Category Archives: Interviews

Eric Fischer: “There may yet be an objective measure of the goodness of places, but I haven’t found it yet“

Eric Fischer
Eric Fischer
Eric Fischer works on data visualization and analysis tools at Mapbox. He was previously an artist in residence at the Exploratorium and before that was on the Android team at Google. He is best known for "big data" projects using geotagged photos and tweets, but has also spent a lot of time in libraries over the years searching through old plans and reports trying to understand how the world got to be the way it is.

Q: You’re coming up on four years at Mapbox, is that right? What do you do there?

A: I still feel like I must be pretty new there, but it actually has been a long time, and the company has grown tremendously since I started. My most important work at Mapbox has been Tippecanoe, an open-source tool whose goal is to be able to ingest just about any kind of geographic data, from continents to parcels to individual GPS readings, numbering into the hundreds of millions of features, and to create appropriate vector tiles from them for visualization and analysis at any scale. (The name is a joke on “Tippecanoe and Tyler Too,” the 1840 US Presidential campaign song, because it makes tiles, so it’s a Tyler.)

Q: I read that you’re working on improving the accuracy of the OpenStreetMap base map. Can you describe that process? I’m guessing one would need to figure out how accurate it is in the first place?

A: I should probably update my bio, because that was originally a reference to a project from long ago: to figure out whether it would be possible to automatically apply all the changes that the US Census had made to their TIGER/Line base map of the United States since it was imported into OpenStreetMap in 2006, without overriding or creating conflicts with any of the millions of edits that had already been made directly to OpenStreetMap. Automated updates proved to be too ambitious, and the project was scaled back to identifying areas where TIGER and OpenStreetMap differed substantially so they could be reconciled manually.

But the work continues. These days, TIGER is valuable to OpenStreetMap mostly as a source of street names and political boundaries, while missing and misaligned streets are now identified mostly through anonymized GPS data. Tile-count is an open source tool that I wrote a few months ago for accumulating, normalizing, and visualizing the density of these GPS tracks so they can be used to find streets and trails that are missing from OpenStreetMap.

Q: In the professional mapping world, I’ve noticed there’s a nervousness around datasets that aren’t time-tested, clearly documented, and from an authoritative source such as the US Census. These official datasets are great resources of course, but there’s a growing amount of data at our fingertips that’s not always so clean or complete. You’ve been successful at getting others to see that there’s a lot to learn about cities and people with dynamic (and sometimes messy) data that comes from many different sources. Do you have any advice on warming people up to thinking creatively and constructively with unconventional datasets?

A: I think the key thing to be aware of is that all data has errors, just varying in type and degree. I don’t think you can spend very much time working with Census data from before 2010 without discovering that a lot of features on the TIGER base map were missing or don’t really exist or are tagged with the wrong name or mapped at the wrong location. TIGER is much better now, but a lot of cases still stand out where Census counts are assigned to the wrong block, either by mistake or for privacy reasons. The big difference isn’t that the Census is necessarily correct, but that it tries to be comprehensive and systematic. With other data sets whose compilers don’t or can’t make that effort, the accuracy might be better or it might be worse, but you have to figure out for yourself where the gaps and biases are and how much noise there is mixed in with the signal. If you learn something interesting from it, it’s worth putting in that extra effort.

Q: Speaking of unconventional data: you maintain a GitHub repository with traffic count data scraped from old planning documents. For those who may not be familiar, traffic counts are usually collected for specific studies or benchmarks, put into a model or summarized in a report… and then rarely revisited. But you’ve brought them back from the grave for many cities and put them in handy easy-to-use-and-access formats, such as these ones from San Francisco. Are you using them for a particular project? How do you anticipate/hope that others will use them?

A: The traffic count repository began as a way of working through my own anxieties about what unconventional datasets really represent. I could refer to clusters of geotagged photos as “interesting” and clusters of geotagged tweets as “popular” without being challenged, but the lack of rigor made it hard to draw any solid conclusions about these places.

And I wanted solid conclusions because I wasn’t making these maps in a vacuum for their own sake. I wanted to know what places were interesting and popular so that I could ask the follow-up questions: What do these places have in common? What are the necessary and sufficient characteristics of their surroundings? What existing regulations prevent, and what different regulations would encourage, making more places like them? What else would be sacrificed if we made these changes? Or is the concentration of all sparks of life into a handful of neighborhoods in a handful of metro areas the inevitable consequence of a 150-year-long cycle of adoption of transportation technology?

So it was a relief to discover Toronto’s traffic count data and that the tweet counts near intersections correlated reasonably well with the pedestrian counts. Instead of handwaving about “popularity” I could relate the tweet counts to a directly observable phenomenon.

And in fact the pedestrian counts seemed to be closer than tweet counts to what I was really looking for in the first place: an indicator of where people prefer to spend time and where they prefer to avoid. Tweets are reflective of this, but also capture lots of places where people are enduring long waits (airport terminals being the most blatant case) rather than choosing to be present. Not every pedestrian street crossing is by choice either, but even when people don’t control the origin and destination of their trips, they do generally have flexibility to choose the most pleasant route in between.

That was enough to get me fixated on the idea that high pedestrian volume was the key to everything and that I should find as many public sources of pedestrian counts as possible so I could understand what the numbers look like and where they come from. Ironically, a lot of these reports that I downloaded were collecting pedestrian counts so they could calculate Pedestrian Level of Service, which assumes that high crossing volumes are bad, because if volumes are very high, people are crowded. But the numbers are still valid even if the conclusions being drawn from them are the opposite.

What I got out of it was, first of all, basic numeracy about the typical magnitudes of pedestrian volumes in different contexts and over the course of each day. Second, I was able to make a model to predict pedestrian volumes from surrounding residential and employment density, convincing myself that proximity to retail and restaurants is almost solely responsible for the number, and that streetscape design and traffic engineering are secondary concerns. Third, I disproved my original premise, because the data showed me that there are places with very similar pedestrian volumes that I feel very differently about.

If “revealed preference” measured by people crossing the street doesn’t actually reveal my own preferences, what does? The ratio of pedestrians to vehicles is still a kind of revealed preference, of mode choice, but the best fit between that and my “stated preference” opinions, while better than pedestrian volume alone, requires an exponent of 1.5 on the vehicle count, which puts it back into the realm of modeling, not measuring. There may yet be an objective measure of the goodness of places, but I haven’t found it yet.

Why did I put the data on GitHub? Because of a general hope that if data is useful to me, it might also be useful to someone else. The National Bicycle and Pedestrian Documentation Project is supposedly collecting this same sort of data for general benefit, but as far as I can tell has not made any of it available. Portland State University has another pedestrian data collection project with no public data. Someday someone may come up with the perfect data portal and maybe even release some data into it, but in the meantime, pushing out CSVs gets the data that actually exists but has previously been scattered across hundreds of unrelated reports into a form that is accessible and usable.

Q: What tools do you use the most these days to work with spatial data (including any tools you’ve created — by the way, thanks for sharing your geotools on Github)?

A: My current processes are usually very Mapbox-centric: Turf.js or ad hoc scripts for data analysis, Tippecanoe for simplification and tiling, MBView for previewing, and Mapbox Studio for styling. Sometimes I still generate PostScript files instead of web maps. The tool from outside the Mapbox world that I use most frequently is ogr2ogr for reprojection and file format conversion. It is still a constant struggle to try to make myself use GeoJSON for everything instead of inventing new file formats all the time, and to use Node and standard packages instead of writing one-of-a-kind tools in Perl or C++.

Q: You’re prolific on Twitter. What do you like about it, and what do you wish was better?

A: I was an early enough adopter of Twitter to get a three-letter username, but it wasn’t until the start of 2011 that I started really using it. Now it is my main source of news and conversation about maps, data, housing policy, transportation planning, history, and the latest catastrophes of national politics, and a place to share discoveries and things to read. I’ve also used long reply-to-myself Twitter threads as a way of taking notes in public as I’ve read through the scientific literature on colorblindness and then a century of San Francisco Chronicle articles revealing the shifting power structures of city planning.

That said, the Twitter timeline interface has become increasingly unusable as they have pulled tweets out of sequence into “in case you missed it” sections and polluted the remainder of the feed with a barrage of tweets that other people marked as favorites. I recently gave up entirely on the timeline and started reading Twitter only through a list, the interface for which still keeps the old promise that it will show you exactly what you subscribed to, in order.

Q: If you could go back in time, what data would you collect, from when, and where?

A: I would love to have pedestrian (and animal) intersection crossing volume data from the days before cars took over. Was the median pedestrian trip length substantially longer then, or can the changes in pedestrian volumes since motorization all be attributed to changes in population and employment density?

Speaking of which, I wish comprehensive block-level or even tract-level population and employment data went back more than a few decades, and had been collected more frequently. So much of the story of 20th century suburbanization, urban and small-town decline, and reconsolidation can only be told through infrequent, coarse snapshots.

And I wish I had been carrying a GPS receiver around with me (or that it had even been possible to do so) for longer, so that I could understand my own historic travel patterns better. I faintly remember walking to school as a kid and wondering, if I don’t remember this walk, did it really happen? Now my perspective is, if there is no GPS track, did it really happen?

Q: Are you a geohipster? Why or why not?

A: I think the most hipster thing I’ve got going on is a conviction that I’m going to find a hidden gem in a pile of forgotten old songs, except that I’m doing my searching in promo copies of 70-year-old sheet music instead of in the used record stores.

Maps and Mappers of the 2017 GeoHipster Calendar: Nathaniel Jeffrey

Nathaniel Jeffrey – August

Q: Tell us about yourself.

Before I fell into GIS, my studies in Environmental Science led me to a freshwater conservation project in Kenya, and down sewer pipes in my home city of Melbourne. Honestly, sewers are kind of fascinating if you have a background in biology.  You think tapeworms can only survive in a digestive tract?  Think again!

Professionally, for the last 10 years I’ve worked as a GIS analyst for Urbis, which is an international urban planning consultancy.  It’s an ever-changing, data-driven job, which makes it a fun geo playground.

Apart from that: I cook, I eat, I game, I poke Raspberry Pis while frowning, and I travel (mostly to Japan, it seems).

Q: Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).

I’ve lived in Melbourne for 25 years after coming over from the USA with my parents as a kid.  And as any Melburnian will tell you if you give them half a chance, it’s the World’s Most Liveable City.

So one of the big factors influencing “liveability” is the ability of a city’s infrastructure to adequately service its growing population. Melbourne has been growing at a rate over 2% per year for more than a decade, adding 80,000+ new people every year. Melbourne’s population has grown from just over 3 million in 1991 to 4.5 million today, and is projected to hit 6 million by 2031.  I can’t do much to solve the many political headaches that spring up due to such rapid growth, but I sure can make a map.

Q: Tell us about the tools, data, etc., you used to make the map.

The population data I used is a mix of counts from past censuses (1991 to 2011), and future projections (2016 to 2036).  I would have loved to go further back in time, but the small-area population data isn’t easy to come by.

I converted the population counts for each year into a raster surface representing population density, and then smoothed the heck out each one.  This was a bit tricky, because I wanted to generalise the data enough to create an easily-readable map, but I didn’t want to misrepresent the truth in the underlying data.

Through trial and error, I then found a density value that more or less matched up with the edge of the suburban fringe for each year, based on aerial and planning maps. Applying that density cutoff to each year gave me the isopleths you can see on the map — lines of constant density.

Obviously this approach makes the assumption that the chosen density threshold has accurately represented to suburban boundary in the past, and will continue to do so in the future.  This might not be the case, with a shift towards higher density developments at the urban fringe.  But I think the approach is fine for a map that’s just trying to give a high-level view of the amoeba-like spread of Melbourne’s population.  I would hope that no one tries to make any policy decisions based on this map!

Cartographically I went with a dead simple basemap — just roads and locality names for context.  I made a deliberate effort to label locations where interesting things were happening in the data — lots of growth in a given year, for example.  The colour scheme I chose for the isopleths is…striking.  What can I say; it’s tricky to find ten colours that are distinct enough when placed next to one another, but still look reasonably harmonious as a whole.  I had a bit of fun with the look of the title and legend — I’m no graphic designer, but I like to dabble in design, and steal things that look cool.

Jim McAndrew: “There’s always going to be some next big thing, but the basics remain the same”

Jim McAndrew is a Geospatial Database Developer. Before adding ‘geospatial’ to his job title, he worked on large Oracle databases for pharmaceutical and manufacturing companies. For the last few years, he has been working with the US Geological Survey and the National Park Service to create tools that provide public access to government data.

He sometimes tweets @jimmyrocks.

Q: How did you get into GIS?

A: I have loved maps for as long as I can remember. I used to study the maps in the phonebook, and I knew where every local road went. In college, I decorated my apartment with maps I had purchased from the Department of Transportation.

After a few years working as a software developer in manufacturing, I saw something called a “Mapping Party” for this open source mapping project claiming to be a “Wikipedia of Maps”. I was in luck, they would be holding a party in New York City the next weekend. I bought a bus ticket to New York, paid the extra fee to bring my bike, and I was introduced to OpenStreetMap.

I was hooked, and I thought that maybe getting into mapping could actually be a viable career option. I started attending different conferences and meetups that sounded interesting, and tried to learn all I could about the industry. I started a graduate certificate program in GIS, and eventually got a GIS job.

Q: Where do you work and what do you do there?

A: I am a researcher at Colorado State University working for the National Park Service (NPS) as a Software Developer. I started working on a new system to collect data from all the NPS units using an OpenStreetMap-style approach. I work on tools that allow data from this, and other internal systems, to be displayed on web maps. Now I am the Lead Developer on some of the NPS tools, including the internal side of the NPS mobile app project.

Q: Tell us about a cool project you are working on

A: The NPS mobile application project is the coolest thing that I’m working on, because it’s easy for everyone to access and use. It also involves working with Park Rangers that are extremely knowledgeable about their parks and are excited about sharing that knowledge. The coolest part of it for me is the opportunity to visit the parks and to do a little bit of field work.

Q: What technology (GIS and otherwise) do you use?

A: I try to do all my work using vi and tmux within an Ubuntu Linux virtual machine. For GIS work, I prefer to do most of the processing in PostGIS with a lot of help from GDAL and OGR. I have been working on some fun projects with Python and GeoPandas recently. For work, I do most development in Node.js and browser-oriented JavaScript.

Q: Open source — Y/N? Why?

A: I prefer to use open source software whenever possible. The best part about open source software is that if you can’t figure something out from the documentation, you can always go look right at the source. If there is a bug in the source, you can find it yourself and suggest a patch. It is also easy to package software in a VM or a Docker image and share it with others as a working system without worrying about licensing.

Q: Is open source for everyone, or just for tinkerers?

A: Open source is for everyone! Open source tools tend to be a little less user-friendly and sometimes lacking in support. This has created a market for companies such as Red Hat and Boundless Spatial to provide support and integration for businesses. While the “Linux on the Desktop” dream may never really come true, the future will include more open source tools packaged in commercial software.

Q: Biking, hiking, any other hipster attributes?

A: I enjoy biking, hiking, and kayaking whenever I get the chance. I enjoy craft beer, I sometimes homebrew beer, and I enjoy working with yeast to make breads, pretzels, and pizzas. I was on a locally-roasted-coffee kick for a while (OQ Coffee in Highland Park is very good), but I have recently switched to drinking mostly tea and tisanes. I enjoy listening to a lot of obscure music. I also love emojis. 🎉

Q: Are you a geohipster? Why / why not?

A: No true hipster would self-identify as a hipster, at least according to the Wikipedia article on the subject. I do enjoy following the latest JavaScript and geospatial trends outside of the mainstream. Maybe not enough that I will go back and refactor code just to use the latest JavaScript functions, although I do really like await/async. I also enjoy hand-crafted maps that capture more than just raw data, but instead show how the cartographer views the world. I make sure to get a GeoHipster wall calendar every year.

Q: Words of wisdom for our global readership?

A: A few years ago I went skiing in Aspen, Colorado. If there’s still snow on the mountain, they open on Memorial Day, and charge a severely discounted price. I brought my skis that were a hand-me-down from the 1980s. People started commenting on how cool and “retro” my skis were. They were so out of date that they were cool again.

There’s always going to be some next big thing, but the basics remain the same. Don’t focus on doing what’s cool now, but instead focus on what you want to work on or learn, even if it’s something completely different than what you’re doing now; eventually, it’ll be cool again.

Amy Sorensen: “Keep pushing the arbitrary boundaries between geospatial and IT”

Grew up on a farm in Iowa. Started my GIS career as an intern for Emmet County, working on first iteration of E-911 for the sheriff's department. Moved to South Dakota from there and worked for the SDDOT for a while with the esteemed title of “Automated Mapping Specialist”. Really enjoyed the work but was looking for a faster pace and more of a challenge. Ended up taking a project-related position with a consulting firm working for DM&E railroad out of Sioux Falls. Had great fun learning all about rail, sidings and frogs. When that project ended, I decided to take a position with HDR and moved down to Omaha, Nebraska, where I am currently.

LinkedIn: https://www.linkedin.com/in/arsorensen/

 

Q: How did you get into GIS?

A: I had been doing in-home child care, I have an Associates degree in Early Childhood Education. I was bored and broke and wondering what I should do. Driving down the road I heard a radio ad for the local community college that talked about computers and mapping. I thought… “I love maps!” and went and signed up for the program after that.

Q: You work for HDR, an engineering company. Tell us what you do there.

A: What I do day by day really varies based on the projects I’m on. The funnest part of my job is the variability of what I am involved with on a week by week basis and meeting new people and learning about what they do. I work on hydrology projects where we are looking at flood zones, levees, or stream flows for one project, and then I am managing the GIS database for a large transportation project and dealing with right of way, utilities, and shifting contracts. I also like to code, so I will put together web maps or write some scripts to automate work flows. It’s really fun to listen and evaluate what is currently being done and then to apply some type of technology to help streamline and document the work as well. Recently I’ve gotten involved with the sustainability group here at HDR, and now there is great potential to mix my love for GIS with my desire to make the world a better place.

Q: Do engineers “get” GIS?

A: Yes! I would say there are varying levels of “get” involved. I find that if you are on a project, and learn as much as you can about the overall big picture, then it is easy to plug GIS into it in ways that make sense and help meet those project goals. If someone isn’t getting the point of using GIS then it could be that you aren’t getting what the big picture is for them.

Q: What technology — GIS and other — do you use at work? What do you / don’t you like about it?

A: Of course the big technology provider I work with is Esri, I work with the full suite of Esri products. I really love working with Python and use Notepad++ for the majority of that type of work. It’s simple and straightforward. When I’m working with JavaScript I have been using Atom, which has been a good editor. I’ve gotten to use Jupyter Notebook on projects as well now, and really have found the power in being able to quickly write code, see the results, then tweak. Being able to revisit later and use the notebooks to document what has been done is priceless. I’m digging into some new (to me) JavaScript frameworks, and am really interested in playing with Ember. I’ve heard good things, and Esri uses it a lot and is starting to push out add-ons using it.

Q: You were a volunteer in a GISCorps project for North Korea. Tell us about the project, why you did it, and what you got out of it.

A: This project was for the World Food Program (WFP) and the information Management and Mining Action Program (iMMAP). We digitized features like roads, cities, rivers, and rail from historical maps. The idea was to create this data to support their humanitarian efforts. I got involved since I had been listed as a GISCorps member for some time and was waiting for a volunteer opportunity to come up that I could do at home. This project was great and I was able to put a couple hours in on a weekly basis. I am always trying to save the world and it really gives me a great sense of satisfaction to be able to do something with the skillset I have to help the world be a better place.

Q: You do lots of volunteer work, not just GIS. Tell us about your other volunteer activities.

A:  I do like to do volunteer work, it’s my desire to make a difference that drives it. Though I would say I’m not doing a ton right now, there are a few things I’m involved in. I do manage a website for a local grassroots organization. For the last couple years I’ve been able to create some mapping for a local group that puts together garden tours in Omaha and hosted for them as well. Over the years I’ve done things like volunteer for Boys and Girls Homes, and also was a baby rocker at a NICU for some time. I think volunteering really does add a lot to your life and gets you out in the community, which is fun.

Q: What is the tech scene like in Omaha?

A:  I think the tech scene is good. We have some good coding school options in Omaha, which have fast turnaround to get people into the workforce. There are coding groups that happen for kids and teens like Girls Who Code, and we have a really innovative tech library that offers classes and opportunities to work with and learn all kinds of software and cool things like 3D printers and maker events. There are also some good groups I’ve found through Meetup — Women In Technology of the Heartland is one, and it has great social events and is a good support group for those working their way up or into technology fields. There are other groups I’ve not joined yet based on Python and JavaScript that I plan on checking out soon as well.

Q: What is the hipster scene like in Omaha?

A:  Isn’t that the same as the tech scene? 😉 Ok, maybe not but there is some crossover. The hipster scene is good. There are some known areas in town where you will find great local music, food, and events. One of my favorite is the Benson First Friday Femme Fest — it’s an amazing opportunity to see all kinds of females taking the lead role and sharing their music, poetry, and art to the masses.

Q: Knitting and Dr. Who — is that hipster or what?

A: Probably. I still need to knit my Dr. Who scarf, I think once that is completed then I can really grab the hipster trophy. My list of nerd interests is strong and I think if I could pull together a group of geohipsters to crash the UC dressed in Dr. Who cosplay, then my life would be complete.

Q: Do you consider yourself a geohipster? Why / why not?

A: Sure. You know, unless in considering myself one I negate the title. 🙂 I think a geohipster is anyone who is constantly striving to do new and cool things with geospatial data. With that definition, I’m 100%.

Q: On closing, any parting words of wisdom for our global readership?

A: Keep pushing the arbitrary boundaries between geospatial and IT. Data is data and we all can and should be playing and working together. Also — volunteer for something. You do need to get away from the computer once in a while, and it will change your life to do so. And finally, if you love Dr. Who we should talk. 🙂

Nate Smith: “Visit a new place in the world; reach out to the OSM communities there”

Nate Smith is technical project manager for the Humanitarian OpenStreetMap Team. He leads out the OpenAerialMap project and dives into all things technical across HOT’s operations. Originally from Nebraska, he is now based in Lisbon, Portugal, slowly learning Portuguese and attempting to learn to surf. 

Q: We met at State of the Map Asia in Manila! What was it that brought you to the conference?

A: I came to State of the Map Asia through my role in two projects with the Humanitarian OpenStreetMap Team: OpenAerialMap and a new project called Healthsites. I had the chance to give short presentations about the projects, plus I wanted to connect with the OpenStreetMap community in Asia about the projects to get feedback and input on the direction of the projects.

Q: Tell us about the Humanitarian OpenStreetMap Team (HOT) and how you got involved.

A: I’ve been involved in HOT in one way or another since 2011. At the time I had just joined Development Seed in Washington DC. I began to get involved in any way I could with HOT, most of it started with trainings about Mapbox tools or collaborating on projects. Most of it initially revolved around helping identify data that could be helpful in an activation or joining in tracing. Over the years, I gradually got more involved in working groups which is the best place to get involved beyond contributing time to mapping. I’ve since joined HOT as a technical project manager to help build and manage projects around some of our core tools like OpenAerialMap or OSM Analytics.

Q: For those who may not be familiar with HOT, “activation” is kind of like bringing people together to participate in disaster mapping or a similarly geographically-focused humanitarian mapping effort, did I get that right?

A: Right, a HOT activation in the traditional sense is exactly that. It is an official declaration that the community is coming together to aggressively map an area for a disaster response. The Activation Working Group is one of several working groups where anyone can get involved, and they define the protocols, monitor situations, and are in contact with many OSM communities and humanitarian partners around the world.

Disaster mapping is a core part of the work HOT does. Not everything but still a big part. If you’re interested in helping think about activation protocols or want to help organize during an activation, come join and volunteer your time to support the work.

Q: What are some interesting projects you’re working on?

A: I’ve been actively working on two interesting projects: OpenAerialMap, and for lack of a better name at the moment, the Field Campaigner app. OpenAerialMap launched two years ago and we’ve been slowly rolling out new features and working with partners on integrating new data since. What’s interesting is the work we’re doing this summer — we’re rolling out user accounts, provider pages, and better data management tools. This is exciting as it lowers the barrier to start collecting imagery and contributing to the commons.

The second project is our new Field Campaigner app. It has a generic name at the moment but it’s part of a move for us to have better tools to manage data collection in the field. A majority of the work the global HOT community does is remote mapping. While this is super critical work and extremely helpful for people on the ground, there is a gap in how work is organized on the ground. This work looks to help improve the way data collection is organized and coordinated on the ground — we want to see field mapping in OpenStreetMap to be distributed and organized well. This work also crosses over some similar work that is happening across the board in this area — Mapbox is working on analyzing changesets for vandalism and a team from Development Seed and Digital Democracy through a World Bank project are working on an improved mobile OSM data collection app.

Q: How easy/hard is it to build these tools? Once they’re out in the world, what are some ways that people find and learn how to use them?

A: It’s not easy building tools to meet a lot of needs. A core thing for success many times is dogfooding your own work. We’re building tools that serve a wider audience but at the core we’re testing and helping spread the word about the tool because we use it.

But just because it’s not easy doesn’t mean people shouldn’t be trying. The more we experiment building tools to do better and faster mapping, whether it is remote or in the field, the more information we will have to improve and address the challenges many communities face.

Q: It looks like your job is fairly technical, but also involves outreach. Is there a particular aspect of your work that you enjoy the most?

A: I think the mix of technical and outreach is what I love most. Spending part of my day diving into some code while the other part talking or strategizing with organizations is what I’ve had the chance to do over the last six years through working with Development Seed and now HOT. I enjoy trying to be that translation person — connecting tools or ways of using data to solve real-world problems. I think one of the things I enjoy the most is the chance to help build products or use data with real world impact. Being able to support MSF staff responding to an Ebola outbreak at the same time working with world-class designers and developers is pretty great.

Q: Looking at your Twitter feed, you seem to travel a lot. What’s your favorite / least favorite thing about traveling? Favorite place you’ve been? Any pro travel tips?

A: I traveled a bit while living in DC but now that I’m living in Lisbon, Portugal I’ve had the chance to do some more personal travel throughout Europe which has been great. This past year I’ve had a chance to travel through Asia a bit more through HOT-related projects. My favorite part of traveling is the chance to meet people and experience new cultures or places. There are some incredible geo and OSM communities around the world and it’s been awesome to meet and work with many of them. Least favorite — awkwardly long layovers – you can’t get out.

I think my favorite spots have been Bangkok and Jakarta. I find that I enjoy big cities that have great food options. As for tips, I would say pack light and do laundry when you’re traveling, and always make time for good local food.

Q: Would you consider yourself a geohipster? If so, why, and if not, why not?

A: Heh, that is a great question. I think I’ve become less geohipster moving to Portugal. I drink light European beer, I don’t bike because there are too many hills, and drink too much Nespresso. Although I’m still a Mapbox-junky, work at a cowork in my neighborhood, and love open source, so maybe I still lean geohipster. 🙂

Q: On closing, any words of wisdom for our global readership?

A: Get out and visit a new place in the world if you can. And while you’re at it, reach out to the OSM communities there and meet them in person. You’ll meet some incredible and passionate people.

Maps and Mappers of the 2017 GeoHipster Calendar: Ralph Straumann

Ralph Straumann – June

Tell us about yourself.

I’m a consultant, data analyst, and researcher with EBP in Switzerland and the Oxford Internet Institute in the UK. My background is in geography, with a PhD in geoinformation science from the University of Zurich. Besides GIS I studied remote sensing, cartography, and political science. In my work for EBP I assist clients with data analysis tasks, do strategy and organizational consulting, and build tools, geodata infrastructures, and workflows. In my free time I occasionally blog about GIS, data, and visualization.

Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).

A few years ago, Stephan Heuel and I developed a raster-based walking time analysis tool. This product has since matured into Walkalytics. Based on OpenStreetMap and/or cadastral surveying data, we can infer the time it takes somebody to walk to or from a place, construct pedestrian isochrones, and compute quality of service metrics, e.g. for public transit. We can carry out these types of analyses world-wide, at high resolution, very fast, and taking into account the topography. The map serves as an illustration of the capabilities and versatility of Walkalytics.

Tell us about the tools, data, etc., you used to make the map.

I used our Python Walkalytics client and a test subscription to the Walkalytics API to derive the data for the map. Besides, I used data from the (in my opinion, fantastic) Natural Earth. I then designed the map entirely in ArcMap. I tried to move away from the ArcGIS defaults. I’m simplifying, but I think a map tends to be good when you cannot tell straight-away which software has been used to make it. For the interested: Below you can see a GIF of some of the revisions of my map for the GeoHipster calendar:

Maps and Mappers of the 2017 GeoHipster Calendar: Jan-Willem van Aalst

J.W. van Aalst, Ph.D. – November

Tell us about yourself.

I’m a cartographic designer and data analyst. I live and work in The Netherlands. My background is in computer science; I did my Ph.D. on knowledge management at Delft University. These days I mostly advise the Dutch Emergency services on (geo) information management.

Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).

The map is part of my Dutch OpenTopo series, which I designed to supply the Dutch emergency responders with the most up-to-date topographic maps possible. I’ve always experimented with combining the best ingredients of various map sources, including the geospatial base registries published by the Dutch government as Open data. The result is now available at www.opentopo.nl.

Tell us about the tools, data, etc., you used to make the map.

The map was made using QGIS. Some post-processing of the raster output of QGIS is done using GDAL. The map data is stored in a PostgreSQL/PostGIS database. The data itself is made “PostGIS-ready” using the Dutch NLExtract tools developed by some geo-wizards also associated to the Dutch branch of OsGeo.org. The map features data from several Dutch Base Registries (BRT, BAG, BGT, BRK) and also contains various elements extracted from OpenStreetMap.

Guido Stein: “Spatial is now a first-class citizen in most databases”

Guido Stein is a Geospatial Data Alchemist who has been working for Applied Geographics for the last 10 years. He is also the founder of AvidGeo, a Boston area meetup group that hosts events for the geospatially inclined, and he is the Co-Chair of FOSS4G Boston 2017. Having lived in Somerville, MA for over a decade, he has truly absorbed some hipster characteristics including growing a beard “ironically” and riding to work on a bike with his little dog Beau.

Q: You are the Conference Co-Chair for the 2017 FOSS4G conference in Boston. Isn’t that like a lot of work? Why do you do it?

A: It is a lot of work. I have been working and organizing community events in the Boston area for over a decade. I do this work because I want to attend these events and the only way these events happen is if someone is willing to do the work. I am not afraid to be that someone. I really enjoy being involved in community work. I like to learn about what cool things everyone else is up to. I also like sharing in the victories and commiserating around failures specific to our community. If these events didn’t exist, who would share a beer with me over the limitations of using shapefiles?

Q: Tell us about the conference. How did you get involved?

A: I think that it was my boss, Michael Terner, who first approached me about it. I have been a big fan of the Open Source Geospatial (OSGeo) community for a long time. Sadly, other than participating as a user, I did not have much interaction with the community. Michael had participated in a few FOSS4G conferences and was ready to get more involved. He approached me to put together a bid knowing that I would jump at the chance to build a stronger tie between OSGeo and Boston.

The reason I want to see FOSS4G in Boston is that I think that there are many groups locally that would benefit from exposure to the OSGeo community and from getting a chance to show off what they are up to. I have been trying to unite people in the Boston area around geospatial for over a decade, and there is such a great and exciting group here doing things in business, government, academia, as well as in the startup tech world.

Q: You and I first met in Boston in 2011 in the offices of Applied Geographics. You are still there, so you must like it. What do you do for AppGeo?

A: I recently celebrated my 10th anniversary at Applied Geographics. When I started I worked on parcel edits, parcel generation using scanned plans and COGO input, utility system development all working with many Esri tools. Since then, I have been really happy to explore many different tools from FME to QGIS to PostGIS for editing, collaborating on, and analyzing data.

My mantra these days is “Spatial, not special”. My work these days focuses on how to use spatial knowledge to solve data problems. I am very happy becoming a more powerful user of all the tools I use, but I feel super powerful when I figure out complex SQL queries to solve problems. I love working on the database solutions.

Q: How did you get into GIS? Why?

A: My father introduced me to GIS in high school. He was a city planner (since retired), and knew that I would really enjoy this use of technology. I have always liked playing and working with computers.

I attended Clark University. Initially I was interested in the psychology department, but found coursework in geography to be very fulfilling and soon started to work with the Clark Labs. I was very fortunate to work closely with the staff and Ron Eastman. They were very supportive of my thirst for knowledge.

Q: Tell us about some of the cool tech you use these days. Describe a cool project that you currently work on.

A: Giggles…

Cool tech, well… the thing I think is coolest right now is my $10 Raspberry Pi Zero W. It is so exciting to have such a cheap computer with Wi-Fi and Bluetooth built in. It is a powerful tool for building IoT projects and also gives me a chance to improve my Linux chops. I really want to buy a bunch of these and create some simple navigation tools around them.

The last year has had me working on projects using Python, FME, PostGIS, CARTO, SQL Server, Oracle, and many other tools. My co-worker Calvin Metcalf has been trying to get me to switch from Python to Node. I really like the work CARTO has done to make mapping and PostGIS available online, it’s really quite powerful.

Q: Do you like open source for pragmatic or ideological reasons? Explain.

A: Both. I like tools that work and I want tools that I can contribute to. I think libre software is the ideal, but I think there is a lot of useful and good grey area in the open source community that is far superior to proprietary solutions.

Q: You knit, which is amazing. I did try it, but didn’t have the patience. Tell us how you got into knitting, and whether you see parallels between knitting and coding.

A: I started knitting when my wife took it up many years ago now. I really love making something with my hands and I also enjoy spending time with people in knitting circles; it’s like the ticket to enter a reality TV show of some very interesting and creative folks. I highly recommend it to everyone.

There is a wonderful tie between raster-based geospatial and knitting; they are both based on basic data principles. Both have rows and columns. Someday I really want to knit some NIR imagery into a hat.

Q: You ride a bike, which — along with knitting and the beard — puts you in the running for the perfect hipster. Is there a hipster attribute that you wish you had but lack?

A: Wait, there are so many more hipster attributes:

  • I live in a hipster community
  • I prefer artisanal chocolate and farm-to-table dining
  • I run my own hipster website http://hipster.country

The only hipster attribute I wish I had that I lack is the hipster gene that makes them all slender and buff. I am now getting ready for a 9K in July and this non-skinny-jean-wearing butt is just not as easy to move around as I would like it to be.

Q: Are you a geohipster? Why / why not?

A: I am a poser. I am a pretender. I am an imposter.

I am not good at defining myself as any particular thing. I love to learn and I love to listen. This makes me less a specific type of person as much as it makes me a person who enjoys being in the presence of others who are specific about who they are.

I have been running a hipster web site for years trying to figure out what being a hipster means and as far as I can tell, no one really knows. Hipster is used as both a positive and negative, so I don’t know if I am or am not.

But let’s do the checklist once more:

  • Lives in hipster community, check
  • Rides his bike to work, check
  • Has a beard, check
  • Goes to farmers markets, check
  • Can tell you about local beer, chocolate, and cheese makers, check
  • Has skinny jeans, nope
  • Can describe a bespoke projection, nope

Q: On closing, any parting words of wisdom for our global readership?

A: Get out of your silo. Spatial, not special.

Spatial is now a first-class citizen in most databases, so we should use databases and other data tools and not be totally reliant on vendor to solve our spatial needs.

Also, check out my friend’s local chocolate CSA (http://www.somervillechocolate.com/) it is the most amazing chocolate made by the most wonderful guy around.

Maps and Mappers of the 2017 GeoHipster Calendar – Johann Dugge and Juernjakob Dugge

Johann Dugge and Juernjakob Dugge – May

Johann Dugge and Juernjakob Dugge
www.papercraftmountains.com

Tell us about yourself.

Johann: I model packing processes of consumer products like laundry detergent to optimise the package design and manufacturing lines at Procter & Gamble in their Brussels office. 

Juernjakob: I work on software for optimising water and wastewater treatment processes. So our day jobs have only little to do with mapping. However, we’ve been exposed to cartography and particularly terrain models from a young age: Our father is a geomatics engineer, and our parents have been collecting raised relief maps for as long as we can remember.

Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).

Juernjakob: I was following Daniel Huffman’s tutorial on generating shaded reliefs using 3D rendering software, and slightly adapted the approach by first converting the DEMs to triangulated irregular networks before rendering them. The faceted appearance reminded me of the low-poly papercraft models that have been in vogue for a while, and I thought it might be fun to build a terrain model out of paper.

Johann: In June 2015 as we were cycling over the hills of Belgium we discussed what the qualities of such a model would have to be to be considered “optimal”. When we returned home to Brussels and Stuttgart we both started to adapt existing triangulation algorithms for this specific problem. In the end I came up with a solution that strikes a good balance between terrain fidelity and having a small number of triangles, avoiding difficult-to-assemble thin and tiny triangles as much as possible. My background in numerical optimisation certainly came in handy for this.

We presented the first results at the ICA Mountain Cartography Workshop in April 2016 and received a lot of very encouraging feedback. Since then we have been working on new models – the Matterhorn is already available through our site www.papercraftmountains.com. Also keep an eye out for Mount Fuji which will be released shortly!

Tell us about the tools, data, etc., you used to make the map.

We developed the triangulation algorithm in MATLAB. The elevation data comes from the USGS National Elevation Dataset, the orthophoto from the US National Agriculture Imagery Program. The quality of publicly available data in the United States is amazing, the rest of the world still has a lot of catching up to do in this regard.

Blender is used to add the stiffening structure to the 3D model. Pepakura is an unfolding software for paper model layouts and the final touches are done in Inkscape.

Dave Smith: “Many of the most satisfied, creative and talented folks I’ve met in geo were multidisciplinarians”

Dave Smith
Dave Smith
Dave Smith is with the U.S. Environmental Protection Agency's (EPA) Office of Environmental Information in Washington D.C.  Dave has a background of over 25 years of experience in working with geospatial technologies in applications ranging from emergency response and field data collection to modeling, analysis, and web mapping. Dave is also a licensed civil engineer and professional land surveyor, where he also worked on writing code and practical applications of using computers to automate data analysis and design. He is currently working with EPA's Chief Data Scientist Robin Thottungal on building out a big data analytics cluster to improve EPA's analytical capabilities.

Outside of the office, Dave has done a lot of volunteer work with various organizations, having been a top contributor to OpenStreetMap, aiding rescue and rebuilding efforts after the 2010 Haiti earthquake, as well as supporting organizations like Red Cross, U.S. State Department, and Team Rubicon in humanitarian and disaster relief mapping. Prior to that, Dave also volunteered his engineering and geospatial knowledge to support Engineers Without Borders on potable water and sanitation infrastructure for communities in Cameroon, Honduras, Rwanda and other communities in need.

You can find Dave on Twitter at @DruidSmith and on LinkedIn at https://www.linkedin.com/in/davidgsmith

Q: How did you get into GIS?

A: Maybe I was born to map. As a kid I grew up reading books such as Lord of the Rings and loved Tolkien’s hand-drawn maps. I started emulating his maps, creating my own maps of the locales in the fantasy and sci-fi books that I would read. You could say mapping was ingrained in me even as a pre-teen and then continued to weave its way through my life.

As a kid we lived in Germany for 10 years, and I was into scouting — I participated in both the German Pfadfinderschaft and a U.S. military-based Boy Scout troop in Germany where we would do orienteering, hiking, camping, and other activities with these wonderfully detailed, large-scale German topographic maps. These maps were essentially their version of our USGS topo quads, which had the funny-sounding name of “Messtischblatt”, as they were originally created with an alidade and plane table (“Messtisch” meaning “measuring table”) and compiled into a published map sheet (the “Blatt”).

Those scouting activities introduced me to more robust concepts like map scale and symbology, for example: differing symbols for hardwood versus pine forests, or contours and hachures to represent terrain. I started incorporating some of those concepts into the maps I would create for the fantasy realms I was reading about in my favorite books at the time. I first got into digital mapping while surveying in high school, with my first exposure to what we think of as GIS today being ARC/INFO in college.

Q: You are a licensed land surveyor and professional engineer. Do you feel that the career move to GIS was a step up from engineering and surveying? Why / why not?

A: Wow, picking between careers feels like asking a mom which is her favorite child. On the other hand, did I really ever leave one discipline for the other? In my case, the various disciplines I’ve been involved in seem to have merged and morphed, with various threads from academic and work pursuits becoming interwoven. I’ve tried steering myself toward the Venn diagram of intersecting circles of “things you enjoy” versus “things you are good at” and “things you can make a living at” to find the sweet spot where they intersect. Over time, I’ve found myself in that sweet spot.

When we moved back to the U.S. I was a teenager. One year in high school I managed to get a summer job with a land surveying firm. That job was great: getting to go out into the woods with the crew, recording a bunch of measurements, and bringing that data back into the office, reducing the notes and using the calcs to create maps. Plus, it was better money and less messy than washing dishes, landscaping, painting houses, or other summer jobs I had been doing. So, I was already a “professional mapper” when I was still in high school. It helped with school too, as I was taught about the techniques and how to do the math, which turned me into a trigonometry ninja. And, later on it helped with some of my college expenses too.

When I first started working in surveying, the company was a small mom-and-pop outfit founded in 1959. They were old school, hand-drafting maps and using transits and programmable HP calculators. Since I had taken drafting in high school they let me test my chops at drawing maps. I found myself already taking shape as a young geohipster, occasionally exercising my artistic side with artisanal hand-drawn north arrows and other creative design elements. A bit later we were in a building boom and needed to modernize to keep up. We hired additional field crews and got electronic total stations and data collectors for the field as well as computers, a plotter, and software for the office. That’s how I learned things like AutoCAD, coordinate geometry software, and digital elevation modeling. To support some of the subdivision and land development work, I also learned about stormwater runoff modeling, storm drainage, roadway design, and other civil engineering basics.

I really enjoyed working with the computers, and I taught myself how to do some automation for some of the repetitive tasks, such as LISP programming to support the CAD work, and writing code to help with many of the design calculations. At home I had a TRS-80 computer that I had saved up for and bought when I was 13. Later, in high school, I saved up and built my first homebrew PC-compatible computer from components, and was endlessly hacking around with it.

When it was time to go off to college, I still wasn’t sure what I wanted to do with myself. I started out in computer science, but became frustrated as the initial coursework seemed like a step backwards when I had already been writing code for several years. As someone interested in such a huge variety of things, whether archaeology, astronomy, or history, I changed majors several times. But I kept working summers and holidays at the surveying firm, and took surveying and civil engineering courses as those seemed a viable and responsible path for my otherwise unfocused youthful exuberance.

But one day I happily stumbled across my university’s Geography department and its GIS program, and I changed majors one final time. Here, the coursework gave me new inspirations, adding new concepts like remote sensing and satellite imagery, human geography, and interesting methodologies for analysis and spatial statistics. One of my favorite professors was Peter Gould, who taught complex statistical methods, analysis of variance, kriging, and other things; no textbook, his class was reinforced via real-world challenges and computer analyses. I recall getting something like a 43% on my first exam. Dejected, I wondered if I would make it through his class. Professor Gould walked over, patted me on the shoulder and said “great job, you got one of the top grades.” I ended up with my degree in Geography — with a specialization in Cartography, Remote Sensing, and GIS. As a student, I also got to work on some interesting projects, like working with the City of Kuala Lumpur, Malaysia on developing their GIS concept and framework.

The challenge of getting into GIS almost 30 years ago, however, was that GIS jobs were pretty scarce. I continued working in surveying and then various civil engineering firms, including working for some of the top engineering firms in the country. As things progressed, I gained enough experience to sit for the exams to become a Land Surveyor, and then for a Professional Engineer. In that engineering journey I ended up being appointed by Governor Rendell to the Pennsylvania Engineering Board overseeing licensure in the surveying, engineering, and geology fields. I chaired that Board for two years, and was heavily involved with NCEES and other organizations on examinations and other aspects of licensure.

In my engineering pursuits I found that civil engineering and GIS are interwoven. Engineering design depends on maps and data, and I was often called on to help out with urban and regional planning. In the early 1990s I was involved in one of the largest land use analyses east of the Mississippi. It required assembling huge amounts of data across paper maps, disparate files, and databases. This project required a lot of digitization, data optimization and management, data reprojection, and data transformation before we could even get to analysis and mapping. Nowadays the GIS kids just push a button; we old timers had to do it uphill, both ways, in 4 feet of snow – and remind me to tell you about the couple of weeks I spent surveying up on the Canadian border, waist deep in snow. At any rate, I ended up writing a bunch of homebrew GIS code to augment and extend the commercial tools we had. In the engineering world I also did a lot of work with hydrology and modeling, HEC-2 water surface profiles, transportation networks, even 3D modeling and rendering, and the worlds of engineering, GIS and code became more and more blurred over the years.

To this day I still rely on engineering principles, like solving complex technical challenges by deconstructing them into their constituent parts, trying to understand the interconnections and dependencies, and figuring out an architecture — whether hydrologic networks, transportation systems, or IT architectures, there are a lot of engineering principles and techniques for approaching them.

Q: How and why did you end up at the EPA?

A: I like to challenge myself and try new things. So, around 12 years ago, I took a big step and started a consulting at an engineering and GIS firm. Eventually, I teamed up with a guy who was also doing environmental and GIS consulting. As my partner was a disabled veteran, we structured as a Service-Connected Disabled Veteran Owned Small Business (SDVOSB), and we approached some larger government contractors about providing geospatial capabilities as a subcontractor. We quickly ended up working as a niche geospatial consultant with companies like Lockheed Martin, Raytheon, and CGI Federal on a variety of interesting projects for the military and other agencies. But because both of us were more interested in environmental protection, we put our main focus on EPA. We provided EPA with GIS support for Hurricane Katrina, and worked on many of EPA’s public-facing web mapping capabilities. While the company grew, I started to tire from the challenges of running a small business; the feast and famine cycles, writing endless proposals, and bouncing from customer to customer, when I really wanted to focus most on supporting a mission around using GIS to understand our environment and help protect it.

During the course of our EPA contracting, I learned that an EPA colleague was retiring. He had been managing EPA’s Facility Registry Service, which integrates and conflates geospatial data from many systems for millions of facilities and sites, and which serves as a geospatial underpinning for a lot of what EPA does. I knew the system well, and when the job announcement came out, I applied and got an offer.

Since then, I’ve been innovating with EPA. Here, I work with groups like the Homeland Infrastructure Foundation Level Data Workgroup (HIFLD) to provide new datasets to support emergency response. I cut costs through automation, improve match logic, and develop web services, including a reusable widget for reporting applications. The widget retrieves and prepopulates information, it validates, standardizes, and geocodes locations. It also allows users to fine-tune the location via a web map. It has since been deployed to a half dozen reporting systems, improved data quality at the source, and helped reduce burden significantly (140,000 hours of annual burden in one program).

Q: What do you do for the EPA? What kind of role does GIS play in supporting EPA in its mission?

A: At EPA we recently created a data analytics division, and hired a Chief Data Scientist. I now work for the Chief Data Scientist, Robin Thottungal, pursuing not only geospatial tech but also big data, machine learning, and other types of analytics. Currently I’m building cloud-based infrastructure to support distributed computing using Apache Spark and other technologies. We’re interested in offloading some of our geoprocessing and analysis to the cloud, along with better leveraging external data sources and emergent technologies for analysis. I’m also looking at how we can handle sensor data more robustly, and how to apply remote sensing for a variety of applications such as detection of Harmful Algal Blooms.

EPA’s mission is to protect human health and the environment. That covers broad territory, whether Emergency Support Function 10 and responding to oil or hazardous materials releases after a disaster, remediating a contaminated former industrial site, or assessing water quality. Environment is all about place, and so much of what we do has that spatial component. GIS is a core piece of support infrastructure at EPA, we have great GIS people across the agency. We’ve had a robust GIS workgroup for well over 20 years, and have had a Geospatial Information Officer for over 10 years. Our people are improving how we collect GIS data in the field, conducting advanced modeling and analysis, and expanding the use of mapping across the agency. We also train with the intent of democratizing technology across the agency, making it easier for non-technical users to map and visualize their data.

Q: What kind of technology do you use at work?

A: It’s quite a laundry list — for desktop mapping I’ve largely transitioned over to ArcGIS Pro, which is great if you already have nice clean data. I use SQL, R, Python, OGR/GDAL and others for data crunching and data prep tasks. A lot of our current infrastructure is Esri-based, with Oracle Spatial on the back end. However, I’ve found that database licensing constraints often lead to enterprise servers that are used for too many competing purposes, whether as a transactional system, data warehouse, or other functions stacked on top of each other. This means that it’s hard to optimize to do any of those jobs well, so I’ve been taking a step back, offloading, decoupling and leveraging more open source, like PostgreSQL and PostGIS, along with other open source technologies. I also do occasional lightweight work using Leaflet and various JavaScript frameworks. Recently we’ve added some point-and-click data viz tools like Qlik Sense for building dashboards, interactive charts, and graphs.

But we’re also looking at how to handle streaming data, big data, and distributed computing clusters — so I’ve started to experiment with an Apache Spark cluster on Mesos, along with Elasticsearch and other technologies that can bring us scale. As we look at deploying in the cloud, I’ve been delving into Docker as a means of containerizing, deploying, and managing our infrastructure in a replicable, maintainable, and cloud-agnostic way. We are working in a test environment in Amazon Web Services (AWS) with our eye on production, one of the big pieces being putting together the Authorization to Operate (ATO) documentation for the AWS environment. I also deployed JupyterHub in our test environment in AWS, which allows a user to create notebooks containing executable code (Python, R, Julia and others) along with annotation, embedded graphics, and other capabilities, with an eye toward supporting some of our scientific computing needs for more technical users in a self-service way. Esri recently rolled out their Python API that enables Jupyter-based approaches. Our little team is also digging into machine learning and analysis in the test Jupyter environment.

Q: Tell us about some of the cool projects you are working on.

A: Aside from building out some cool new infrastructure in the cloud, I get to tinker with a pretty wide variety of projects. For example, I recently built a tool to visualize streaming water sensor data, using Open Geospatial Consortium sensor standards. The tool allows users to slice and dice the data temporally; almost immediately we detected a recurrent anomaly in the data that the sensor operator hadn’t previously detected. Additionally, it provides dynamic, interactive ways to look at relationships between different sensor parameters, such as suspended solids, nitrates, and e. coli concentrations. I’d love to be able to set that up so that it can traverse upstream or downstream, pulling in other related sensor data from the stream network, since the devices are now all spatially indexed to the National Hydrology Dataset (NHD). I’ve also been building out tools that provide better insight on environmental impacts affecting tribes and American Indian country, using a combination of spatial queries and other approaches.

Q: You ride a bike and like craft brews — two mandatory geohipster attributes. Do you have any other?

A: For the male geohipsters, I possess an epic beard, however I’ll pass on the waxed handlebar mustache. And for the foodie geohipsters, I’ve brewed my own beers, I make things like pickled daikon, homemade mustard, gochujang chicken and Thai curry, and while I have been known to eat hipster foods like kale and quinoa, you won’t find me washing it down with a can of PBR. And I definitely have hipsterishly eclectic music tastes, ranging from funk to punk, blues to ska, a lot of artists not well known in the mainstream. I do actually own vinyl discs and a turntable, but I confess: most of what I listen to is digital.

But to get serious, I’ve noticed that the real geohipster attribute is to be innovators, makers, creators with broad interests and backgrounds. I came from a creative and resourceful “maker” family. When my parents settled in Pennsylvania, I was a nerdy city kid – born in Massachusetts, spent most of my childhood in Germany, then to El Paso, Texas. As I grew up, I became a country kid on a hundred-acre farm in the wooded Pocono mountains of Pennsylvania. There we raised goats, sheep, chickens, and other animals. We had a huge garden and did a lot of canning. We would hunt and fish, and were pretty self-sufficient. My mom and stepdad make a great team. She is a very talented sculptor and painter, and he is incredible at woodworking. She spins and weaves with their sheep’s wool, and he researches and rebuilds damaged antique spinning wheels using his lathe. My mom, with her undergrad in Chemistry and grad work in Germanic languages does things like reading Old Norse sagas in the original tongue to pick out tidbits on ancient Viking wool dyeing, spinning, and weaving.

So, I grew up in a very resourceful and creative “maker”-oriented environment: I did pen and ink drawing and sculpture; I learned to work on cars, solder electronics, and hang sheet rock; I learned how to build and make things. And that creativity and tenacity to figure out how to make things still influences how I approach many things, even if these days it involves a multidimensional dataset, or a homebrew sensor built with a Raspberry Pi.

Q: Do you consider yourself a geohipster? Why / why not?

A: We grizzled geohipster silverbacks might be tempted to say something hipsterish like “I was geo before geo went mainstream,” but one look at me and you’d probably think more geohippie than geohipster. I don’t possess any colorful skinny jeans (not that I’d fit into skinny jeans), I don’t wear Vans or ironic t-shirts under a blazer. My natural state is more likely to involve hiking boots and a tie-dye. But hipster jokes aside, it’s actually what’s on the inside that makes one a geohipster. It’s about out-of-the box thinking, being creative, passionate and innovative, and weaving together a lot of different knowledge and experience into your work.

Q: On closing, any words of wisdom for our global readership?

A: For one thing, remember that Venn diagram of things you enjoy / things you are good at / things you can get paid for. I’ve found that many of the most satisfied, creative and talented folks I’ve met in geo were multidisciplinarians. Some came into geo from another field, some were folks who started in geo but who then did a deep dive into another field. I also think that shows that we need to mix it up, keep thinking outside of the box and looking across the sciences.

Don’t get too hung up on specific tools and technologies or stay in your comfort zone. Instead, get comfortable being uncomfortable, get outside of your comfort zone, and stay curious. Keep learning. Focus on outcomes, keep adding new techniques and approaches to your toolbox, and apply your knowledge to a wider variety of problems.

Don’t be afraid to learn to code — it can make your life easier through automation, overcoming limitations of boxed software, or wrangling data. You don’t need to be an expert coder, often just a smattering of skills and googling code snippets will get you there. Though I’ve hacked around with over a dozen languages, these days I mostly use SQL, Python, JavaScript, R, and some shell scripting to get things done.

And, get involved in your local geo and tech community. Here in the nation’s capital we are particularly blessed to have a number of great geo and techie Meetup groups like Geo DC, always bringing new presentations and insights and great networking over beer – but even if you don’t have a strong local geo and tech community, there’s a strong and vibrant online geo community on twitter and social media.