Tuesday, 17 March 2015

Drug crazed mapping

I had told myself I wasn't going to bite when @Amazing_maps screamed once more for my attention. But the more I tried to ignore, the more it reeled me in so eventually I thought it worth a few comments.

Here's the so-called amazing map:

I've no idea who made it. It doesn't really matter. What I feel matters is the impact maps like this have on those that view it. This is more about the consumption of maps but, of course, their design and construction goes a long way to underpinning the message people take away.

Quick look and take away: Holy drug barons Batman,...San Bernardino is full of crack-heads! So are a few smaller areas I don't even know....but they're really small so they can't be as important eh? Right, must be time for Alaska State Troopers, turn on the TV...

That's how a lot of people will look at this map. Message delivered. Warped view of reality perpetuated. Job done. Wait for the next Amazing Map.

Here's the longer look and take aways I formulated...

Hmm. Something's not quite right with this map. Let's talk it through. It's a choropleth. We can assume from the title...well, the line that doubles as the legend title, what the subject matter is. It's about the labs, not the population so it's about production, not consumption. And the colour scheme goes from light to dark so we see where there are more meth labs and where there are fewer. I'll not repeat myself like a cracked record about it being totals (but it is) and not normalised (but it isn't) suffice to say it needs to have the data transformed into per capita or something equally sensible to allow us to compare like for like. Though critical for a choropleth, let's ignore that for the purposes of this because there's other 'take aways' in this map.

Look at San Bernardino County again...jeesz, it's heaving with meth labs.

This makes me a little more interested (perhaps concerned) as it's where I live. Notwithstanding it's totals, look at that large, expansive area filled with loads of meth labs. How many?...there's about...errr, well, let me look at the legend. hmm. It's dark blue. Does that make it 300, 500, 1000 meth labs?

It's impossible to tell without doing some assessment of the actual RGB values. It's actually closest to the RGB value about 1/3 along the legend colour ramp which would make it about 330ish...though there are no RGB values in the legend that match those found in San Bernardino County so it's impossible to be certain and why am I having to do an RGB analysis of a legend anyway? It shouts out from the map yet is nearer the lower end of the legend. That doesn't seem right.

So San Bernardino leaps out because 1. it's the largest county in the US 2. It has a lot of meth labs (though possibly not per capita or in relation to counties with many more) and 3. It's dark blue and that means 'more' except there's virtually no differentiation between the blue used at 330 and that for 1000. All the variation in colour value is at the lower end.

The map uses an unclassified choropleth approach. That means every data value is given its own position along the chosen colour ramp. I'm not a huge fan of unclassified choropleths. Choropleths are generally used to show where places are similar and that relies on classifying your data into groups that display similar characteristics. All you can really see from an unclassed choropleth is the extremities...which areas tend to the maximum and which to the minimum. It's really difficult to assess where those in-between values might sit...and that's assuming the scale is linear and the colour scheme is applied linearly. Of course, you can stretch colour to be applied non-linearly but then it's an even more confusing picture that's arguably more difficult interpret visually. If you don't classify data before mapping it then you're painting by numbers and it's a bypass to considering your data and teasing out the message through careful classification and symbolisation.

I'm going to add a caveat here - if the map is for interactive web display and the user can hover or click an area to retrieve the value directly, then unclassed choropleths are, arguably, less problematic because people can retrieve values across the map. I'd still contend, however, that if we know the map is classified into, say 5 classes using natural breaks then every county symbolized in the same shade of blue is 'similar'. It's an important metric we can easily see in the map and it's a good default. Other classification schemes exist to suit alternative purposes. If we use, say, a quantile scheme of 5 classes then we know each class shows 20% of the data values in rank order - again, similarity between values, across the entire range values, can be easily seen and it's simple to see which areas are in the top 20% of values.  If you make two choropleths then using something like a quantile scheme allows you to compare the two maps on a comparable cognitive basis. Clicking to retrieve a value is an additional step in the map reading process. Trying to remember values from one hovered-over area to another is equally taxing because our short term recall is not our best cognitive function (think of memorizing and recalling a pack of cards in order...it's not easy!). I like maps to 'show and tell' rather than require further processing or actions by the user to reveal the message.

Onto the colours. Because there are just so many different shades of blue across the map we get a sense of some overall pattern but we can't really tell which are similar to which. How similar is San Bernardion COunty's colour compared to the other dark blues across the other side of the map? It's called simultaneous contrast and is a problem for our map reading. Our perception of a colour (or shades of a colour) varies as we look across the map due to the colours that surround it. Look at the following two grey squares and how they are affected by the surrounding shades:

The grey square differs in perception depending on whether it's surrounded by dark or light.  A darker surround makes us see it lighter than if it has a lighter surround. Now look at how different colours modify the grey square:

The grey squares, despite being the same, take on a perceived tinge of colour based on what's around it. And when the image gets even more complex we have even more difficulty processing what we see. In the following animation, which grey square, A or B, is darker?


Of course, the greys in A and B are the same. In the above diagrams all the grey squares are seen differently simply because of their surroundings. The map of meth labs has over 3,000 counties, each shade of blue being surrounded by it's own different mix of blues.

These perceptual issues are also a problem in classed choropleths of course - but not nearly to the same degree because it's much easier to distinguish and differentiate 5 or 6 shades of blue across a map than it is to try and make sense of several hundred (thousands?) different shades of blue.

And what about labels? Yes we can probably all recognise it's the U.S. I know where my home is so I recognise San Bernardino County. I've no real way of describing where other patterns exist in language that makes sense. Giving people context is important. Interactive maps support this through basemap labels or, again, hover and click...but however you deliver the map, give people a way to reference the patterns they see.

So the take-aways for me...
  • It's totals. If you can't or won't change to a rate or ratio then use something other than choropleth like a dot density, proportional symbol, dasymetric or cartogram.
  • If you have to use unclassed choropleths then scale your data across the range of colour so that extremeties don't dictate the way values map onto the colours. Make the legend more useful by providing labels at key positions and make your map interactive so people can retrieve values.
  • Go with a classed choropleth if you want people to 'see' more than just the extremeties in your data and how different areas are similar to others for all values that display similar characteristics. Learn which classification techniques are going to manage your data most appropriately for the message you want to share.
  • Be aware of the problems of simultaneous contrast.
  • Include some form of labelling to give people a way of referencing the geographical patterns they see.

Other problems...no real title, no source, no credits, no dates, no contact details. Nothing. Like I said, I don't know where the map came from but as is, it's a fail in every respect.

Finally, I tried to get the data to recreate this as a per capita but after a quick search I wasn't able to find it at county level. Instead I came across this abomination on the Drug Enforcement Administration web site:

I don't even know where to start with this one, and they've made one per year for the last few years. They were clearly on something or other. And if we assume the DEA reporting is accurate (and the most current) AND that the Amazing_Maps one is broadly of the same time period (OK, a lot of assumptions) then what's with San Bernardino having over 300 meth labs given California as a whole has only 79?

Clearly something's wrong somewhere. Amazing map? Possibly. It's just poorly designed and constructed and gives a totally misleading impression of a dataset that cannot be verified. It's another potentially mildly interesting dataset that's poorly mapped.

And by the way, San Bernardino County is the 5th most populous county in the US so per capita...we may even have a paucity of meth labs so a different map might support the assertion we need more to get our supply increased*. Additionally, while the overall area of the county is about 20,000 sq miles, the populated areas are predominantly crammed into the south west corner in an area roughly 450 sq miles...which makes a choropleth map of totals covering mostly desert even less useful (unless the meth labs are in the desert). And all those less important smaller areas...Seattle, St Louis, Tulsa and Grand Rapids. But because of the way the boundaries lie, choropleths are always going to cause difficulties in interpretation. That's the Modifiable Areal Unit Problem...and a whole different blog entry.

* this is a joke

Tuesday, 24 February 2015

Messy heat map

My last blog post on heat maps was an attempt to persuade map-makers that the term actually means something other than what you might think it means...and that doing cluster analysis of some form or other on your data more than likely requires a better understanding of data and technique than a so-called heat map generator provides.

My Twitter feed lit up today as Manchester City played Barcelona in the Champions League Round of 16. Lionel Messi, the Barcelona forward, had a fine game by all accounts (to be fair he pretty much always does) and Squawka were on the ball with their live analysis during the game.

Squawka provide a web-based view of sport that collates and presents data as it happens. They tweeted the following:

Needless to say it brought on a nervous carto-twitch. If you read the previous blog you'll know by now that whatever the above is, it's not a 'heat map'. It's a density map of some form of cluster analysis but it illustrates far more than just another example of an inappropriately named map.

Here are some of the issues I see with this map and how similar issues are seen in almost all of these sorts of maps. They may help to understand that it isn't, in fact, a map of Messi leaking all over the pitch.

What is the data that was used?

Messi presumably ran about the pitch yet the splodges look like they are based on point data. Is this where he had the ball? Where he received the ball? Where he passed the ball to another player? Where he was stationary for a period or simply where he stood watching as Suarez scored the goals? Etc etc. While logic suggests that a map of Messi's running should be linear we are immediately confused in trying to decipher what this data actually represents because it looks like points that have been analysed to create a representation of clusters (more points = larger or more intense splodge). Without knowing what data the map represents we cannot decide whether he was all over the final third or not. If the data is indeed points then is that an appropriate metric and can it justifiably be used to show what they purport the map to be showing?

What are the fuzzy splodges?

The typical symbology on these sort of maps tends to go through some form of spectral colour scheme and this is no different. The hazy blue splodges are likely where less clustering occurred but is this a fleeting movement or pass or where he tripped over his laces? As Messi moves more or passes more (or whatever more) the intensity of the symbology increases. But what precisely does this represent? We have no legend to tell us what changes in colour mean and whether colour is mapped onto the clustering values linearly or logarithmically or...

Indeed - if you look at the overlap of two hazy blue splodges near the bottom centre of the map you'll notice that a simple overlap at the edge of two hazy blue splodges results in a bright, intense change in symbol. But if these hazy blue splodges are built from point data (presumably at the centre of the hazy blue splodge) then the overlap is simply an artifact of overlapping symbology...not necessarily overlapping data. These artifact overlaps occur everywhere on the map so it's unclear what the relationship is between data and symbology and how that then translates to Messi's actual movement or involvement.

The statement of being all over the final third also doesn't exactly stack up either. The main splodges are in a zone towards the top of the pitch graphic...a little left of centre but certainly not all in the final third. We'll assume Barcelona are attacking the left half of the pitch graphic and that even though teams switch sides at half-time the graphic maintains teams in the same half for mapping purposes.

All in all it's a graphic that reveals very little except gross error and uncertainty and which is utterly impossible to interpret in a way that reveals anything sensible about Messi's contribution to the game. 

These sort of back of an envelope 'heat maps' are unhelpful for any visual or analytic task. Quick to produce yes, but you can't make any sensible or quantifiable interpretation. Finally, we have no-one elses maps to look at so we simply have to presume that every other player's heat maps are in some way visually inferior to Messi's map.

Messy data. Messy clustering. Messy symbology. Messy map. Messy communication and very messy ability to interpret, compare or understand. Poor old Lionel Messi who is, quite literally, an innocent bystander in all of this...as the map, sort of, shows.

Tuesday, 17 February 2015

When is a heat map not a heat map

Actually, the answer to the question in the blog post title is most of the time...

The term 'heat map' has gatecrashed the cartographic lexicon. It has seemingly replaced other, more established, more accurate and perfectly good terms. It's used as a catch all for any map that portrays a density of point-based pieces of information as a surface. Here, I try and explain why I find it unhelpful.

Heat maps have become a popularist way to label a surface representation of data that occurs at discrete points. On one hand the search for a better way of showing point based data which avoids death by push-pin is a sound cartographic approach. Imagine simply looking at a map of points and trying to make sense of the patterns. Chief Clarence 'Clancy' Wiggum would certainly struggle to make sense of the pattern of crime in Springfield just from coloured dots.

It's difficult to process patterns of dots (other than more here, less there) and even harder when you're looking at thousands of dots that overlap (death by pushpin) so...let's make a heat map!

In analytical terms there's a number of ways one might approach the problem. One way is to bin your data into regularly shaped containers like hexagons, effectively a spatial summary of the point data. Another way is to interpolate lines of equal value across the map to create a surface which then helps us to see areas that display similarly high or low values across the map. Of course it's important to remember that any interpolated surface is effectively inventing data values for the areas on the map for which you don't have data or sample points. It's therefore important to think whether you want data making up for the areas between your data points when you know damn well nothing exists.

For instance, make a map of temperature and you'll likely use sample points. It's perfectly reasonable to infer that temperature exists everywhere as a continuous surface so filling in the voids where you have no data is fine. If, on the other hand, you have accident data for road intersections and you interpolate a surface it makes much less sense. Intersections do not exist across space so filling in the voids with made up data values is not really appropriate.

Let's assume Chief Clancy is making a surface based on some sensible logic. These interpolation methods collectively result in an isarithmic map. That is, the planimetric mapping of a real or interpolated three-dimensional surface. So Chief Clancy might see the pattern of crime in Springfield a little more like this (and before someone suggests the interpolation is a little off..yes, it's just for illustrative purposes. I'm a cartonerd but seriously...):

These sorts of isarithmic maps are used everywhere for displaying temperature (isotherm) to atmospheric pressure (isobar); and from height (isohypse) to population distribution (isopleth). They're also commonly, and erroneously,  referred to as heat maps.

Technically speaking, the map of crime activity points that Chief Clancy is looking at might be termed isometric data since it shows locations of discrete events that do not necessarily exhaust space. The fact that the dots are coloured actually suggests more than one incident. Rather than a simple interpolation of discrete points, he might instead do a kernel density analysis that uses the values at each point as a weighting factor to end up with a map like this:

His analysis needs to very carefully decide the shape and size of the kernel (search area) used to compare nearest neighbours. A small kernel will create a map that looks much like the original...just discrete points on the map but displayed as splodges. Choosing a kernel that is too large will create an over-smoothed, highly generalised map.

A more advanced version of kernel density analysis might be used to calculate a K function which constructs zones around events as a way of summing and weighting values which end up on the interpolated surface. It goes beyond simply looking at nearest neighbours and can help map patterns across a wider area. They're also commonly, and erroneously,  referred to as heat maps.

Hopefully Clancy knows what he is doing and not simply using a slider in a haphazard manner to achieve a map he likes the look of. There's nothing fundamentally wrong with sliders but it's also useful to know what the slider is doing to make what you're seeing.

If Chief Clancy is feeling particularly brave he may even fire up his favourite geo-analytical powerhouse and calculate the Getis-Ord Gi* statistic for the variable in question. The resulting p-values are mapped to show where statistically significant high or low clusters occur spatially.

This is often termed a hot spot map (again, sometimes referred to as heat maps) and which typically use red to show 'hot' areas (or a lot of something) and blue to show 'cold' areas (or much less of the thing in question). It doesn't show hot places and cold places and, frankly, if you don't use the right data inputs and know what it's mapping it can distort your view of reality beyond comprehension. It's a map of statistically significant clusters of data based on a multitude of decisions taken in setting the parameters. It's a complex but perfectly good analytical technique though the fact we're now beginning to see the introduction of terms that reflect 'heat' and, often, colours that connotate temperature, it begins to form a basis for misinterpretation.

These techniques form a valuable collection of related methods that create interpolated surfaces from discrete data points. They create isarithmic maps; predominantly isopleths because they map distributions of populations of some variable or another. Referring to them as a heat map is wrong because a heat map is something else entirely. Badging them as isarithmic maps is fine but it's important to recognise that they have very different data demands, functionality, complexity and potential and knowing these differences helps you understand and interpret the maps they create.

Just for the cartonerds, let's decipher the definition...an isopleth is a form of isarithmic map and shows change in the quantifiable variable over space. They differ from choropleth maps in that the data is not grouped into a pre-defined region (e.g. countries, census areas). They also work particularly well for data that exists continuously and which doesn't necessarily change at an abrupt point or a pre-defined boundary. In this sense, any surface of population based data that can be interpolated from point data is an isopleth. That's ISOPLETH from iso- Ancient Greek ἴσος (ísos, equal) +‎ Ancient Greek πλῆθος (plêthos, a great number)....or more easily understood as a map that shows equal numbers. The equal numbers being demarcated by isolines of equal value which divide areas which display similar characteristics.

So I've hopefully established that isarithmic maps are not heat maps but if we turn our attention to the use of descriptors and colours in such maps it helps to understand the misrepresentation a little more.

In all of the example maps Chief Clancy looked at earlier the colour schemes suited the analytical technique. The kernel density maps used single hue colour schemes which went from light to dark. The Getis-Ord Gi* map used a diverging scheme to show clusters of positive p-values (red) and clusters of negative p-values (blue)...though there's no particular reason why red to blue should be used other than to reinforce the use of terms such as 'hot' and 'cold'.

In all of these maps you can clearly see the areas that represent higher data values and the areas that represent lower data values. It's easy for us because darker shades are interpreted as 'more'. We describe patterns using terms such as 'more' or 'less' rather than 'hot' or 'cold' because that helps us understand the data using language that refers to quantities., 'With diverging colour schemes we naturally interpret the data as diverging away from a central value.  Again, 'more' and 'less' is useful as is 'statistically significant cluster' if we're referring to the result of the Getis-Ord Gi* statistic. Using heat related terms adds unnecessary confusion which is further compounded by the colour schemes often used for heat mapping.

The idea of showing heat using a rainbow colour scheme is likely based on the sort of colours you get when you 'see' something through an infra-red camera like so:

This technique is specifically designed to measure heat and display hot and cold areas so the use of reds and blues makes sense as it matches the data and the phenomena being mapped. It matches our cognitive ability to process what the colour actually represents. Greens in the middle are still odd though because it doesn't really suggest a mid-heat value. Sometimes these colour schemes avoid green altogether.

While this colour scheme arguably works when we're talking of temperature, when the 'mapped' phenomena is actually hotter or colder, transposing this colour scheme for any mapped data variable causes problems in our ability to cognitively process patterns like so:

Here, Chief Clancy is looking at a beautiful rainbow. Blues for colder areas, reds for warmer and yellow and white for, well, white hot areas. The middle is glowing white hot with progressively 'cooler' colours radiating out in a beautifully smoothed gradient. Springfield is on fire and the interpolated values appear to show a lovely linear gradient reflected by the colours. Does the data Clancy is working with vary spatially according to a linear distribution away from source locations? Linear rarely happens in reality so such maps can present a distorted view of what's going on with the underlying data.

More likely, such a map presents the results of an over-generalised cluster algorithm that has been combined with the application of a linear spectral colour ramp. Given the point of this blog post, it's not a heat map either...it's just a version of some form of density analysis that results in a surface that does away with isolines altogether and compounds the problems of interpretation by using an unhelpful colour scheme. It's averaged out the analysis and complicated the symbolisation which doesn't help the user task at hand. There's even some transparency thrown in for good measure to see a little of the basemap which also adds to the visual clutter. Of course,defaults can always be changed so let's not get too bogged down in colour choices though using spectral schemes to show quantitative data is never a good idea.

Clancy needs help but unfortunately the routine use of a red and blue diverging colour schemes and spectral colour schemes on such maps has become so ingrained in popular use that's he doesn't know it's not helping him understand the map particularly well. Why should more of something be 'hotter' and less of something be blue and 'colder'? We're talking about data here...not the weather, though given the dreadful use of colour schemes on weather maps it's unsurprising we've seen them re-purposed for the sort of maps people call heat maps.

Of course, if you move your heat map slider in a different way to define a different implementation of the cluster mapping method and choose different colours you may end up with this:

Or even this...

Different sized splodges with different colours that tickle your fancy. And all from the same data. What a cornucopia of colour that means, well... very little. And once you've made your data look like something and added some random colours...you can even animate it. Doh!

The key to understanding the utility of this type of map is having a good understanding of your data in the first place and choosing an appropriate technique and colour scheme to go with your analysis. While making these type of maps simply is helpful to many, it's also really important to support them in making better maps rather than in allowing them to fall into the trap of making basic mapping errors.

So, back to the main point... if none of the above are heat maps...and the misnomer has been reinforced through the use of poorly defined colour ramps...what exactly is a heat map? Does such a thing actually exist?

Yes...but strictly speaking it's not really a map; it's a visual representation of a data matrix.

A heat map has been around in statistical analysis for a good while. It's defines a graphical approach to code a matrix of data values into a graphical representation using colours. It's designed to reveal the hierarchy of row and column structure. Rows in the matrix are ordered so that similar rows are near each other and you see cluster trees on the axes. A heat map looks like this:

The closest spatial representation of data that might reasonably have similarities to a real heat map is a tree map...and that's a cartogram which has destroyed geography for the sake of creating an ordered matrix. At a stretch I understand why raster surfaces used to represent geography might be mistaken as heat maps because they are formed from a rectangular grid of pixels. But as I've set out here, I'd contest that the use of the mapping term isopleth already differentiates it from other map types and that we don't need to borrow a term from graph theory to simply replace one that already suits the technique. Proper heat maps go further than the mis-named cartographic versions anyway because the matrix is designed to illustrate correlations between variables through linked lines and other axis annotation.

Want to read more about real heat maps? Check out this rather good paper by Leland Wilkinson and Michael Friendly who explored the history of the term and of heat maps in 2008 (and from where the above illustration was sourced). It's prior art for heat maps in their true sense.

Tempted to make a heat map? I'd suggest doing a density analysis on your point data, experiment with different bandwidths (and understand what it is you're trying to map) and symbolize the resulting surface as an isopleth map using anything other than a rainbow or spectral colour scheme. If you're using a slider and default colours...think about what the slider is doing. Use it sensibly and try and understand how it's interpolating your data and, if you can...change the bloody colours from a rainbow palette! If you can't, then maybe try a different map type altogether.

More generally, I find that the introduction of replacement terminology where perfectly good terms already exists creates a further division between what we might call professional cartography and the wider world of map-making. I'd prefer to see those worlds come together under the same umbrella and for a better understanding to emerge through the use of a standard and accurate nomenclature. We don't need to dumb down mapping or how we talk about it to encourage better mapping.

PS - with resspect and apologies to The Simpsons whose image I have used and abused in this post.

Wednesday, 14 January 2015

Cartography is a great word

Before you start reading, grab a coffee or, possibly, something a little stronger...this is a lengthy stream of thought that I've tried to fashion into something that makes sense. Sometimes it may wander...

We’ve all heard, seen (and possibly written) the meme’s that have heralded the death of cartography, the death of the printed map and so forth but these slow-news-month scare stories couldn’t be further from the truth. More maps are made by more people than ever before and if anyone is worrying that print mapping is dead then Mapbox might just have precipitated the second coming with their new printing capabilities. The irony.

The number of books on maps published in the last year has also rocketed and someone you know was likely spoilt for choice when choosing their 2014 Christmas gift for a map-nerd son or daughter. Plenty of delicious coffee-table books full of great maps are currently available (see Jonathan Crowe’s review to which I’d add the NACIS Atlas of Design and James Cheshire and Oliver Uberti’s superb The Information Capital as two of my recent favourites). 2015 looks set to bring more of everything to our browsers, our desktops and our bookshelves. The appetite for maps has never been greater and sure, we see a lot of cartographic crud that we have to wade through but in some senses it makes the gems even more special when you find them. Map-making and the interest in maps, then, is in rude health…but what of cartography? We rarely see mention of ‘cartography’.

Cartography is defined as the discipline dealing with the art, science and technology of making and using maps.  The International Cartographic Association (ICA) has recently been accepted as a full member of the International Council for Science (ICSU) which is the international non-governmental organization devoted to international cooperation in the advancement of science. Cartography just graduated but I find that the term and what it stands for remain a term of derision for many. My feeling is we need to re-establish cartography as modern and relevant, because it is.

There’s no doubt cartography has undergone significant change in the last decade and a number of people have claimed we’d be better off if we just forgot about ‘cartography’ as a definition or as a framework to talk about mapping. Change is nothing new in the mapping sciences because evolution has always radically alter the mechanisms of map-making from time to time. This is usually a technological change (engraving, lithography, computers, cellphones, Google…) which has a massive impact on both the design and production of maps and also the people involved in map-making. New people enter the mapping landscape which both challenges and reinvigorates but it usually goes hand-in-hand with cartographer’s moaning because it usually means they have to retrain, reinvent or let go of ageing techniques. Feeling threatened or at least a little frustrated by change is inevitable if your skills and experience are overtaken so frequently by the new kids on the block. It’s tiring to perpetually invest the energy to keep pace; and also to face the challenge of people trying to constantly rename what it is you do.

Cartography is a word that many new to map-making seem reluctant to use. Not so long ago, up stepped the self-proclaimed ‘neo-cartographers’ whose moniker describes the people and processes of making a map outside of the community of professional map-makers. That’s everyone right? I’ve written about my views of neo-cartography being a fallacy before but don’t we already have a definition that’s relevant? It’s called ‘amateurism’; and before you baulk and rip me to shreds I say that not in a derogatory sense but merely as a perfectly good differentiator. An ‘amateur’ is a person attached to a particular pursuit, study or science in a non-professional way. Amateurs may have little professional training. Many are self-taught. The negative connotations of amateurism mean that sub-par work is often easily explained but that’s also broadly true as most of the time a non-professional will not be able to produce work to the same standard as a professional. So why do we constantly need new terms to describe making and using maps when the word cartography, whether it’s as a professional or amateur pursuit, seems to fit? It’s perfectly acceptable to have professional and amateur cartographers making maps. Many of the best maps were made by amateur cartographers anyway.

New terminology tends to be sought to describe a movement that wants to be seen as different from the past. New. Fresh. Exciting. Maybe being unencumbered by the perceived shackles of formal training is what defines a neo-spirit but they’re just bringing different skills and new insight to bear to cartography which is no bad thing. The open source movement, Volunteered Geographic Information and Citizen Science have been the backbone of the rise of ‘neo’ because computer scientists and programmers need to have something to programme and geographical data (and lots of it) has coordinates which lend themselves very well to computer processing, particularly if there are other numbers attached to these coordinates. Coders saw geo as a vast untapped marketplace and jumped on the mapping bandwagon…partly because cartography and professional cartographers were too slow to grasp the mettle. There’s a lot of positive work that these ‘amateur cartographers’ (and professional computer scientists) have brought to bear and I don’t disagree that formal definitions of cartography don’t need challenging. But I do take issue with the creation of a new species called neo-cartographer (or whatever) because it seems to go hand-in-hand with decrying what’s gone before while at the same time hyphenating the label to bring a sense of stature to their own efforts. They are fledgling cartographers whether they like it or not, albeit not necessarily in the sense of what has gone before. Rather than embrace cartography they prefer to distance themselves and even become vocal in their anti-cartographic sentiment because for some reason they know best. I got into a brief twitter exchange recently because a ‘designer’ had stood up at a small conference gathering and proclaimed they were a designer and that meant they need not talk with a cartographer because they wouldn’t have anything to add that they couldn’t already better. That arrogance and derision is quite common. My retort was simple…everything is designed and cartographers design maps; so what’s the domain specialism of a generic ‘designer’? Truth is, if the designer had collaborated with a cartographer the map product would likely be far better than sum of the parts anyway. Same goes for your average coder…in fact the same goes for probably 99% of amateur cartographers.

This issue with the word cartography goes deeper. This is about people’s perceptions and misconceptions of what cartography is and what a cartographer does. Of course, the term cartography isn’t as old as map-making anyway and so the claim that it’s the defining framework for mapping can be plausibly challenged. The term cartography is modern, loaned into English from the French ‘cartographie’ in the 1840s, based on Middle Latin carta "map". While relatively new, it has nevertheless become synonymous with the definition of the art and science of making and using maps. It helps to define a discipline (and now an official science). Yet the public perception of cartography is also awash with a lack of understanding of what a cartographer does. To many, cartographers just make maps ‘pretty’. They are more concerned with finessing the aesthetics of the map than the need to make the damn thing and publish it. Maybe that perception bears fruit in some instances but it’s a gross generalization and most professional cartographers I know take a healthy approach to the graphical marriage of form and function.

And these misconceptions can get quite alarming. I recently had a conversation at Border Control at Los Angeles International airport where the Officer (wearing the obligatory hand-gun and devoid of humour) asked my occupation. I often say something nebulous that will get me through unscathed but increasingly I feel I should just say it as it is so I said ‘cartographer’ when asked my occupation (curiously, despite the fact I have never had the term ‘cartographer’ as part of any job title). Stunned silence ensued and the Officer eventually asked ‘what part of the cars do you fix?’. My British sense of humour wanted to say any number of things but the lack of humour and obligatory hand gun made me pause and simply reply that I made maps. The Officer retorted that she never knew that; so we had a brief conversation about how her map gets on her cellphone and yes, that there are places that still need mapping. After I’d been processed I wished her a pleasant day as I wandered through and pondered on the fact that her impression is probably quite common…and it’s really not that far removed from people’s knowledge and understanding of cartography in the geo and mapping industries themselves. I’m serious. The number of people I know who work in the geo industries who wouldn’t know a decent map if it reared up and bit them on the arse is staggering. Sometimes they make maps. Sometimes they market or herald maps made by others. Mostly they just carry on in their own ignorant way satisfied that their own facts are perfectly OK…and get annoyed if people point out deficiencies. I also recall reading the jacket notes of a book on cartography, published in 2009, that claimed they wrote it because no other books on cartography existed. That’s a blatant lie. Just because you didn’t look very far doesn’t make it a fact. And there’s the problem…people prefer their own facts rather than making the effort to learn those that have already been proven or written. So these negative connotations about cartography begin to blur into personal facts by people predisposed to that argument and view of identity.

So if you’re a coder, journalist or designer (or anyone new to making maps) and you make maps as part of your work…you’re involving yourself in cartography, but you likely never call yourself a cartographer because of those connotations and perceptions. If you’re going to play in the same sand-pit as other cartographers I propose it would help rekindle respect for the discipline, rather than perpetuate divisions, if you learnt a bit about what being a cartographer is really all about. I don’t propose you take a class because you’ve done that already to become an expert in your own field but appreciate that some have taken classes in cartography and that makes them experts in that field. We can’t all be experts in everything and with such crossover between job requirements these days we inevitably need to tool ourselves in ways that make us amateurs in some things while professional in others.

The sweeping technological changes and turnover of people at the forefront of cartography means change takes place almost as regularly as fashion but like fashion, most new is actually old and reinvented for a new audience who are simply arriving at their map-making using a different approach. The rise of open this and that has brought this new set of people to the light table who use spatial data as a way to flex their computing muscles or to tell their data-led stories. Modern browsers, new programming languages, SDKs, APIs, open geospatial data and the freedom of the internet created the perfect storm and there were many storm chasers just waiting to jump into the mapping milieu. I recently compared the internet to Mos Eisley spaceport from Star Wars (Episode IV) and the famous Obi-Wan quote “Mos Eisley spaceport. You will never find a more wretched hive of scum and villainy. We must be cautious.” It’s true. The internet has brought wonders but also troubled times for cartography because it has largely tried to denounce it (mostly with terrible maps it must be said). The neos got their mapping hands dirty but also made cartography a little grubby in their dismissal of much that had gone before. The mindset of many of these amateurs has sullied cartography because the quality of the result rarely matters even though there’s been some beautifully disruptive gems amongst the general mess that’s been created. But here’s the sting…the more these amateurs work with maps, the more their work matures and the less they remain amateurs – they become part of the profession through practice and experience. No one ever said you have to have qualifications to be a professional. On-the-job experience counts for a lot if you’re willing to learn, develop and develop knowledge and understanding to go along with your experience.

Because change is inevitable (and why should we stop anyone from having a go or getting involved in map-making anyway?), it’s beholden on cartographers and those geospatial experts who know something about high quality, meaningful mapping and data visualization to accept change because it’s part of the territory. It’s not particularly unique to cartography either so the idea that we get a raw deal is perhaps simply part of a stereotypical view of reality. The fact is, change happens and it happens rapidly. Becoming part of the change, being the change you want and working to ensure the fundamental basis of cartography is retained is vitally important. If we leave cartography to the amateurs we’re running the risk of leaving behind all the good stuff for short-term gain, reinvented techniques and an approach that tends to prefer butting heads with convention rather than embracing it and making good use of it.

I’m simplifying and generalizing of course (it’s what cartographers do) but the brain and skill drain is palpable in much of what we see in cartography. Academic programmes are largely gone or where they do exist they’re seen as too theoretical and not practical enough (by neos) or are too far down the buttonology road to be considered ‘proper’ courses (by academic cartographers). National Mapping Agencies have had to rapidly alter their course to take advantage of new approaches. Maps are now personalized and mostly we default to the ubiquitous offerings on our desktops or mobile devices…and we consume transient maps about this whimsical topic or that fanciful theme daily. And cartographers still moan. We’ve got to get with it as much as we want our new map-making friends to get with it. Embrace change but work to promote what cartography is, how it can be inclusive, not exclusive and what knowledge and skills one might reasonably expect a cartographer to possess as they develop from amateur to professional. That may render some people as perpetual amateurs but that shouldn’t be negative. We are all amateurs at something or other (sport, cooking, writing…).

In pondering how to encourage people to value cartography; to encourage cartographers to stand up for their profession and expertise; and show those new to map-making what cartography is about I was inspired by some parallels in the debate on User Interface design (UI) and User Experience (UX). Up until only a few years ago you’d never hear of a job title with either UI or UX in it, let alone in combination with the ultra-trendy ‘designer’ or ‘architect’ monikers. These labels have even entered the mapping domain…map designer, map architect etc (never cartographic designer or cartographic architect you’ll note). As a tangent, it’s an improvement to ‘GIS cartographer’ or someone who can make ‘GIS maps’. What is that? I digress. It’s meaningless, that’s what; and it demonstrates if you’re hiring that you don’t really know what it is you want or need. So what of the label of cartographer? It’s a perfectly good label but it carries baggage (to wit…the moaning guys hunched over draughtsman’s tables with pens). Erik Flowers’ excellent look at the differences between how User Experience wants to be seen and how it is seen (www.uxisnotui.com) has many parallels in how cartography and cartographers are viewed and how they might wish to be viewed. His thesis is, effectively, that UX is poorly understood, that people don’t really understand what it means and, consequently, they have little idea of the scope of work that a User Experience Designer might be capable of. He’s right. And one could argue that this is the problem that faces cartographers and cartography whether we’re talking about Border Control Officers or the latest neo-map-hacker. Flowers produced this fantastic sheet that explains in very simple terms how UX wants to be seen and how it is typically seen:

The point of Flowers’ list is to try and debunk what a User Experience expert is, what their skillset and expertise is and what roles they are able to fill. Some are entire jobs or careers and some are perhaps a little more transient but what he wants you to realize is that UX designers are not just people who do UI design or who think the world can be solved through UI design. He wants you to appreciate that there is much more to being a UX expert than many might immediately think.

So I made a similar list for cartography and the sort of expertise and roles cartographers are involved in.

How cartography might be seen

Before you claim that not every professional cartographer wants to be seen like this let me be clear…I agree. The list is of expertise and skills that cartographers will possess in different combinations and to different levels. Possibly not every cartographer can claim they are proficient in every part of this list (actually, I’d be wary of any that do) but it shows the breadth and depth of the cartographic profession.

And on the other hand, the following version of the same list is generally the way in which cartographers tend to be viewed: as an ill-defined, nebulous group of grumpy people who tend to just make maps and complain about everyone else’s maps, note the perception of this has also seen a subtle change from the word critique (constructive, supportive, rigorous and justified) to Police (simply critical). And yeah…it’s in Comic Sans.

How cartography is generally seen

This is an unfortunate situation but I’d challenge anyone within the cartographic community to refute that this is how many others look at us and what we do. It’s no wonder people claim all we do is colour in with computers (a phrase my old Dean of Faculty used in describing the geo, GIS and cartography courses at Kingston University…he’s risen to Senior Deputy Vice-Chancellor while GIS and geo have all but closed…terrible sign of the times). But these sort of narrow-minded people never seem to really understand or want to understand what it is that a cartographer brings to the table. In fact, you’ll see this lack of understanding permeate across job adverts and specifications and even within organisations that should know better. Whose fault is this though? Well I began this by complaining that cartographers simply complain and in many respects I feel that as a community we have largely been the architects of this perception. Where once cartographers were Royal appointments they are now backroom staff and, to be frank, you’re likely to need to be a coder or something else first and foremost and an amateur cartographer second. The ability to know how to make a map is tangential to many other job requirements. It’s also the case that when you make a map many employers wouldn’t know the difference between a good and poor map anyway. Quality is low on the list of priorities for many. Speed and turnover is more useful. And so the path to the dark side is complete as apprentice becomes the master. A new order is formed that eschews the past and leads to the rise of an alternative with a new mindset. Yes, I’m using a Star Wars analogy again which even had those on the good side like Han Solo mocking the Jedi: “Hokey religions and ancient weapons are no match for a good blaster at your side, kid.” Trouble is…actually, most people side with the Jedi in Star Wars and ultimately appreciate it. Good triumphs evil. Just like the Jedi, cartography gets bashed about a fair bit from time to time but it needs to reinvigorate, return and prevail as the way in which we set out the cartographic order in our universe.

We’re currently awash with neologisms because, frankly, if you’re a new player in the mapping landscape you want to be seen as new, avant garde. You want to make your mark and not be viewed as simply regurgitating the cruddy old stuff you think cartographer’s of yesteryear hold so dear. Neologisms such as GIS mapper, map-maker, map designer and…neo-cartographer. In fact, you’ll have to hunt hard to find any ‘modern’ map-maker wanting to use the simple term cartographer to describe what it is they do. These neologisms have become personas. They take on new meaning as they attempt to shake off the past and define a new set of skills and expertise. They also define a way to divide the past from the present but that, to my mind is simply divisive for the sake of it. Why does everything have to be seen as new? Why is there such a determination for people to want to break from the past and to differentiate themselves so markedly. There are clearly now improved ways of doing cartography that replace older ways but it’s evolutionary, not revolutionary. Does the fact I can’t code in Javascript or I prefer to make a map using a GUI rather than code up CSS make me a bad cartographer? No. It just means I do my work a different way. The International Cartographic Association’s definition of cartography covers it I think. Let’s not reinvent what it is and let’s accept amateurs as well as professionals and see them as bringing different things to the table. Let’s also try and ensure the rest of the world understands cartography and what it is to be a cartographer a little better. And that starts with the geo-professions more broadly developing a better understanding of the broad church of cartographic expertise and practice rather than constantly trying to avoid it, ignore it or reinvent what it is they do.

My point is simple (despite the lengthy essay)…whether we call it cartography or not (and we should call it cartography), cartographers have much to offer. They are rarely seen as people that have such a varied skillset as I’ve set out here but I would encourage us to shift our thinking. Being a cartographer is a fine profession. What needs to happen is to explain far better to people what we do. We need to go beyond simply saying ‘I make maps’ because that reinforces the stereotypes. We need to avoid infighting between those who prefer to print their maps and those who prefer to code. We need to accept that some make maps using GIS software and some use Illustrator and Photoshop. You know what…some people use a wide range of approaches and I have Esri software, Adobe products, QGIS and Tilemill installed on my computer. I use ArcGIS a lot (inevitably, I’m paid to…though in the past this has been by choice also). I also have Mapbox and CartoDB accounts. It’s allowed.

Beyond the different ways in which we approach the craft, we can start re-establishing cartography by encouraging people inside and out to acknowledge the expertise a cartographer can offer and see them as vital in an organizational context. ICA are making efforts to underpin this with the designation (by the United Nations Committee of Experts on Global Geospatial Information Management) of 2015-2016 as International Map Year which is formally launched at the 27th International Cartographic Conference in Rio de Janeiro in August. My good friend, current ICA President Georg Gartner has also written in a similar vein recently on The Relevance of Cartography and Challenges to Cartography. As cartographic professionals, we all need to help develop a better public image; one that encourages amateur cartographers to see themselves as such (or as working towards becoming professional) and that allows people more generally to understand how the map on their mobile phone arrived there. It’s not magic. It’s cartography. It's a great word so let's embrace it.

Postscript: I'm no longer Editor of The Cartographic Journal after a 9 year stint but if I were...this would be the first Editorial of 2015.

Postscript 2: Well done if you got to the end. I hope it's provoked some thinking.