What if There Were No Cars?

Here’s a map of the top method of travel to work, for each “small area” (~250 people) in the UK, for people aged 16-74 and in employment, at the time of the 2011 Census (or try the interactive, zoomable version):

traveltowork_car

The pattern is, fairly evenly, that car use (light blue) dominates except for people living in the very centre of cities, where walking to work (green) is the most popular method. The two big exceptions are London, where rail/metro travel (orange) dominates for the inner city zone, separating the walking core and car-driving outer London ring; and Cambridge, where the cyclists (red) really are king. There are some other interesting results in small areas (e.g. walking is popular in central Leicester but not in the centre of Peterborough), but overall, the map doesn’t tell you much more.

So, I’ve considered what the map would look like if we removed cars from the calculations – what form of transport is used by the people that need to work but don’t own or otherwise have access to cars, either as a driver or passenger? How does the UK commute, without cars, right now – and what might a UK landscape look like without the great rush-hour traffic jam, if the alternatives, pro-rata, were adopted? A whimsical hypothesis – cars are always going to be essential for certain kinds of commutes in certain parts of the UK – but let’s see what happens anyway, as it will still tell us something about public transport provision, city walkability and maybe attitudes to working life in general.

Here is a map of the top carless commute method for small areas, right across Britain:

traveltowork_nocar

(Here is the interactive, zoomable version).

Suddenly, all sorts of interesting trends emerge. In rural areas, working from home dominates – with no public transport, and motorbikes being an uncommon form of transport in the UK, this is the only option. In towns and villages, and in city centres, walking to work dominations. Both are obvious – the interesting results appear if you zoom in:

  • In London, the central walking-to-work area (green) coincides almost perfectly with the congestion charge zone. Other walking areas include the large outer London town centres of Hillingdon, Croydon and Kingston that have been absorbed into the metropolis, and the traditional community of Stamford Hill.
  • Rail/metro (orange) dominates throughout Zones 2-6 London and beyond.
  • London has four major areas of bus dominance (purple) – Burgess Park in the south, Hackney in the north-east, the western Lea Valley in the north and a huge zone surrounding Heathrow Airport in the west. Three of these not surprisingly coincide with areas of poor rail/metro provision, but the western Lea Valley result is interesting – there are two rail lines down through this area with stopping services. However, notably, this area’s most popular employment type is cleaning – cleaners typically have to work nights, where the bus is the only public transport option.
  • York versus Leeds – both have a similarly sized walking core, but then the rest of Leeds has bus users, while York’s outskirts are dominated by cyclists (red). The flatter nature of York is likely the major reason.
  • Buses are pretty crucial in the Birmingham conurbation.
  • Cycling dominates in almost every part of Cambridge but less so in the other famous cycling city, Oxford. In London, Hackney’s famed cycle community actually has roughly equal prominence with both bus and train/metro use.
  • Stoke-on-Trent has a very large walking core, larger than for the larger cities, covering the whole area almost, rather than being surrounded by bus/cycling/train commuters as normally happens. Stoke-on-Trent is actually a conurbation of six towns, with employment scattered throughout rather than concentrated in the normal core. Alternatively this could be due to poor bus provision or a dominance of driving.
  • Ilkley and Bingley like their trains – nearby Keighley and Skipton, nearby and on the same network, don’t. The former two towns perhaps act more as commuter towns for Leeds while the latter two have a tradition of more local employment.
  • The very richest areas have a high proportion of people working at home (brown) – live in help, aka domestic servants? See Knightsbridge and Hampstead Garden Suburb in London, or Sutton Park near Birmingham, are two examples.
  • The new towns in central Scotland seems to have a greater proportion of working-at-home than equivalent new-town areas in England.
  • Fishing communities (yellow – other) are obvious in north-east Scotland:

traveltowork_nocar_fish

These are just a few of the spatial patterns I’ve spotted – there are I’m sure many more interesting ones. Sometimes, removing the dominant factor reveals the interesting map.

The technique of mapping only the most dominant mode of transport has a serious flaw, in that, depending on how you merge or split other transport modes, you can significantly influence which appears “top”. I have merged some modes together (driver+car passenger, train+metro+tram, and taxi+motorbike+other, e.g. boat), hopefully in a meaningful way that shows interesting results without hiding the bigger picture. Another mitigating factor is that, where a second mode of transport has nearly as much use as the first, I include its colour too, in narrow vertical banding, and highlight this in the interactive “area information” panel.

All the maps in this article use the CDRC Maps platform, created by the Consumer Data Research Centre, to map small-area consumer and other demographic data for the UK. Because I am using Census data, I am able to map for the whole of the UK (including Scotland and Northern Ireland), as, for the Census at least, the activity is coordinated across the nations, and while the outputs are arranged differently, they are sufficiently similar to combine and use together with care. The data comes from the National Statistics agencies – the ONS, NRS and NISRA, and is Crown Copyright, licensed under the Open Government Licence.

Have a look at some other CDRC datasets mapped, download the data yourself or find out more about the CDRC.

traveltowork_nocar_cambridge

Visit the new oobrien.com Shop
High quality lithographic prints of London data, designed by Oliver O'Brien

The Age of Buildings

liverpool_houseages

We don’t have individual building age open data in the UK, unlike in some other countries (the data has been used to great effect in New York City and Amsterdam) but the Valuation Office Agency, which amongst other things decides council tax bandings for residential properties, has published some interesting data on how old houses are in England and Wales – it’s their “dwelling ages” dataset. A separate governmental organisation, the ONS, publishes house prices summaries, at a relatively small-area* scale, on a quarterly basis for the previous year. I have combined both these datasets into a record on CDRC Data. and have mapped them both on CDRC Maps.

bristol_houseagesThe dwelling age data is supplied grouped in approximately ten-year age bands (+ a Pre-1900 catch-all) with a count of the number of houses in each band, for each small area (LSOA) in England/Wales. I’ve mapped just the modal band, that is, the band with the most number of houses in it**. In some cases, houses were steadily built in an area throughout the 20th century, so that the band assigned to that area is not actually very representative of the houses there – this can be spotted by looking at the “Classif. %” number which appears on the right.

Many UK cities show a pattern of Pre-1900 inner-city (dark grey on the map), with early 20th century houses out towards the edge (lightening blues). The “Green Belts” of the 1940s stopped this radial outward development, so, some old housing was instead overhauled to build 1960s-70s housing estates (shown in yellow) and more recently, the urban core has seen much of the recent housebuilding activity. This shows up on the map as an area of red in the centre of many cities. There are some exceptions – Milton Keynes is a large, and new, town, its map showing mainly yellows and reds.

Not all areas are constrained by Green Belts but some have other, physical constraints, such as the sea. Weston-super-Mare, for example, has steadily expanded westwards over the last 150 years:

westonsupermare_ages

A second map concentrates just on post-WW2 (1945+) building, showing the proportion of such houses in each area. Hello, riverside east London:

london_riverside

The house price pattern in England/Wales is quite familiar to many people – basically London is eye-wateringly expensive, particularly in the central and west, along with some satellite towns and cities (e.g. Oxford and Cambridge) but not others (e.g. Luton and Harlow). I’ve mapped the median house prices for each small-area as I think this better provides an indicator of a typical price paid. 50% of properties sold in the previous 12 months, in each area, sold for less than this amount, and 50% for more. As only a few houses in an area typically get sold in a year (I have included this number in the metric data) it is worth noting that the values can jump around a lot.

Explore the interactive maps:

houseprices

* There is separately individual house transactions (with prices) released regularly by a third organisation, the Land Registry, however I have not mapped this at this time.

** Where an area is fairly equally split between two bands, I’ve included the “runner up” band as well, shown thinner vertical stripes. This only appears where the runner up housing count is 90% of the modal band, and the two bands account for more than half of the total housing. I’m using Mapnik compositing operations to get the vertical stripes, rather than a very long and repetitive stylesheet. I calculated the modal band in Excel from the original VOA dataset by using MAX (to find the value) and nested IFs (to display the category). Calculating runner up (i.e. second from mode) was a little more tricky, but I was able to do this but using COUNTIF and LARGE (to find the value – which could the same as the mode, ie. multimodal) and then nested IFs/ANDs to display the category.

Visit the new oobrien.com Shop
High quality lithographic prints of London data, designed by Oliver O'Brien

London’s Bikeshare Needs A Redistribution of Stations

bikes_journey_day

Here’s an interesting graph, which combines data on total journeys per day on London’s bicycle sharing system (currently called “Santander Cycles”) from the London Data Store, with counts of available bicycles per day to hire, from my own research database. The system launched in summer 2010 and I started tracking the numbers almost from the start.

You can see the two big expansions of the system as jumps in the numbers of available bikes – to all of Tower Hamlets in early 2012, and to Putney and Fulham in late 2013. Since then, the system has somewhat stagnated in terms of its area of availability, although encouragingly at least the numbers of available bikes has remained constant at around 9500, suggesting that at least the operator is on top of being able to maintain and repair the bikes (or regularly source new ones). Some of the individual bikes have had 4000 trips on them. There is a small expansion due in the Olympic Park in spring 2016, but the 8 new docking stations represents only a 1% increase in the number of docking stations across the system, so I doubt it will have a significant impact on the numbers of available bikes for use.

There is a general downward trend in the numbers of uses of each bike per day, since the halycon Olympic days of Summer 2012, over and above the normal seasonal variation, which concerns me. The one-year moving average recently dipped below 3 uses of each bike per day, this summer, and I am not confident it will pick up any time soon. (The occasional spikes in uses/bike, by the way, generally correspond to sunny summer bank holidays, tube strikes and Christmas Day).

To rejuvenate the system and draw in more users, rather than relying on the established commuter and tourist flows which likely dominate the current usage, I am convinced that the system needs to expand – not necessarily in terms of the number of bikes or docking stations, but in its footprint. I think the system would be much improved by dropping the constraining rule on density (which approximates to always having one docking station every 300m) and instead redistributing some of the poorly performing docking stations themselves further out. It’s crazy that, five years on, there are no docking stations in central Hackney, Highbury, or Brixton, three areas with an established cycling culture and easily cycle-able into the centre of London. Conversely, Putney and Tower Hamlets simply don’t need the high density of docking stations that they currently have, except in specific areas (such as around the train/tube stations in Putney, and Canary Wharf).

Ideally we would have a good density of docking stations throughout cycleable London but, as docking stations (and bikes) are very expensive, I would suggest that TfL instead adopts the model used in Bordeaux (below). Here, the city retains a high-dense core serving tourists, commuters and other centrally-based workers, but adopts a much lower density in the suburbs, so that, while tourists can still “run into” docking stations they don’t know about in the centre thanks to the high density, local users can benefit from the facility in their neighbourhood too, even if it requires a little longer walk to get to it.

bikes_bordeaux

Technical note: Before November 2011, the London numbers included bicycles that were in a docking station but not available to hire (i.e. marked as broken). This exaggerates the number of available bikes (and correspondingly reduces the number of hires/bike/day from the true value) in this period by a small amount – typically around 3-5%, an effect I am not considering significant for this analysis.

In/Visibility and Difference – Visual Methods Workshop in Berlin

vmw1

I presented a talk on geodemographic mapping, at a visual methods workshop “In/Visibility and Difference” which took place in Berlin at Bard College (formerly the European College of Liberal Arts). The workshop was organised by the TransformIG project at Humboldt University in Berlin, which was also the venue for the keynote part of the meeting. Thank you to the organisers for organising an interesting and intensive workshop which presented a wide variety of visual and geographical techniques which are becoming key ways to structure and analyse sociological studies.

I structured my talk into four sections:

  1. An primer on improving choropleth mapping of socioeconomic data, moving beyond the basic “heat map” by adding regular geographical feaures (see photo below), labels and clipping coverage to populated areas, to explain the demographic patterns and highlight external influences. This is the technique used by DataShine to display Census 2011 aggregate statistics, and CDRC Maps to show geodemographics. I also outlined alternative approaches used by other research groups, such as cartograms and dot density maps.
  2. A tour of the geodemographic maps in CDRC Maps, including the Output Area Classification and a map of the latest Index of Multiple Deprivation. I also touched briefly on the problems of geodemodemographic classifications, where good/poor fits to the classification are typically mapped identically, and the “second choice” classification doesn’t get shown, showing some techniques to try and map these subtleties.
  3. An introduction to more novel methods of mapping demographic data, such as Lives on the Line and Tube Tongues, but highlighting the shortcomings of such maps too.
  4. Finally, a brief mention of mapping more novel datasets, showcasing the Twitter language maps for London and New York – again discussing the flaws as well as strengths of such maps.

vmw_me

I found many of the other talks very interesting – particularly the work by plan b – two performance artists who have essentially tracked their entire outdoor life over the last 8+ years, both creating GPS traces which they have turned into artworks at various scales and on mediums (including a 3D mould), but also temporal activity indicators which they have grouped together into small multiples. They term these the “birch trees” due to their characteristic stripy white/black columns (see top photo). I also liked the striking pictograms created by Migrantas who have created simple and powerful graphics, from stories from the migrant community in Berlin and elsewhere. Their work can be seen on billboards and walls in various places across the city. There was a good talk by Stefan Lindemann on “SuperLUX”, focusing on linear development along commuter lines to Luxembourg City and corresponding population changes – essentially an international take (due to the country’s size) of the more recent “Northern Powerhouse” project to connect the cities of the north of England.

There was one more map “treat” for me at the workshop – the closing keynote given by Caroline Knowles included her investigative journalistic project where she tracked the complete journey of a pair of flip flops – from oil in Kuwait, to factories in South Korea, then to and through the markets in east Africa, to the consumer, and then finally to the rubbish dumps of the region. A map illustrating the life cycle of the flip flops is below.

Thanks to Agata Lisiak and the TransformIG team for inviting me to speak at the workshop and the opportunity to learn as well as disseminate. (Photo credit for the top photo: Agata).

vmw2

Changes in Deprivation in England, 2010-15

Click any of the images in this article to go to the interactive map.

imd2015_londonup
Above: A significant reduction in relative deprivation in Blackheath and Maze Hill since 2010.

I’ve just now published a number of maps on the CDRC Maps platform which uses the DataShine mapping style (more about DataShine) to show demographic data relating to consumer and other datasets.

The maps relate to the Indices of Deprivation 2015, small-area measures of deprivation in England, which were compiled and published at the end of September by OCSI on behalf of the UK Government.

imd2015
Above: Deprivation varies between Tottenham, Walthamstow and Woodford Green, in 2015.

The Indices of Deprivation (of which the Index of Multiple Deprivation, or IMD is the overall index) split England into around 32000 areas (“LSOAs”), each containing a typical population of 1500. Each area is scored for several components, which are then combined (with different weights) to produce an overall score of deprivation for the area. Note that areas with little deprivation may be mainly compared of people who are not “wealthy” but just not deprived, and therefore rank the same as areas mainly populated by extremely affluent people. IMD is a measure of deprivation, not affluence.

The look of these maps, with their Red-Yellow-Green colour ramp, is intentionally similar to my New Booth map of the 2010 IMD deciles which was my first “colour the houses” map and the precursor to DataShine and therefore CDRC Maps.

imd2015_miltonkeynes
Above: Milton Keynes has a characteristic strip of high deprivation, running north/south.

These scores cannot be directly compared with those from previous exercises (2010, 2007 and 2004 are the recent ones) due to slight methodological alterations, however we can rank each area based on the overall score – this is the Index of Multiple Deprivation – and then compare ranking changes between the years. It should be noted that a decrease in rank (i.e. an increase in deprivation measure compared with other areas) does not mean that an area has become more deprived in absolute terms – it may be just becoming less deprived at a slower rate. I have mapped the overall rank change from 2010 to 2015, and also the rank change of the component which measures the effects of crime on deprivation, as this shows some particularly interesting spatial characteristics.

Looking at the overall changes, London’s pattern is striking:
imddelta_london
Above: London has an inner-city “ring” of blue showing a large reduction in relative deprivation since 2010.

London’s inner city areas – Zones 2-4 – have becoming significantly less deprived in the last year. Indeed London, in general, has done very well recently relative to the rest of England, with only a few areas (St John’s Wood, Thornton Heath, Mill Hill, East Barnet and Hounslow) showing a significant increase in relative deprivation levels. Again, this may mean that they are still becoming less deprived, just at a slower rate. By comparison, Blackheath, Ealing, Upton, North Wembley and Crouch End have become dramatically less deprived since 2010. There are smaller pockets throughout the city who are are also showing marked moves in both directions – see the interactive map. I use a different (Red-White-Blue) colour ramp for these maps, to emphasise that they are showing changes.

imddelta_readingbury
Above: The contribution of crime to deprivation has significantly dropped in Reading and increased in Bury.

Some of the more notable results for changes in the crime component ranking of the IMD are in Reading (where the impact of crime on deprivation has significantly reduced) and Bury (where it has had a significantly greater impact). In both towns (see above, presented at different scales) however, other components have acted in the opposite direction, such as the deprivation ranking of these two places, with respect to the rest of England, has not significantly changed in five years. Bury, was, and still is, already significantly more deprived than Reading, the difference between the two has increased.

Another example: comparing Gateshead with nearby South Shields. The former coming up, the latter going down:
imd_gateshead
Gateshead is almost universally moving out of deprivation at a faster rate than the rest of England, while South Shields is change much more slowly.

The components are income, employment, education, health, crime, barriers to housing and services, and living environment. Their weights are summarised in this nice infographic from gov.uk.

There is also an official summary which maps the data slightly differently. One of its analyses – Chart 6 – shows the local authorities (LA) where relative deprivation has significantly fallen, by measuring the proportion of areas within the LA that have moved out of the bottom 10% in the IMD, between 2010 and 2015. The top four are: Hackney, Tower Hamlets, Greenwich and Newham. These are four of the five Olympic Boroughs. The fifth, Waltham Forest, is also in the top 10. East London is changing.

See these maps and various geodemographic classifications at CDRC Maps.

imd2015_midlands
Across middle England, cities are more deprived than the countryside, with notable exceptions (such as Shrewsbury, Cambridge, northern Leeds and western Sheffield).

DataShine Wins the BCS Avenza Award for Electronic Mapping

bcsavenza

DataShine Census has won the British Cartography Society’s Avenza Award for Electronic Mapping, for 2015. The glass trophy and certificate were presented to DataShine creator Oliver O’Brien at the award ceremony and gala dinner for the combined BCS/Society of Cartographers conference “Mapping Together” which took place in York, earlier this September. The prize was presented by Peter Jones MBE, the BCS President.

Additionally, DataShine Election was Highly Commended for the Google Award for mapping of the UK General Election 2015.

The book “London: The Information Capital” which DataShine PI James Cheshire co-authored with Oliver Uberti, won three awards at the same ceremony, the Stanfords Award for Printed Mapping, the John C. Bartholomew Award for Thematic Mapping (for Chapter 3 of the book), and the meeting’s grand prize, the BCS Trophy. Dr Cheshire was on hand to receive the trophies and certificates.

The awards cap a successful year for the DataShine project which has seen hundreds of thousands of viewers, several key media articles and four key websites launched, along with a number of variants, most recently including DataShine Scotland Commute which was commissioned by the National Records of Scotland. Full details of the project can be found on the project blog.

neocartography_presentationThe awards were just a small part of a eye-opening and rewarding two-day conference held in central York. A wide variety of talks were held, from academics, company representatives and field enthusiasts. They ranged from detailed discussions of subtle automated cartographical techniques that improve the legendary “Swiss Topo” national maps of Switzerland, to a not-so-serious critique of maps supplied by the floor – a sea/land temperature gradient map proving to be particularly controversial due to its multi-hue, repeating colour ramp. A particular highlight was a discussion on “neocartography” by Steve Chilton, he framed the presentation around an email conversation he’d had with myself and another “experimental” mapper SK53.

The theme “Mapping Together” represented the combination of the annual conferences of the trade-focused BCS with the academic-weighted SOC, the two professional cartography bodies of the UK, for the first time in several years. The format worked well and there was enthusiasm at the meeting for it to be repeated in future years.

bcs_datashinecertificates

This is an extended version of an article that first appeared on the DataShine blog. Photo below courtesy of the Society of Cartographers Publicity Officer.

bcs_avenzaaward2015

ECTQG 2015

ectqg_2015_alistair

Just a quick report on the 19th European Colloquium on Theoretical and Quantitative Geography, which took place near Bari in Puglia, South-east Italy, earlier this month, and which a significant amount of the quantitative geography group here at UCL attended, including myself. The meeting was held at an agricultural college in a university town a few miles from Bari itself, and was held Friday-Monday, which emphasised the residential nature of the meeting.

A couple of frustrating aspects, which persisted throughout the weekend, were some relatively uneven grouping together of talks on unrelated topics into a single session, and also a relatively large number of talks were included on the programme despite being from presenters who had submitted abstracts but weren’t present at the actual meeting, resulting in quite a few gaps or sessions. In one case, the first of three OpenStreetMap sessions was cancelled after most presenters were absent, but the three sessions were later being regrouped (unannounced, so I missed it) into a single session with seven presenters squeezed into the time for five. In another case, one person had been allocated to chair one session while giving a presentation simultaneously in another stream!

Positives from the conference though were the excellent food provided, the weather meaning that several of the meals could be taken outdoors – as well as at the grand gala dinner in a hotel in central Bari. The local feral kittens also provided entertainment, particularly for us Brits who are suckers for such things! We also managed some time off to visit Monopoli, a lovely little town about 30 minutes from Bari, with a pleasant old town and central square, a small (sadly, too small) bikeshare system, and apparently almost completely off the tourist radar.

Next ECTQG is much closer to home – the Leeds part of the CDRC research group that I am affiliated with are organising it somewhere in Yorkshire in 2017.

Above: Alistair Leak discusses Ward’s hierarchical clustering for surnames, as part of his presentation at the colloquium. Below: An evening meal outside.

ectqg_2015_dinner

Living Somewhere Nice, Cheap and Close In – Pick Two!

eastsheen

Skip straight to the 3D graph!

When people decide to move to London, one very simple model of desired location might be to work out how important staying somewhere nice, cheap, and well located for the centre of the city is – and the relative importance of these three factors. Unfortunately, like most places, you can’t get all three of these in London. Somewhere nice and central will typically cost more, for those reasons; while a cheaper area will either be not so nice, or poorly connected (or, if you are really unlucky, both). Similarly, there’s some nice and cheap, places, but you’ll spend half your life getting to somewhere interesting so might miss out on the London “experience”. Ultimately, you have to pick your favoured two out of the three!

Is it really true that there is no magic place in London where all three factors score well? To see the possible correlations between these three factors, I’ve calculated the ward* averages for these, and have created a 3D plot, using High Charts. Have a look at the plot here. The “sweet” spot is point 0,0,0 (£0/house, 0 score for deprivation, 0 minutes to central) on the graph – this is at the bottom left as you first load it in.

Use your mouse to spin around the graph – this allows you to spot outliers more easily, and also collapse down one of the variables, so that you can compare the other two directly on a 2D graph. Unfortunately, you can’t spin the graph using touch (i.e. on a phone/tablet) however you can still see the tooltip popups when clicking/hovering on a ward. Click/touch on the borough names, to hide/show the boroughs concerned. Details on data sources and method used are on the graph’s page.

The curve away from the sweet spot shows that there is a reasonably good inverse correlation between house prices and deprivation, and house prices and nearness to the city centre. However, it also shows there is no correlation between deprivation and nearness. Newington is cheap and close in, but deprived. Havering Park is cheap and a nice area, but it takes ages to get in from there. The City of London is nice and close by – but very expensive. Other outliers include Merton Village which is very nice – but expensive and a long way out, while Norwood Green (Ealing) is deprived and far out (but cheap). Finally, Bishop’s in Lambeth is expensive and deprived – but at least it’s a short walk into the centre of London.

Try out the interactive graph and find the area you are destined to live in.

kingspark

p.s. If you are not sure where your ward is, try clicking on the blobs within your borough here.

* Wards are a good way to split up London – there are around 600 of them, which is a nice amount of granularity, and importantly they have real-world names, unlike the “purer” equivalent Middle Super Output Areas (MSOAs). Using postcode “outcodes” would be even better, as these are the most familiar “coded” way of distinguishing areas by non-statisticians, but statistical data isn’t often aggregated in this way.

UKDS Census Applications Conference

census1

I was in Manchester a couple of weeks ago for a UKDS conference on applications of the Census 2011 datasets that have been made available, through the ONS, NOMIS, UKDS and other organisations/projects. The conference was to celebrate the outputs and projects that have happened thus far, now that the Census itself is four years old and most of the main data releases have been made.

It was a good opportunity to present a talk on DataShine, which I made a little more technical than previously, focusing on the cartographical and technological decisions behind the design of the suite of websites.

I enjoyed an interesting talk by Dr Chris Gale, outlining graphically the processes behind creating the 2011 OAC geodemographic classification. Chris’s code, which was open sourced, was recently used by the ONS to create a local-authority level classification. There was also some discussion towards the end of the two-day meeting on the 2021 Census, in particular whether it will happen (it almost certainly well) and what it will be like (similar to 2011 but focused on online responses to cut costs).

All-focus

After the conference close I had time to look around MOSI (the Museum of Science and Industry) which is mainly incorporated around an old railyard, terminus of the world’s oldest passenger railway and containing the world’s oldest station (opened in 1830, closed to passengers in 1844). But I was most impressed by the collection of airplanes in the adjoining hangar (once a lovely old market building), which included a Kamakaze. I also had a quick look around the Whitworth Gallery extension which has been nominated for this year’s Stirling Prize.

census3

China: Fuzhou

fuzhou7

I spent a week in Fuzhou earlier in July, in China’s Fujian provice, presenting and attending a summer school and conference, respectively, at Fuzhou University. I’ve already blogged the conference itself (read it here) but during the week I got plenty of time, outside of the conference to get a feel for Fuzhou and this small part of China. Here are some notes:

fuzhou5

Bikesharing
There is a bikeshare system in Fuzhou, but it is small (by Chinese standards). I saw a few bikeshare docking stations during my trip, in particular one outside the university, which was complete with a (closed) booth for an attendant (I think this is where you get a smartcard to operate it). Each station has 10-20 docks, generally nearly full of the bright orange and green bikes, docked under a bus-stop-style shelter that also contains an alarm light, CCTV and loudspeaker, and red scrolling LED information screen. Adjacent there were typically 10-20 further bikes chained together, presumably for manual restocking by the attendant when they are there. The one thing I did not see, at any point during the trip, was anyone actually using the bikeshare bikes. The modal share of cycling is low anyway in Fuzhou (the roads are intimidating, but this doesn’t stop the swarms of electric bike users) but I wasn’t expecting to see a completely unused bikeshare system in a country so famous for the transport mode.

fuzhou4

Transport in General
Fuzhou is a city of nearly five million people – half the size of London. And yet it has no metro, tram or commuter rail (apart from a couple of stations right on the outskirts). So everyone travels by car, taxi (very cheap – £1 for most journeys), bus (10p per journey, air-conditioned and frequent), or electric bike. Probably 50% car, 15% bus, 30% electric bike, 5% taxi. Walking is not so popular as the roads are generally very wide and difficult to cross (you don’t generally get much space given to you at zebra crossing!) and likely because of the hot climate at this time of the year. The one mode that I saw extremely little of, is pedal cycling. I had heard that cycling has quickly become an “uncool” thing to do in China, it is interesting to contrast with the rapidly rising cycling use in London – albeit from a low base. London’s cycling mode share was also once much higher and also had a sharp fall – maybe London is just ahead of hte curve.

fuzhou3

Climate and Pollution
Fuzhou is a southern Chinese city. It’s around an hour’s drive in from the coast, where its airport is. It’s north of the many cities near Hong Kong – about 90 minutes on a plan from the latter – but south of Shanghai, and a long way south from Beijing. The climate is therefore quite hot and muggy at this time of year. As you might expect from a city of five million people where most people drive, a haze of pollution was often visible where I was there. However, the haze is not too bad. Fuzhou is helped in this by being surrounded on most sides by thickly forested mountains, which often rise up steeply, immediately beyond the city limits. One of these ranges indeed forms the Fuzhou National Forest Park which contains a wide variety of trees, including a 1000-year old tree with its elderly branches supported by concrete pillars! The masses of trees on all sides no doubt help with some soaking up of pollutants. Many of the large roads have lines of thickly foliaged trees running along them, and the bridges for pedestrian crossings, and highway flyovers, also have lines of shrubs and bushes all the way along them, which doubtless also help absorb pollutants and keep the haze under control. The street foliage also has the side effect of making many views of the city look quite pretty, with lines of green and purple plants softening the concrete structures and making the city seem to blend into the landscape.

fuzhou1

Urban Structure
Fuzhou is a city largely of apartment blocks. Strikingly, the centre of the city has virtually no construction going on – it is as dense it as needs to be, Fuzhou’s population does not need to increase, and the congestion need not get any worse. A few from the central hotel reveals almost no cranes, anywhere on the horizon, apart from some small ones for the aforementioned metro construction project. This is starkly different to the edges of the city, at the few gaps between the mountains, particularly along the road leading to the airport and the coast. There is a brand-new high-speed railway station at this edge of the city, and it also is the direction towards the shipbuilding and electronics industry factories that are a few miles distant. The area around the station is relatively free of apartment buildings, but huge numbers are currently being built, many 30-40 stories high and often built very close to each other, in clusters with distinct designs. The new station and the good road leading outwards it presumably the spur. This is infrastructure building, and developers responding to this, on a grand scale.

fuzhou6

Consumer Culture
One thing I noticed was that most of the Chinese attendees of the conference I was at had iPhone 6 phones. I’m not sure if this is representative of the Fuzhou population at large, but I was surprised to see no Huawei or Xiomai phones (both Chinese brands, i.e. home-grown). I have a Huawei myself – it is excellently built and I am very happy with it. Apple has done hugely well out of convincing people to pay thousands of extra yuan for the a phone with the Apple branding. Talking about luxury brands in general, Fuzhou has a cluster of these (Christian Dior etc) in a small mall in the centre, and also I spotted a Starbucks and McDonalds lurking nearby. But, Apple aside, in general western brands have little impact. And as for the popularity of the iPhone, the (official) Apple Stores have not made it to Fuzhou yet.

More generally, the food in China takes some getting used to, both the variety of produce and also the local varients. Lychee trees are everywhere (the region is where they were originally from) and there were plenty of other unusual fruits. The look of lychees takes some getting used to, but the taste is very pleasant. Fish features in a lot of dishes, as do various meats – the buffet and “lazy Susan” format though thankfully means the more mysterious items can be ignored! Our host also took us to an upscale restaurant where we had a lot of very spicy food (rare for the region) and also some weak but pleasant Chinese beers.

fuzhou2