Monday, 2 September 2019

Globe projections and insets in QGIS

Proper QGIS boffins may remember Hamish Campbell's excellent post on the topic of 'Azimuth Orthographic Projections with QGIS' from 2014, in which he described a method for creating maps with an azimuthal orthographic projection - or, what non-boffins might call a 'globe view'. The good news is that in QGIS 3.4 you can do this very easily using the built in The_World_From_Space projection and you can easily create new ones with the view of the world you want. See below for an example and then read on for the method. The great thing is that you can also have a globe view and a normal, flat view in the same Print Layout as QGIS now supports multiple projections in the same layout.

The graticules are from Natural Earth

Okay, how to find this 'world from space' projection? The easiest way is to click the little CRS button at the bottom right of the QGIS window, or go via Project > Properties > CRS. Then you just search for it in the CRS search box, as you can see in the screenshot below - where you'll also see a couple of modified CRS versions I created. Just in case you don't know, CRS stands for coordinate reference system and they all have an EPSG code so that's why you'll see 'EPSG' and some numbers. If you're a proper carto boffin you'll know your 4326 from your 3347.

You can see the properties of the projection here

Okay, so that's how you set it. How to create a view centred on where you want? For that, you first need to copy a bit of the text in the above box, which I've pasted below so you can try it yourself.

First, copy this text: 

+proj=ortho +lat_0=42.5333333333 +lon_0=-0.53333333339999 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs

Then replace the 42.5333333333 wih the latitude you want and the -0.53333333339999 with the longitude you want. You don't need so many decimal places! So, for example, I did this for New Zealand and this gives me:

+proj=ortho +lat_0=-40.5333333333 +lon_0=-157.3333333339999 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs

You can see by looking at the numbers above that I have centred the projection on 40 degrees south and 157 degrees east. In order to create this new projection you just need to go to Settings > Custom Projections > click the green plus symbol and then at the bottom of the window give your project a name and then paste in your new projection information into the box. See below for what this looks like for my NZ projection.

Click OK to activate it

Once you've created a new projection you can just go back to Project > Properties > CRS and search for the new projection and use it. This is what the NZ one looks like, below.

This is not a map without New Zealand

I mentioned above that you can have a globe view like the above in a Print Layout with a different, flat projection in the main map. This is very easy. See below for an example where I have the globe view projection in the top left and a different projection with the same layers in the bottom of the image - in this case using the Equal Earth projection.

One Print Layout, two different projections

This is where you set the CRS for each map

I've put this together quickly so it's not very polished but see below for an example of the kind of thing you could do using this method - I quite like the globe locator inset, and it's also used by a good few news organisations, including the BBC. This used to be quite a lot of hassle in QGIS but now it's very easy and, I think, quite effective.

I've used Layer Effects to add a drop shadow to the globe

The only thing I should add is that in order to get the sea showing in the background here - rather than having the land floating on a blank canvas, I took the 1 degree graticule and converted it to a polygon layer via Vector > Geometry Tools > Lines to Polygons...

One last example below, with lots of cities, just for fun.

Cities are from Natural Earth as well

That's all for now. I hope you find this useful.

Friday, 16 August 2019

Constituency cards

Everything is fine 
This blog post provides some background to my attempt to create a simple 'constituency card' for every UK constituency in the run-up to an election that may or may not happen. This is at a time when, if I may be so bold, it can seem like everything is not as fine as it perhaps once was. I had the idea for this when I saw that there was a new set of official MP portraits that anyone could download and use but then other things got in the way. So, this is part open data experiment and part election prep, though mostly the former. Consider it my attempt to take lots of ugly data and turn it into useful information in an easy-to-digest way. See, I told you everything was fine.

Just want to see the maps? Okay, here you go. Want the underlying data files ? No problem.

An example of one of the constituency cards

Things keep changing

Here's an easy-to-remember short url you can use to go straight to the cards:

What's the point of this?
I'm not a member of a political party and never have been so there is no underlying conspiracy here. Or at least, if there is, nobody told me about it. Aha, but why did I put the '% Swing required' figure on the individual constituency cards? I put it there because I think it's an important thing to know, for everyone with an interest in politics, regardless of whether you're the MP for Knowsley (George Howarth, Labour, majority: 42,214) or the MP for North East Fife (Stephen Gethins, SNP, majority: 2).

The point of this project (which is a spare time thing, not part of my day job) is to provide a single card for all 650 UK constituencies, which tells us who the MP is, what they look like, how they did at the last election or by-election, what it would take for the seat to change hands, plus a bit of other information.

On the last point, I calculated the straight-line distance from the centre of each constituency to the Palace of Westminster. The closest, unsurprisingly, is Cities of London and Westminster at 0.86 miles and the furthest is Orkney and Shetland at 581 miles.

Ideally, people will be able to click the link to the image files on their phone, tablet or computer and then flick between individual cards and make comparisons between places, find out more about individual constituencies and generally learn stuff.

How did I do this?
I compiled a list of official portraits using this blog post on the API, spent ages trying to figure it all out and then once I had a list of official photos, I added to it with other photos in the public domain because there isn't an official photo for every MP. Then I supplemented this data with information on current MPs from mySociety, then I put together a UK-wide geo file and chopped out the loughs of Northern Ireland so it looked right. I then added information on distance to Parliament and the size of each constituency, and mashed it all together. Then, in QGIS, I spent a while sorting the layout, editing and editing and tweaking and tweaking until arriving at the final result. The image below shows you what I ended up with.

The labelling is always tricky

Stuff wot I got wrong or not quite right enough
Sometimes the labelling isn't perfect. That is, sometimes places you might expect to be labelled are not, and some that you think should not be actually are. My labels file has 42,000 or so place names and I use a variety of rules and filters to decide what gets placed on each map but occasionally this doesn't work that well. I could devote tons of hours to it and make further improvements but I think I'm at the point where I'm happy enough with it.

Independent MPs. I'm sorry not to have given the different MPs, who are in different independent groupings, different colours but too many colours would not work well in my opinion. So this is not quite right but to be honest I found it hard to keep track of who is in what independent grouping and who is not.

Occasionally my swing figures are 0.1% out or so compared to some of the figures I've seen elsewhere online but in general they agree with the Target Seats lists on Election Polling.

Colours, maybe. I tried a few different versions without a white dim surrounding the featured constituency on each map but it became a riot of colour at times so I've gone with a 33% opacity white mask layer to dim it a bit. Sometimes this isn't perfect but I like it better than the alternatives, including a dark dim.

Style stuff, etc.
This can be controversial! But I'm quite chilled about it really. I tried my best to make them look good and also to build in some kind of logic and flow to the individual cards but of course they're never going to be perfect but I'm happy enough with them.

If you want to play around with the files you can find them in the files repo. You can style the files using the html colour codes in these - there is a colour for the winning party and one for the second placed party.

Decisions, etc. Well, the name of the constituency goes at the top left and the whole top row of each card is reserved for these names. I have sized everything so the longest constituency names (e.g. Cumbernauld, Kilsyth and Kirkintilloch East) don't run beyond the end of the map image below it. I have also placed the little black-dot-locator inset there as it is close to the name of the constituency and your eyes don't have to move much to locate it, and then you can scan down to the main map image.

I've given the sea a muted blue colour, which also applies to Northern Ireland loughs. I decided not to add lochs and lakes elsewhere as I couldn't decide where I would place the size cut off (e.g. include Loch Morar but not Loch Shin?). Too messy, but I made an exception for Northern Ireland because people are forever making maps without their loughs and they are really big and important.

I included a 1 mile scale bar not because I'm a devotee of the imperial system but because people know how far it is and for most constituencies the bar is big enough. The one mile scale bar is of course tiny on the Ross, Skye and Lochaber card and some of the other huge constituencies.

I included a bit of foreshore for Great Britain (didn't have the foreshore data for Northern Ireland) as I think this makes things look a bit better - usually - in coastal areas although of course it gives some places the appearance of having a lovely sandy beach when they actually don't. But I like to bequeath beaches to people who don't have them - in the spirit of mending the nation's divides.

You'll notice that in lots of maps you can see a nearby city. This is because I wanted people to be able to look at a map and say, for example, "Ah, so Aberavon is near Swansea" or "Oh, right, Meon Valley is kind of between Southampton and Chichester". I don't know about you but some of the constituency names I find quite baffling as they give little clue to the uninitiated where they actually are.

I also included a fairly dull building mask layer. The idea here is you can see the layout and form of the built environment and say things like "this is quite a densely built up area" or "this constituency is quite sparsely populated". You may know this already but I think it adds an extra dimension of knowledge for places I'm unfamiliar with so I like it. Just hard to get the balance right so it doesn't mess too much with the colours.

I decided it would be good to add a party-coloured frame round each photo in lieu of a legend and then the MP's majority below that in large bold text. The MP name and party go in the coloured box below the image. But then I thought it would be useful to show who came second and what kind of swing would be needed for the second place party to win. In some cases they are not likely to win, to say the least, but in a good few things are very close. I make it 171 constituencies (out of 650, so 26% of the total) where the required swing is less than 5%.

The descriptive text below each map has some other information I found useful and I thought I'd add some geographical context with distance from parliament and also area in square miles - these figures make more sense when you compare them with others I suppose. I calculated these in QGIS.

I also added a sources box to the bottom right of the image so it's clear where the data come from.

The font is Montserrat.

Nerd notes
Not too much else to say here but I did use Excel for some vlookup type stuff but basically everything was done in QGIS using the Atlas tool and a range of quite messy looking functions. For example, where the required swing was below 0.05% I had to make sure two decimal places were displayed instead of the default one I used otherwise it would say 0.0% there. For that, I used this:

 format_number( "REQSWING"  ,  if( "REQSWING"< 0.06, 2,1))||'%'

Making sure there were no 0.0% swing figures was fiddly

I used a rule-based symbology for the place name labels - using this rule to only show places in the current Atlas feature:

intersects( @atlas_geometry ,$geometry) 

And then to make sure cities outside the current Atlas feature were always showing I used this on a copy of the same layer, filtered to only show cities:

NOT intersects( @atlas_geometry ,$geometry) 

But how on earth do you get data defined text colours? Well, I couldn't figure out a way to make this happen in QGIS Print Layout as it's not a default option on font colour so I figured out a workaround.

What I did was add the swing figure to the layout using the  "REQSWING" variable in the layer driving the Atlas. I added this as black text. Then I added a white text box over the top of it. Then what I did was set the colour of this text box overlay to match the colour of the party that came second. This was easy because I already created a field called "secondcol" with the html colour code of the party that came second. Then I changed the Rendering blend mode to Screen so that the black text changes to whatever the colour of the second placed party is. Sounds complex but works perfectly - see below for screenshot.

A workaround that works - I couldn't figure out another way

You may also notice that in the five constituencies that have had a by-election since 2017 there is a little '(By-election)' indicator below each Majority figure. Since I created a field in my data table with data on when the last election was (i.e. either 2017, 2018 or 2019) I was able to add a text box with '(By-election)' in it and then set it to be 100% transparent if the value was 2017. Otherwise it is displayed. This is the text I used in the data defined over-ride box on Rendering for that.


WHEN  "last_vote" = 2017 THEN  0

ELSE  100


Again, a bit fiddly but effective

That's about it really. There's nothing super-fancy going on behind the scenes and of course I still haven't really figured out how to make friends with date and time formats. Drives me mad, but I got there in the end. See below for the mess that makes up the data-driven text at the bottom of each card (using the 'Render as HTML' tick box in QGIS).

  • '<b>' ||  replace(  "cname1" ,'Kingston upon H','H')  || '</b> had an electorate of ' ||  format_number("elect17" ,0) || ' and a population of ' ||  format_number("pop17",0)  || ' in 2017. The distance from the centre of the constituency to Parliament is ' ||  format_number( "mi_fr_pow" ,0)  || ' miles, and the constituency covers an area of ' ||     format_number("sq_mi" ,0)  || ' square miles. At the 2017 general election, this constituency was number ' ||  "dec_order"  || ' out of 650 to declare the result, at ' || format_date( "just_time" ,'hh:mm') || ' on ' ||  format_date(  "dec_time" ,'dddd d MMMM') || '.'

Whose shoulders am I standing on here?
Ordnance Survey, mySociety, the amazing team at the House of Commons Library, Philip Brown, Elvis Nyanzu, Alex Parsons, the Parliamentary Digital Service, the very nice voters of the UK, and of course the team who make QGIS.

Without the hard work, expertise, knowledge and experience of a wide variety of people and organisations it is impossible to do stuff like this. I am simply trying to bring data together and make it into useful information. Trying to contribute to the open data ecosystem I suppose.

Who will win the next General Election?

Saturday, 3 August 2019

Playing around with shaded relief maps

Last summer I was playing around with maps of where ships go, but this summer I'm back on land with some shaded relief maps, just for fun - and also a bit of an experiment in styling, labelling and all that. This is all very simple: I took a single tile from NASA's SRTM 1-arc second dataset (basically a map tile with a resolution of about 30 metres, covering a rectangular area about 110km north to south and 60km east to west). This data was captured in 2000 by the Space Shuttle Endeavour from 233km above the earth. The easiest way to download it is using Derek Watkins' 30-meter SRTM Elevation Tile Downloader. Anyway, enough of this for now, here's one of the final maps, which I'll explain below alongside more maps and technical information and stuff about the area I've mapped.

This is the 'single malt' version

The map you see above is of a single SRTM 30 metre tile - NG57004 - and I did it all in QGIS 3.8, no other software involved. There are a few layers here with different blend modes and transparencies plus some basic labelling that I added manually. The area itself is more or less a bit of the Cairngorms plus Speyside in the north of Scotland and is just to the south east of where I grew up so I'm very familiar with it, and if you like Scotch whisky or shortbread you might be as well.

Movie stars, whisky and shortbread

This area is world-famous for whisky production and indeed the Speyside area is one of the five whisky regions of Scotland. It's also where I went for school trips to places like Boat of Garten and Nethy Bridge. If you're into this kind of thing you'll also notice that some of the place names relate to famous Scotch whiskies, including Glenlivet, Knockando and Dallas (as in Dallas Dhu single malt, which actually comes from just outside of Forres, nearby). You may notice in the bottom right corner of the map some places associated with the royal family - including Braemar and Crathie. You might also see Aberlour in the north east portion of the map and that's where Walkers shortbread comes from - one of Scotland's big food exports and which I have seen for sale in airports and shopping malls from LA to Beijing.

You'll find polar bears down in Kincraig and even a bit of surfing at Lossiemouth beach (cold water! I've tried it). But I mainly chose this area because of the lumps and bumps of the Cairngorms - not very high elevation-wise but at 57 degrees north and with five of the highest six mountains in the UK (named on the map) this part of Scotland can be a very harsh environment - though also extremely beautiful. I've shared some more traditionally-shaded versions below but this first one above is deliberately a bit different, since I have gone with something close to a single malt whisky colour scheme. See below for the a slightly more exotic one.

A bit exotic but I like this version

Growing up, I also used to take the bus from Inverness, hire some skis and go skiing at Cairngorm Mountain and remember feeling like I was suffering from severe frostbite on the old 1960s chairlift (in the days when sledges and giant inner tubes were not uncommon and it was all a bit hot tube time machine fashion-wise). Anyway, if you like 50 mile an hour crosswinds, boilerplate ice and zero visibility conditions the Cairngorms are the place to be. If you've visited in summer during midgie season then you'll definitely prefer the former (seriously, see this video).

Right, back to the maps - the next three are as follows: a slightly subtler version of the original above, then a couple more traditional shaded reliefs - one in purple shades then one in orange shades.

I was going for 'single malt' but might be burnt toffee

Some people prefer this kind of thing

This is a lot subtler but not as interesting I reckon

A few other things to say about the maps. I've added a couple of place names to the Tarbat peninsula to the north west of the map - they're not part of the area I wanted to map but I always like to know what's what. Plus Portmahomack is an interesting place and you pronounce it just as it's written (like Drumnadrochit!), unlike loads of places in the Highlands, such as Avoch, Kilravock, Ballachulish and many more. Oh, and Moray Firth is not 'Mo-ray' - it sounds just like 'Murray'.

I've also added a curvy 'Cairngorm Mountains' label and a smaller 'Monadhliath Mountains' label on the west side of the Spey - hopefully it doesn't stray too far north although I'm not 100% sure on what's considered the northern limit of this range.

I did do a version in a more traditional topographical style so I've posted this as well. Below that, you'll see one more version - in greyscale, just to complete the set.

Possibly a bit more realistic
This actually looks quite pleasing

I was just playing around with a bit of relief data here during some down time, and wanted to make a few maps that looked a bit different but were still recognisable as shaded reliefs. I could probably spend hundreds of hours on this if I wanted to get everything right. The underlying data is pretty good resolution (30m, roughly), better than the 50m resolution open data from Ordnance Survey. I do have access to the 5m resolution data but since it's not open I didn't use it this time, but it would give a lot better quality image. We do have 1m resolution Lidar data for small chunks of Scotland, so it's a shame the 5m Ordnance Survey data isn't yet open.

'What does this area look like on the ground', I hear you ask? Well a good chunk of the off-road stuff, including the Lairig Ghru and Ben Macdui is actually on Google streetview, and you can see a screenshot below.

If you're looking for some seriously fantastic NASA-related mapping, check out the amazing work of Joshua Stevens.

That's all for now. The original 300dpi maps are in this Dropbox folder, if you want a closer look.

All six in one

Technical notes: the elevation data you see on the map is released via NASA's Jet Propulsion Laboratory at the California Institute of Technology. The highest resolution data - used here - was released in late 2015. Here's what the SRTM user guide says about the data:

  • "datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA – previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. Together, this international space collaboration generates a near-global digital elevation model (DEM) of the Earth using radar interferometry"

The data were collected from the Space Shuttle Endeavour over an 11 day period in 2000:

  • "SRTM was the primary, and virtually only, payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. Following several hours for instrument deployment, activation, and checkout, systematic interferometric data were collected within a 222.4-hour period. The instrument operated almost flawlessly and imaged 99.96 percent (%) of the targeted landmass at least one time, 94.59% at least twice, and about 50% at least three or more times. The goal was to image each terrain segment at least twice from different angles (on ascending, or north-going, and descending, or south-going, orbit passes) to fill areas shadowed from the radar signal by terrain"

See this BBC article from 2000 about the SRTM mission - it's a very clear explanation. And here's a picture of the 60 metre long mast that was used to collect the data in the maps above. It's called the Able Deployable Articulated Mast (ADAM). There were radars on the mast and on the Space Shuttle itself.

This is where the SRTM data came from - more info here

* I don't even drink whisky, but I'll lose my Highlander card if anyone finds out so please keep it quiet

Sunday, 14 July 2019

All English Premier League Grounds in 60 Seconds

I've been thinking a lot about information design, urban density and mapping things in context lately as part of my day job so I thought I'd experiment with all these things using an interesting example. To cut a long story short, I took Ordnance Survey data, extracted all 20 Premier League grounds, edited them slightly to match what's on the ground and then did some mapping. The results are shown below for all 20 teams (as of the 2019-20 season), starting with a gif and followed by some further explanation. All you need to know for now is that the main image in each graphic shows the stadia at the same scale as the rest and the little stadium silhouettes at the bottom are all shown at the same scale as well so you can make comparisons between the size of different grounds. I've made all files (including geo files of the stadia) available on this page.

Three seconds per frame, 60 seconds in total - mp4 here

I've always been interested in cities, urban history, football, deprivation street patterns and that kind of thing - and how they all interact - so this seemed like an interesting way to look at it. I did map this before in relation to deprivation but this post is just about showing each ground in its local context - all at the same scale and with just a little bit of additional information for each one.

What I've attempted here is to produce a set of individual images and a gif (I also did an mp4 version) that loops through each ground, gives some basic information about each, shows where it is in England and also shows you how it compares in size and local context to all the others. I've added in some street names but not all, because that makes it far to busy and harder to read. 

I've been to a few of these grounds to watch a match - most recently to Goodison - and I used to drive past Old Trafford almost every day on my way back from work but for lots of others I'm not familiar with them so I wanted to produce a set of simple visuals that clearly shows each ground in context. I really find some of the older ones interesting, the way they are packed tightly into their surroundings - like Anfield. But this was really an experiment in layout and map design more than anything else; I just used this dataset as it was interesting.

As for the colours along the bottom, I used the colours for each team on their official website, though for some there are more than one and for at least one (e.g. Man City) I used a darker one so it would show up better on the stadium silhouette. 

And, oh yes, I was probably thinking about this topic because for the first time in a decade Sheffield will have a team in the Premier League (just not the one in my neighbourhood!).

You can find all the original files - 20 high resolution images plus a gif and an mp4 - here.

Data sources: for the club information it was a mix of Wikipedia and the Premier League website. For the outlines of the grounds I extracted these - then edited them - from Ordnance Survey's OpenMap Local product. The background imagery is Google satellite view. The street names are also from Ordnance Survey but I extracted these from the OS Open Zoomstack local roads layer and then symbolised them just to show a few of the road labels. I thought this was important given the significance of some local roads and how several grounds are named after them - or parts of grounds are. 

Software: I did the mapping in QGIS 3.4, I created the gif with GIMP, and the mp4 I made with ffmpeg. For resizing and cropping and otherwise editing the image outputs I used IrfanView. The font is Montserrat, one of the free Google Fonts and a current favourite of mine.

Saturday, 1 June 2019

New Book: GIS for Planning and the Built Environment

A bit of a different blog post today, because a new GIS book by Ed Ferrari and me has been published. It’s called GIS for Planning and the Built Environment: An Introduction to Spatial Analysis and it’s an intro text aimed at anyone with an interest in GIS and the built environment, from geography and planning students to aspiring architects and landscape majors, plus people working in professional practice. We’ve included examples from across the world, from the bustling streets of Manhattan to the zig-zagging ski slopes of Austria. We hope you’ll see that we love GIS and what it can do, but we recognise that not everyone shares our passion, so when we were writing the book we also had one eye on the reluctant GISer - that’s why you can use our book to dip in and out of topics as and when you need to. 

Front cover

Want to know how to make better maps? Okay, no problem, head straight to Chapter 6. Desperate to know more about Waldo Tobler (pictured below with the authors in Santa Barbara on a previous GIS  world tour), his famous ‘First Law of Geography’ and why everyone goes on about it? Then head directly to Chapter 7. You'll also learn about the much less well known 'Second Law'. Want a good overview of contemporary GIS for planning and the built environment more generally? Excellent! Read the whole book. Looking for more information on a specific technical topic? Then our comprehensive index is the place to begin.

The authors, with the late Professor Waldo Tobler

There are many GIS texts out there, from the comprehensive to the highly technical. Ours sits somewhere in the middle as what we think is an accessible, easy-to-read reference for anyone with an interest in the topic as it relates to the built environment and planning more generally. We begin by establishing the book’s aims, and set out our hopes that anyone who reads the book will:

  • Obtain the knowledge, skill and experience to understand how the spatial analysis of data about the ‘real world’ can be used to understand planning problems;
  • Be able to apply a broad range of spatial analytic and visualisation techniques using industry standard GIS software packages; and
  • Understand how maps and data can be used effectively as evidence for planning- related issues.

In the introduction, we also include a little guide to what you’ll find inside so that, for example, if you want to know more about data (including open, big and ‘bad’ data) we tell you to head straight to Chapter 4. In here you’ll also find a bit more on the parts of GIS that really can be baffling if you’re just starting out (like what file formats to use, things like dots per inch, and more about ‘the mighty shapefile’!). 

An example of new Ordnance Survey data we use in the book - this is Manchester

Although we recognise that much of what might be considered core elements of GIS and spatial analysis change little over time, we also recognise that things have changed a lot in the past decade, with new technologies and platforms like QGIS and CARTO helping shape and re-shape an already vibrant discipline. New open data sources have added fuel to the GIS fire and social media has fanned the flames to such an extent that maps are now everywhere, or so it seems. We cover some of this in Chapter 5, where we note that such developments have often led to the creation of what one might charitably describe as ‘bad maps’, and which the book's authors have been guilty of many times! 

But because we’re optimistic people, we do of course focus mainly on the positives, with reference to GIS and cartographic pioneers like Kenneth Field, Anita Graser, Gretchen Peterson, Joshua Stevens and the inspirational Atlas of Design series, where you’ll find some truly breathtaking examples of what can be done with spatial data. We also offer a good bit of advice on how to avoid common pitfalls in your work, so if you’re a student doing GIS and you want to get better grades/marks then we can probably help with that too!

But this an introductory text, and we don’t try to cover everything (far from it) because we thought that would be overwhelming. But we aim to cover the most important things for those working within built environment disciplines more broadly. That’s why the book is peppered with examples of GIS in the real world that people might be able to understand without having to look up a reference book!  Often, we use separate boxes for these, like when we were trying to explain the topic of generalisation in GIS, as you can see below.

Our new book is in part an attempt to bring GIS back to the forefront of planning and built environment disciplines, but also partly an attempt to show how it can help us understand the world just a little bit better, so long as we don’t get carried away with ourselves. We’ll end here with what we say in our concluding remarks in Chapter 9: 

“GIS lets us see. It opens up a world of visualisation that spreadsheet models can never hope to rival. It helps us make links between phenomena on the basis of the attribute that is common to so much of what goes on in our world – the attribute of where.”

We hope you’ll agree and that you’ll find our book useful if you choose to take a closer look - if you want to find out more, head to the book's homepage.