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.

Daytime and nighttime population density in Europe

This is a short post about a relatively new data series from the European Commission's Joint Research Centre. It comes from their 'Spatiotemporal activity and population mapping in Europe (ENACT)' project and - in simple terms - it provides gridded population data for the daytime and nighttime, so that we can compare population patterns at different points in the day. It's very similar to the GHSL data that I've written about before but the key difference is that we can compare the population of 1km cells in the daytime vs the nighttime. The data are from 2011 and it is available for each month of the year, and in two different projections, for the EU28 (as it was when the project began). But what does it look like? See below for a snapshot of nighttime population for January.

This is basically a 'where people live' map

The data were released in mid to late 2020 so many people might have missed it but I think it's a great new addition to the European data infrastructure. You can read more about the specifics of the project and the data fusion approach in this open access Nature Communications paper written by the research team. It's a nice piece, and it also sets out why - if you weren't aware - it is important to understand both the spatial and the temporal distribution of population. These issues have of course come into focus more during 2020 and beyond with the rise of Covid-19.

As for the data, I'll let you explore that yourself if you're interested but I'd certainly recommend spending some time on the website and also reading the notes and information about it. For now, here's another map of the January data, but this time for during the day (so I've turned the lights up). You can see how the settlement patterns thin out as the population is concentrated in towns and cities. Greater London's daytime population normally swells to over 10 million, for example - although this has all changed since the advent of Covid-19. Will it ever rise so high again?

Daytime population - notice the higher spikes

You can find the actual values for each 1km cell by importing the data into QGIS (or any other software that will read a tif) and then querying it. I've done this below for the January data for a small area of central London so you can see how the day and night populations differ. I've added the raster cell values to the images - these are the populations for each 1km cell either at night or during the day, so you can really see how the population changes with commuting in this example. You may have to click on the image and zoom in so you can read the numbers.

Day vs night population

Right, that's all for now. This is a great new dataset and even though the time point is 2011 it provides a really useful resource for spatiotemporal analysis. It will be very interesting to see what things look like in future in relation to daytime vs nighttime populations with the impact of Covid-19 on the nature and location of employment.

Download the data (each tif file is about 14.5MB)

ENACT seasonal nighttime and daytime population grids for 2011. Values are expressed as decimals (Float). The data is published at 1 km resolution in Lambert Azimuthal Equal Area (EPSG:3035), 12 monthly nightime grids and 12 daytime grids, and at 30 arc-seconds in WGS-84 (EPSG:4326), 12 monthly nightime grids and 12 daytime grids. The compressed ZIP file contain TIF files and short documentation.

https://data.jrc.ec.europa.eu/dataset/be02937c-5a08-4732-a24a-03e0a48bdcda

Citation

Schiavina, Marcello; Freire, Sergio; Rosina, Konstantin; Ziemba, Lukasz; Marin Herrera, Mario; Craglia, Massimo; Lavalle, Carlo; Kemper, Thomas; Batista, Filipe (2020):  ENACT-POP R2020A - ENACT 2011 Population Grid. European Commission, Joint Research Centre (JRC) [Dataset] doi:10.2905/BE02937C-5A08-4732-A24A-03E0A48BDCDA PID: http://data.europa.eu/89h/be02937c-5a08-4732-a24a-03e0a48bdcda

45 Minute Cities

Have you ever wondered what a city would look like if it only included areas within 45 minutes of the centre? Of course you have, otherwise you wouldn't be here. Well, even if you haven't, please read on and be sure to check the nerd notes at the bottom of the page. The idea here was to produce a set of maps, based on a central point in 26 different British cities, and a 45 minute time cut off. More specifically, from everywhere shown on the maps below you should be able to reach the centre (in this case I chose a main railway station) within 45 minutes of leaving home (note, not from when you get on a train or in your car). I included travel by public transport, driving, or driving plus train in my model and an arrival time of 08:45 on a Monday morning. I then calculated the population within each area and the maps you see below are the result. These '45 minute cities' are of course not actual cities from an administrative point of view, although they are a kind of functional entity that makes more sense from an economic point of view. But really this is just a first-stage experiment and I thought the results were interesting so I'm sharing them here.

What's this all about, then?
Partly it's about me using a new tool more regularly - TravelTime platform's QGIS plugin - and partly it's about some other work I've been doing over the past few years relating to functional urban areas, polycentricity, functional regionalisation, and whatnot. You can read a bit more about one of the off-shoots of this work here, which is all about a little graphic I tweeted in February 2020. It also relates to some of my previous research, including this work on transport-related barriers to employment for the Joseph Rowntree Foundation.

But for now, in this case, it's mostly about curiosity and me wondering 'how big are these cities if we see them as 45 minute commuter-sheds based on arriving at some central point with enough time to get to work for 9am on a Monday morning?'.


What about Marchetti's constant?
The idea that on average we travel 30 minutes each way to work each day is of course well known, but I thought a 45 minute time frame, from leaving the house to arriving at the workplace (and including interchanges, walking, and the actual realities of the daily commute) would be more interesting and also realistic. My door-to-desk journey these days takes about 20 to 25 minutes but in the past it used to take anything from 50 minutes to an hour and a half when I commuted between Liverpool and Manchester each day.


How does this compare to travel-to-work-areas?
I'm glad you asked, because I had the same question early this year so I added up the 2018 population data for most TTWAs and the results are shown below, as well as on this Twitter thread. This is particularly interesting when we look at, say, Leeds and compare it to the Leeds map above. Or even the Warrington one above. But of course TTWAs are based on a commuting self-containment threshold and my maps here are just experimental outputs created as part of a little curiosity project (for now at least).

I only had English and Welsh data for this

The numbers for my city look wrong
This may be the case, but it may be a bit more complicated than that. The specific point of arrival I have chosen (the central railway station in each city - or three in London) and the specific time I chose can have an impact on the results. However, I've been through the maps and I think they look right, or at least plausible for sure. In some cases it may very well be possible to get from a location off the map to the central point I chose, but the idea here is it needs to be reliable and without rushing for connections and allowing enough time to get to and from the mode of transport at either end. It's a whole journey commute time rather than a single point-to-point, best case scenario trip. So, you may be able to do better yourself but this is based on a daily journey that most people can realistically make within 45 minutes.


'Why is Warrington so big?', and other FAQs
The population of Warrington isn't 4 million, so let's all calm down. But maybe it is and nobody had noticed. Okay, maybe not. Yet at the same time it is highly accessible, with the M6 and M62 plus the Liverpool-Manchester railway line passing through as well as the West Coast mainline. I'd say Warrington has the Connectivity Double Whammy (CDW) nailed down pretty well. I used to travel through Warrington each day on my day to work and I definitely had the 'if I lived here I'd be home by now' thought more than once. In this sense, then, Warrington may be the Ultimate 45 Minute City.

You might also ask why Leeds has such a high population in this little 45 minute city experiment, when we compare it to the TTWA population above. I suspect it's due to a mix of possibly unique not-very-good internal connectivity and other things Tom Forth knows a lot more about. Also probably has a bit to do with overlapping job markets nearby - e.g. Manchester, Sheffield and the like.

What's the deal with Glasgow vs Edinburgh? Well, if you take the combined populations in the 45 minute city for both of them, you get a good chunk of Scotland - more than half. But why is Glasgow so much bigger than Edinburgh? Again, a mix of things but Glasgow has excellent suburban rail, a subway, motorways and so on, whereas Edinburgh has epic congestion and a good few buses and not too much other stuff, despite the new-ish tram system. Plus the wider Glasgow area just has more people in it.

Why did you choose the central railway station? Just because it is generally very central and close to the jobs. I could have put the arrival point somewhere else in the city centre without it making too much of a difference but I wanted to be consistent and there is no agreed 'this is definitely the city centre' point in each city anyway.

I've spotted something wrong, who do I tell? Feel free to get in touch. You should probably treat this in the spirit it is intended - i.e. an experimental take on functional urbanism - but if you do see something that looks egregiously wrong please let me know. I can also feed back to the people who make the TravelTime platform tool as they're always trying to improve their models and are very keen on feedback.


Nerd notes
I mapped this in QGIS 3.4 using the TravelTime platform plugin. I created a little model (see below) to automate the process than ran a batch process on it for the 26 cities I chose. The 26 include a few that may seem like odd choices but I did try to include places far and wide. For the population counts, this is based on the most recent LSOA (England and Wales) or data zone (Scotland) population estimates from mid-2018. The boundaries here do not align perfectly with the 45 minute isochrone so I suggest you consider the specific numbers in each case as being 'roughly' or 'about' rather than exact. I did use the population-weighted centroids though so having said that I expect the population figures are close to the real numbers. The TravelTime platform api used real-world transit information and if you want more details on it just check out their website.

This is the QGIS model I made

A few flow maps + data to play with

I've written a fair bit about mapping flows (e.g. migration, commuting) over the past decade or more and here I am again. The point of this post is to a) share some data so that anyone who wants to play with it can have a go; b) talk a little bit about visualising flows; and c) to look at the functional economic geography of England.

First of all, here's the underlying data - it's 2011 MSOA-to-MSOA commute data for England and Wales, which comes from the Census. Yes, it's getting quite old now but it's still a useful dataset. It's a big shapefile, with columns for total commute flow between LSOA and also different modes (train, bus etc) and some other stuff (e.g. distance, area codes and names).

Here are some of the maps I extracted from it. The lines are the commuting connections between places, with an addition blend mode added in QGIS, plus a scaling factor is used to make smaller flows dimmer and larger flows brighter. I've filtered the dataset to show only MSOA-MSOA flows of 10 or greater, otherwise it's a total mess. Here are some of the maps.

Okay, lots of shiny maps to see. With the opacity of the lines set to reflect the volume of the flows you get a slightly better overview of commute patterns. The addition blend mode gives the shiny effect, which is in some ways just a bit fancy but actually it serves a purpose here: making the main economic centres brighter, which fits in well with the underlying economic geography of England and Wales.

Talking of the functional economic geography of England more broadly, there are connections to my previous work with Garrett Nelson on US megaregions (e.g. our interactive map site, with similar shiny maps but driven by an algorithmic partitioning process) and you can get a sense of where the break points are between different areas.

Talking of which, if you're interested in this kind of thing, check out the AMA Garrett and I did on Reddit about the US commute work as part of the PLOS Science Wednesday series. The comments here are pretty interesting, particularly those from commuters about where they draw the line themselves between travelling to city A vs city B.

That's all for today - feel free to download the data and have a play, use the maps as you see fit, or get back to me with any questions or suggestions.

Oh, and if you do download the dataset, you'll also see that I have added origin an destination codes and names for MSOAs and local authorities. This is useful if you want to, for example, only look at all flows into Manchester, or all flows between Leeds and Bradford.


Data notes: MSOAs are small geogaphic areas with between 5,000 and 15,000 people in them, or between 2,000 and 6,000 households. Most have about 8,000 people and 95% of MSOAs had a population of between 5,443 and 11,579 at the time of this dataset is from. Want to know how to do this in QGIS? See my flow map tutorial from a few years ago. Want to know more about the glow effect? See this tutorial on glowing lines in QGIS. And the place labels? I put together a single file of Great Britain place names if you want to use that. I've used a couple of rules on which places to display, plus added some in manually. Thanks to Allan Walker and Richard Mann for making me think a bit more about this again.

Visualising Bay Area Commutes with Kepler.gl

A couple of months ago I saw on Twitter that Uber launched a new visualisation tool called kepler.gl. Apart from the fact that it looked seriously amazing, it also looked very useful but I hadn't managed to have a play with it until this week, so here I am with some visuals and a few words.

I've already looked at commuting in the San Francisco Bay Area quite a bit in the past (including this in Wired) so I had the data already and when Bay Area urbanist Alex Schafran got in touch about this very topic a couple of days ago it prompted me to dust off my Bay Area commute data and have a play around. These are the results. 

If you want to have a play yourself, I've put the csv in a shared folder, alongside a ready-to-upload json, some images, and a bonus csv of commuting and image folder for England and Wales as well, which is good fun.

Each line connects two Census Tracts, which have a resident population of just over 4,000 people. 

Commutes to San Francisco (100+) coloured by origin county

Bay Area commutes (colour = destination county)

Commutes flows (50+) within Alameda County 

Commute flows (150+) within Alameda County

Same as above, but with a different base map

Bay Area commutes (100+), coloured by destination county

As above, but showing the Filter tools in Kepler.gl

As above, but showing a tooltip for one flow line

Commute flows (50+) in Contra Costa County

Commutes to Santa Clara County (100+)

Commutes to San Francisco County (50+)

Commutes to San Francisco County (100+)

San Francisco to Santa Clara County commutes (all)

The final four images are just to show you the styles and settings I've used with the data, so that you can understand how it works a little better if you want to have a play around with it yourself.

You can see that lines are based on orig, dest x and y coords

I've applied a single filter here, but you can add more

These are the fields used in the tooltip - very useful

You can choose different base maps and turn layers on or off

That's all for now. Like I said, you can play around with this data if you want or even try the England and Wales dataset I created, which has some useful fields for filtering (e.g. local authority name, distance, flow volume).

Notes: Kepler? Presumably named for Johannes Kepler, the German mathematician. The Bay Area commute data includes commutes within and between the nine Bay Area counties of Alameda, Contra Costa, Marin, Napa, San Mateo, Santa Clara, Solano, Sonoma, and the City and County of San Francisco. The dataset is one I put together for another project, and comes from the American Community Survey. The England and Wales file is from the 2011 Census and includes all commutes of 25 people or more. I've found that you can't always export images from Chrome directly but it worked in Firefox. I haven't tried on Safari but I'm guessing image export works on that. Have a go yourself with the data I used to create the images above.