Using CartoBuilder As a TARDIS For Exploring Geologic Relationships

Using CartoBuilder As a TARDIS For Exploring Geologic Relationships

If you're a Dr. Who Fan I apologize for the potential clickbait but geoscientists also frequently have to navigate the space-time continuum, sadly without the luxury of a time machine. However after receiving a few emails from CARTO I finally decided (and had the time) to take CartoBuilder for a spin and I think some of the new functionality could be used in interesting ways to help teach geologic concepts and relationships. After watching the introductory video on widgets I immediately wondered how CartoBuilder could be used to explore the recent earthquake swarms in Oklahoma. A number of studies (including this nice summary) have concluded the increase in seismicity is linked to subsurface fluid injections associated with increased fracking activities. However it's pretty difficult (Google's results aren't compelling) to find a nice looking map summarizing this seismic anomaly. The only visual I really liked was created by Dan Nguyen who used a nice Tufte-inspired, small mutiples approach:

Source: http://blog.danwin.com/oklahoma-earthquakes-r-ggplot2/

If you're interested in teaching or exploring your own data with R, Dan also provides a nice Github repository with all the necessary code and R notebooks to explore how he created both this image and his earthquake animation. For research purposes there's no question R has a more extensive suite of user-supported packages and is without question more customizable, but for out-of-the-box simplicity in teaching environments I think CartoBuilder is really exciting.

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Unfortunately I didn't discover Dan's repo until after I finished my map or I would have used some of his data. To create my map I used the following data:

• I used the USGS Earthquake Catalog to download all the earthquakes within Oklahoma since 1980 of magnitude 1.5 or greater, which resulted in this CSV file.
• I downloaded and merged shapefiles of the gas/oil plays from the Energy Information Administration.
• I downloaded shapefile from the Oklahoma Geological Survey depicting faults.
• I used a file of gas/oil wells I already had from a previous project

NOTE: Click here or on the map to view in a new window. The layout works better in a full-size window rather than as an embedded element.

In the static map above you can see the earthquakes as orange dots and since the timeline widget below the map is using a date field within the earthquake table, the vertical bars are also orange. There are three widgets on the right side of the interface: total earthquakes, earthquake magnitude, total wells, and play name. You can filter the map results by highlighting data of interest within the timeline, magnitude and/or play widgets. For example you could isolate only those earthquakes that happened between 2009 and 2015 or only those earthquakes with a magnitude greater than 3.0 or combine those two selections to focus in on the recent seismicity of concern. I included the play name widget to demonstrate the ability to include and symbolize categorical data but also overlay drilling operations with known fault systems. The really cool thing about widgets in CartoBuilder is they are included with your final map so that others can explore the data you want to share. This is pretty similar to the shareable Dashboards in Tableau, but in my opinion easier to create and share. 

Another nice feature is the ability to reveal the underlying SQL and CartoCSS syntax in most of the panels - it doesn't appear possible yet to expose the CARTO.js used to create widgets. From a teaching perspective this is a great way for learners to explore and tinker with the code in a stable sandbox. But it is also useful for greater cartographic control over your map features. For example, since the oil and gas plays aren't an integral part of this story, I wanted them to have dashed lines so they were less obtrusive. To accomplish this I switched into the CartoCSS editor (lower left corner in the first image) and added a line-dash-array property to the layer symbology (second and third images). The only disadvantage is once you opt into the editor environment you need to stay there or reset all your changes (fourth image) - so you're no longer using the WYSIWYG interface. I don't think this is a real issue other than potentially slowing down new users unfamiliar with CartoCSS. But if you're a frequent user of halos or patterns, you'll quickly be switching to this Sublime-esque environment.

It's pretty easy to imagine the variety of inquiry-driven discussions we could jump start using the CartoBuilder TARDIS. Ultimately the bottleneck is finding relevant and sanitized geologic data. However it's worth the extra effort because in addition to exploring geology over space and time, geology students would also develop greater fluency with geospatial concepts and be required to navigate common syntax used to query and visualize spatial data. TheSQL statements used to filter or join data in the CARTO environment are transferable to desktop PostGIS environments. Most importantly for introductory courses, all of this is performed relatively easily compared to a full-blown GIS or by using Python or R. The Torque animation below (pretty similar to Dan's version) was created using the same earthquake data and CartoBuilder in under 5 minutes.

I'm developing another interface to help explore geologic relationships using the new analysis tools available in CartoBuilder. In my preliminary testing though, the underlying PostGIS engine supporting these cloud-based analyses is blazing fast compared to other... um, "competitors." I have shared my opinion about using PostGIS before to explore geologic relationships and I think the installation and database management required of a typical desktop setup may be a factor in seeing a slow adoption but CARTO offers painless access (both GUI and command line) to this powerful tool.

Header Image Source: http://j-vh.me/2ghv2Dr

The Wikification of GIS Education?

In December 2014 Anita Graser published the second edition of Learning QGIS, which provides a great introduction to the basics of the QGIS platform. Last month I had the pleasure of co-authoring Mastering QGIS with Kurt Menke (GISP), Dr. Richard Smith Jr., (GISP), and Dr. Luigi Pirelli. With all that alphabet soup it's a wonder we had any letters left to write the book... It was an outgrowth of curriculum from the GeoAcademy spearheaded by Dr. Philip Davis at Del Mar College and I think emblematic of our shared interests in furthering access to quality educational materials in the FOSS4G arena.

In 2008 Daniel Sui published one of my favorite academic articles titled "The Wikification of GIS and its Consequences: Or Angelina Jolie’s New Tattoo and the Future of GIS." Depending on who you talk in the GIS community the concept - and inherent value - of Goodchild's notion of volunteered geographic information (VGI) varies. And folks get even more feisty about the term neogeography - although some sectors are bit more measured in their response to this term. And it was a comment by Bill Morris about OpenStreetMap that spurred me on to summarize my reflections during the process of contributing to the book. 

“... in many ways the power of nakedly-open crowdsourcing surpasses Google’s proprietary muscle...”

— Bill Morris

Aside from the disturbing image of OSM contributors digitizing edits in their birthday suits, I think this sentiment reflects a similar shared value among the contributors to this project; putting as many open-source resources in the hands of novices and experts alike. Sui's states:

“The core of this new trend lies in web-based mass collaboration, which relies on free individual agents to come together and cooperate to improve a given operation or solve a problem.”

— SUI (2008)

                       Source: http://j-vh.me/1GsBXB4

Using a conventional definition for GIS, he identified four areas that would likely be impacted by the rise in wikification: hardware, software, data, and people. Given his connection with academia, it's surprising there wasn't any reference to the impact of this 'web-based mass collaboration' on education. 

But his ideas about the crowd-sourcing effect on 'doing' GIS are transferable to teaching and/or learning GIScience. I think the more common wiki-based model is excellent for documentation purposes and short tutorials but often those resources are best-suited for folks who already have a background in the fundamentals not novices (I'm not going to make the unnecessary and condescending differentiation between 'experts' or 'non-geographers' versus 'amateurs'). It is easier if you have some experience with GIS to hunt and peck through YouTube videos or isolated online wiki tutorials, to figure out how to create convex hulls or perform a least-cost path analysis in either an updated version of your go-to platform or when exploring - possibly migrating to - a new GIS platform.

So educational endeavors like the GeoAcademy the CartoDB Map Academy and even more traditional wiki-based 'getting started' resources supporting Fulcrum, Mapbox Studio, and PostGIS are offering more guidance than just what buttons to click. What's fantastic about this book project is that other than Phil Davis, I haven't met any of my collaborators in person. We followed Sui's idea of 'web-based collaboration' and developed, reviewed, and revised  a variety of educational resources to help novices through GISPs engage with FOSS4G options. What's even better is these efforts are spawning folks armed with new resources who are in turn engaging in their own educational activities. This is the cycle we want. I want to yet again re-purpose Sui's claim that "we are witnessing the emergence of a new geography without geographers" and argue we are experiencing a new education model without educators.

That being said, there's something very satisfying working through a book to ensure a more thorough understanding of a particular software or database format. I own the first edition of PostGIS in Action (and will order the 2nd shortly), An Introduction to R for Spatial Analysis & Mapping, Open-Source GIS: A GRASS GIS Approach, and The Geospatial Desktop. I haven't had time to tackle the R book yet but even with the University of Google at my fingertips, find myself reaching for bookmarked pages. In the same way that engaging in GIS often involves a quiver of tools, teaching and learning GIS should include a diversified portfolio.

I wanted to respond to Adena’s question below with more than just a comment to make sure it was included in the original post because she raises an important point of clarification: “Do you simply mean people are teaching themselves via tutorials and books like the one you’ve just published? Or something else?”
 

UPDATE: 4.27.2015

I was definitely imagining more than just people teaching themselves and not just following an xMOOC ‘sage-on-the-stage’ transfer model. I see a lot of instances where folks who perhaps don’t identify as educators take the time to share their expertise and experience - historically though platforms like ESRI’s forums (what would I have done without William Huber in grad school?!), to StackExchange, to even less structured tutelage through Twitter.

And that is where i see Sui’s concept of wikification crossover to education; folks with widely varied backgrounds (including the ‘experts’) are learning from one another. I don’t think the success of Mapbox and CartoDB occurred because geographers, GIS Analysts, GIS Admins, geospatial ninjas, (or whatever label you want to use) embraced those platforms and pushed the creativity envelope. More often than not when I’m looking for insight into why my CartoCSS isn’t cooperating or my PostGIS SQL query is bonking in CartoDB, I find help - and learn - from journalists, activists, etc.

Bill describes many of the criticisms we commonly hear about crowdsourcing, especially about QA but as I type this response, hundreds? of non-expert volunteers are building a map to inform disasters response to the Nepal earthquake. We often hear a similar criticism of MOOCs (cMOOC or xMOOC alike) about quality control and the risks of letting learners 'cherry pick' their educational pathway. Fortunately higher education can only control the crediting process, not the learning process and students of GIS have an increasingly diverse options for how they 'learn' versus how they are credentialed. Job announcements like this will continue to be more frequent even though the number of traditional GIS programs 'teaching' CartoDB, Fusion Table & RStudio is probably fairly limited (no, I didn't do a comprehensive study, just my experience interacting with folks at regional and national GIS conferences - many programs are dominated by ESRI). So this is what I meant by a new education model without the educators, because being an expert in GIS does not equate to an expert in PostGIS or CartoCSS. 

An Atlas of Remote Tweets and The Popularity of Null Island

                    Source: http://j-vh.me/1HMmSGi

When I see the word 'atlas' it often conjures up memories of a tattered monstrosity my father saved from the local transfer station. Although its glossy dust cover had long been lost, the embossed majuscule on the front book board still held stubborn scales of faux-gold gilding. It covered the entire coffee table when opened and was a frequent co-pilot in helping make better sense of books like Treasure Island, Around the World in 80 Days and Tom Sawyer. That atlas represented - and in many ways, still does - an opportunity to explore unfamiliar territory, to refine spatial relationships and more often than not, provide an outlet for escape and imagination to run wild. 

This was no less true when I purchased Judith Schalansky's 'Atlas of Remote Islands' published in 2010. I enjoyed both the simplicity in her cartographic representation and the accompanying text for each island, which provided an historical and social context. The emphasis on remote islands ensured that readers would be lured to far away lands while learning about how those places are still related to modern geopolitical realities. Shalansky clearly recognizes her bias in selecting locations by asking "Whether an island such as Easter Island can be considered remote is simply a matter of perspective." 

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“Whether an island such as Easter Island (pg.100) can be considered remote is simply a matter of perspective.”

One Thousand Remote Tweets 

I went back to Schalanksy's atlas after Eric Fisher at Mapbox released an interactive map depicting 6 billion tweets. Eric noticed that tweets were missing along the Prime Meridian, which resulted in anomalous banding. This made we wonder if the opposite were true - could we see individual tweets where we wouldn't expect them? So with the atlas as my guide I explored Fisher's map to see just how remote Shalanksy's selections were. 

First I created a table containing coordinates for all 50 islands - and for the geohipster crowd I added in Null Island, just because. I took the link to Eric's map and concatenated his link with each set of coordinates in CartoDB and then visited each island. My initial thought was to query the databases by a geographic bounding box using the q=&geocode=0,0,10km parameter but there isn't a publicly available database to query. Fortunately after I started exploring I realized there were so few I could literally just count them on-screen. I tallied the approximate number of tweets (approximate because yes I probably missed some) and added them to the table. The resulting CartoDB map depicts the location of Sholanksy's islands with an info window that reports out the total number of tweets, provides a link back to Fisher's map and a Wikipedia link for further exploration.

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I tallied a total of 1,005 tweets from the 50 islands that Shalansky included in her atlas but only 16 of the islands reported any Twitter activity. The majority of the tweets were - not surprisingly - from Easter Island (49%) followed by Robinson Crusoe (15%) and Diego and Christmas Islands (5%). Although a third of the islands have internet access, after re-exploring these islands through the lens of Eric's map it is probably unlikely we will see that number dramatically increase. There are enough localities like Southern Thule or Rudolf & Franklin Islands that will remain offline until we start seeing Twitter-supported satellite phones (or maybe that is already a thing).

                     Source: http://j-vh.me/1xVAyPn

Null Island, Where Art Thou?

It was also interesting to see how many people had 'visited' Null Island. While it is likely impossible to decipher whether this was intentional geolocation anarchy, geohipsters at play or geocoders gone wrong, given that Null Island doesn't exist it tallied an impressive 10% of the total Twitter activity. And this is where the realities of the traditional atlas intersect the realities of spatial data collection. 

I wouldn't have found Null Island in my childhood atlas (or using Google Earth today) but it IS a spatial entity - albeit two simple coordinates that could have just as easily been named 'Origin Island.' So while the framing of Fisher's map as the most detailed ever resulted in the folks at Floating Sheep to state it 'rubs us the wrong way' it does provide some interesting details about the intersection between access to and adoption of the Twitter platform and how that data is aggregated and interpreted by various geolocation services (something they also elaborate on).

Null Island is a spatial reality that became a visual reality - and geohipster totem - after accepting that the multiple reports of a position of [0,0] represented 'something' (although initially just a geogoder failsafe).  Visualizations of global flight patterns or wind dynamics also represent these spatial realities we can't 'visit' or see without the visualization process. And without Eric's visualization - most detailed or not - it wouldn't be as easy to decipher something like the distribution of the most remote tweets. And though the medium of the atlas by which we explore the world may be changing, Shalanky's caution about what we consider remote is still warranted.

Although Shalanky's subtitle is "Fifty Islands I have Never Set Foot On And Never Will" I feel fortunate to have visited at least one of them - Deception Island. I was a participant on the Geological Society of America's 125th Anniversary field trip to explore the geology and ecology of South Georgia Island, South Scotia Arc and Antarctic Pennisula through Cheesmans' Ecology Safaris. For a more in-depth geologic exploration of the island you should read Anne Jefferson's and Chris Rowan's recent blog entry on Highly Allochthonous. However I can safely add Null Island to the list of islands that I will never set foot on...

Van Hoesen and Edwards on Deception Island

Photo credit: Heather McFarland

Defining Projection versus Re-projection: A Lesson In Translation

I frequently encounter students and new users of ArcMap that are incredibly frustrated because their data isn't cooperating. More often than not their frustration is related to projection compatibility, which is often exacerbated by the lack of metadata to help decipher the projection of each data layer. But there is often confusion over when to use 'Define Projection' versus 'Project' when trying to line up their data.

I like to use the analogy of visiting a foreign country to differentiate between these two tools. Imagining you travel to Spain and never learned any Spanish - it would be difficult if not impossible to communicate using spoken language. If I then just tell you that you can speak Spanish (loudly and convincingly) it won't make the situation any better, you still won't actually know any Spanish. This is analogous to 'telling' data that it has a projection that it doesn't actually have. For example, using the Define Projection tool to tell a shapefile with data stored in the geographic coordinate system WGS84 that is suddenly in UTM NAD27 will not be productive in aligning that shapefile with data that is 'actually' in UTM NAD27. 

However if you travel to Spain with a translator, you will be able communicate with anyone who only speaks Spanish. Similarly, if you translate your data from one projection into another (i.e. - from WGS84 to UTM) using the Project tool, your data will align with data stored in UTM NAD27.

ArcMap will usually warn if you add data with an undefined projection (i.e. - missing a world file, *.prj file, etc) and that is when you should use the Define Projection tool - assuming you have access to metadata that informs you what projection you should use.

CartoBokeh: Usefulness As A Fog Of War Technique In Cartography?

I was never much of gamer. The last time I lost hours of my life in front of a console - thumbs furiously flying across a controller, eschewing food and sleep - it was 1992... it was the first Nintendo and Princess Zelda needed me. The princess didn't hold my attention for long but the frustration I felt towards the developers deliberate choice to reveal each stage little-by-little was my first experience with the 'fog of war' concept in gaming. 

'Fog of war' in Legend of Zelda: http://j-vh.me/1nc7nMP

I've seen this FOW approach (often as masking or vignetting) used cartographically to focus attention on the most important map elements while still providing some regional context. The FOW is described in the gaming strategy literature but I couldn't find any 'scholarly' articles about this strategy in the cartography world (at least explicitly using this language). The word war is often applicable in gaming interfaces but in my opinion, doesn't translate as well to cartography (I don't want to be at war with map users or cartographers)...

I often find myself trying to merge varied interests and it occurred to me that I've employed this same technique in my photographic pursuits. Isolating foreground images from background images using a shallow depth-of-field is common and in addition to isolating subjects, many photographers strive to achieve a nice bokeh effect. Bokeh is the deliberate blurring of background objects that are typically lighter than foreground objects.

Juvenile King Penguin isolated from 100s of adult and juvenile penguins in the background

I thought I would try and create a similar cartographic effect. The process involved importing NASA's Blue Marble Imagery into Photoshop, duplicating the image, applying a lens blur to the top image and then burning a 'hole' into the top image over the area of interest. I created three silly maps with different applications of this CartoBokeh technique:

Probably my favorite output from this process

I had a Walmart KML and figured I'd see what this looked like with points rather than background raster images

Overlay of Walmart points on Blue Marble imagery after CartoBokeh

So I'm wondering if this technique seems useful - or in the words of Kai Wong "bokehlicious" or is it too much effort for too little cartographic return? Personally I like the effect it has with the Chicago image but like all map decisions there needs to be a reason/value for using this - needs to add to the map rather than seem like an afterthought (which is how the Walmart on Blue Marble feels). So just exploring a new approach and new language to the fog of war.