​I prefer audiobooks because I can multitask trail running with my passion for books without having to give up those valuable hours at the end of the day when I fall asleep 2 paragraphs into an anticipated read. 

​But I did find time to read through Risk: A User’s Guide* by General Stanley McChrystal, retired 4-star general in the U.S. Army.
*as a member of Amazon associates they give me a portion of the sales if you click through. Easy way to support my work at no cost to you!

The military assessments are riveting but the part I wanted to share really resonates with the data analysis I perform in the healthcare realm.

This is also the book I use as a framework for working with health system executives.

Confucius says, “The beginning of wisdom is the ability to call things by their right names.”

Discussions about risk are no different.

​Are we envisioning the same threats when I use the word risk?

What do we mean when we talk about calculating risk?

What can happen when we delay focusing on data that measures risk?

Threat x Vulnerability = Risk 

What a seamless definition of risk.

“If there are no threats--our vulnerabilities don’t matter and
​If we have no vulnerabilities--threats don’t matter

​The foundation of the book for me is the ten dimensions of control present in all organizations but typically in varying amounts of effectiveness.

​I am sharing this not as a substitute for the book but to hopefully stimulate the same ‘aha' moment I experienced.

Communication: Exchange of information
|Clear, concise, accurate, and timely information is hard to come by|
Narrative: Telling others what you do
|How we think of ourselves--and tell our story--does much to drive our behavior and determine outcomes| 
Structure: Organizational design and processes
|Ultimately, organization matters. An improperly assembled weapon will not fire|
Technology: Applying resources and expertise
|Technology raises a new question: Who or what is in control|
Diversity: Leveraging a range of perspectives
|If you don’t see and address a problem from multiple perspectives, you’ll miss something--and it may be the critical thing|
Bias: Assumptions of how the world influences us
|Just because we believe something doesn’t make it so|
Action: Overcoming inertia to respond
|Typically, for anything to get done, someone has to do something|
Timing: When we act impacts effectiveness of response
|When you do something is often as important as what you do|
Adaptability: Responding to changing risks
|If it’s stupid and it works--it isn’t stupid|
Leadership: Directing the overall Risk Immune System(Detect--Assess--Respond--Learn)
|Leadership, more than any other factor, can make the system function or fail|

Each of the above tactics are explored in detail in the book but I think you can see a path emerging. Combined they are an assessment of an organizational Risk Immune System but that organization can also be an N of 1 independent consultant (me) or a large Fortune 500 company (not me).

These 10 elements build the framework for defining a data question, how to respond, select datasets, and execute a plan.

​I never argue with Confucius.

What do you think?

​What are you reading?

For a few hours on a Sunday I try to read something from the list of books, articles, emails, and other things ignored during the work week. There seemed to be more than usual this week so here are a few insights from the pile.

One of the best things about a blog is the archive.

Second only to the relationships I have grown with so many of you.

First, here is a podcast episode I keep listening to and sharing.

​Important distinctions should be made between skills and knowledge. I absorbed this through the lens of my work life but I think this is broadly applicable anywhere.

Sprinkle it everywhere.

I am listening to a podcast episode of Minds Behind Maps. This one is particularly timely. Bruno Sanchez is the, Program Director of the Planetary Computer at Microsoft and the author of "Impact Science: The science of getting to radical social and environmental breakthroughs".

Knowledge can be placed in buckets, skills should not--Bruno Sanches

Anyone working in a scientific field recognizes these buckets. They are deep and not connected willingly from within. We spend so much time positioning our expertise and “othering" our colleagues into their buckets--perhaps to simplify dragging them from the stage of debate. 

Bruno brilliantly explains what we lose when we don’t value skills but instead strain our necks to see if the knowledge checklist has been evaluated. My point remains that your scientific, technical, mathematical, focus ignores that we often need more than the facts.

“How many people need to die before we understand that facts are not enough? One? Ten? A thousand? This is a blunt question to draw attention to the failure in facts alone, even when lives are at stake.”--Bruno Sanchez, Impact Science, The Science of getting to radical social and environmental breakthroughs.”*affiliate link

Many either new to or trying to elevate careers in data science aren’t feeling the love.

​We read the debates over which degree, school, or path to a career in data is the best.

None.

​Hiring managers want the work done. They value critical thinking skills and how you solve problems. The ability to ask data questions and revise them when needed is critical. I would call it common sense but you might already know--it isn’t as common as you think.

Do the work, get the skill--the subject knowledge will grow with you.

A recent conversation on Clubhouse had me thinking about how I work and specifically the best way to navigate a conference. I notice there is a typical approach and those geospatial careers paths seem to be fairly beige. There doesn’t seem to be a platform for less traditional analysts to share their journey. 

Case in point. I was invited to speak on a popular geospatial podcast. Our preliminary interview was scheduled for 20 minutes and lasted at least an hour. I prepared, we recorded but the host decided they had no idea how to shape it into a conversation because it was not the same sort of conversation they typically record!? My time was wasted, my thoughts weren’t shared, and perhaps other women that look like me will have no idea about how anyone can begin including geospatial or geographic context in their analyses.
​

I admit being shocked that there wasn’t a different outcome. Maybe feature the discussion as a one-off, packaged as something a little different. I don’t think it is professional to mute a voice that you find interesting but hesitate to share. Because of this, I take the time to pause and listen to all stories. Clubhouse is a great opportunity for all to join the conversations, ask questions, and share expertise.

I wasn’t trained as a geologist or geographer. Granted I did attend an online executive education applied analytics program from an Ivy but whatever your foundation--you will find the right resources. I discovered spatial analytics while looking for a dynamic visualization to explore food deserts in Washington, DC. I examined the data and noticed a few pockets with hardly any grocery stores. A physical divide by a river created a physical boundary where low-income and resource challenged wards were located. I began to explore other “not so physical” barriers  contributing to this insight. Working as a data analyst, the location aspect was often an afterthought--mistakingly. I participated in every MOOC by ESRI or available on Coursera with special emphasis on GIS Lounge.

Perhaps my consultancy is successful because I understand the language necessary to make the project collaborative. Here are a few nuts and bolts:

• I don’t work hourly--because I don’t sell dollars--I deliver outcomes. This is critical for me.
• You don’t need to beat the bushes for clients if you have an open narrative and are willing to be of service before “the ask”.
• Do your homework--there are brilliant resources provided by ESRI or open source QGIS, and Google Earth Engine.

The important take-away is that there is not a singular way to pursue geospatial skills. 

It is better to fail in originality than to succeed in imitation.
Herman Melville

I am writing a book on Python for Geospatial Data Analysis. Most of the really cool widgets or packages I discover end up in the book but every now and then--something is cool but not really aligned with the rest of the story.

​Open route service is free with a robust API that allows you to use navigation information, geocoding, isochrones, points of interest (POIs) and more. I wanted to immortalize the tool so decided you might find it interesting too. Did I mention its free?

I suggest you navigate to the Jupyter Examples and download the binder on GitHub. Or you can just follow along here. I work exclusively on a Mac but it is pretty easy to adapt to whatever your system your preference might be.

You need to enter the following into terminal:

mkdir ors-apartment
conda create -n ors-apartment python=3.6 shapely
cd ors-apartment
​pip install openrouteservice ortools folium
​

You are basically creating an environment called ors-apartment that acts like a container for the necessary packages and dependencies. I used my later version of python and it still worked but if you run into problems you should be able to go as high as 3.7.

I am going to assume that you will be working in the notebook so I am simply screen casting my computer images. You will be requesting a key after clicking on the API Playground and entering it in the code below where it says ‘your key’.

The task at hand is you have located 3 potential new apartments/homes. Maybe you would like to find certain neighborhood features and you want to see which location has more bang for the buck.

I left the locations the same but here is where you can have some fun with customization. It looks like they list lat/long as long/lat when creating the apt_dict so make sure you make the switch in your code if pulling coordinates from another website.

You can also change the map tiles (Stamen)as they are open source tools from OpenStreetMap.

If you are relying on walking distance head over to the link https://github.com/GIScience/openrouteservice-docs to pick the features and the locations from the index--search for Places Response to find the category_ids.

I selected museums, supermarket, and dog-park.

The code as written in git-hub will throw an error if you don’t add the [0] between clnt.places and features (see below).

Once you run the code you can see one of the locations is completely out of the running. None of the attributes you were seeking are within your radius. The syntax for the code can be reviewed in the Folium documentation.

​The style_dict includes icons to identify the requested items you would like to see in your chosen neighborhood. I simply selected free font-awesome icons that would identify the locations. If you render your map and the tags are blank you either picked or dud or a paid service so keep looking.

The map that renders below shows the neighborhood with the most of what you wanted to see near your new home.

If the search had shown tighter results between the properties you could compare the distances and pick a winner. For the data I selected the zero values were problematic and only yielded distance for apartment 1.

There is one more cool thing below. You can design your own atlas and bring it into the field with you. Click the link to find out how.

What’s in your genes? About 98% homology with chimpanzees for starters.

​The human brain has 100 million times the number of neurons a sea slug's brain has. Where do those differences in quantity come from? At some point in their development, all embryos — whether human, chimp, rat, frog, or slug — must have a single first cell committed toward generating neurons. That cell divides and gives rise to 2 cells; those divide into 4, then 8, then 16. After a dozen rounds of cell division, you've got roughly enough neurons to run a slug. Go another 25 rounds or so and you've got a human brain. Stop a couple of rounds short of that and, at about one-third the size of a human brain, you've got one for a chimp. Vastly different outcomes, but relatively few genes regulate the number of rounds of cell division in the nervous system before calling a halt.-- Robert Sapolsky Discover Magazine

The first time I heard this statistic I was working on a children’s book, “There is a Frog in My Genes”.This was around the time that Dolly the sheep had been cloned and it seemed appropriate to level-set the conversation. The file with rhymes and draft illustrations is buried in a hard-drive somewhere but I find the 98 to 99% chimp analogy to be most compelling.

First of all, and maybe the most important take-a-way, is the power of 2 percent.

Think of what you could do 2% better that could make a big difference in your life? There is an article that outlines what a 1% improvement can do--let’s use that and think that clearly 2% would be even more massive--excluding the law of diminishing returns for a moment.

James Altucher reinforces the idea that you don’t have to be better than the other person. The improvement should come from within. I haven’t worked the math but the claim is if you are 1% better every day you will actually be 3800% better in just one year. Tragically if you become 1% worse you basically disappear. You lose 97% of your value after one year. You can read Altucher’s nudges in Physical, Emotional, Mental, and Spiritual health here.

I am going to list my rag tag thoughts in no particular order. They are making the cut because I don’t see them on many lists but I think they belong.

They matter to me but your mileage may vary.

​1. Begin your day with a small success -- Atomic Habits
I commit to running most mornings. When you meet your commitment it is a win. They begin to build throughout the day and lead places you didn’t even know you were headed.

2. Learn something
Seth Godin said something in his book, The Practice: Shipping Creative Work
You are not the boss, but you are in charge. Unless you only create work for your amusement, you clearly are not the boss. Those you serve have preferences and needs--this might require a modest amount of coloring within the lines to reach your smallest viable audience. But because you are in charge, you can decide what skills you learn, what books you read, and even what conferences you choose to attend.

The links to the books mentioned throw a few pennies my way. I don’t know how it works but I notice tiny payments of $3.50 here and $1.95 there from time to time.

3. Skill up--don’t get mad, get data.
In full disclosure, although I authentically claim autodidacticism in geospatial analysis, there were several university degrees in the hopper before I tackled location intelligence. None of them specific to analysis. I launched with an executive online program in applied analytics from an Ivy League institution. The investment was in credibility because the real skill development, at least in Python, was due to Ted Petrou. If you want to learn Python--I mean really learn Python--explore his company website, Dunder Data. He also has YouTube videos.

I enrolled in every MOOC I could find--bonus points if they were ESRI. One weekend I watched every ESRI conference video from my bike trainer. I took notes, made recordings, and referenced material I wanted to return to for several hours a day.

I run quite a few trail miles each week and use those hours for learning. I listened to every episode of Mapscaping podcast. I wanted to understand the acronyms, ideology, and theory behind the science of geospatial analysis.

The final bit was to code. Learn how to code Python to explore geospatial platforms like ArcGIS, QGIS, Google Earth Engine, and more.

4. Monkey see, monkey do
​Communicating needs to explore the other 2%--the bit we don’t share with our chimp friends. Although visual cues, auditory calls, and even fecal tossing may have their place in primate culture, we need storytellers.

Are you able to describe a spatial algorithm clearly and concisely? Provide context about why we should care? I have been recommending the Opportunity Projects, Product Development Toolkit. The Opportunity Project has the tagline, “Putting America’s data to work for the people".The datasets are all worthy of a deeper dive. They hold stories and opportunity.

What are your thoughts?

Follow along on Substack or your social media of choice.

I was recently interviewed for a podcast. I will share more when it is released as there are a few in the pipeline that I am very excited about. In a great conversation, with a great podcast host, you can easily forget that you have an audience. An audience that is far from homogenous so I made a list of topics that might appeal to folks interested in geospatial analytics.

It sounds easy but only if you think of two simplified but fairly accurate buckets of geospatial professionals. In one bucket you have the geospatial professionals that have an academic background in an university setting.They have studied theory, application, engineering, and perhaps a specific enterprise or open-source tool has emerged as their preferred platform. In the other bucket you can find deep subject expertise in medicine, public health, environment, finance, public policy--the list is vast. Along the way, the discovery has been made that “where” something is occurring on the earth’s surface might hold clues and insights that can become actionable. Geospatial tools start to bubble up to the forefront and the research and education become colocated in the question formulation. No more big data for the sake of volume. We now start exploring with a specificity and focus. I belong to this latter group.

​Google Earth Timelapse Glacial Retreat Alaska

While preparing for my podcast debut I pulled my thesis off of the shelf in my office. Constraints of Landscape Pattern and Fish Mobility on Ecologic Genetics of Lake Trout (Salvelinus namaycush) and Arctic Char ((Salvelinus alpinus) in Arctic Watersheds. I used microsatellites (genetic markers that follow inheritance patterns of genes) to measure the genetic distance in fish populations as a proxy for the mathematical basis for evolutionary theory. I may even have introduced the geomorphic tropic hypothesis model to my readers--gasp. The model is a decision tree that predicts where certain species are located based on characteristics of the landscape.

This bit of forgotten history did reveal a deep interest in cartography. I hired a cartographer to create maps for my thesis and was invited to watch his work. I might honestly say he still is one of the most interesting people I ever met. He introduced me to ebay before it was a big thing. He hunted for old maps with a depth and skill that was admirable. One of his unrelated finds was a coin machine recovered from an old motel that would allegedly shake the mattress if you fed it enough quarters. Years later his girlfriend at the time was a student of mine and we laughed at this shared absurdity. Chris also downloaded the theme of Sanford and Son to my old windows computer so I wouldn’t have to listen to the default chimes.

​For years, when I launched my computer the distinctive song would play loudly and make me smile.

What has evolved is a deep appreciation for geospatial analysis--and Fred Sanford.

​

The fast fish, not the big fish, eats the small fish. Ali Babacan

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 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.

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

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.