Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Stephen Smith
Q: Tell us about yourself.
A: I’m a cartographer by night and a GIS Project Supervisor by day. I work for the Vermont Agency of Transportation where I help our rail section use GIS to manage state-owned rail assets and property. Most of the time my work entails empowering users to more easily access and use their GIS data. I’ve used Esri tools on a daily basis since 2008, but recently I’ve been playing with new tools whenever I get the chance. I attended SOTMUS 2014 in DC (my first non-Esri conference) and was really excited about everything happening around the open source geo community. I got some help installing “Tilemill 2” from GitHub and I haven’t looked back. Since then the majority of the maps I’ve made have been using open source tools and data. Lately I’ve been heavily involved in The Spatial Community, a Slack community of 800+ GIS professionals who collaborate to solve each other’s problems and share GIFs. I’m also starting a “mastermind” for GIS professionals who want to work together and help one another take their careers to the next level.
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 born out of an inspirational post shared on Marty Elmer’s MapHugger blog. In it he featured a wonderful map of Great Britain from the 1940s. I really fell in love with the style of the map and thought it would be a fun exercise to try to replicate and update it using modern tools and data.
Q: Tell us about the tools, data, etc., you used to make the map.
A: I’ve done a full writeup on my blog which discusses in depth the color palette, specific data sources, software used, manual processing, and the stylistic choices I made while creating the map. It also features a high resolution download of the map perfect for a desktop wallpaper.
‘The United States – Her Natural and Industrial Resources’ by Stephen Smith
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Joachim Ungar
Q: Tell us about yourself.
A: I’m a cartographer working at an Austrian IT company, named EOX IT Services, based in Vienna. We are mainly involved in the Earth Observation domain, most of the time being contracted by the European Space Agency (ESA). The first time I got in touch with GIS was in high school where we first heard about vector and raster data, projections, and so on. I liked it from the first day, so it was a rather easy decision to study Cartography and Geoinformation at the University. Back then I was rather drawn into data processing and learning more about the Open Source tools (GDAL, QGIS) than analyzing data or creating thematic maps with commercial tools.
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: There were a couple of things coming together. I want to mention that we, at EOX, build an Open Source, back-end system for managing and serving EO data to portals for viewing and download, called EOxServer. Soon thereafter, we started to build clients as well and needed background maps. As we are committed to Open Source and Open Data, it was clear that we wouldn’t use one of the big commercial solutions (e.g. Google Maps). First we used a couple of voluntarily maintained OSM servers where we got our maps from, but we needed to have guaranteed uptime.
Mapbox was just starting off getting huge, but at this point I decided to get back to my cartography roots and do it myself. Moreover, ESA needed to have the maps in the WGS84 projection and not in Spherical Mercator, but almost all of the aforementioned solutions just supported the latter. But there was TileMill, which in my eyes revolutionized the process of styling maps for the web. I probably would have failed, mainly in keeping the motivation up, if TileMill (or something comparable) weren’t there already.
Downloading some SRTM data, creating a hillshade, and combining it with some OSM data shouldn’t be that hard, I thought. Well, let’s say I pretty soon found out this couldn’t be done in two weekends.
In the end it took us over a year and several iterations until we were able to publish Terrain and later Terrain Light. I was pretty obsessed for quite a while about all kinds of details. But it was worth it, I think. If you look close enough you’ll discover many neat details, like that the contour lines are light blue over ice, blue on the ocean floor and brown over land. We recently published a blog post about creating smooth centerlines from polygons if you are interested: https://eox.at/2015/12/curved-labels/
Of course we use EOX::Maps on a daily basis, and even ESA is now one of our customers, paying for the maps as a service. We also use it for our new product, called mapalupa (www.mapalupa.com), a tool where you can easily publish your global (or large area) data on your website, overlaid on an interactive globe using Cesium. Terrain Light is the default background layer there, and I think it works quite nicely on a globe as well.
However, there are always things to improve. So I guess what I have learned is that it can be very satisfying to map the world once, but you are never done doing so.
Q: Tell us about the tools, data, etc., you used to make the map.
A: For preprocessing I wrote some tools in Python which make use of GDAL, OGR, rasterio, and so on. For styling we used TileMill and some Mapnik hacks, so we could render the maps in a WGS84 projection. The tile cache is created and hosted by MapCache, a MapServer project.
For the hillshading we first used SRTM, but later switched to ASTER GDEM and filled some messy parts (bigger ice shields e.g. in Greenland) with GTOPO30. For the rest we used, of course, OpenStreetMap and some datasets from Natural Earth.
It’s great that there is so much data and software out there which can be used. Especially, I would like to mention the person from USGS who was extremely friendly and supportive when we asked to get the global ASTER data. We are not even US taxpayers, but this commitment to Open Data and customer support was very impressive. Last but not least I like to thank all people who spend their time and effort for the Open Source / Open Data idea and therefore provide so many powerful tools which help to compose and realize new things.
‘Mont Blanc web map at various zoom levels’ by Joachim Ungar
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Dr. John Van Hoesen
Q: Tell us about yourself.
A: I’m an Associate Professor of Geology & Environmental Studies at Green Mountain College in Vermont. I stumbled into GIS as an undergraduate and for most of my graduate work focused on using it as spatial analysis tool exploring geologic processes and environmental issues (and I made a lot of fugly maps along the way). Straddling the fence of academia and the consulting world it became clear that fugly wouldn’t cut it and I eventually embraced cartography as an art rather than an afterthought.
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: A post by Geomorphology Rules on Facebook prompted me to celebrate “Worldwide LIP Appreciation Week.” Much to the disappointment of Steven Tyler, this is related to Large Igneous Provinces. I couldn’t find any other reference to this during the official week of celebration, however it seemed like a great topic to mention in my Intro to Geology course. But when I went looking for a good map of LIPs I was disappointed, so I decided to create one.
Q: Tell us about the tools, data, etc., you used to make the map.
A: I started with some Blue Marble imagery from NASA and following some great advice from John Nelson with idvsolutions applied a little desaturation and knocked down the brightness in the bright white polar regions. I found ready-made shapefiles (LIPs, hot spots, etc.) created by Mike Coffin and provided by the Institute For Geophysics at the University of Texas, Austin. I also used a dataset from the Large Igneous Provinces Commission to create a simple graph illustrating the frequency of igneous pulses over geologic time (this is meant more as illustrative than definitive, of course).
Cartographically I chose the orange and red based on the USGS Cartographic Standards palette, and the yellow, blue and purple mainly for contrast. I re-projected all the data into Winkel tripel (see Goldberg and Gott (2008, PDF)). I couldn’t figure out how to reproject to Winkel tripel in QGIS so that was done in ArcMap 10.1 and then symbolized in QGIS and exported to Illustrator for the marginalia (I know, I know, I could use Inkscape but we have a site license…)
‘Large Igneous Provinces (LIPS) – Atlantic Ocean’ by John Van Hoesen
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Markus Mayr
Q: Tell us about yourself.
A: I’m a University Assistant at the Technical University of Vienna in Austria at the department of Geodesy and Geoinformation. But more than that, I’m an OpenStreetMap contributor and GIS nerd that can not decide whether it is more exciting to do JavaScript mobile programming or Python based backends. So, instead, I sometimes end up doing cartography with OpenSource tools.
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: One lecture of the bachelor study program “Landscape Architecture and Landscape Planning” at the University of Life Sciences and Natural Resources (also in Vienna) is about learning trees — lots and lots of different trees. To make it more practical, the students walk around in the nearby Türkenschanzpark and study the different characteristics of the trees planted there. About 8 years ago, I was one of these students. One semester of learning how an e.g. one-year-old Juglans regia looks (and tastes) like left a lasting impression with me.
When data about all these trees became publicly available, it became of great interest to me. Still remembering the days of searching for one specific tree in the park, I started drafting a map…
Q: Tell us about the tools, data, etc., you used to make the map.
A: The map was composed using only open source software and data from freely available sources. Since the cadastre of trees of Vienna was imported into the OpenStreetMap some time ago, I was able to use solely data from OSM for this map.
The base layout and main cartographic work was done using QGIS. The map then was exported as a vector file and post-processed in Inkscape. While a lot of cartographic work can already be done within QGIS, some cartographic details just have to be crafted manually (e.g. the smoothing of the walking paths, more precise labelling, or slight generalization of the few buildings). (a hooray for traditional cartographers 😉 )
Inkscape proved to be up to the job — despite the slightly sluggish performance because of the many elements on display. The possibility to search for similar elements via XML-SVG attributes was a powerful feature that helped me a lot. Maybe, at some point in the future, Inkscape will be extended by a set of cartography tools?
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Christina Boggs
Q: Tell us about yourself.
A: I’m an Engineering Geologist with the California Department of Water Resources in the Division of Integrated Regional Water Management’s North Central Region Office, in the Geology and Groundwater Investigations Section. I like helping people collaborate and work together better, GeoHipster, URISA, @womeningis, volunteer teaching deaf folks, upright bass, competitive strongman lifting, live music, coffee, and photography!
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: I made this map years ago during my very first GIS class! This project taught me to be creative with my data sources. I found this data through an obscure place for people to upgrade their GPS unit data. It was fun to play with color and textures for this map!
Q: Tell us about the tools, data, etc., you used to make the map.
A: I used publicly available data and the latest ArcMap of that time (9.3).
‘Starbucks Locations in the US Normalized by Population’ by Christina Boggs
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Andrew Zolnai
Q: Tell us about yourself.
A: I’m a geologist who turned to computer mapping 30 years ago and GIS 20 yrs ago — high school Latin helped me transition to coding just short of programming — and I now started my third business and assisted two others. I’m taking a ‘business process first’ approach, using mind mapping as a ‘talking point’ to help firms help themselves, which will determine workflows in resources planning that may invoke web maps. My Volunteered Geographic Information also helps individuals and academics put themselves on the maps such as this one.
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: CLIWOC (CLImatalogical database for the World OCeans) maps all ships captains logs, 180 attributes such as temperature & wind speed per reading typically twice a day, collected by British, Dutch, French and Spanish navies (and a few minor ones but not the Portuguese). From 1750 to 1854 they’re the best climate data available offshore, making about 1/4M points. The global sailings by time slices were posted on the so-called Stamen backdrop: its black oceans bring out the ships’ location colour coding. This map shows in the late 18th c. the French traffic to New France (E Canada) in yellow, in blue the Hudson’s Bay Company sailings to N Canada and the British East India Company sailings to India, and the Dutch triangular trade to the Caribbean and W Africa in green. http://bit.ly/1T1bblS
Q: Tell us about the tools, data, etc., you used to make the map.
A: Original CLIWOC climate data MXD was imported into File Geodatabase. 1/4M original points turned into 1/2M points after the four navies’ look-up tables normalised the climate data. Realistically, ArcGIS Online only posted these in decade time slices thru the time stamping. http://bit.ly/1Mgrgyy These were exported as GeoJSON both as time slices and in bulk, and uploaded into AWC EC2 stack. From there they were rendered in MapCentia GeoCloud2. http://bit.ly/1Yg3xL8
‘CLIWOC ships captains logs for 1750-1774 time slice’ by Andrew Zolnai
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Dr. Farheen Khanum
Q: Tell us about yourself.
A: I am a Geographic Researcher, I have done Ph.D in Environmental Geography. I love to work in the field of Environment and GIS applications. I am a simple person having lots of wishes to explore the world, fond of meeting new people and learning new technologies.
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: I was writing my Ph.D thesis when I decided to participate in the GeoHipster Call for Maps 2015. This map is also a part of my thesis as well. The most special thoughts for serving the nature I learnt by my dear mentor Dr. Jamil Kazmi, whose passion is to work for the environment and sustainability. Afterwards it also became my interest too. It encouraged me to do something better for the future of Marine Megafauna and their conservation. Marine turtles are highly migrated and intrinsic species in the Globe. They are under the threat of various problems and declared to be protected. They are an extremely important part of our ecosystem.
Q: Tell us about the tools, data, etc., you used to make the map.
This map is designed with the help of an old reference map of WWF. It helps me to identify the territories of Marine Turtles in the world. This map defines the spatial distribution of Marine turtles in the world. It highlights the nesting and foraging areas of Marine turtle species as found all over the large oceans and terrestrial areas. I used ArcGIS software for making this map. It is a simple map designed by using coordinates of all marine turtles nesting sites in the world. Point distribution method was also used to indicate the locations. Furthermore, the special symbols of marine turtle in ArcGIS were utilized to differentiate every species in a unique colour.
Last but not least, it is my wish to serve the nature by spreading the knowledge of conservation.
‘Worldwide Distribution of Marine Turtles’ by Dr. Farheen Khanum
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Ralph Straumann
Q: Tell us about yourself.
A: I’m a senior information management consultant with Ernst Basler + Partner in Switzerland as well as a Visiting Researcher at the Oxford Internet Institute (OII) of the University of Oxford in the UK. In my day job I consult clients regarding effective and efficient data infrastructures, data processing, and information-centric workflows. With the OII, I do research on Information Geographies. Besides these topics, I have a strong interest in information visualisation, cartography, politics, journalism and blogging.
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: My map in the 2015 GeoHipster calendar was inspired by my interest in politics and the MAUP (modifiable areal unit problem). Switzerland (where I live), through its approach of direct democracy, has frequent votes on popular initiatives and referenda. For illustration: since 2000, 225 votes have been held at the federal level alone! (There are also votes on cantonal (state) and municipal levels)
In Switzerland, political division often shows itself not so much between language regions (anymore) but between urban and rural parts of the country. At the federal level, metropolitan areas don’t have direct representation, but cantons do (and of course citizens through political factions). However, some of the bigger Swiss cities represent a clearly larger population than a considerable number of small cantons or half-cantons. Thus, there are ongoing debates about the political representation of cities’ particular challenges and policy interests at the federal level.
Additionally, in many situations, for a vote to pass it doesn’t only require the majority of voters agreeing but also the majority of the cantons and half-cantons. Here, small cantons and half-cantons (that are found mainly in rural parts of the country) can have what some people perceive as unfair political weight – especially compared to their political counterparts, the metropolitan areas. So we have discussions if the voting weights of cantons and half-cantons should be adapted to also reflect their population size in some way.
Within this setting, I wanted to come up with an innovative display of the actual weight of cities as compared to cantons. I opted for combined and linked cartogram, map and bar diagram. In March 2013 and while I was still working on the visualization, Switzerland held a controversial vote that met the agreement of 54.3% of the population but did not pass, because it didn’t get the required majority of cantons. This result was quite particular because of the rather solid majority of the people in this case.
On the way back from a trip to the mountains I got news of the forecasts of this vote and I decided on the spot that I had to finish the (interactive) visualization on this day and put it online. I managed to do just that and the results did indeed draw much attention: the map has been broadcast on Swiss national TV as well as re-published in various media and reports. So I gladly think it helped foster discussion.
Q: Tell us about the tools, data, etc., you used to make the map.
A: Let me take a step back and talk about the contiguous, hexagon-based cartogram I made and acknowledge its source: This is an idea I got from Leicestershire (UK) when I was researching cartograms. The UK has the concept of Lower Super Output Areas (LSOAs). These conveniently contain roughly 1,500 residents each. So Leicestershire Statistics and Research Online could use them as base-units for their hexagonal cartograms; which in turn served as my inspiration. I talked to a Leicestershire representative and he kindly told me that their cartogram design workflow included the use of a lot of post-its. This was clearly not a viable approach for me 😉
Instead, I used a combination of ArcGIS, a custom tool from Jenness for hex grids and Scapetoad to design my hexagonal cartogram. The process is not too complicated but [for] quite some thinking and also (in my opinion: indispensable) manual work went into it. I explain all the various aspects, data sources and tools in twoblog posts. For the interactive version of the visualization I used Mike Bostock’s D3.js. The version in the GeoHipster calendar was made using Crowbar (to extract the SVG shapes) and in Inkscape.
Given the production history of this visualization in which I got input from various people (from Leicestershire, but also e.g. Danny Dorling and Adi Herzog), I felt a duty to document my approach and methodological considerations in order to help others in the community to build on my work. I was very pleased to see Mike Bostock, Stefano de Sabbata, Xaquín Gonzalez and Andy Towpicking it up. And I re-used and refined the approach for work with the OII on Wikipedia and internetaccess as well as for the Guardian’s coverage of the Generel Election. They often get a bad rap, but I think there is a niche where cartograms are valuable.
‘Governance and Population Distribution of Switzerland’ by Ralph Straumann
Sending off the year 2015, we present to our readers the mapmakers who contributed their work to the 2015 GeoHipster calendar.
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Damian Spangrud
Q: Tell us about yourself.
A: I like maps and playing with data (aka analysis). I’m a failed Biologist who became Geographer (CU Boulder, MSU Bozeman) and have been working with GIS for around 25 years now (The last 22 of them at Esri). Over the years I’ve been fortunate to be able to take on a number of ad-hoc mapping, analysis, and visualization projects. These have allowed me to explore creative ideas, some fairly “out there” analysis, and “what if” scenarios of data combinations.
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: I experimented with hexagon mapping many years ago, and while it was well established (for 100s of years), I never really saw the attraction of it. I was into modeling and needed to do weighted surfaces and interpolation.
But a few years back I started working on more of the visualization and comprehension aspects of information. Aggregating data (or binning) into well-known shapes is a great approach for providing a higher level view of data. I was CERTAIN that squares (maybe rectangles) were the correct way to do this, but after some experimentation I found that hexagons in many ways worked better, as they don’t impose rigid linear sightlines. And the tessellation of nested hexagons is fascinating in multi-scale maps. (It still pains me to praise hexagons!)
So when the call for maps came out, I was working with hexagons and combined that with my fascination for map projections and showed the nested hexagons across a Goode Homolosine projection. (I also sent one in for another projection (Stereographic) that I thought was better — but the aspect ratio didn’t work for the calendar).
What did I learn? Other than there is a “cult” for Hexagon mapping out there? The nesting hexagons worked great, except that some of the bigger shapes got distorted and didn’t line up quite right. I realized that the hexagon sides with 2 point lines, and when projected they needed to be densified to make the smaller hexagons inside.
Q: Tell us about the tools, data, etc., you used to make the map.
A: Making the map was pretty straightforward. In ArcMap I used a sample script to produce the hexagons at several sizes in *un-projected* WGS 84 coordinate system, and then just played with changing the projection till I had a couple maps I liked.
I am a Cornell graduate in the natural resources field, and can still be found spending part of the work week absorbed in data analysis and mapping for the greater environmental good while reserving the rest of the work week for broader mapping endeavors, which includes keeping up on the multitude of innovative map styles coming from all corners of the profession.
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 just a snippet of a world-wide basemap created specifically to be placed underneath users’ data layers to provide geographic context. To that end, the color palette is muted and, though the product for which it was originally created was discontinued, it still works nicely as a stand-alone map.
My team created it using OpenStreetMap data pulled down with imposm3 via a custom mapping JSON file to pull down the specific bits of OSM that we wanted. Osmosis was then used for processing, and at one point we even had a good working changeset procedure to keep the map continuously updated.
The styling went through several iterations to arrive at this look and feel, and was then continuously tweaked as the project evolved. This is pretty typical when designing a webmap that has to be incorporated into a website’s overall aesthetic, which itself evolves through time. The surrounding site and the webmap must have a design alliance achieved through continuous dialog.
Q: Tell us about the tools, data, etc., you used to make the map.
A: The map was tiled with GeoServer and the styles were written out in SLD. When I began the project I wasn’t familiar with SLD, so I used the GeoCatBridge product to get some good initial SLDs created with the kinds of filters that I needed, and then proceeded to develop them as straight-up SLD from there. Getting proper RegEx statements going is always a challenge, but we also got that going in the end to get the right features and labels to show.
As far as labeling goes, a font was used that had enough character sets to style all the languages we wanted to support world-wide so that labels wouldn’t show up as empty glyphs.
From beginning to end I’d say the project took about 3 months. There’s definitely some robust infrastructure that’s needed for these huge OSM world-wide pulls, especially if you want to set up a dev environment to try out new styles with — as we did — while still running the full map in production for existing users.
