being what it is, can be revealing in ways you might never imagine. I have been listening to Stephen King’s book--On Writing: A Memoir of the Craft. Because you can never pull too many threads without ripping the entire seam it will suffice to say that I have made a pivot to audiobooks for company on long trail runs. I figure at least I won’t have to hear the rabid coyote rip me apart if I bring along a distraction.
The bit about the coyote is funnier if you live in my city. There was a coyote on the loose that could not be confirmed or denied as being rabid. It simply attacked a few people on the bike and running trail and either died or became bored. A friend mentioned her pepper spray would save her. She would spray it directly into her own eyes so she didn’t have to watch her demise. Maybe its ultra-runner humor but I still think it’s pretty damn funny.
Stephen King made a reference to “eat your eggs”. In his context (I believe) he was referring to the scene from a Raisin in the Sun where bold dreams and aspirations are being articulated but fall on skeptical--if not deaf ears. Almost like saying, “yada, yada, yada” that is all well and good--now just eat your eggs.
Newly minted data professionals are not too different. When I am approached for career advice they often want to know the “how” before even appreciating the “what” or the “why”. I may need to stare at them blankly while reciting, “eat your eggs”.
I believe you need to cultivate a passion for your data projects. Admittedly easier to do if your responsibilities aren’t limited to spreadsheets and dull finance reports. For example, Mapping Inequality is a website describing the “redlining” of the 1930s.
In 1933, faced with a housing shortage, the federal government began a program explicitly designed to increase — and segregate — America's housing stock. Author Richard Rothstein says the housing programs begun under the New Deal were tantamount to a "state-sponsored system of segregation."
In the map below you can see how the neighborhoods were classified in Decatur Illinois in the 1930s. You can select the geographical areas you are interested in exploring.
We can see patterns of the districts impacted by segregation in the maps available for inquiry. Census Viewer filtered by census tract and black race demonstrates the population demographics still somewhat predictable in the graphic below.
ArcGIS has a native living map depicting urban heat islands. Focusing on the same geographic area we can identify where the zones of heat intensification are within the city. An important thought or additional layer of exploration might be considerations of historically established land-use systems and the modern impact on climate change. Think about neighborhoods with higher square footage of asphalt, low levels of tree line, less green space and you will see higher land surface temperatures and down stream impacts of climate discrimination.
Additionally, most cities capture informative demographic data to enhance perspectives on the distribution of economics at the county, city, or census tract level. Additional questions create hypotheses to identify drivers of poverty density or non-low income diaspora from areas lacking economic investments.
Tom Lisi from the Herald and Review highlights abandoned homes in Decatur cited for potential demolition."The left side shows a "heat map" illustrating pockets of Decatur with the most abandoned homes left to either county officials to manage, or already on the city's demolition list."
These examples are to show you how important it is to “scramble your eggs” before trying to make a souffle.
You need to build curiosity before you can bring a bit of creativity to your data questions or analyses.
The quote by Glennon Doyle is often repeated in the edited form I borrowed for the title. The full quote, “This life is mine alone...so I have stopped asking people for directions to places they've never been.” Basically I interpret this as a reminder not to compare yourself to others. They haven’t walked your path. It also reminds me of discussing big issues in public health across our communities. If you aren’t including spatial perspectives I think the road is too narrow and perhaps the insights you are curating are distorted.
Location intelligence adds an additional layer (pun intended) to data analysis. Herds of data analysts are guilty of mooing the sound “big data” perhaps attempting to direct attention to their own efforts or skills.
When I hear the term, I think of data without a story, lacking a formulated question, and most importantly helpless to motivate or change behavior.
When we appreciate the “where” of large datasets we can suddenly reveal deeper insights. We create buckets and filterable data readily available to tell unique curated stories.
For example, it appears easy for many to wave away the inevitability of climate change. We can do better. As journalists, data visualizers, analysts, and professionals that use graphics to communicate and share narratives we should focus on data literacy, skill development, and if nothing else--how not to be duped by low value information.
How else can you explain the persistence of the spreadsheet? How compelling of a story can you weave with this spreadsheet?
Once we visualize the data we can see trends missing in our initial glance at the spreadsheet. Fluctuations in global temperatures reveal the temporal aspect of climate change but what we can’t see here is the spatial component.
What happens if we sacrifice the temporal aspect for the spatial? This snapshot demonstrates a global perspective but how about getting both integrated into a single visualization.
Temperature Anomalies by Country
This pill plot does a great job on the global setting to show when anomalies become more routine than exception. Look what happens in the 1970s on a systemic scale.
The last graphic reminds me a bit of my recent discussion on Chord Diagrams hosted by Jon Schwabish, One Chart at a Time (below).
We can now look at country level (US is at the top) and region level once we watch the temperature change that also included predictions into future decades.
I was welcomed to Clubhouse recently and joined a GEOSPATIAL CONNECTIONS community where we discuss climate change and how best to communicate the risks and potential solutions. You can reach out on twitter for an invite. I have a few that I am happy to share with anyone looking to join the community.
Creative Commons License I first saw these images in a class that I recommend for the geospatial data curious--GEO-PYTHON 2020
You can follow along on Substack. Subscribers can get step by step “how to” sessions either within the newsletter or pop-up zoom sessions. So far we have talked about geo-python skills, QGIS, Tableau, and even blogging.
Don’t become a mere recorder of facts, but try to penetrate the mystery of their origin--Ivan Pavlov
Leaning into the silver lining a bit more, my consumption of podcasts tripled. I didn’t become brilliant or anything but my questions were definitely better. Nothing makes you brilliant-adjacent faster than better questions.
For example, I often facilitate data discussions about Anti-PD-1 Therapies. These are straightforward discussions because you can access de-identified open source data from the US FDA Adverse Event Reporting System (FAERS). Many of the pharma types in my workshops are focused on the profits made from the leading drugs. Executives and marketing agencies dissect the positioning and marketing strategies of the branded drugs in the space and point to strategies you can emulate.
I like to talk about the patients. These are highly toxic drugs being introduced into sick patients. What are the risks? What are reasonable side effects to expect? How does overall survival compare to standard therapies? Are there clues as to which patients might have better outcomes?
My point here isn’t to discuss the actual data although we can if that is of interest. A recent podcast did an interesting job walking through the latest research utilizing a creative but impactful study.
I like to share examples of how to add a deeper insight by looking at a variety of data perspectives and resources.
Thanks for reading along!
A starter level ArcGIS account is reasonable. I think I paid $100 for one solid year. Buyer beware though--this is definitely an enterprise solution unless you are really careful not to bring in any data that will be automatically geocoded ($$$$$) or you store large maps and data on the platform. The price tag can bloat quickly.
It was a great platform to simply learn about GIS and creating maps. Their conference sessions from last year are great educational opportunities and they offer an abundance of free tools, courses, and webinars. What they don’t offer is ArcGIS Pro compatible with MacOS. A deal breaker for me. Back in the early days of Tableau, I had to bootcamp my hard-drive and run windows on a partition. I hate Windows. When Tableau was available for MacOs I vowed to never return. Buh bye Alteryx. Yes, it might be my loss but there was also a lot to gain. ArcGIS online may be all you need. No software to install but there aren’t scripting functions unless you are on desktop version (ArcGIS Pro).
The Living Atlas is amazing and after a quick tutorial you are able to build maps using their Census data layers and a wide variety of other ready built datasets for you to explore. Happy to share a few videos I created on how to create a story map.
I am sharing a professional hack with you. Once I felt comfortable in my geospatial skills I switched to an open source platform--QGIS. There is a python plug-in readily available and you are off to the races. This will be the focus of the book.
I am also trying out a little live forum--link below--where we can chat in real time.
Python is versatile (and free). I often demonstrate how to address the same question using 3 different methods. Look for these sessions to increase as I move through the book. I create and test a variety of visualizations and often perfectly great maps are too twitchy for a book where many users will be using different software. I will include them here for our discussion and in future blogs and newsletters.
Click to register Geospatial Roundtables and other discussions
There was an art exhibit called “Fields of Light,” by Bruce Munro, which consisted of acres in the desert on a hillside of lights that slowly pulsated, asynchronously, in different colors. It was the nighttime. It just made me think in a different way about what it might be to wander inside a mind and what thought looks like. And to me, it was awesome, because it brought together so many analogies and metaphors and ways of thinking about thinking and visualizing it. I suddenly thought that this is what thought really is.--Frank Wilczek, On Being
Different artists have different styles. We don’t expect to find Renoir’s shimmering color in a different world entirely, the Beatles’ from another, and Louis Armstrong’s from yet another. Likewise, the beauty embodied in the physical world is a particular kind of beauty. Nature, as an artist, has a distinctive style.--Frank Wilczek
Like many of you, I am balancing equal measure stacks of work to finish out the year and a wandering eye toward the prep for a scaled down holiday feast and celebration.
Last night my husband suggested we watch Charlie Brown Christmas. By we, it was just us. The kids were off in their own homes or upstairs enthusiastically engaged with an online posse of Valorant tactical team players.
Steve is not known for sentimentality but we have always watched the special; either live on a network or from our own personal copy. The first holiday music to play is more than likely our Vince Guaraldi CD. I know we can stream it from dozens of options but there is nothing like tradition--especially around the holidays.
Curious about a credit at the end of the cartoon reading, “Graphic Blandishment by...” I found the director, production designer, storyboard and layout artist, and background painter--Edward Levitt.
He also coined the famous credit used for many years at the end of the Peanuts specials—Graphic Blandishment. “Blandishment” is defined as “something that tends to coax or cajole,” which speaks to Levitt’s modesty and his view of the role he played in the filmmaking process.--RIP: Edward Levitt, 96, Disney Background Painter and Cartoon Modern Designer
Thinking of the word ‘blandishment’ in the context of graphics is brilliant. “A flattering or pleasing statement or action to persuade someone gently to do something.” Well, yes. That is pretty much data visualization in a nutshell, right?
Here are a few year end thoughts from the newsletter, Mumble, Ponder, Delegate over on Substack. I pulled it out from the paywall to share with you.
This specific post is from The plausibility of factual information--here are summarized ideas from a year that has been nothing but challenging, heartbreaking, and illuminating.
Be safe my friends and have a happy holiday season...
Hope Smiles from the threshold of the year to come, Whispering 'it will be happier’--Alfred Lord Tennyson
A few years ago I entered the entrepreneurial space as a “newly" minted applied data analyst. Although I had been working along analysts and data scientists in my work as a medical writer and outcomes professional, it was more of an observed curiosity than an immersive existence. Somewhere along the line I began asking questions. I hesitated at first not wanting to appear ignorant but quickly noticed vague responses to questions I would have thought had straightforward responses.
Curiosity morphed into agency as problems presented themselves and needed viable solutions. But whatever that sound is when a record scratches to halt forward movement in a movie--insert that here. Collaborative efforts to improve data collection and processes were not hailed as the “secret sauce” I had imagined. Here is the rub. Data literacy was lagging the needs by quite a significant gap. Most data departments (and I use the term loosely) consisted of finger pointing, a small measure of chest pounding, and gasps of “But we have always done it this way...”.
Fast forward to today and I am contractually obligated to author my first book on geospatial analytics. How I got here and why will be the subject of more than a few future posts. I want to first introduce you to an organization that does a great job illuminating the importance of thinking spatially (below).
We all respond to graphic images. Instinctually we are grounded in the what and where of an image. Any student of data visualization recalls pre-attentive attributes--the preliminary detection of the image. But we often don’t appreciate the attentive attributes as well. Attentive attributes call in to play the higher centers of the brain to make inferences following four principles as described by Eric Kandel--Nobel prize winner in Physiology or Medicine:
1. Disregarding details that are perceived as behaviorally irrelevant in a given context,
2. Searching for constancy,
3. Attempting to abstract the essential, constant features of objects, people, and landscapes,
4. Comparing the present image or graphic to images encountered in the past.
“Perception is the process whereby reflected light becomes linked to an image in the environment, is made enduring by the brain, and becomes coherent when the brain assigns it meaning, utility, and value.--Eric Kandel, Reductionism in Art and Brain Science."
What do we mean by “spatial literacy”? Let’s take a look. Location data looks at the environmental or first-order effects and the second-order or interaction effects. We can simplify processes into data-driven and model driven. But let’s not get ahead of ourselves. We are first looking to summarize the data. What are the characteristics of the data? The testing begins during model-driven analysis.
WorldPop data is the perfect place to start understanding the process of mapping and providing “high resolution, open and contemporary data on human population distributions, allowing accurate measurement of local population distributions, compositions, characteristics, growth and dynamics, across national and regional scales.” You won’t find a lot of US data here but I have successfully used the methods discovered here on US CENSUS data for example.
There really isn’t--at least not yet--a handbook to guide you through the insights needed to make granular assessments about poverty beyond quantitative assessments but applying even a few of these insights to our data questions can only improve our ability to provide a 360 perspective.
Spatial data reminds me of a useful definition I once heard of big data. It isn’t the volume that makes it big--it is the interactivity of blending different datasets to answer complex questions. This is visually appreciated when we look at layers of data integration to examine patterns in geographic regions.
The ability to explore characteristics that influence differences in population density. It isn’t enough to simply drop a pin onto a map to indicate populations--we need context.
Summaries of the workflows will continue to be posted here. If you are interested in the detailed "how-to"--subscribe below:
Graphics are from HDX Dataset Deep Dive on WorldPop’s Gridded Population Datasets.
HDX Humanitarian Data Exchange.
There are 46 million Americans with Alzheimer's disease in their brain right now, but no symptoms. --Richard Isaacson
I have two blogs. More like one and a half. The other one is sort of a repository for information. I don’t pay for that one and at times I think it may have run its course but then I think of something else I want to park over there. The name of it is Alzheimer’s Disease: The Brand and there is plenty of value over there but also plenty I have learned that I replicate over here.
For example, you really need to do your homework. The hard tedious bits. I long advocated the work of Dr. Dale Bredesen and I am not exactly recanting but it never occurred to me to look at the data he cited from the literature in support of the claims made in his writings.The person that dug into the findings and the data in the resources cited by Dr Bredesen was Dr Peter Attia. I have listened to his podcast and read his posts for years. He has evolved into more of a pay to play model for some of his podcast show notes and communications so I was unable to locate the conclusion. Regardless I still follow many of the earlier recommendations simply because they still make sense.
An article in The Washington Post, Atypical forms of dementia are being diagnosed more often in people in their 50s and 60s caught my attention. All gloom and doom and no grounding in the granularity needed to describe the known heterogeneity of Alzheimer’s Disease.
My dad had Alzheimer’s disease likely because of head trauma in a car accident years before we were able to make the probable diagnosis. So with uncertainty regarding any long term benefits from the lifestyle recommendations in the literature I decided to focus my attention on longevity and prevention--the focus of The Drive.
Here is a direct link to the podcast Alzheimer’s disease prevention--patient and doctor perspectives
And it sounds like what you're saying is Alzheimer's is not really one disease. it's an umbrella term that encompasses many different diseases of the brain that have some common features in the way that all cancers have some common features, cells don't respond to normal signaling, but there's this notion that someone could have a form of Alzheimer's that largely spares the frontal cortex and therefore preserve some higher order functioning versus another person that has.--Peter Attia
Here is an additional resource--an article authored by both Peter and Richard (as well as others). Click on title for full article.
Multidomain intervention for Alzheimer's disease (AD) risk reduction is an emerging therapeutic paradigm.
Patients were prescribed individually tailored interventions (education/pharmacologic/nonpharmacologic) and rated on compliance. Normal cognition/subjective cognitive decline/preclinical AD was classified as Prevention. Mild cognitive impairment due to AD/mild-AD was classified as Early Treatment. Change from baseline to 18 months on the modified Alzheimer's Prevention Cognitive Composite (primary outcome) was compared against matched historical control cohorts. Cognitive aging composite (CogAging), AD/cardiovascular risk scales, and serum biomarkers were secondary outcomes.
One hundred seventy-four were assigned interventions (age 25–86). Higher-compliance Prevention improved more than both historical cohorts (P = .0012, P < .0001). Lower-compliance Prevention also improved more than both historical cohorts (P = .0088, P < .0055). Higher-compliance Early Treatment improved more than lower compliance (P = .0007). Higher-compliance Early Treatment improved more than historical cohorts (P < .0001, P = .0428). Lower-compliance Early Treatment did not differ (P = .9820, P = .1115). Similar effects occurred for CogAging. AD/cardiovascular risk scales and serum biomarkers improved.
Individualized multidomain interventions may improve cognition and reduce AD/cardiovascular risk scores in patients at-risk for AD dementia.
I will continue to share information, preferentially in this blog, due to the limits of a free Weebly account.
“Sentient beings are numberless; I vow to save them all.”--Bodhisattva vow
ivart was one of the first scientists to call attention to the observation that major transitions in evolution do not involve a single organ changing; rather, whole suites of features across the body have to change in concert.
Some Assembly Required Decoding Four Billion Years of Life, from Ancient Fossils to DNA--Neil Shubin
If you aren’t familiar with St George Jackson Mivart, today is your lucky day. In a nutshell, Mivart was trolling Darwin’s findings in his seminal work, "On the Origin of Species".
But his question was important:
If entire bodies have to change for any great transformation, and many features need to change simultaneously, then how could major transitions happen gradually?--Some Assembly Required Decoding Four Billion Years of Life, from Ancient Fossils to DNA--Neil Shubin
Charles Darwin responded thoughtfully and respectfully...
All of Mr Mivart’s objections will be, or have been, considered in the present volume. The one new point which appears to have struck many readers is, ‘That natural selection is incompetent to account for the incipient stages of useful structures.’ This subject is intimately connected with that of the gradation of the characters, often accompanied by a change of function.”--Neil Shubin
Lungs aren’t some invention that abruptly came about as creatures evolved to walk. Fish were breathing air with lungs well before animals ever stepped onto terra firma. The invasion of land by descendants of fish did not originate a new organ--it changed the function of an organ that already existed...the change did not involve the origin of a new organ; instead the transformation was, as Darwin said more generally, “accompanied by a change of function.”--Neil Shubin
My thesis was in population genetics so this book is a win for me but it also reminded me of data. We take a class and are often surprised when the skills or tools are not easily assimilated into a work flow. It reminds me of the fish with the genetic equivalent of a lung.
He doesn’t effortlessly stroll onto the beach and become a land dweller. There are gradients of success. There needs to be changes in a whole host of functions. Suddenly his watery environment has a drop in dissolved oxygen--mysteriously he relies on his lungs to weather the storm.
Perhaps our data skills are like air sacs. They exist--we simply need to challenge them to innovate and evolve along with us.