Tag Archives: 2018 maps and mappers

Maps and mappers of the 2018 GeoHipster calendar — Atanas Entchev, October

Q: Tell us about yourself.

A: I am an architect and urban planner by training, and GISer by circumstance since 1991. I founded ENTCHEV GIS in 2005 and GeoHipster in 2013. Currently (since 2015) I am the GIS specialist for Franklin Township, NJ. Read more about me in my GeoHipster interview.

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).

A: This map is one in a series offering visual representation of all reported animal-vehicle crashes in Franklin Township over the course of several years. The map series informs environmental policy decisions in the town, particularly with regard to hunting regulations. I felt that discrete representation of point events was not communicating well the story behind the data, being that many animal crashes locations were concentrated in tight clusters — hence my choice of heat mapping for the series. I learned that deer population moves over time, which is probably obvious, but I never thought about it before.

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

A: The map uses data from police reports. The project started in a MapInfo derivative, moved to QGIS, then ArcMap, then Paint.net. Data was originally created in MapInfo TAB, moved to SHP (hi, @shapefile! 🙂 ), then to GeoTIFF, to PNG, to PDN, to PNG, ultimately to PDF (of course!).

 

Maps and mappers of the 2018 GeoHipster calendar: Nathaniel Jeffrey, September

Q: Tell us about yourself.

I’ve been making maps professionally for over 10 years now.  But when I’m not doing that, I could be cooking, messing around in VR (how exactly do you ingest geojson into Unity, anyway?), or running about as fast as the world’s fastest 90 year old.  Seriously, I looked it up; his name is Frederico Fischer. My sprinting pace is terrible, but it keeps my legs thicc at least.

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).

Oh hey, speaking of running – my boss had the idea for this map while he was training for his first marathon.  He came into work on Monday and explained how cool it would be if we could produce a map that showed the bounding boxes of every map our business had ever made.  I agreed that it would indeed be cool. Then I promptly forgot about it.

Working on something completely unrelated a couple of months later, which required me to programmatically extract the coordinates at the corners of some map documents, I was reminded of his idea.  A bit of Python frankenscripting later – with StackExchange acting as Igor – and I was able to unleash this on our entire corporate directory of map files. Turns out, in ten years of using our current GIS, we’ve collectively authored over eighty thousand maps.

Zooming in to Melbourne (which accounted for 30,000+ maps on its own), I started to play around with layered transparencies to visualise the data.  This eventually evolved into a nice glowy blue colour scheme, which reminded me of deep space images of clusters of stars and galaxies, connected by glowing filaments.

This map has no practical use.  I’m fine with that. There’s still something really satisfying about it, how it just hints at the tens of thousands of hours of work that went in to making all of those maps, which are reduced down to their most basic representation.  It looks nice too (I think). If you got a GeoHipster calendar, I hope you think so too, because you’re stuck with it for this month.

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

To scrape the data: A simple, custom Python script, run over a big and messy nested directory structure, full of .mxd files.  It extracted the x/y min/max coordinates of every map document, and reconstituted them into a shapefile full of rectangles.

To visualise the data: A mixture of ArcGIS Pro (I love the feature-level transparency), InkScape, and Paint.net.  

 

Maps and mappers of the 2018 GeoHipster calendar — Kurt Menke

Q: Tell us about yourself.

A: I am the owner of a small geo consultancy Bird’s Eye View based out of Albuquerque, New Mexico. My biggest focus areas are conservation, public health and training, but my clientele have become more and more diverse in recent years. I am an avid open source proponent and have authored two books on QGIS: Mastering QGIS and Discover QGIS. In the small amount of spare time I seem to have, I like working out, getting out into big wild spaces/mountains, playing board games while spinning some vinyl, raising chickens, and good coffee. I also love having the time to be creative and put together a nice map.

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).

A: This map was produced for a coalition working to protect the San Gabriel Mountains called San Gabriel Mountains Forever. The target audience was U.S. Congresswoman Judy Chu’s staff and the general public. It shows a series of proposed protections: expansion of the existing San Gabriel National Monument, a new National Recreation Area, expansion of several existing wilderness areas along with 6 new wilderness proposals, and several new wild and scenic rivers. The goal was to create a map highlighting these proposals with a clean modern look.

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

A: This was created with the QGIS nightlies, which last fall was version 2.99. This gave me a chance to check out some of the new emerging features coming with version 3. The proposal data was digitized using QGIS. The Stamen Terrain basemap is being seen through a similarly colored State boundary layer employing some transparency and the multiply blending mode. Existing wilderness and proposals also employ the multiply blending mode. Wilderness areas were obtained from Wilderness.net and highways were sourced from CalTrans. Highways were styled as white lines so that they would fall to the background. They look better digitally than in print form…is a map ever done? Cities were shown simply as labels.

Maps and mappers of the 2018 calendar: Andrew Zolnai

Live WebScene

Q: Tell us about yourself.

A: I’m a geologist by training and turned to computer mapping and GIS 30 years ago in Canada. Why? I took eight years high school Latin in France, and while I cannot write code I sure can fix it… Handy on Unix then Java scripts when you’re posted from Bako thru BK to Baku (that’s Bakersfield CA, Bangkok and Azerbaijan). So I’ve been in the petroleum service sector all my life, crossing over to remote sensing, geodata management and web services as GIS is apt to do. After hours, I do VGI (volunteered geographic information) to help academics and agencies find free data and publish (almost) free maps, and thus promote better citizen engagement.

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).

A: Oil & gas has a fantastic array of 3D subsurface data currently locked up in expensive and bespoke software and services. Using Esri for Personal Use for VGI work in England, working with US and Norwegian business partners Eagle Info Mapping and Geodata, and pulling basic data from Esri and US Bureau of Ocean Energy Management, helped conflate all the relevant data in one simple 3D stack in the US Gulf of Mexico. Posting on WebScene displays a vast array of data in an easily-accessible form, to help engage agencies, operators and the public around complex geo-science. This is critical in matters ranging from smart fields (like smart cities, only in oil & gas) thru emergency response to environmental protection and beyond.

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

A:

– Esri ArcGIS Pro and Web Scene, and Geodata Seismic extension.

– Seafloor bathymetry: Esri data.

– Oilwell polylines, surface block and subsurface strata polygons, and salt dome multi-patch: US BOEM open data and courtesy Eagle Info Mapping.

– Seismic imagery not local and courtesy Geodata.