Each year MSc students at CASA demonstrate their spatial data visualisation skills with a group project. The theme this year was ‘Global to Local’, and the class of 2022 has produced some particularly excellent work, experimenting with a range of visualisation tools and techniques.
Global Digital Divides Finally, another interesting take was to think about online communities as interactions between global and local, including the changing geography of internet access and the division of the world into different online platforms by language and political and economic divides.
Here is the full list of project groups and websites-
Geographers have long grappled with the complex and ever changing configurations of global urbanism. Many terms have been coined to describe new 20th and 21st century urban forms: conurbations (Geddes, 1915), multi-nuclei cities (Harris & Ultman, 1945), megalopolis (Gottman, 1961), world cities (Hall, 1966), desakota (McGee, 1991), fractal cities (Batty & Longley, 1994), network cities (Batten, 1995), postmetropolis (Soja, 2000), splintering urbanism (Graham & Marvin, 2001), polycentric mega-city regions (Hall & Pain, 2006)…
These concepts are diverse, coming from different perspectives with different methods and archetypal case studies. But there are shared themes: a focus on more diffuse and polycentric urban forms; recognition of city connections across multiple scales; and the rise of ever larger urban regions embedded in thicker global networks.
Representing and exploring the diversity of contemporary global urban forms is a challenge for cartographers. We often focus on mapping the amazing richness and diversity of dominant global cities like London and New York. Yet this is clearly a very biased lens from which to frame the vast majority of the globe, as researchers have noted. Postcolonial critiques like Robinson’s ordinary cities (2006) argue for a much more representative and cosmopolitan comparative urbanism. From a different angle, provocative research like Brenner’s (2014) ‘planetary urbanism‘ has critiqued the contentions of a universal urban age, arguing that urban/rural distinctions are no longer meaningful where capitalist networks reach to every corner of the globe.
I recently released an interactive map of the new Global Human Settlement Layer (GHSL) produced by the European Commission JRC and CIESIN Columbia University. This dataset makes several advances towards an improved cartography of the diversity of global urbanism. Firstly it is truly global, representing all the world’s landmass and settlements at a higher level of detail, down to 250m. Secondly the population density and built-up layers are continuous: there are no inherent city boundaries or urban/rural definitions (the GHSL includes an additional layer with urban centres defined, but the user can ignore these and create their own boundaries from the underlying layers). Thirdly the dataset is a time-series, including 1975, 1990, 2000 and 2015. Finally the data layers and the methods used to create them are fully open.
Diversity and Structure of Global Urban Constellations
The complexity and scale of the GHSL data is both beautiful and beguiling. In China and India there are continuous landscapes of connected urban settlements with hundreds of millions of people, scattered across many thousands of square kilometres. The cartographic appearance of these regions is like constellations of stars coalescing in vast nebulae of diffuse population. Densities of South and South-East Asian towns and small settlements in semi-rural regions exceed many major cities in Europe and North America. These are complex evolving landscapes at a scale and extent unprecedented in the history of urbanism.
Similarly there are unique trends in other major regions of urbanisation such as Latin America. Here major centres are very high density, but the extent of diffuse rural populations is far less prevalent. As a result countries like Colombia and Brazil have some of the highest urban population densities in the world.
The recognition of this global diversity does not mean abandoning global theories of urbanism. Even amongst such complexity and diversity, we can still observe shared spatial patterns and connections. Clearly we are observing landscapes heavily influenced by our current era of intense globalisation, as well as retaining inherited patterns from previous eras. Spatial logics of globalisation are apparent across the globe, though differentiated between regions, economies and societies.
The pull of coastal areas for global trade is an obvious spatial pattern. The importance of port cities is also applicable to historic periods of ancient civilisations, and indeed to globalisation in the 18th and 19th centuries. But the difference in the 20th and 21st centuries appears to be the more intensive links between major ports and global megaregions of production and manufacturing. We can observe this in the huge megaregions of China: the Pearl River Delta and Yangtze Delta (both with around 50m population depending on where the boundary is drawn), which are China’s leading manufacturing centres.
It also applies to Europe, with the higher density spine of the ‘blue banana’ linking low country ports to manufacturing centres in western Germany and north-eastern France, and more loosely to south-east England and northern Italy. As well as the manufacturing roles, it is clear that most major global financial centres are closely linked to megaregions, either at their core (e.g. Shanghai, New York, Tokyo) or within a couple of hours travel (e.g. Hong Kong, London, Paris). These centres provide the capital and business services that embed megaregions in global networks.
The importance of ports is also evident in South Asia. Port cities in South Asia are amongst the fastest growing in the world, such as Dhaka, Mumbai, Karachi, Kolkata and Chennai. But megaregions here appear as yet to be less extensive and well connected. Latin American cities are even more spatially separated and precisely defined in density terms, though there are signs of increasing connections between for example the two great Brazilian metropolises, Sao Paulo and Rio de Janeiro, and in the north between Venezuelan and Colombian port cities.
Another fascinating pattern relates to large previously rural areas of population in developing countries that are urbanising in more diffuse and bottom-up patterns. McGee used the term desakota (village-city) to describe patterns of disperse rural development in Java Indonesia. There appear to be similar patterns emerging across regions of China and India, including many areas of the vast Ganges plain, and along the great rivers of China. One of most striking features in China is the concentration of semi-rural and urban populations radiating south-west from Beijing towards Shijiazhuang and then south towards Zhengzhou (this follows one of China’s oldest rail routes, built 1903 and is nearly 600km long).
There are several areas of sub-Saharan Africa where desakota-like patterns seem to be apparent. The west coast around Nigeria and Ghana is one such area. Another is the many developments around Lake Victoria in Uganda, Kenya and Tanzania. Clearly the cultural and geographical diversity is very high in these regions, and my own knowledge of these countries is very limited. But the similar density patterns is still of interest.
Population and Density Statistics The World Population Density map includes density statistics at national and city scales, with population totals classified into density groups (turn on the Interactive Statistics button at the top left). These help to identify differences in patterns of settlement, and how city densities relate to national distributions.
If we view the world’s highest density cities, we can see the clear links to the above discussion of urbanisation in South Asia and East Asia, and major global port cities. Note however there are many issues with defining and measuring density, which need to be borne in mind when interpreting such statistics. These are measures of residential density, and results will likely be affected by the scale and accuracy of the underlying census data. It would also be better statistically to measure peaks as the 95th or 99th percentile to prevent a single square km cell skewing the results, as there are some outliers in the results.
Highest peak density cities GHSL 2015 1km scale-
Peak Density (000s pp/km2)
Mean Density (000s pp/km2)
We can also consider the highest population city-regions based on the GHSL urban centre boundaries. These are defined as continuous built-up areas, with polycentric regions linked into single cities. This leads to quite different results for world’s largest cities, with the Pearl River Delta measured as the world’s biggest urban agglomeration at 46 million (and that’s not including Hong Kong or Macao). It is interesting to compare this to results from the UN World Urbanisation Prospects data, which keeps these regions as separate cities and identifies Tokyo as the world’s largest city-region.
Highest population urban centres GHSL 2015 1km scale-
Peak Density (000s pp/km2)
Mean Density (000s pp/km2)
Future Cartography of Global Urbanism
Population density is clearly a very useful base from which to understand urbanisation and patterns of settlement. But we can also see its limitations too in the World Density Map if urbanisation is viewed only in terms of density. Many US city-regions are very low density, much lower than semi-rural parts of Asia and Africa, but these US cities are amongst the most affluent and highly urbanised areas of the globe.
Clearly a more comprehensive cartography of global urbanism would combine population density with measurements of development and economic activity, and the flows of people, goods, energy and information that describe the dynamics of how cities and networks function. The development of open global datasets like the GHSL will greatly help in these endeavours.
Another important issue is improving the sophistication of spatial statistics to include multiple urban boundaries and limit Modifiable Areal Unit effects. This would be possible with the GHSL dataset, and I have tried including national and city statistics, but clearly MAUP effects remain when using fixed city boundaries. Something along the lines of my colleagues’ research testing statistics for multiple boundaries simultaneously and showing their influence would be a good avenue to explore.
A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- https://luminocity3d.org/WorldPopDen/
The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time. A few highlights are included here and I will post in more detail later when I have explored the dataset more fully.
The GHSL is great for exploring megaregions. Above is the northeastern seaboard of the USA, with urban settlements stretching from Washington to Boston, famously discussed by Gottman in the 1960s as a meglopolis.
Europe’s version of a megaregion is looser, but you can clearly see the corridor of higher population density stretching through the industrial heartland of the low countries and Rhine-Ruhr towards Switzerland and northern Italy, sometimes called the ‘blue banana’.
The megaregions of China are spectacularly highlighted, above the Pearl River Delta including Guangzhou, Shenzhen and Hong Kong amongst many other large cities, giving a total population of around 50 million.
The Yangtze Delta is also home to another gigantic polycentric megaregion, with Shanghai as the focus. Population estimates range from 50-70 million depending on where you draw the boundary.
The form of Beijing’s wider region is quite different, with a huge lower density corridor to the South West of mixed industry and agriculture which looks like the Chinese version of desakota (“village-city”) forms. This emerging megaregion, including Tianjin, is sometimes termed Jingjinji.
The term desakota was originally coined by McGee in relation to Java in Indonesia, which has an incredible density of settlement as shown above. There are around 147 million people living on Java.
The intense settlement of Cairo and the Nile Delta is in complete contrast to the arid and empty Sahara.
Huge rural populations surround the delta lands of West Bengal and Bangladesh, focused around the megacities of Kolkata and Dhaka.
There is a massive concentration of population along the coast in South India. This reflects rich agriculture and prospering cities, but like many urban regions is vulnerable to sea level changes.
The comprehensive nature of the GHSL data means it can be analysed and applied in many ways, including as a time series as data is available for 1975, 1990, 2000 and 2015. So far I have only visualised 2015, but have calculated statistics for all the years (turn the interactive statistics on at the top left of the website- I’ll post more about these statistics later). Change over time animations would definitely be an interesting approach to explore in the future. Also see some nice work by Alasdair Rae who has produced some excellent 3D visualisations using GHSL.
Recent advances in public sector open data and online mapping software are opening up new possibilities for interactive mapping in research applications. Increasingly there are opportunities to develop advanced interactive platforms with exploratory and analytical functionality. This paper reviews tools and workflows for the production of online research mapping platforms, alongside a classification of the interactive functionality that can be achieved. A series of mapping case studies from government, academia and research institutes are reviewed.
The conclusions are that online cartography’s technical hurdles are falling due to open data releases, open source software and cloud services innovations. The data exploration functionality of these new tools is powerful and complements the emerging fields of big data and open GIS. International data perspectives are also increasingly feasible. Analytical functionality for web mapping is currently less developed, but promising examples can be seen in areas such as urban analytics. For more presentational research communication applications, there has been progress in story-driven mapping drawing on data journalism approaches that are capable of connecting with very large audiences.
And here are some example images from the mapping sites reviewed in the paper-
One of the best datasets for understanding the explosive growth of cities across the world in the last 65 years in the UN World Urbanisation Prospects research, which records individual city populations from 1950 to 2014, and includes predicted populations up to 2030. I have been meaning to create an interactive map of this fascinating data for a while, and have now completed this at- luminocity3d.org/WorldCity/
The map uses proportional circles representing city populations in the years 1950, 1990, 2015 and 2030, highlighting the regions in the globe with the most spectacular urban growth, and the time period when this growth occurred. This technique of overlaying proportional circles to show population change over time was first developed in a static map at LSE Cities Urban Age by Guido Robazza. Naturally China, India, Africa and Latin America jump out in the map, while Europe is largely static (except for Turkey). You can also explore time-series graphs and statistics for individual cities by moving your cursor over each city.
The site also includes queries of the city statistics, for example highlighting the world’s largest cities in different years. It’s amazing to see the dramatic changes between 1950 and 2015. London was the 3rd largest city in the world in 1950, and is now the 36th. In 1950 there were no African cities and only one Indian city in the world’s top 12, but by 2030 this list is dominated by South Asian, East Asian and African cities.
Mapping Tools Used
This map is the first time I’ve tried out CartoDB for interactive mapping, and I’m impressed with this tool. The main advantage of CartoDB for thematic mapping is the ability to perform SQL queries on the client-side, allowing map features to be highlighted interactively (this is used for the map queries on the World City site). There is also the ability to comprehensively restyle map symbology from the client using CartoCSS (this feature requires a full map refresh). Certainly sophisticated interactive mapping functionality is possible using CartoDB. It’s also Leaflet.js based, which is what I’m used to from the previous LuminoCity3D project.
Cities and Mega-City Regions
Measurements of city populations inevitably depend on where regional boundaries are defined, and the UN database is by no means perfect. The job of trying to integrate the hundreds of different city definitions used by each individual nation-state is no easy task. The UN tries to apply the concept of metropolitan agglomerations across the globe, but data is not always available and some cities are measured using administrative boundaries, which leads to population underestimation (full details on the UN methodology).
One of the interesting definitional issues that arises is around how very large polycentric regions have emerged in parts of the globe and beginning to look more like a single giant city. One of the most famous is the Pearl River Delta Megacity Region-
There are so many giant cities in close proximity that the map symbology struggles. Hong Kong, Guangzhou, Shenzhen, Foshan and Dongguan are all huge cities. Shenzhen in particular has experienced the most rapid growth of any city in history, growing from small town in 1980 to 10.7 million people in 2015. The combined population of these cities would make the Pearl River Delta the largest city in the world if a wider regional definition was employed.
CASA and UCL Geography have substantial experience in developing online interactive mapping sites for research outreach. The purpose of these tools is to take spatial analysis and visualisation outputs from the research lab and make them accessible and useful for many users from a wide variety of sectors and backgrounds, including: wider academia, central and local government, built-environment professionals, business, technology, community groups and the general public. Interactive mapping tools are part of the movement to make science and research more accessible, supported by the main UK research funding bodies as well as specific campaign movements like Open Data and Open Science.
The positive media coverage of recent projects and our communications with users has indicated that interactive mapping sites do reach a wide audience, including various expert users as well as the general public. These mapping projects are however a relatively new set of tools, and there is a lack of detailed information and evidence on who is using interactive mapping sites and the degree of research impact that they can deliver. In this post I explore two recent interactive mapping projects, DataShine.org.uk & LuminoCity3D.org, and analyse who has shared these sites using data from Twitter. This method is not without its flaws as described below, but is an early attempt to gather evidence and understand the user base.
‘Engaged’ Users and Social Media Sharers
A well designed interactive mapping site can generate a lot of hits, particularly if it gets picked up by national media sites. DataShine generated a huge 99,000 unique users in its first three months after launch in June last year, while LuminoCity had a reasonably large 24,000 unique users in its first three months from September 2014.
How many of these hits are truly engaged users? We can approach this question in terms of web statistics. On the LuminoCity site during the first three months, 16% of users made at least one return visit; 18% of users stayed for at least three minutes; and 26% of users explored at least four different maps during their session. So we can estimate that around 20% of the total users are exploring the site in some depth. That’s not a bad return where there is a high number of total users, e.g. this would equate to 19,800 people for the first three months of DataShine, and 4,800 people for the LuminoCity site.
We do not know however who these users are. Are they mainly interested members of the general public? Are they expert professional users? This is harder to gauge.
Classifying Twitter Users
We do have further information about the most engaged group of users- the social media sharers. These are the people who actively promoted the site to their network of followers/friends. The two major social media sites are Facebook and Twitter, with 4% of visitors of both DataShine and LuminoCity either sharing/liking the site on Facebook or posting the link on Twitter in the first three months. This is a high proportion of social media sharers, and reflects the novel and accessible nature of the sites which helped to generate enthusiastic users.
In this analysis I have classified Twitter users who shared site links to Datashine and LuminoCity according to their profession. Naturally there are some problems with this approach- this selection reflects only the most enthusiastic users of the mappings sites; Twitter users are a biased sample (generally towards affluent professionals, tech and media users); many users have multiple professions (I tried to pick the main one); and professional and personal opinions on Twitter overlap significantly. However this is an early effort to explore types of users of interactive mapping sites, and hopefully this can be built on in the future.
The DataShine Census Site
Below is the classification of 350 Twitter sharers from the DataShine site. It is clear that a wide variety of users are covered, including both professional and community groups (a more detailed table is at the end of the post)-
Geographers were not surprisingly the main group of academic users, but DataShine also attracted many users from across the natural sciences, social sciences and the humanities. Health researchers were particularly well represented, as the site provides many useful health related maps from the 2011 census. This result also chimes with a high number of business users in the public policy sector, mainly with a health and planning focus.
The innovative visualisation technology behind the DataShine site appeals to IT users, and there were many sharers from IT, cartography, data journalism and data science backgrounds.
One of the biggest successes with the DataShine site was in reaching beyond academic and professional experts to local communities. The site provides high quality maps of census data at the neighbourhood level, and this successfully appealed to local community groups, campaigners (e.g. cycling campaigns, local environment campaigns) and to local government users. Several councillors tweeted the site, as well as users from DCLG and local government planners. Media coverage also helped to generate many interested users from the general public.
The LuminoCity Site
The data from the LuminoCity site is based on a smaller sample of 140 Twitter shares. This covers a similarly wide variety of users, with more of a focus on built-environment professionals, and less on local government and the general public.
The LuminoCity site provides a range of maps and statistics for the comparative analysis of UK cities. This functionality appealed strongly to planners and transport consultants, as well as some business users in economic development and real estate. Academic users also had a more urban focus for the LuminoCity site. The site did not chime so strongly with local government and community users who generally want a more local scale of analysis. There were some users from Central Government who used the site for measuring economic performance in northern cities.
The more abstract minimalist aesthetic used on the LuminoCity site attracted quite a few architects and designers to the platform. These users are enthusiastic about visualisation while being less familiar with the range of open data available at city and national scales.
The ‘Other Education’ sector, which was popular for both sites, includes high schools, geography departments, museums and the wider education sector beyond universities. This was an unexpected outreach success for both of the websites, and shows how the open approach can help to create new connections.
This analysis of twitter shares from interactive mapping platforms shows how these tools can successfully appeal to a wide range of users, both professional and the general public. Academics are well respresented, but also business users, government, local communities and the wider education sector.
Twitter users are inevitably a biased sample and it would be useful in the future to look at methods that can capture a larger proportion of engaged users and assess to what extent the most engaged social media users represent the wider engaged audience for the sites.
Household energy use is a key indicator for understanding urban sustainability and fuel poverty, and is a timely topic now that winter has arrived. The LuminoCity3D site maps domestic energy use in England and Wales at 1km2 scale using data from DECC. This map has also just been published as a featured graphic in Regional Studies Regional Science. The household energy use distribution is really fascinating, with large scale regional variation and fine scale intra-urban patterns identifiable-
The lowest energy use per-household is found in cities and towns in the South-West region such as Plymouth and Exeter, and also along the South coast. While the highest energy use per-household is found in commuter belt towns around London. The variation within city-regions is very high, with for example London and Manchester averages varying by up to a factor of 5, from a mere 8kWh to over 40kWh per year.
The main drivers of energy use are generally housing type (more exposed walls=more energy use; larger house=more energy use), household size, wealth and climate. Often these factors are correlated at household and neighbourhood levels- so for example wealthier households in England and Wales are more likely to live in large detached houses, and these households tend to be clustered together. These trends produce the high energy use pattern seen in London’s commuter belt, as well as in the wealthier suburbs of other large cities like Birmingham, Manchester and Leeds. South West England on the other hand benefits from the mildest climate in the UK, has a relatively high proportions of flats and generally lower average household sizes, thus resulting in the lowest energy use.
We can see how these factors play out for London in the map below. The height of the hexagons shows density, with higher density areas clearly using less energy. City centre households have considerably lower energy use, with a strong bias towards Inner East London where incomes are lower.
Energy use areas correlate strongly with the most prevalent housing type map (also on the LuminoCity3D site), with flats and terraced housing the lowest energy users, and detached and semi-detached areas the highest.
The relationship with household size is less clear cut, but it can be seen that average household sizes are smaller in the city centre. On the other hand, areas with high average household sizes such as Stratford and Wembley, do not have particularly high average energy use.
Overall domestic energy use patterns tend to mirror transport sustainability, in that higher density city centre areas perform more efficiently compared to low density suburbs. On the other hand the link to city size (which tends to be strong in transport sustainability relationships, with bigger cities reducing car use) is much weaker, and the most efficient locations are often small and medium sized towns and cities. It is not clear in this analysis whether more recent green policies (such as improved insulation or CHP schemes) are having much effect, but several cities with green reputations like Brighton and Bristol are amongst the best performing cities.
Urban policy is currently riding high on the UK political agenda. A combination of the desire to rebalance the UK economy away from financial services; debates over massive high-speed rail investment; the worsening housing crisis in the South-East; and city devolution demands following the Scottish referendum, all point to major reform. As we move towards the 2015 general election, addressing city concerns is going to be a key, perhaps even decisive, election debate.
It is therefore a good time to take stock of recent urban growth and change in Great Britain, assess policy successes and failures, and consider how better outcomes might be achieved in the coming decades. This post draws on map visualisations from the LuminoCity3D.org website.
London and the South-East: Global Boom Region to Elite Island?
London’s recent growth has been phenomenal, gaining over a million residents (+13%) between 2001 and 2011. As we can see in the figure below, population growth has occurred across all of Greater London (except Kensington & Chelsea), with the strongest concentrations in Inner London and East London, reflecting the priorities of successive London Plans. This spectacular growth has not been confined to Greater London either, but is found across the South East region. The fastest growing UK towns and cities are nearly all in London’s orbit, including Milton Keynes with 20% growth, Ipswich with 15% growth, Cambridge with 16% growth and Ashford with 21% growth. This shared growth clearly illustrates that the South East is a closely integrated region, as further demonstrated by extensive commuting flows.
Inevitably it is strong economic growth that underpins this rise in population. London gained 650,000 jobs (+15%) between 2001-2011, strongly focussed in Inner London and Canary Wharf. Employment growth is much more unevenly spread across the South East, and arguably booming Inner London is taking jobs away from other centres, or pressuring some into becoming dormitory suburbs through soaring demand for housing. This is most clearly seen in Outer London in centres such as Croydon and Bromley where employment has fallen, while resident population has risen.
So with so many success stories, you be forgiven for thinking everything looking rosy for London and the South East. Unfortunately this is not the case. Soaring population growth has in no way been matched by new housing construction. What was previously a housing affordability problem in the South East is now an outright crisis that threatens to put the brakes on the entire region. Mean house prices just passed the incredible figure of £500,000 in July of this year, and a recent survey placed London as the most expensive city in the world to live and work. This is a looming disaster for future growth prospects. The crisis is not limited to London either, as shown below, with median prices above £300k for much of the South East, and the most popular cities experiencing similar extremes to London.
Soaring prices may seem like great news for property owners, but ultimately cities rely on their ability to attract talent and new businesses. And as London’s competitiveness falls, growth will go elsewhere. What has traditionally been a region of opportunity risks becoming a closed-shop for the wealthy.
And the situation is in danger of getting worse before it gets better. The current UK government did not create the housing shortage, but have overseen a period of historically low house building, with 2014 rumoured to hit rock-bottom. Mapping new-built housing sales leaves a sea of white, largely because there have been so few new houses constructed to sell. The recession presented an ideal opportunity for investing in housing and addressing unemployment, but this opportunity was missed. Trumpeted planning reforms have achieved very little, while right-to-buy policies have simply further increased prices.
Solving the housing crisis requires reform on a number of fronts. More power for local authorities to borrow money and make compulsory land purchases would certainly help. Linked to this is a desperate need for property tax reform to encourage housing to be used efficiently. Currently a £300k house pays the same council tax as a £10 million house, while empty housing is not discouraged, leaving many houses in Inner London as empty or underused investment vehicles. Similar arguments are made in favour of a land value tax to encourage land to be used efficiently and stop land banking.
Perhaps the most controversial issue is whether the green-belt can be retained in its current form. Calls from the eminent Richard Rogers that all new development can still be on brownfield frankly look out of touch with the reality in the South East. The debate really needs to switch towards how a controlled release of green belt land can be managed to avoid car-based sprawl and develop sustainable urban areas. Mapping rail infrastructure and urban density in the South East as shown below indicates that there are many potential locations with rail stations and room for growth. This approach would only however create more commuter towns, and ultimately there needs to be stronger planning for the entire South East region, likely with big urban extensions for successful cities such as Milton Keynes, Cambridge and Brighton. It is interesting that recent entries for the Wolfson prize were focussed on this approach.
Northern Evolution: an Emerging Hierarchy of Urban Centres? While the South East is in danger of overheating, the majority of the UK’s city-regions have been focussed on post-industrial regeneration and stimulating growth. And in the last decade there has been significant change for many northern cities. Starting in the North West and Yorkshire we can see rising populations in all the major city centres. Greater Manchester in particular has experienced high levels of growth, gaining 200,000 residents (+8%) and 100,000 jobs (+10%) between 2001 and 2011. By the regional definitions used in LuminoCity3D.org, Greater Manchester has overtaken the West Midlands to become the second largest city-region in the country with 2.6 million residents. Manchester city centre has also experienced high rates of employment growth and is the primary centre in the North West, with positive signs in the business services and science & engineering sectors.
The Leeds and West Yorkshire region is also growing quickly, gaining 120,000 residents (+8%) and 50,000 jobs (+6.6%). Population growth is greatest in Leeds city centre, but is evident across the region, particularly in Bradford and Huddersfield. Similar to Manchester, employment growth is focussed strongly on the largest centre, Leeds, with a concentration in financial and business services. Despite West Yorkshire and Greater Manchester being two of the most dynamic northern regions, there is very little travel interactions between them due to poor transport links, and this surely needs to be a policy priority.
Sheffield also displays significant city centre led growth, gaining 45,000 (+6.3%) residents and 21,000 jobs (+6.7%), as does Liverpool although there has been some population decline in the suburbs. Liverpool’s figures are a gain of 21,000 residents (1.8%) and a more impressive 44,000 jobs (10%).
The house prices map for the north-west and Yorkshire makes a very interesting comparison to London. The dramatic gentrification that has transformed Inner London towards increasing affluence and polarisation has not (yet?) occurred. The wealthy areas are mainly suburban in the north-west, often where large cities merge with national parks such as the Peak District and the Yorkshire Dales. There are some signs that wealthier South Manchester is beginning to move towards the city-centre, but this is still in earlier stages of city-centre transformation.
Similar to the North West and Yorkshire, city centre housing markets are relatively inexpensive in the Midlands, with wealthier areas in the suburbs, particularly between Birmingham, Coventry and Warwick/Leamington Spa. There are signs that wealthier groups to the south of Birmingham are moving further into the city centre.
Will Growth Transfer from the South East to the North? With the South East struggling to accommodate growth and northern regions trying to attract more growth, the answer seems obvious- transfer growth to the north. Unfortunately urban economics is seldom that straightforward. London is a global leader in a range of service sectors, and it does not automatically follow that existing firms and new firms would choose northern cities over the South East. There are however many encouraging signs in cities such as Manchester, Leeds and Birmingham with growth in a range of knowledge-economy sectors. The gap with the South East still remains extensive, and this essentially is the crux of the debates about city devolution and infrastructure investment: whether or not these policies can enable northern cities to bridge this gap. London currently has great advantages in terms of public money invested in infrastructure like public transport, and also in terms of political power to plan and manage growth through the Mayor and Greater London Authority. The argument in favour of empowering northern cities looks increasingly convincing, and we shall see in the coming months whether politicians are brave enough to instigate this process.
Recent urban growth in the UK has further emphasised the role of cities in influencing economic prosperity, quality of life and sustainability. If we are to meet 21st century social and economic challenges then we need to plan and run our cities better. Data analysis can play a useful role in this task by helping understand current patterns and trends, and identifying successful cities for sharing best practice.
Taking for example employment density change in northern English cities as shown below. Current growth is mainly in ‘knowledge-economy’ services that generally favour being clustered together in city centres, generally reinforcing a select few larger centres rather than many smaller centres. There is clear growth in Manchester, Leeds and Liverpool city centres, particularly Manchester which displays the biggest increase in employment density of any location in GB. But around these success stories there is a much more mixed picture of growth and decline for many other centres that are finding it more difficult to compete for firms and jobs.
Interactive City Statistics
City statistics are available to make more precise comparisons between urban areas. Statistics can be viewed on LuminoCity3D.org by moving your mouse pointer over a city of interest, or by hovering/clicking on the GB Overview Chart at the bottom left of the screen. The graphs and statistics change depending on the map indicator selected, so that the LuminoCity maps and statistics are interactively integrated.
The example below shows public transport travel, a key sustainability indicator that also has important economic and equity implications. Greater London is by far the public transport centre of the UK with nearly 50% of commuting by public transport. Without the investment and historic advantages of London, city-regions like Manchester and Birmingham do not even manage 20% PT commuting. But we can see that it is not essential to be as gigantic as London to achieve more sustainable travel. Edinburgh, with a compact form and extensive publicly owned bus network, achieves 36% PT commuting.
All the datasets used are government open data. Websites such as LuminoCity would not be possible without recent open data initiatives and the release of considerable government data into the public domain. Links to the specific datasets used in each map are provided to the bottom right of the page under “Source Data”.
UK cities have been undergoing significant change over the last decade, and the 2011 census data provides a great basis for tracking current urban structure. I’ve mapped population and employment density for all of England and Wales in 2011, using a 1km2 grid scale approach-
The main themes that emerge are the dramatic intensification of London, high densities in some medium sized cities such as Leicester and Brighton, and the regeneration of the major northern conurbations, with Manchester and Birmingham as the largest employment hubs outside of London.
Mapping all of England and Wales together is a useful basis for considering city-regions and their connections (note Scotland has not yet published census 2011 employment data and is not mapped). Certainly this is a major theme in current policy debates grappling with the north-south divide and proposed high-speed rail links. I’ll be looking at densities in relation to network connections in future posts as this topic is part of ongoing research at CASA as part of the MECHANICITY project.
It is also possible to directly map changes in density between using the same visualisation approach (note the grid height describes density in 2011, while colour describes change in density between 2001-2011)-
The change map really highlights the pattern of city centre intensification combined with static or marginally declining suburbs in England and Wales. This trend was discussed in a previous post. The two statistics of peak and average densities reinforce the city centre versus suburbs divide, with peak density measurements growing much more than average densities. But the peak density statistic is somewhat unreliable (such as in the case of Birmingham/West Midlands) and we will be doing further work at CASA to define inner cities and produce more robust statistics of these trends.
Notes on the Analysis Method-
The density values were calculated from the smallest available units- Output Area population and Workplace Zone employment data from the 2011 census. This data was transformed to a 1km2 grid geography using a proportional spatial join approach, with the intention of standardising zone size to aid comparability of density measurements between cities. The transformation inevitably results in some MAUP errors. These are however minimised by the very fine scale resolution of the original data, which is much smaller than the grid geography in urban areas.
The workplace zone data is a very positive new addition by the Office for National Statistics for the 2011 census. There is a lot of new interesting information on workplace geography- have a look at my colleague Robin Edward’s blog, where he has been mapping this new data.
Defining city regions is another boundary issue for these statistics. I’ve used a simple approach of amalgamating local authorities, as shown below-
It’s been 14 years since the landmark Urban Task Force report, which set the agenda for inner-city densification and brownfield regeneration in the UK. Furthermore we’ve seen significant economic and demographic change in the last decade that’s greatly impacted urban areas. We can now use the 2011 census data, mapped here on the LuminoCity GB site, to investigate how these policies and socio-economic trends have transformed British cities in terms of population density change.
The stand-out result is that there’s a striking similarity across a wide range of cities, with overall growth achieved through high levels of inner-city densification (shown in lighter blue to cyan colours) in combination with a mix of slowly growing and moderately declining suburbs (dark purple to magenta colours).
We can see this pattern in the growing urban regions of Manchester, Birmingham, Leeds and Sheffield above. Manchester has the fastest population growth after London, with 8.1% growth in the city-region, and a massive 28% growth in the core local authority. Average densities in Manchester have gone up by 28% (+35 residents per hectare), but it’s not a uniform growth. There are new development sites at a very high 300 or 400 residents per hectare, contrasting with low density surrounds and the extensive remaining brownfield sites. There is a patchy nature to the current urban fabric of Manchester, indicating that much further development could still take place.
The West Midlands Conurbation is the third fastest growing city-region at 7.3%, with a higher 10% growth in the core city authority Birmingham. Density increases are more modest here (+13 residents per hectare) but the same general pattern remains. Similar patterns of high density inner-city growth are also clear in Leeds (5% growth) and Sheffield (8% growth).
The trend applies to medium size cities also. Those cities with the highest growth rates like Leicester (+18%), Nottingham (+14%), Cardiff (+13%) and Bristol (+12.5%) show fewer signs of suburban depopulation-
Scottish cities have a stronger tradition of high density inner-city living. With compact cores already in place, Edinburgh (+6.5%) and Aberdeen (+5%) have been expanding the inner city into Leith and Old Aberdeen-
Meanwhile the UK’s former industrial powerhouses of Glasgow, Liverpool and Newcastle display a more problematic variation on this pattern. City centre intensification is still much in evidence, with core city authority populations growing at 8% in Newcastle, 6% in Liverpool and 4% in Glasgow. But this growth is in combination with outright decline in some surrounding towns and suburban areas, particularly around Glasgow. These patterns are linked to major programmes to overhaul poor inner-city housing stock, but are also inevitably linked to weaker economic growth in Glasgow and Liverpool. The picture is better in Tyne & Wear, where there are more positive employment signs (8% growth in workforce jobs 2001-2011).
What is driving this urban dynamic?
In addition to planning policy shifts, a series of economic and demographic changes are contributing to the pattern of central growth and struggling suburbs, as commentators have variously been observing in the UK and US (e.g. gentrification researchers, Erenhalt, Kochan). Demographic aspects include more students, immigrants, singles and childless couples. Economic aspects include city-centre friendly service and knowledge economy jobs, as well as increased costs of petrol. For these trends to occur over a wide range of demographically and economically diverse cities in the UK and beyond, clearly there are multiple factors pulling urban populations and growth in similar directions.
We’ve avoided the gigantic outlier of London so far. It’s a city apart in many ways- much larger (8.1 million in the GLA area) and faster growing (+14% 2001-2011). It’s also massively higher density, with average residents per hectare 50% higher (nearly 200 residents per hectare) than the next most dense city-region in GB. The biggest changes have been in Inner East London. Tower Hamlets (where Canary Wharf has boomed) is 1st on every indicator- highest population change (+28.8%), highest employment change (+50%!!), highest population density (324 residents / hectare). The pressures for growth in London are so high that there is little surburban decline in population terms (although employment has been declining significantly in Outer London).
Yet the high rate of densification in London has come nowhere near meeting housing demand. London is the midst of a massive housing shortage and crisis, with some of the world’s highest property prices. The debate is currently raging about what needs to be done to accelerate construction, with advocates of transforming more land to community ownership (e.g. Planners Network UK), relaxing planning regulations such as the green belt (e.g. LSE SERC), and implementing an array of measures simultaneously (e.g. Shelter Report). We can see London’s challenges in the maps, such as the failure thus far of the flagship housing expansion programme, the Thames Gateway, to deliver. Some high profile development sites like Stratford and Kings Cross have only recently opened for residents and so do not show in the 2011 data.
Another more surprising result is the fall in the population of Inner West London, particularly Kensington and Chelsea. While this finding does need some context- K&C is still the forth most densely populated local authority in the country- it’s still an amazing trend given the extreme population pressures in London. It is in line with arguments that the most expensive properties in London have become investments for international capital rather than homes for living. Such trends push prices up, cut supply and bring questionable benefits to the city. Addressing this issue would require tax changes, and macro economic factors like the value of the pound and yields on alternative investments are also clearly influential.
Summary- an Ongoing Renaissance and Suburban Challenges
Well to state the obvious GB cities are, with only a few exceptions, growing significantly. That’s not to be sniffed at given the history of widespread urban decline throughout the second half of the 20th century. And secondly the pattern of growth in density terms is clear- densifying inner cities, and fairly static or declining suburbs. The scale of London and the severe housing crisis has it’s own unique dynamics, while Glasgow and Liverpool are still dealing with significant population loss in many areas of the city region. But on the whole, the pattern is surprisingly consistent across cities in Great Britain.
Clearly this review prompts a series of further questions analysing the economic, demographic, gentrification, deprivation and property market processes inherent in this urban change, and what future city centres and suburbs will be like. Hopefully this mapping exercise should is a useful context for the ongoing research.
Our cities have been changing dramatically in recent years, with the intensification of urban centres, redevelopment of old industrial spaces, new demographic trends, and the pressures of a volatile global economy. The aim of the LuminoCity website, which launches in beta today, is to visualise urban form and dynamics to better understand how these trends are transforming cities in Great Britain. Explore the site for yourself here- luminocitymap.org.
The visual style developed for LuminoCity combines urban activity data with built-form. Density values are calculated by dividing fine-scale (LSOA) employment and population data by built-up area, and then mapping the results to the same building footprint data (Ordnance Survey VectorMap). The result is a novel city perspective on common demographic indicators like population and employment density, with links between density and the texture of the built-environment clearly highlighted. So for example in the London map above, we can see the patchwork pattern of recent high density developments in Docklands (along the river to the east), and high density clustering around major rail stations like Paddington.
There are three layers included in the beta version of LuminoCity-
Each layer provides a complementary angle on urban form, with Employment Density showing business agglomeration patterns, and Population Density Change highlighting where intensification is occurring and where population losses are found. Examples of these three layers for major cities are shown above. The Population Density Change is particularly interesting in light of clear patterns of city centre growth and static or declining suburbs in many British cities, such as Manchester above. There is also in London a distinct pattern of population loss in the western inner-city, likely due to international capital speculation leaving under-occupied housing (see image below). These trends will be discussed in a further post later this week.
Multi-Scale Interactive Statistics
As well as browsing the map you can also click on particular locations to get a set of core statistics and rankings of that area for the current map layer. The statistics are at three spatial levels- City Region, Local Authority and LSOA. This feature shows how typical a particular area is compared to the wider city-region and the country as a whole. It also helps to communicate the variation in density measurements according to scale.
The site concept was partly inspired by Ollie O’Brien’s ‘New Booth’ Map of Deprivation for Great Britain.
The population data comes from the UK 2001 and 2011 Census, published by Office for National Statistics and National Records of Scotland. The employment data is derived from the Business Register and Employment Survey 2009-2011, also published by Office for National Statistics. The building footprint and urban area data is from the Ordnance Survey Vector District and Meridian products. These datasets have been published by the OS as Open Data, which is a fantastic recent development enabling sites like this to happen.
Spatial Analysis Method Details and Errors
All socio-economic mapping contains a degree of error, and the building footprint density approach used here introduces some issues. The Lower Super Output Area zone geography at which the population and employment data is published is fine scale but is not at the individual block level. Each LSOA zone represents groups of adjacent city blocks. The density results are therefore an average of the adjacent blocks in each zone. The results are affected by a particular version of the Modifiable Areal Unit Problem, and represent the density of fine-scale city neighbourhoods rather than of particular buildings. You can view the specific geography of the LSOA zones by turning on the ‘Admin Boundary’ layer on the LuminoCity site to see how blocks are aggregated.
Additionally the analysis does not consider building use (there are several technical and copyright challenges with this) and so population and employment density measures include all buildings rather than distinguishing residential and commercial property densities.
Finally, the ONS has not yet published census 2001 and 2011 population counts at the same LSOA geography, and a proportional spatial join method by building area was used to convert the 2001 LSOA census data to 2011 LSOAs for the Population Density Change layer.
Feedback and Comments
If you like the site or have any feedback or comments then you can tweet me @citygeographics, or email email@example.com. The site is in beta at the moment, and I plan to add more layers and interactivity in future releases. I’ll be blogging here in more detail about what the visualisations reveal about the changing geography of British cities over the coming weeks.
We know that knowledge networks and intensive competition within cities boosts innovation. There are also further scales to this dynamic. The networks and competition between cities at regional and global scales promotes the adoption of new ideas- as cities buy, borrow and adapt ideas from their competitors. It’s this latter global dynamic that we’re exploring in this post, investigating the spread of new ideas in a sector that’s intrinsically urban in nature- public transport. After widespread decline in the second half of the 20th century, transit has recently undergone an impressive renaissance linked to the dramatic growth of urban populations, high density forms and sustainability policies.
The spread of new ideas between cities is clustered in space and in time, as cities are strongly influenced by nearby competitors, as well as economic investment cycles. Therefore a natural way to visualise these spatial and temporal patterns is through animated cartography. This is the technique used here with the help of Processing and the MapThing library by Jon Reades (allows GIS data to be imported into Processing).
So first up we’re going to head back in time to the invention and dispersion of the underground/subway metro (data from metrobits.org; best viewed HD fullscreen)-
London celebrated 150 years of the Underground this year, and it was three decades after 1863 before other cities in Europe and North America had their own high-frequency high-capacity city centre networks. This delay can be linked to varied levels of industrialisation between countries, as well as the time taken to improve the metro concept with electrical power (the original Underground amazingly used steam locomotives). It’s interesting that the youthful American metropolises of Chicago and Boston were quicker off the mark to build metro systems than many European capitals.
Buenos Aires in 1913 and Tokyo in 1927 (now the world’s largest metro) were early exceptions to the European and North American monopoly on metro systems. Yet it took until the 1980’s onwards with the rise of Newly Industrialised Countries like Brazil, Russia, India, Mexico and Turkey for metro systems to become truly global. China is now in a league of its own with gigantic metros in Shanghai, Guangzhou, Beijing and Hong Kong.
Underground metros may seem like the best answer to cities’ transit demands, but they are highly expensive and disruptive to build, and are pricey to maintain also. These difficulties underlie another key innovation in the global rise of public transport- bus rapid transit. The use of segregated roads, specially designed stations and articulated buses enables BRT to have similar capacity and speed advantages of subways at a much lower cost. We can see from the animation that BRT begins as a Brazilian innovation (data from brtdata.org)-
Initially BRT adoption is highly clustered in Brazil’s major cities, with a few early adopters including Santiago de Chile, Quito, Pittsburgh and Essen in Germany. Then in the late 1990’s the dynamic changes with a burst of new systems in Central America, Canada, Australia, and mainly second-tier cities in Europe. Taipei has spearheaded the adoption of BRT into China, with many new large systems emerging. Sizeable BRTs also recently opened in Istanbul, Tehran and interestingly in Lagos where hopefully further investment in African cities will follow.
In our highly connected globalised world, new city innovations are likely to spread more quickly, and that seems to be the case with BRT. Indeed this acceleration effect is even more marked in the last innovation we’re going to investigate- the bike sharing phenomenon. Now bike share schemes are of course small investments compared to city-wide metro systems, yet they are still an interesting recent advance with similar global dispersion dynamics (data from Bike Sharing World Map and O’Brien Bike Share Map)-
The original pioneer of bike sharing is not as clean cut as the BRT and Underground examples as there have been several generations of innovation (see pdf article). In 1995 Copenhagen successfully created a reasonably sized (1,000 bikes) coin operated system with specially designed bicycles that tried to reduce theft. A small number of cities in Germany and France followed suit. The next generation began in Lyon in 2005 with a larger (4,000 bikes) system using smart card technology that greatly reduced theft. Subsequently bike sharing has exploded globally across Europe, North America, China and South Korea.
Paris has by far the largest system in Europe with 20,000 bikes. But even Paris’s Vélib’ is small compared to two huge Chinese systems in Wuhan (90,000 bikes) and Hangzhou (70,000 bikes). China’s strong cycling tradition has recently been in decline with rising car ownership, and hopefully the Bike Share boom will reverse this trend.
So to conclude, we are experiencing an age of truly global transit adoption with innovations spreading more rapidly through global city networks. While innovation has traditionally arisen in Western European and North American contexts, by far the greatest urban growth is in Newly Industrialised Countries, increasing demand for innovations like BRT. The rapid rise of bike share systems shows that relatively modest innovations can have a global impact when the innovation is popular and effectively implemented.
The ridership and scheme size data relates to current passenger levels rather than the size of the system at the time of construction. Would be great to do this visualisation with time-series ridership data, but this is not to my knowledge currently available.
The definition of metro and BRT systems used here comes from the database providers, and there is some ambiguity, e.g. in defining when a regional urban rail system can be classed as a metro (see metrobits.org).
One of the most recognisable visualisation techniques used by LSE Cities in the Urban Age publications is the 3D density map- an intuitive and engaging way to represent built form, and enable comparison of very different city environments across the globe. I’ve been producing 3D density maps in my own research for around five years now, and so it was a nice challenge to produce the 3D density maps for this year’s Urban Age conference, the Electric City in London. In this post I focus on the contrasting densities in three leading world cities- London, New York and Hong Kong- with the added twist that both residential and employment densities are mapped for comparison.
Higher urban densities can facilitate more sustainable travel patterns, improve service delivery efficiency, reduce building energy use and promote urban vitality. These advantages depend of course on good urban planning to minimise congestion and pollution problems. High density mixed-use development is central to the compact city planning movement, and remains a foundation of sustainable planning policy today. Here we map the number of residents in each square kilometre of a 100 by 100 kilometre region for London, New York and Hong Kong. Lower urban densities apply to suburban-like neighbourhoods, while high densities generally represent medium or high rise buildings clustered on a tight urban grid.
The city that stands out in the mapping is Hong Kong, with its extremely high residential densities exceeding 110,000 people per km2. Here planners have responded to scarce land availability with very tall (over 30 storeys) high-density development. Scarce land has also influenced the development of New York City, where Manhattan densities peak at 59,000 people per km2. London in comparison is much lower density. The heritage of suburban housing and generous greenspace has created a residential culture at half the density of New York and a quarter the density of Hong Kong. Despite current intensification in London, residential densities remain a world away from other global cities.
Where people live is not however the only perspective needed to understand urban density. We can also examine employment densities for an important point of comparison (both residential and employment maps are at the same scale). Taller spikes in the employment maps represent higher numbers of jobs concentrated in business centres. London, New York and Hong Kong feature very intensive central employment clusters. The highest peak of over 150,000 jobs per km2 is in Midtown Manhattan. London is surprisingly close behind at over 140,000 jobs per km2, concentrated in the City of London and the West End. Hong Kong peaks at 120,000 jobs per km2 in Central (note the Hong Kong survey data is less comprehensive and may underestimate peak densities). These intense spikes represent very strong agglomeration economies, where financial and business services and creative industries cluster together to access labour markets, share fast-changing information and engage in face-to-face interaction with clients, customers and partners. Despite living in an age of instant telecommunication, proximity is still critical for many world city business activities.
The extreme employment density peaks are indicative of economic success in these world cities. Demand for office space is so stong that developers get sufficient returns to build high and businesses use their space more intensively. Central employment clustering also means these cities are dominated by public transport rather than car travel (particularly Hong Kong). On the other hand the divergence of living and working densities can signify a lack of integration between living and working locations. London is very polarised between its low density living and high density working environments. This contributes to the long distance and long duration commuting travel for many Londoners (recent surveys find an average one-way commute times for Londoners of 38 minutes). New York has a better integration of living and working locations (average commutes are around 31 minutes). Hong Kong appears to have the closest integration of living and working spaces, though unfortunately commuting time survey data is not available to test this.
The analysis here supports the medium-rise inner-city residential intensification that the London Plan prescribes to improve the balance of urban functions, and increase accessibility for residents and businesses. The gap in residential densities between London and many world cities is so large that modest intensification can be achieved while keeping London’s distinct character, providing development is on the much remaining brownfield land rather than London’s treasured greenspaces.
Another interesting thought is whether the highly concentrated office clusters we see in London and New York will continue to be the way most businesses operate in the future. Greg Lindsay gave a good talk last week on how businesses are changing the way they use work space towards more shared and flexible environments that will likely be less space demanding.
As the costs of recent droughts spiral from USA to Australia, West Africa to India, we’re getting a taste of what a significantly warmer climate would be like. Critically as the scientific evidence mounts up that climate change is occurring, global carbon dioxide emissions are soaring. Why is this?
I’ve designed a new website Carbon Chart visualising current data to answer this question.
There’s no single ideal metric to determine the contribution of different countries towards global warming, and a range of different perspectives need to be considered, as well as related issues of economic development and poverty reduction. The design of Carbon Chart is intended to allow the comparison of several perspectives.
So where are the maps? I’ve gone for a graph approach to focus on change over time. See Kiln’s excellent Carbon Map website for a cartogram-based approach to understanding global warming.
Current emissions data do not make happy reading. CO2 output is increasing in the developed world in consumption terms, and is rocketing in the developing world, especially China. We’re replicating our carbon intensive economic model on an incredible scale.
Maybe the climate models are wrong, or maybe an international climate agreement with substance is just around the corner. But right now it’s difficult to see how the more extreme scenarios of 4°C+ warming are going to be avoided.
Every so often you come across a dataset that really amazes you in its richness and ability to change perspectives on understanding the world. One such dataset has been produced by academics at Stanford and Oslo tracing the global supply chain of CO2 emissions.
Traditionally emissions are attributed to countries depending on where fuels are burned- the point of production. This approach puts big industrial polluters like China at the top of the emissions pile. Yet globalisation means that we are linked into an increasingly complex web of trade that challenges a production-based understanding of emissions. A quarter of fossil fuel CO2 emissions can be considered as being embedded in manufactured goods that are consumed away from the point of production.
To address this issue Davis, Peters & Caldeira have created a database charting the global supply chain of CO2 emissions from extraction to production and finally to consumption. The database covers coal, oil, gas and secondary fuels traded by 58 industrial sectors in 112 countries for the year 2004. Even better, the entire database is available online.
Maps of the major carbon transfers included in the paper highlight firstly the massive flows from the energy rich Middle East and Russia, and secondly how production emissions from industrial countries such as China are ultimately driven by consumption in the affluent core of USA, Europe and Japan.
Being a mapping type, I feel that the flow maps in the paper miss out much of the amazing detail in the dataset, such as extraction to consumption flows within countries (half of all emissions). So I decided to put my visualisation skills to the test…
First up I produced a proportional bubble map of extraction and production, giving a good sense of the relative scale between countries. Economies with high levels of both extraction and consumption (e.g. USA and China) exploit their own energy resources and have large emission flows within their national boundaries. Other large consuming nations that lack energy resources (e.g. the EU, Japan and South Korea) must import them.
Next I mapped the transfers of CO2 embedded in trade flows, using the same black-red colour scheme to indicate flow direction. While the visualisation is not as straightforward as the simpler flow map above, it gives a strong sense of the amazing complexity in global trade relationships and highlights clear patterns and structures.
Black lines emanate from the major energy exporters of the Middle East and Russia. Indeed the degree to which all of Europe is dependent on Russian energy is highly alarming. Major industrial countries act as intermediaries, both importing and exporting emissions. For instance China and Japan import energy and materials from the Middle East, Indonesia and Australia, then export manufactured products to the USA and Europe. The USA is top predator in the emissions food chain, spectacularly drawing in goods and resources from every corner of the globe and racking up over 25% of global emissions by consumption.
The data is for 2004, so some current trends like the strong growth of South America, continued growth of China and the strengthening relationships between China and Africa are not fully captured. Hopefully an update will come in the not too distant future.
On the cartography side, I went for the Azimuthal Equidistant projection to emphasise the close North America-Europe-Asia links. This projection is recognisable as the basis of the United Nations logo. Here however it is global capitalism and environmental exploitation drawing the world together like some kind of tightening noose. After another empty environmental conference at Rio+20, burning billions of tonnes of fossil fuels is set to remain a defining characteristic of our age.