World Population Density Map Update with GHSL 2023

The European Commission JRC recently released a new 2023 update of the Global Human Settlement Layer (GHSL) data. This update has greatly improved the GHSL data, with a 10 metre scale built-up area dataset of the entire globe which has been used to create a 100 metre scale global population density layer. The level of detail for cities and rural areas is impressive, and it overcomes the limitations of previous releases of the GHSL. I have updated the World Population Density Map website to include this new 2023 data, with both the cartography and statistical analysis now based on the new data.

Improved Level of Detail for Cities and Rural Landscapes
The new GHSL 2023 data has produced a much more detailed 10 metre dataset of built-up area (using recent European Space Agency Sentinel data), and this is the basis for creating the updated population layer. The results are much improved, particularly for complex rural and peri-urban landscapes in the Global South, such as for India shown below. The tens of thousands of small villages are identified and used to more accurately distribute India’s huge population. This is also the case for other key regions such as Sub-Saharan Africa, and China.

The added level of detail also improves the representation of cities, with more accurate density analysis, and improved techniques to differentiate residential from industrial and commercial urban land uses. Previous releases of the GHSL were underestimating urban densities for cities where census data was weaker, but this appears to no longer be the case. The dataset can now be used for more accurate comparisons of population and density for cities across the globe. Example images for Shanghai and New York City are shown below.

Country Density Profiles – the Diversity of Human Settlement
The statistical analysis on the World Population Density Map website has also been updated using the 2023 GHSL data, so you can view the density profiles for all countries around the globe. Some highlights are shown below.

To complement the graph of the population in each density category, this updated version of the World Population Density Map includes Population Weighted Density statistics for each country and city. Population Weighted Density is a measure of the typical density experienced by residents in the country/city, in this case using the 1km2 scale GHSL data. The PWD is calculated by weighting each 1km2 cell according to the population, summing all the cells for the city/region, and then dividing the sum by the total population of the country/city (i.e. the arithmetic mean). This is a more representative measure than standard population density, which is affected by low density suburban/peri-urban and rural land, even where the population in these areas is relatively low.

China and India have very high density cities, but their large rural populations translate into moderate Population Weighted Density statistics overall. India is 9.9k pp/km2 and China is 8.9k pp/km2. The table below shows the top 20 countries by Population Weighted Density using the 2020 data-

Rank (by PWD 2020)Country NamePopulation Weighted Density 2020 (pp/km2)
1Singapore30.9k
2Republic of Congo25.2k
3Somalia24.1k
4Egypt21.8k
5Comoros17.4k
6Djibouti17.2k
7Iran16.8k
8Yemen16.7k
9Jordan15.8k
10North Korea14.8k
11Democratic Republic of the Congo14.2k
12Bahrain13.9k
13Colombia13.5k
14Equatorial Guinea13.5k
15Turkey13.5k
16Morocco13.4k
17Bangladesh13.3k
18Taiwan12.9k
19South Korea12.7k
20Western Sahara12.6k

For comparison, the equivalent Population Weighted Density figure for the UK is 4.1k, France is 3.7k and Germany is considerably lower at 2.7k. The USA is renowned for its low density living and suburban sprawl, and the Population Weighted Density measure for 2020 is 2.2k. This is the lowest figure for any large developed country in the world. Smaller developed countries have similar figures to the USA, including New Zealand, Norway and the Republic of Ireland.

Analysing the World’s Largest City-Regions Using the GHSL
The Built-Up Area and Population layers in the GHSL are used to define a settlement model (GHSL-SMOD) layer, which classifies land into urban and rural typologies. We can use this layer to define the boundaries of city-regions across the globe. This has been done using continuous areas of the highest urban category (urban centres) for the 2020 data. When you hover over cities on the World Population Density website, these city boundaries are highlighted-

This land use based method of defining city-regions produces different estimates of city populations to analyses based on administrative boundaries. The GHSL method generally emphasises large continuous urban regions, such as the megacity region of the ‘Greater Bay Area’ in China shown above, which has formed from the fusion of Guangzhou, Shenzhen, Dongguan and Jiangmen. This is the largest city-region in the world by this measure, with a population of 43.8m in 2020 (rapidly developing from a base of 5.8m in 1980). The top twenty city-regions in the world are shown below-

Rank (by Pop. 2020)City-Region NamePopulation 1980Population 2000Population 2020Pop. Weighted Density 2020 (pp/km2)
1Guangzhou-Shenzhen-Dongguan-Jiangmen5.8m30.9m43.8m20k
2Jakarta16.1m26.3m38.7m13.4k
3Tokyo27m31.3m34.1m10.2k
4Delhi8.3m19.1m30.3m29k
5Shanghai6.7m15.1m27.8m27.9k
6Dhaka6.2m14.8m26.8m47.9k
7Kolkata16.3m22.9m26.7m36.4k
8Manila11.3m18.3m24.8m27.1k
9Cairo9.8m16.6m24.5m44.9k
10Mumbai11.3m18.4m22.9m52.4k
11Seoul13.3m19.7m22.7m19.8k
12São Paulo13.7m17.4m19.7m14.5k
13Beijing7.2m11.6m19m20.2k
14Karachi5.8m10.9m18.7m48.8k
15Mexico City13.8m17.9m17.8m13.2k
16Bangkok5.4m9.2m17.4m11.6k
17Osaka17.2m16.8m15.6m8.1k
18Moscow9.9m11.9m14.9m16.7k
19Los Angeles10m13.1m14.5m4.6k
20Istanbul6.1m10.4m14.3m25.2k

One of the most impressive aspects of the GHSL is that it is a timeseries dataset going back to 1975. Therefore we can create historical indicators such as the population change data shown in the table above. Many cities have more than doubled, or even tripled in population size since 1980, including Delhi, Shanghai, Dhaka and Karachi. Rates of growth in the USA, Japan and Europe are inevitably much lower, as seen in Tokyo and Los Angeles in the table above. Tokyo is often measured as the world’s largest city (for example in the UN World Urbanization Prospects), though with the GHSL method Tokyo the third largest at 34.1m in 2020. Tokyo is also distinctive in terms of its Population Weighted Density at 10.2k pp/km2. While this figure is more than double the density of Los Angeles, Tokyo’s medium density is much lower than cities in China and South Asia. Incredibly, Mumbai’s density figure is five times higher than Tokyo at 52.4k pp/km2, and Karachi is not far behind at 48.8k.

Credits and Links
The Global Human Settlement Layer is published by the European Commission Joint Research Centre. All the GHSL layers are available as open data and can be downloaded on their website. The GHSL population data uses as an input the Gridded Population of the World data by CIESEN at Columbia University.

The World Population Density Map website has been created by Duncan A. Smith at CASA UCL. The mapping data is hosted on a tileserver at CASA UCL kindly set up by Steven Gray. The interactive mapping layers are hosted on Carto.

Global Data Visualisations by CASA MSc students

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.

Sustainability and Climate Change
Several groups interpreted the main theme in terms of global sustainability and climate change. These projects included investigating flood events, both locally and in terms of impacts on the UK; global heating in the Arctic and sea-level rise; and the effects of global temperature changes on the wine industry, with both positive and negative changes. Some interesting visualisation trends here included the use of high quality spatially detailed global datasets; using non-Mercator web projections (a recently added feature to Mapbox); experiments with animated data; and also use of 3D globes.

Global Flooding Events Map by Group 1 (Nina Fabsikova, Sangbin Lee, Murray Chapman, Xinyi Huang, Henry Song)
Global Temperature Change and the Wine Industry by Group 5 (Andras Gelanyi, Ruby Johnson, Haofu Wang, Shuyi Zheng, Kameliya Staneva)
Arctic Sea Ice and Climate Change by Group 3 (Nayomi Kasthuri Arachchi, Haisu Chen, Daphne Badounas, Tianyi Li)

Global Cultural Interactions
Taking a very different tack, several groups looked at Global to Local interactions in terms of cultural and culinary exchanges. One group used story-mapping techniques to show Chinese cultural diffusion, including the history of the Silk Road and Zheng He’s early voyages of discovery. Another approach was to look at global interactions through food, including creating a virtual kitchen as an interface to global food journeys, and mapping major food importers to the UK.

The Silk Road and Chinese global cultural diffusion by Group 4 (Rongrong Xue, Yuning Jiang, Zhonghao Li, Ce Hou)
The World to your Kitchen, global food study by Group 7 (Zicheng Fan, Jingran Ma, Hangjin Cai, Haotian Meng, Liyuan Dong)
Global food imports to the UK by Group 10 (Shengwei Deng, Zhenlei Gu, Jing Liu, Yujie Hu, Xinwei Kang)

Energy and the Cost of Living
Returning to the sustainability theme, several groups zoomed in on energy and affordability challenges that the world is currently experiencing. One group used some advanced D3 charting to tell the story of the UK’s varying energy imports and wider global affordability challenges (see image below). A different take was to chart the energy generation mix in major economies around the world. Another topical affordability challenge relates to housing in major cities, and one group mapped relative affordability of housing in major cities across the globe.

UK energy import dependency over time by Group 6 (Sunny Zhao, Elika Sinha, Val Ismaili, Ankur Shanker, Bolin You)

Global energy consumption by type by Group 2 (Jiani Gong, Jeonghwa Kang, Linhan Cao, Seren Shi)
House price affordability comparison in global cities by Group 8 (Mengjiao Luo, Jieqi Tan, Wei Wang, Siyi Cai, Yanpu Huang)

Covid-19 Visualisations
The aftermath of the pandemic is still with us, and tracking Covid-19 was another interpretation of the Global to Local theme. One group experimented with animation to track the spread of the virus. Another used graphs and interactive mapping to investigate how effective lockdown policies were in a series of case studies.

Global Spread of Covid-19 by Group 9 (Ian Liu, Peizhao Wang, Shirao Zhang,  Miaomiao Pan, Lin Sheng)
Global Covid-19 infections by Group 11 (Jikai Song, Lucia Zhang, Jianqiang Li)

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.

Global internet connections and the digital divide by Group 12 (Ruijie Chang, Maidi Xu, Zhiheng Jiang)

Here is the full list of project groups and websites-

  1. Delugeo Global Flooding-
    Sangbin Lee, Murray Chapman, Xinyi Huang, Nina Fabsikova, Henry Song
    https://nfabsikova.github.io/delugeo/
  2. Clean Energy Now-
    Jiani Gong, Jeonghwa Kang, Linhan Cao, Seren Shi
    https://linhanccc.github.io/CASA0003-GROUP2/website.html
  3. 30 Degrees Rising-
    Nayomi Kasthuri Arachchi, Haisu Chen, Daphne Badounas, Tianyi Li
    https://eloquent-crepe-6cc40a.netlify.app/
  4. Chinese Cultural Diffusion-
    Rongrong Xue, Yuning Jiang, Zhonghao Li, Ce Hou
    https://casa-chinese-cultural-diffusion.github.io/digital-viz-Chinese-Cultural-Diffusion/html/main.html
  5. A Change in Climate, a Change in Taste-
    Ruby Johnson, Haofu Wang, Andras Gelanyi, Shuyi Zheng, Kameliya Staneva
    https://agelanyi.github.io/climate-and-uk-wine/website-frontend/index.html#firstSection
  6. Why are energy prices rising?-
    Sunny Zhao, Elika Sinha, Val Ismaili, Ankur Shanker, Bolin You
  7. The World in your Kitchen-
    Zicheng Fan, Jingran Ma, Hangjin Cai, Haotian Meng, Liyuan Dong
    https://world2kitchen.github.io/The_World_in_your_Kitchen/P0_Panoramic_and_Fullpage/fullpage.html
  8. San Francisco: Global to Local-
    Mengjiao Luo, Jieqi Tan, Wei Wang, Siyi Cai, Yanpu Huang
    https://casa0003group8.github.io/HousepriceSF/index/housepriceSF
  9. How does Covid-19 influence the world?-
    Ian Liu, Peizhao Wang, Shirao Zhang,  Miaomiao Pan, Lin Sheng
    https://peizhaowang.wixsite.com/casag9covidimpact
  10. From the Global to your Table-
    Shengwei Deng, Zhenlei Gu, Jing Liu, Yujie Hu, Xinwei Kang
    https://zhenleigu.github.io/CASA0003_Group10/
  11. Did Lockdown really Stop the Virus?-
    Jikai Song, Lucia Zhang, Jianqiang Li
    https://jikaisong1997.github.io/
  12. Worldwide Digital Divides-
    Ruijie Chang, Maidi Xu, Zhiheng Jiang

Environment & Planning Featured Graphic: World City Populations Time-Series Map

The World City Populations Interactive Map is now available as a static map, and has been published as a Featured Graphic in Environment and Planning A. The EPA article includes details on the UN World Urbanization Prospects data, and the methods used to create the map.

For a high resolution version of the static map, click below-

UNWUP_WorldPopMap2014_DSmith

Mapping the Global Urban Transformation

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/

UNWCP_global

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.

UNWCP_shanghai

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.

UNWCP_largest2030

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-

UNWCP_shenzhen

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.

Exploring the Users of Interactive Mapping Platforms

Datashine

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)-

DataShineSectors

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.

LuminoCitySectors

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.

Summary
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.

Full Tables of Twitter Sharers

DataShine Twitter Sharers Classification

Sector Sector Percentage Group Group Percentage
Academic 18.4 Geographer / Urban Academic 3.8
Other academic 11.4
Social Science Org 0.9
Student 2.3
Other Education 5.8 Geography Education 1.8
Other Education / Museum 4.1
Built Environment Professional 7.0 Transport Consultant/Planner 2.0
Architect 1.2
City Planning/Housing Org. 3.8
Business 9.6 Economic development 0.0
General Business / Marketing 6.1
Public Policy 3.2
Real Estate 0.3
Design & Journalism 8.2 Design- graphic, interactive 2.3
Data Journalism Specialist 1.8
Journalist General 4.1
IT 16.7 Cartography & GIS exp. 4.7
IT / Tech General 9.1
Data Scientist 2.9
Government 7.3 Central Gov 1.8
Local Gov 3.8
Open Data 1.8
Local Community & Charity 8.8 Community / Place Activist / Charity 8.8
General Public 18.1 General Public 18.1

 

LuminoCity Twitter Sharers Classification

Sector Sector Percentage Group Group Percentage
Academic 19.4 Geographer / Urban Academic 8.1
Other academic 7.3
Social Science Org 1.6
Student 2.4
Other Education 7.3 Geography Education 4.8
Other Education / Museum 2.4
Built Environment Professional 16.9 Transport Consultant/Planner 4.8
Architect 4.0
City Planning/Housing Org. 8.1
Business 10.5 Economic development 3.2
General Business / Marketing 6.5
Public Policy 0.0
Real Estate 0.8
Design & Journalism 12.1 Design- graphic, interactive 7.3
Data Journalism Specialist 2.4
Journalist General 2.4
IT 18.5 Cartography & GIS exp. 5.6
IT / Tech General 8.1
Data Scientist 4.8
Government 3.2 Central Gov 1.6
Local Gov 0.8
Open Data 0.8
Local Community & Charity 3.2 Community / Place Activist / Charity 3.2
General Public 8.9 General Public 8.9

Overheating London and the Evolving North: Visualising Urban Growth with LuminoCity3D.org

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.

LondonSE_PopChange
Population Change 2001-2011 in the South East region.

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.

LondonSE_EmployChange
Employment density change 2001-2011 in the South East region.

Inner London is dominant for many employment sectors, not just financial and business services, but also creative industries, research, tourism, and increasingly for information technology, helping London to bounce back successfully from the great recession. The IT industry is an important growth sector, and has traditionally been concentrated in Reading, Bracknell and surrounding towns, an area dubbed the Western Sector by Sir Peter Hall in the 1980s. The Western Sector still retains the highest percentage of IT jobs in GB, but recent growth here has been sluggish. The current stars of the IT industry are now online and social media businesses, and these are attracted to the creative pull of Inner London. Meanwhile the most significant South East growth story outside the M25 has switched north, with Oxford (12% jobs growth), Milton Keynes (14% jobs growth) and Cambridge (22% jobs growth) forming a new northern arc of science and engineering based growth.

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.

LondonSE_HousePrices copy
House prices 2013 in the South East region.

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.

LondonSE_greenbelt
Rail infrastructure, the green belt and urban density in the South East region

 

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%).

NorthWest_PopChange
Population change 2001-2011 in the North West and West Yorkshire regions.

LuminoCity3D_EmpDenChangeNorth
Employment density change 2001-2011 in the North West and West Yorkshire regions.

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.

NorthWest_HousePrices
House prices 2013 in the North West and Yorkshire regions.

Moving on to the Midlands, again we can see population growth across all major city centres. Birmingham and the West Midlands gained 162,000 residents (7.3%) and 47,000 jobs (+4.8%) between 2001 and 2011, with similar city centre employment density levels to Manchester. The most dynamic cities in the Midlands seem to be medium sized cites, with Leicester growing 12.8%, Nottingham by 8.1% and Derby by 11.8%, although jobs growth is more mixed. There is a significant concentration of business service jobs in Birmingham city centre, but by far the most distinctive sector in the Midlands economy is hi-tech manufacturing and R&D jobs linked to the automotive industry. Clusters around major factories can be seen in Solihull Birmingham, Coventry, Derby, Telford, Warwick and Crewe, with manufactures including Jaguar Land Rover and Toyota. The distributed nature of employment contributes to considerable travel flows between neighbouring cities.

Midlands_PopChange
Population change 2001-2011 in the Midlands region

Midlands_JobsChange
Employment density change 2001-2011 in the Midlands region.

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.

Midlands_HousePrices
House prices 2013 in the Midlands region.

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.

 

 

Explore the performance and dynamics of GB cities at LuminoCity3D.org

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.

LuminoCity3D.org is a mapping platform designed to explore the performance and dynamics of cities in Great Britain. The site brings together a wide range of key city indicators, including population, growth, housing, travel behaviour, employment, business location and energy use. These indicators are mapped using a new 3D grid-based approach that allows consistent comparisons between urban areas to be made, and relationships between urban form and city performance to be identified (technical details are provided here). Press coverage of LuminoCity3D has included Londonist, Wired.co.uk, Independent Online and Guardian Cities.

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.

Employment density change in the north of England (blue is an increase and orange decline). Manchester and Leeds city centres have established themselves as the largest centres, with the biggest increase in Manchester.

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.

Public transport commuting in central Scotland. Hovering over urban areas highlights indicator statistics and highlights the city’s position on the GB Chart.

Indicator Themes

The map indicators on LuminoCity3D.org are split into five themes- Population, Transport, Housing, Society and Economy- which are selected from the Indicators Selection box to the top right. Population covers resident and employment density; Transport looks at journey-to-work, accessibility and air-pollution; Housing covers house prices, types, tenure and household size; Society looks at various inequality measures; and finally Economy covers the distribution of growth industries such as ICT, creative industries and hi-tech manufacturing.

LuminoCity3D_HousePPSE
House prices 2013 in the South East of England.

Comments and feedback on the site are very welcome. Have a look at the Comments & FAQ page, tweet @citygeographics, or email duncan2001@gmail.com.

LuminoCity3D Credits

Site design and cartography © Duncan A. Smith 2014.

Duncan is a researcher at the Bartlett Centre for Advanced Spatial Analysis, University College London. Data hosted at CASA with generous help from Steven Gray.

Maps created using TileMill opensource software by Mapbox. Website design uses the following javascript libraries- leaflet.js, mapbox.js and dimple.js (based on d3.js).

Source data Crown © Office for National Statistics, National Records of Scotland, DEFRA, Land Registry, DfT and Ordnance Survey 2014.

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”.