Tag Archives: 2017 maps and mappers

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.

Maps and Mappers of the 2017 GeoHipster Calendar: Mark Brown

Mark Brown MSc – February

Tell us about yourself.

I am an ecologist and conservationist with strong technical skills in GIS & Remote Sensing. My interests lie in the application of geospatial technologies to help solve ecological and environmental problems. I love developing novel techniques in areas where these technologies might not have been previously used.

For the past several years I have been working on landscape scale habitat restoration schemes such as the Yorkshire Peat Partnership in the uplands of northern England. There is a very special type of habitat found here known as a blanket bog. Referred to as the ‘rainforests’ of the UK, they are home to many unique plants and animals. These areas of land are not covered by trees however, but a ‘blanket’ of peat material that has formed over thousands of years through the accumulation of dead plant material such as Sphagnum Mosses. Due to this they act as a massive carbon sink. They are also a very important source for much of our drinking water and help to regulate runoff and therefore flooding. They are also an important site for leisure and recreation.

Despite this, most of these habitats are in a heavily degraded state. Mismanagement of this land through burning and overgrazing, as well as natural causes such as wildfires, is causing much of it to erode away. Once peat is exposed to the elements, it rapidly wears away releasing carbon and entering waterways, where it turns the water a brown colour similar to English Breakfast tea. Peat that is dissolved in waterways is causing a problem for water companies, as they have to spend millions of pounds each year to remove the peat through chemical treatments.

The project I work on aims to reverse degradation of the blanket bog by providing the stable conditions necessary for its recovery

In order to do this we need to identify those areas currently undergoing or most at risk of erosion and this is where I come in.

As well as being qualified with an MSc in Geographical Information Systems, I am also a certified Unmanned Aerial Vehicle (UAV) pilot. I use a fixed-wing drone known as a senseFly eBee to capture aerial imagery and to generate digital surface models (DSMs) of the habitats that we work on. This data is of a very high resolution (up to 1.5cm!). This gives me the capability to examine and analyse the blanket bogs in an unprecedented level of detail.

I use this data within GIS and remote sensing software to carry out a whole range of analyses. This includes but is not limited to topographic modelling, hydrological analysis, feature extraction, Object-Based Image Analysis (OBIA) to detect different types of vegetation communities and exposed peat, 3D visualisation, and the creation of cross-sectional profiles to determine the dimensions of eroding gullies.

If you’re sat there reading this and wondering what on earth is a blanket bog?! I’d suggest having a look at the Yorkshire Peat Partnership Facebook page. There are lots of interesting photographs of the landscapes as well as our restoration works and imagery of the UAV in action. See link below:


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’m sorry to admit it but blanket bogs just aren’t sexy! With no charismatic fauna, cold, wind and rain, to the untrained eye there’s not really much there. They are often seen as barren wastelands. However if you delve down among the undergrowth, there are many interesting species of lichens, mosses, and England’s only carnivorous plants!

This map was part of a series of images that I created in an effort to make blanket bogs more interesting to the public eye. It has been used in Yorkshire Peat Partnership reports as well as at international conferences.

I was interested in creating a series of photorealistic terrain models of blanket bogs. I visualised the terrain in open-source 3D modelling software with a light source over a white background. This creates the interesting shadow effect so the terrain looks like it is floating over a white surface.

This was a departure from my usual workflow as I normally work exclusively with GIS software such as QGIS. However I found that this was providing too many limitations for graphical visualisations and it was interesting to learn an entirely new piece of software from scratch.

Actually I was quite surprised to be selected for the calendar. I think if I was to enter the image again I would put more information on the image regarding what the image actually is and how it was generated. Hindsight however is a wonderful thing!

I was super pleased to be selected and hopefully this will introduce a very novel application of GIS and remote sensing to a wider audience!

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

The UAV I used to survey the site was a senseFly eBee. (https://www.sensefly.com/drones/ebee.html). This is a small fixed-wing drone that is proving to be one of the most popular commercially-available drones for surveying and mapping purposes. The UAV is fully automated and flies along pre-defined transects,taking photographs along the way.

The imagery that was captured was then processed within photogrammetry software known as Pix4D Mapper (https://pix4d.com/). This software is used to generate point clouds, digital surface and terrain models, orthomosaics, and textured models.

The resulting orthophotograph and digital surface model was then imported into Blender (https://www.blender.org/) where the image was rendered. Blender is an open source 3D modelling software package. It is more commonly used for 3D art, animation, and the creation of computer games.

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: