Thursday, 7 October 2021

The Big Spatial Reconfiguration of Housing and Labour Markets?

Instead of - as I might have done previously - writing a long, possibly very boring academic paper on the spatial reconfiguration of housing and labour markets in the UK, I am instead going to write a relatively short, possibly only mildly interesting blog piece about The Big Spatial Reconfiguration of Housing and Labour Markets. What on earth am I on about? Well, let me explain, but first let me show you a map.

Think of these blobs as mega-commuter zones

Okay, let's begin. If you type 'housing and labour market interaction' into Google you should see some academic papers among the results. In fact, depending upon where you are in the world and how things pan out, you might even see a paper called 'The Spatial Interaction of Housing and Labour Markets' as the first result. This was written by my former colleague Ste Hincks at the University of Sheffield alongside Cecilia Wong at the University of Manchester. You can read the paper if you have access, but the basic idea here is that housing markets and labour markets have distinct geographies and they are not the same, usually. But, sometimes they interact and overlap in different ways, depending upon lots of things, like by income and occupation, by gender, life stage, in relation to transport infrastructure, housing costs and all those kinds of things. If you're really into the subject, and talk about things like spatial arbitrage at parties, then see Ste's more recent paper on the geodemographics of commuting. It's very interesting and has cool maps.

Um, isn't this just a long-winded way of saying some people live in Warrington so they can work in Liverpool OR Manchester? 

Yes, kind of. But the reason I'm writing this now is because I've been thinking a lot about these issues for years, and writing various papers on commuting and connectivity - among other things. I've also written a few things on the topic here, including a particularly badly timed '45 minute cities' piece in March 2020. Now, I know that the idea of the 'return to the office' is somewhat (very) exclusionary, but all the same lots of people do work in offices and there has been talk lately about the need to 'get back to the office', for a variety of reasons. I can see both sides to this, but my feeling is that we may, possibly, be witnessing the 'Big Spatial Reconfiguration of Housing and Labour Markets' - at least for a chunk of the population and parts of the country. 

What would this mean? Well, we'll see. 

In the meantime, I thought I would look at things from the perspective of what might be called The 2 Hour City. Freed from the constraints of having to live in daily commuting distance of the office of the old world (say, under an hour, or even the famed Marchetti's constant - 30 mins each way), what might things look like if people saw a 2 hour door-to-door commute two to three days a week as an acceptable compromise for a) options for a better place to live and/or b) more flexibility and freedom in the labour market? I'm not entirely sure of the answer, but what I did was plot the 2 hour travel zones around the three biggest English cities - Manchester, Birmingham and London. Let's have another map below and then I'll say more about it. It was James Blagden that got me thinking about this, as he's been doing a lot of work on the general topic lately.

The overlaps between areas are the brighter bits

The map above shows a zoomed in version of the original. Just to be clear, this is based on public transport only, and getting from door to door in 2 hours or less on a weekday morning by 9am reliably. Sure, you could do it from further away if you try hard but this is supposed to be a realistic, 'you can bank on it' commuter zone rather than a high stress 'will I be late for work?' type best-case scenario map. I set the arrival time to 9am, the maximum travel time at 2 hours - and this includes every part of a journey, including time for any connections where necessary, and the arrival point to the city centres of Manchester, Birmingham and London - roughly Piccadilly, New Street and Westminster. Or, as Gareth from the actual The Office might have said about the vagaries of such parameters, "different frogs, different times". As in the past, I used TravelTime for this (I don't work for them or take money off them!).

What's particularly interesting to me in all this potential spatial reconfiguring are two things - a) the absolute size of these areas in terms of population and b) the overlaps - where might people live if they want their household to be able to take advantage of new 'we only need you at the office two days a week' type working patterns in multiple labour market areas? I've used total population because I was thinking about housing markets first and how the new context has been shaping things (see Neal Hudson for more on this kind of thing) but you could of course do the same thing with jobs data (e.g. BRES). We know people were already doing this kind of thing a bit pre-2020, but if it became possible for millions more people, where might the optimal housing and labour market overlaps be?

Well, let's check the maps for the overlaps.

Hello Northampton!

Hello Crewe, hello Chesterfield!

Of course, I've only done this for three cities, so you can imagine what it might look like if I added in lots more - say all the big employment centres. We'd get one big overlapping blobby Venn and everyone would probably want to move to Kettering.

Here's another map, more zoomed in to the London area - note Reading in particular here. Note also places that are not red and then feel free to plug in a journey to Google Maps and see if you can get it to get you there - occasionally it says it is possible but in my experience mostly not, but either way, you know how commuting actually works. It's not safe to leave it tight and to be sure of a 2 hour door to door connection on a regular, reliable basis you need to be realistic and also you want to avoid any Olympic sprinting.

Live in Reading, work in Birmingham

Let's all come back in 5 years and see how this all pans out. My feeling is that the Great Return to the Office will be partial, patchy and prolonged. Possibly also painful. I'm out of that game now, and my commute involves about 15 stairs (I can't be sure of the exact number, will report back) and the careful transportation of a cup of hot tea up said stairs. 

Okay, go on then, one more map. The bright green spot is where you can do central Manchester and Central Birmingham within the 2 hour limit and the yellow is where you can do London and Birmingham.

Yellow bits can reach Birmingham or London


So, in the meantime, how might we know if The Big Spatial Reconfiguration of Housing and Labour Markets is actually happening. Well, we should find early clues in the most obvious of places, like in house prices in the overlapping areas. But might we also see jobs themselves move as a response? Maybe. Might we see more housing in different places? What might all this do to the geography of housing demand, the geography of housing markets, housing search (which I've written a bit on before) and all that. Again, wait and see.

I'd be interested in what others think and have to say about this too. Will we see a enlarged Hebden Bridge Megaregion? A Warrington Employment Megaplex? A Rush on Corby?

For now, only time travel will tell us how travel time changes in future.


Thursday, 9 September 2021

10km City Squares

In 2020 I wrote a piece on here along with some maps showing 10km by 10km (that's 6.21 miles) city squares for places across Great Britain. I'm fascinated by the different forms and types of urban development we see on the ground, so I'm back for more - this time adding in blue and green to the grey of building footprints. You may even have seen my Twitter thread on this, but here I'm sharing some final versions of these grey-green-blue maps and also saying a bit more about what they actually are - and are not. But first, let's look at a few of the final map outputs for some interesting places. There are currently maps for 199 different places across Great Britain in the project folder. Sadly I don't have the data for Northern Ireland, otherwise I'd have included that too.

























What are all the different colours?

The grey bits are buildings, roads and railways. The blue bits are surface water (lochs, lakes, ponds, rivers and the like) as well as coastal water. The green bits are less straightforward in that they do not represent every bit of green in a place - and are not intended to. They are the areas included in the OS Open Greenspace dataset from Ordnance Survey. You can read more about it on the dedicated webpage for it but in short it includes public parks, playing fields, sports facilities, play areas, allotments and more. So, things like some large green areas (e.g. national parks, gardens, and the like) are not green on these maps. What I am trying to do here is compare like-with-like across the country to examine density, the patterning of urban fabric, water coverage and a particular kind of green. Nothing too profound but something I find particularly interesting.


My town isn't included - you scoundrel!

I can in theory do this for about 43,000 places across Great Britain so if the place you want to see has been overlooked and is not included in the web folder then feel free to get in touch and I might be able to add it. Alternatively, strike me off your Christmas card list and consider me a non-person from this point onwards.


Can I print one of these and hang one on my wall?

Be my guest. I have outputted them at 300dpi (png files) so they should print at a reasonable size. I may yet add some to my print store but not sure yet.


How did you make these?

I downloaded the map data from Ordnance Survey and then put the layouts together in QGIS. I then automated the exporting of each map using the QGIS Atlas function. If you're desperately keen to learn how to do such a thing then I also do training on that. Once you get one layout set up, it's fairly straightforward to export a big batch, so long as your computer is up to it. See below for how they look in QGIS just before I export them.

This is how it looks in QGIS Print Layout

Why did you choose 10km?

Partly because it's a nice round figure within which a lot of towns and central cities fit, and partly because 20km was too big and 5km was too small. I chose it through trial and error and it works reasonably well for most places.


That's all for now. Happy browsing.



Wednesday, 1 September 2021

A village called Trumpet (and how to find it using open data + QGIS)

This post has two main purposes. The first is to highlight the often weird and wonderful place names of Great Britain, including the village of Trumpet in Herefordshire, recently visited by my colleague Philip Brown - hence the title. The second purpose is to show anyone who is interested how to filter and find them using freely available Ordnance Survey data and free GIS software (QGIS). I'll start with a little bit of information about the data, then some maps of interesting place names, and then at the bottom I'll share more for any aspiring data boffins who might not yet know how to do this. I use these kinds of datasets in my QGIS training sessions, so that's the real reason I've been playing around with this stuff lately.

Trumpet, Herefordshire

The first step is to grab Ordnance Survey's OS Open Names dataset - a free download, available in several formats, and no registration required. There are nearly three million points in this dataset (2,952,321 as of the August 2021 file) and for each point we have the name of the feature in English, plus Gaelic or Welsh where applicable. You can read all about it in the official user guide, but for now all you need to know is that for each point there is a column which tells us whether it's a populated place, transport node, landform, some kind of landcover (e.g. a forest), hydrography (e.g. a loch) or 'other' (e.g. a primary school). For this little blog post, I filtered the data so it relates to populated places only, and this gets us a total of 43,120 places across Great Britain. This is how the places are broken down by type in the OS Open Names classification

There are four places called 'City' in Great Britain - two of these are hamlets and two are suburban areas. there is also one hamlet called 'Hamlet' and one suburban area called 'Hamlet'. 

Maps of interesting place names

Right, let's try some maps of interesting place names now - I'll leave the labels on and messy, so you won't be able to see them all on every map but that's okay, this is just a bit of trivia.

Place's, right?

Note 'London Apprentice'

Lots of Saints

Map-on-the-blog

The only exclamation in the nation


Rhymes with scone

Nicely lined up


I detect a pattern

England has cornered the market

Cat places

Such vital, vital work

Hello Romans

Yes, you get the idea. I could go on forever but I won't - I'll just add a few more and then say a little about how to structure queries for this in QGIS.




How much longer caan I go on?

I thought this should go at the bottom


How to filter to find different places in QGIS

If you are a regular reader, or if you follow me on twitter there is more than a tiny chance that you might already be a bit of a GIS nerd, so this bit may be surplus to requirements. If not, and you want to know how to filter layers in QGIS, read on.

In the final map above I have shown places containing the word 'bottom', because I am very mature. To do this in QGIS, all I did was right-click the layer in the Layers panel, click Filter... and then enter the correct query into the Provider Specific Filter Expression box, as you can see in my screenshot below.

This is using the OS Open Names points layer

Here's the filter text, if you want to copy and paste it:


"TYPE" = 'populatedPlace'

AND 

(

"NAME1" LIKE '%bottom%' 


Explanation? There is a "TYPE" column and a "NAME1" column in the OS Open Names dataset so I've used these to filter the data to show only those places that are categorised as populated places, and only where the name contains the word 'bottom'.

On the last point, the LIKE operator looks for anything between single quotes - the % signs just ensure that QGIS finds anything with bottom, including anything with text before or after it. If you put in 'bottom' it would find only places called 'bottom', if you put in '%bottom' it would find only places ending in 'bottom' and if you put in 'bottom% it would find only places that start with 'bottom'. Note that you may read that the LIKE operator is case sensitive, but in my case I was using QGIS 3.16 and it was not case sensitive (I believe this is because only certain data types will require you to take case sensitivity into account - and in those cases you can just use the ILIKE operator).

Where you have variations of spellings, etc. then you might need to be a bit more creative - e.g. in the case of the 'st.' / 'st' / 'saint' map above. This was my query for that.

"TYPE" = 'populatedPlace'

AND 

("NAME1" LIKE 'st. %' OR
"NAME1" LIKE 'st %' OR
"NAME1" LIKE 'saint %' )

One thing that can be confusing is how to search for text that contains an apostrophe, because if you just put one apostrophe between single apostrophes (i.e. single quotes) and % symbols then it won't work. The solution is to add two apostrophes, as below.

"TYPE" = 'populatedPlace'

AND 

(

"NAME1" LIKE '%''%' 


How about the hyphen-hyphen-hyphen one? Well, that goes like this:

"TYPE" = 'populatedPlace'

AND 

("NAME1" LIKE '%-%-%-%')


If you just wanted to filter to show only places of your choosing, rather than filter to find some that contains certain bits of text, then it's just a case of using the IN operator and adding places in a comma separated list, in brackets, as shown below.

"TYPE" = 'populatedPlace'

AND 

(

"NAME1"  IN ('Aberdeen','London','Southampton','Middlesbrough','Liverpool')


Slightly different example below, where I've filtered the OS Open Names dataset using a different data type so that I can show all the 'lakes' in Scotland.

"TYPE" = 'hydrography'

AND 

(

"NAME1"   LIKE '%lake%' AND "COUNTRY" = 'Scotland'

Lots of these are tiny, but they do exist


Hopefully you get the idea - hours of fun, limitless possibilities for silly blog posts, but ultimately all this is actually very useful (as well as simple) and it's the kind of thing I do day-to-day in the course of my work.

Thanks for reading.