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BODMAS Conferences

UKDS Census Applications Conference

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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).

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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.

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Categories
Conferences

China: Fuzhou

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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:

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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.

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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.

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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.

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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.

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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.

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London OpenStreetMap

OpenStreetMap: London Building Coverage

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OpenStreetMap is still surprisingly incomplete when it comes to showing buildings for the London area, this is a real contrast to other places (e.g. Birmingham, New York City, Paris) when it comes to completeness of buildings, this is despite some good datasets (e.g Ordnance Survey OpenMap Local) including building outlines. It’s one reason why I used Ordnance Survey data (the Vector Map District product) rather than OpenStreetMap data for my North/South print.

The map below (click to view a larger version with readable labels and crisper detail, you may need to click it twice if your browser resizes it), and the extract above, show OpenStreetMap buildings in white, overlaid on OS OpenMap Local buildings, from the recent (March 2015) release, in red. The Greater London boundary is in blue. I’ve included the Multipolygon buildings (stored as relations in the OSM database), extracting them direct from OpenStreetMap using Overpass Turbo. The rest of the OSM buildings come via the QGIS OpenStreetMap plugin. The labels also come from OS OpenMap Local, which slightly concerningly for our National Mapping Agency, misspells Hampstead.

The spotty nature of the OSM coverage reveals individual contributions. For example, Swanley in the far south east of the map is comprehensively mapped, thanks presumably due to an enthusiastic local. West Clapham is also well mapped (it looks like a small-area bulk import here from OpenMap) but east Clapham is looking sparse. Sometimes, OpenStreetMap is better – often, the detail of the buildings that are mapped exceeds OpenMap’s. There are also a few cases where OSM correctly doesn’t map buildings which have been recently knocked down but the destruction hasn’t made it through to OpenMap yet, which typically can have a lag of a year. For example, the Heygate Estate in Elephant & Castle is now gone.

The relative lack of completeness of building data in OpenStreetMap, for London, where the project began in 2004, is – in fact – likely due to it being where the project began. London has always an active community, and it drew many of the capital’s roads and quite a few key buildings, long before most other cities were nearly as complete. As a result, when the Bing aerial imagery and official open datasets of building outlines became more recently available, mainly around 2010, there was a reluctance to use these newer tools to go over areas that had already been mapped. Bulk importing such data is a no-no if it means disturbing someone’s prior manual work, and updating and correcting an already mapped area (where the roads, at least, are drawn) is a lot less glamorous than adding in features to a blank canvas. As a result, London is only slowly gaining its buildings on OSM while other cities jumped ahead. Its size doesn’t help either – the city is a low density city and it has huge expanses of low, not particularly glamorous buildings.

An couple of OpenStreetMap indoor tracing parties might be all that’s needed to fix this and get London into shape. Then the OpenStreetMap jigsaw will look even more awesome.

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Click for a larger version. Data Copyright OpenStreetMap contributors (ODbL) and Crown Copyright and Database Right Ordnance Survey (OGL).

Categories
BODMAS Data Graphics London

The City of London Commute

Here’s a graphic I’ve made by taking a number of screenshots of DataShine Commute graphics, showing the different methods of travelling to work in the City of London, that is, the Square Mile area at the heart of London where hundreds of thousands and financial and other employees work.

All the maps are to the same scale and the thickness of the commuting blue lines, which represent the volume of commuters travelling between each home area and the City, are directly comparable across the maps (allowing for the fact that the translucent lines are superimposed on each other in many areas). I have superimposed the outline of the Greater London Authority area, of which the City of London is just a small part at the centre.

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There’s lots of interesting patterns. Commuter rail dominates, followed by driving. Car passenger commutes are negligible. The biggest single flow in by train is not from another area of London, but from part of Brentwood in Essex. Taxi flows into the City mainly come from the west of Zone 1 (Mayfair, etc). Cyclists come from all directions, but particularly from the north/north-east. Motorbikes and mopeds, however, mainly come from the south-west (Fulham). The tube flow is from North London mainly, but that’s because that’s where the tubes are. Finally, the bus/coach graphic shows both good use throughout inner-city London (Zones 1-3) but also special commuter coaches that serve the Medway towns in Kent, as well as in Harlow and Oxford. “Other” shows a strong flow from the east – likely commuters getting into work by using the Thames Clipper services from Greenwich and the Isle of Dogs.

Try it out for your own area – click on a dot to see the flows. There is also a Scotland version although only for between local authorities, for now.

Click on the graphic above for a larger version. DataShine is part of the ESRC-funded BODMAS project at UCL. I’ll be talking about this map at the UKDS Census Applications conference tomorrow in Manchester.

Categories
Conferences

China: ICSDM Conference

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Last week I was in China for the 2nd IEEE International Conference on Spatial Data Mining (ICSDM), travelling with my lab’s director who was keynoting and giving a day’s teaching at the conference’s accompanying summer school. The conference was based in Fuzhou University, on the western edge of Fuzhou in Fujian Province, a city of five million people about 90 minutes north east of Hong Kong by plane, and an hour’s drive inland from the ocean. The city’s setting is rather dramatic – it is surrounded by forested mountains, and the greenery extends into the city too, where it helps absorb pollution.

IMG_20150709_165709ecThe conference consisted of a number of keynote presentations given by domain experts on topics such as Big Models for Big Data, to Social Media geographic data mining and classification, to multi-source pollution monitoring and modelling. Interspersed with the keynotes were parallel tracks of project presentations, many (but not all) of which were given by Ph.D. candidates and other students at various universities elsewhere in China, as well as at Fuzhou itself. Remote sensing was a major theme of the conference, but other topics included modelling house prices based on demographic information and looking at movements of people using the Chinese equivalents of Facebook and Twitter.

As well as the conference itself there was time for a number of walks in the local forest parks and up some mountains – tough in the heat and humidity of southern China in the summer, but well worth it for the views. We also visited a number of temple buildings and other areas popular with tourists.

It was a well organised conference and was interesting to attend – not least to see that the sorts of research topics that we are familiar with here in quantitative geography at UCL, are carried out in China too – but with a local perspective, based on the different datasets available and cultural habits. The keynote talks also added a good, rounded perspective on the spatial data mining field as it currently stands. All in all, an eye-opening week.

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