The interactive map of world city populations – https://luminocity3d.org/WorldCity/ – has been updated with the newest release of the UN World Urbanization Prospects (WUP), the leading dataset for understanding global urban dynamics. The new UN WUP 2025 release supersedes the 2018 version. It’s a major revision with updated demographic data a new harmonised methodology for calculating urban agglomeration populations based on urban land analysis (using the Global Human Settlement Layer data) compared to previous use of national administrative boundaries. The dataset also covers an updated time frame of 1975-2050. The dataset shows some dramatic changes in the ranking of the world’s largest urban agglomerations and predictions for urban growth in the next 25 years.
The online interactive World City map allows you to explore the overall trends in urban populations and the detailed dynamics of particular cities of interest. I have also created a non-interactive high-res publication version of the map below-
Jakarta and Dhaka Measured as the World’s Largest Urban Agglomerations The UN WUP 2025 uses an updated harmonised global methodology for defining urban agglomerations as contiguous areas of high density urban land (above 1,500 persons per km2) with a minimum population of at least fifty thousand. This new methodology results in several changes to the ranking of the world’s largest city-regions. Tokyo, with a population of 33.4 million is 2025, is often measured as the world’s most populous city, but with this methodology Jakarta, current population a gigantic 41.9 million, replaced Tokyo in 2010. This change in Jakarta’s population appears to be due to updated demographic data, and a misalignment between Jakarta’s administrative boundary and its metropolitan region that caused previous underestimations (I got similar results for Jakarta with my own analysis of the Global Human Settlement Layer data). The projection is for Dhaka and Jakarta to become the first urban agglomerations to exceed 50 million people in 2050.
Stabilisation of Urban Populations in China and India Global urban growth in recent decades has been dominated by China and India. The next 25 years however project a more stable urban picture in the world’s most populous countries, with China starting to follow Japan and South Korea with an ageing and more static population. For example, Shanghai gained a massive 15.5 million people between 2000 and 2025, but this growth is predicted to slow to 5.5 million between 2025-2050. Some cities in West China such as Chongqing, Wuhan and Chengdu are predicted to lose population between 2025-2050, though there are larger population falls predicted in Japan and South Korea.
Population change in India’s largest urban agglomerations follows a broadly similar stabilisation pattern. New Delhi gained 12.3 million residents between 2000-2025 to reach 30.2 million people, but the population growth prediction for the next 25 years is lower at 3.7 million. A similar levelling off can also be seen in the Indian megacities of Mumbai and Kolkata. Urban growth is higher however in India’s South Asian neighbours. Dhaka in Bangladesh continues with very high rates of growth. Dhaka is currently the world’s second largest urban agglomeration at 36.6 million in 2025, and may become the world’s largest urban agglomeration in 2050. Pakistan shows a similar picture, with Karachi predicted to gain 11.5 million people between 2025-2050 to become the world’s fifth largest urban agglomeration.
Highest Growth Rates in Africa, Though Also Lower than Previous Predictions Africa countries are generally much earlier in their urban transition and so have proportionally higher predicted rates of growth. Luanda is set to gain 8.9 million people between 2025-2050. Dar es Salaam and Addis Ababa are predicted to both become megacities of over 10 million in the next 25 years. Even In African cities however, this release of World Urbanization Prospects shows lower predicted growth for coming decades than previous predictions. Cities such as Lagos and Kinshasa have reduced populations with this methodology.
About the World City Population Visualisation The interactive map has been built using Mapbox JS. The technique of overlaying proportional circles to show urban population change over time was first developed in a static map at LSE Cities Urban Age by Guido Robazza.
Each year CASA master’s students demonstrate their spatial data visualisation skills with a group project. The theme this year was ‘Urban Futures’, and students have produced some very impressive and diverse work, experimenting with a range of visualisation tools and techniques. Click on the images below to visit the project websites.
Urban Resilience Projects Two groups explored at urban resilience and climate change. Some excellent interactive mapping work was created analysing Urban Heat Island effects in the Greater Bay Area of China (one of the world’s largest and fastest growing city regions) and at the potential impacts of sea level rise in New York City.
Transport Sustainability Projects Transport sustainability was another popular topic in urban futures. This included investigating 15 Minute City Accessibility in London (mapping the recent UBDC data), exploring the sustainability of the freight and logistics industry in the USA, and mapping the growth and sustainability of Los Angeles.
Economic Change and Innovation Projects Several groups explored different aspects of economic futures. This included analysing the UK’s international trade and sub-regional performance using interactive dashboards; and charting innovation policy in Singapore and its international competitiveness with animated charts and mapping.
The phenomenon of mobile knowledge economy workers or Digital Nomads was explored in terms of analysing London compared to other global cities. Finally the importance of the Creative Industries to the UK economy was visualised using interactive maps and charts.
Social Change and Ageing Societies Projects Another increasingly important aspect of urban futures is planning for ageing societies. Two groups explored this topic, the first looking more particularly at ageing societies and facilities globally and with the case study of Bournemouth, and the second group exploring ageing societies both globally and in Manchester.
Although Greater London has an extensive transit network, this is not the case for many UK cities where underinvestment and privatisation has seen bus, metro and rail networks stagnate in recent decades, falling well behind European peers. Improving public transport is an important aspect of addressing the UK’s regional inequalities and poor productivity, and is a prominent issue for the 2024 general election.
Accessibility measures are an ideal tool to gauge the comprehensiveness and efficiency of public transport networks – they describe the ease with which populations can reach key services by different travel modes. The leading UK urban thinktank, the Centre for Cities (see their new Cities Outlook report 2024), has been doing some accessibility analysis of English cities compared to continental European cities, and this was recently republished in the Financial Times in an article on productivity challenges-
It’s great to see accessibility analysis feature in the media. The measure used above however has some serious problems leading to nonsensical results (e.g. does Manchester really have half the accessibility of Liverpool and Newcastle?). The Centre for Cities measure uses a single time threshold (30 minutes) when we know that accessibility varies considerably at different time thresholds. It is based on a single destination point, when cities can have multiple employment centres. And it describes accessibility as a percentage of all city jobs, which means that the smaller the urban settlement is, the higher the accessibility result will be using this measure. In reality, larger city-regions have better jobs accessibility.
Creating Robust Public Transport Accessibility Measures – R5R and PTAI-2022 We can create much better and more reliable accessibility measures for UK cities. There have been significant recent advances. The open source R5R software has solved many of the computational challenges for accurately calculating public transport accessibility, allowing the calculation of full travel matrices for all possible trips and handling accessibility variation over time. In the UK, Rafael Verduzco and David McArthur at the Urban Big Data Centre have taken this one step further and pre-calculated accessibility indicators for all of Great Britain at a range of time thresholds in their Public Transport Accessibility Indicators dataset. This dataset is calculated using R5R, and is based on the median travel time across a three hour travel time window, 7am to 10am on a typical weekday (Tuesday 22nd November 2021), and uses the latest public transport service datasets such as the Bus Open Data Service. The results are at LSOA scale for GB only (no Northern Ireland), based on census 2011 zones (so I have used 2020 population data in the below analysis).
Origin and Destination Accessibility Measures This article focuses on jobs accessibility, and this can be analysed from either the perspective of trip origins (residential-based accessibility to jobs) or from the perspective of trip destinations (workplace-based accessibility by residents). Both perspectives are complementary, and are developed below. For residential measures, if we take the average accessibility for all residents in a city then we get a good overview of how extensive and efficient the public transport network is. This requires city boundaries to define all the residents in each city. The analysis below uses the Primary Urban Area geography.
Public Transport Jobs Accessibility Trip Origin Results The table and chart below show average accessibility to jobs for residents in all major GB cities by three travel time thresholds- 30 minutes, 45 minutes and 60 minutes. London’s accessibility results are inevitably much higher than any other GB city, being around 3 to 4 times higher at all three travel times, and emphasising just how big the gap is between the capital and all other GB cities. The 30 minute threshold describes shorter trips, and identifies higher density compact cities where residents are on average closer to employment centres. Small compact cities such as Cambridge and Oxford score well at 30mins (though note this is not the case at 45 or 60mins). Edinburgh and Glasgow have the highest residential average accessibility outside of London at both 30 and 45 minutes. This is due to Scottish cities historically following a higher density European urban model, and maintaining better public transport networks by avoiding some of the worst effects of privatisation.
The 60 minute accessibility measure picks up longer distance commuting on regional rail and metro networks. This is where the strengths of larger city regions such as Greater Manchester and the West Midlands are highlighted, with Manchester second and Birmingham forth in the ranking (Glasgow is third and also has a large regional rail network). Given their large populations, Manchester and Birmingham should however be scoring higher in absolute terms and closing the gap on London. Both have poor accessibility for the shorter 30 minute accessibility measure, reflecting the need for further inner-city densification (as the Centre for Cities have argued). For longer commutes, Manchester and Birmingham metro networks should also continue to be extended regionally. Leeds scores relatively well at 30 minutes due to its medium-density urban core, but it lacks a metro and is behind Birmingham, Glasgow and Manchester for the longer commuting times.
Peak Public Transport Accessibility by Trip Destination We can also analyse accessibility by trip destination, which produces similar results to the trip origin residential measure but is more from the perspective of employment centres. The table below shows the peak accessibility by workplace within each Primary Urban Area, which is a measure of labour market size and agglomeration potential for the UK’s largest city centres. London retains its huge advantage with this measure, at 3 to 4 times higher than the next best cities. City-regions with larger rail and metro networks score better with the peak destination measure, with Birmingham and Manchester ranked second and third respectively, exceeding 2 million people at 60 minutes. Cities with strong rail connections to London, such as Reading and Crawley, also score highly at 60 minutes, but have much lower accessibility at 45 and 30 minutes. Smaller compact cities such as Edinburgh and Cambridge rank much lower by the destination measure compared to the residential analysis.
Both the trip origin residential average accessibility measure and the trip destination peak accessibility measure provide useful perspectives. The residential average measure is a good summary of the coverage and extent of public transport across a city, and how likely residents are to use public transport modes. The trip destination peak accessibility measures employment centre labour market size, and summarises the total number of people that can reach city centres by rail and metro. This is a better measure of agglomeration potential and is more closely correlated with city-region size.
Mapping the Accessibility Results We can also map the results to view the geography of accessibility to jobs. Firstly the trip origin accessibility to jobs measure. This emphasises how large the area of high accessibility is across Greater London, with parts of Outer London and the South East having higher accessibility to jobs than residents in the city centres of the next largest cities, Manchester and Birmingham. The Primary Urban Area geography is also shown, which is the basis of the residential average accessibility chart and table shown above.
Next we map the trip destination accessibility to population measure. This has a very similar geography, but with more of an emphasis on city centres, as we are measuring average accessibility on a weekday 7am-10am when there will be more commuting services going to, rather than from, central areas. Again London has a huge advantage, peaking at 7 million people. We can also see the centres of Birmingham and Manchester reaching accessibility levels above 2 million people, while Glasgow, Leeds, Newcastle and Liverpool exceed 1 million.
Conclusion- Open Data and Software is Available to Create High Quality Accessibility Measures With software such as R5R (see this workshop for an intro) and the exemplary and easy to use PTAI-2022 dataset from the UBDC, it is easier than ever to produce accurate public transport accessibility measures. The comparative accessibility analysis of GB cities shown here has highlighted the huge accessibility gap between London and all other UK cities. It has also shown the generally better accessibility performance of Glasgow and Edinburgh, and the high regional accessibility of Birmingham and Manchester which contrasts with their weaker accessibility in these regions for shorter travel times, which supports inner-city densification. There is no single perfect accessibility measure that answers all questions we are interested in – this analysis has confirmed that variation at different travel times reveals contrasting patterns in local and regional accessibility; that average and peak accessibility in cities emphasise different aspects of transit networks; and that trip origin and trip destination measures provide complementary perspectives. We therefore need to test a range of measures to understand accessibility patterns.
Future Improvements This has been a relatively quick demonstration of the PTAI-2022 data and there are several areas for further improvements-
Including European cities for comparison would be very interesting, as the Centre for Cities explored in their original analysis. A recent major paper in Nature has shown how accurate international accessibility comparisons can be done- https://www.nature.com/articles/s42949-021-00020-2.
The PTAI-2022 dataset is a really good tool that makes GB accessibility analysis much more straightforward for researchers. Currently it uses the 2011 census boundaries, and the next update should use the 2021 boundaries allowing the latest census data to be used. Additionally, the current PTAI-2022 release uses 2021 public transport data, and updating this with the latest rail and bus data would also be a useful update. A related issue is that reliability on UK public transport networks can be poor, and that timetables can overestimate transit accessibility. This topic has been analysed by Tom Forth in this blog post.
This analysis has used the Primary Urban Area geography, which is a useful description of GB city-regions, but there are some issues with PUAs due to the underlying local authority geography. A few PUAs for medium-sized cities have quite large hinterlands (e.g. Sheffield) and this lowers the average accessibility measured in these PUAs due to lower accessibility outside of the urban core. A more thorough analysis of accessibility would need to test multiple urban geographies and gauge the extent of Modifiable Areal Unit Problem variation.
The housing crisis in London has become increasingly severe in the last decade with much higher prices, rents, and largely static incomes, while housing development volumes have remained consistently below targets. Green Belt reform is often cited as a solution to boost development, though this has been off the agenda during the last 13 years of Conservative government. Recent announcements by the Labour leadership, supporting Green Belt reform and setting ambitious targets for housing development, could change this state of affairs with the general election coming in 2024.
This article analyses housing development in the London region from 2011-2022 (full CASA Working Paper here), using the Energy Performance Certificate Data. There is strong evidence that the Green Belt is a major barrier to development and is in need of reform. On the other hand, there are very substantial challenges around the quality and sustainability of new build housing in the South East. The analysis shows that, outside of Greater London, new build housing typically has poor travel sustainability and energy efficiency outcomes. Any release of Green Belt land needs to be dependent on travel sustainability criteria and improved energy efficiency for new housing. Sustainable housing outcomes are much more likely to be achieved through prioritising development in existing towns and cities and in Outer London.
London’s Housing Affordability Crisis House prices in London doubled between 2009 and 2016, pricing out households on moderate and low incomes from home ownership, and translating into rent increases, longer social housing waiting lists, increased overcrowding and homelessness (see Edwards, 2016; LHDG, 2021). Price rises are linked to on the one hand to the financialization of housing (exacerbated by record low interest rates and Help to Buy loans in the 2010s) and on the other a long period of low housing supply, stretching back to the 1980s and the erosion of public housing.
The impact is record levels of unaffordability, with Inner London average house prices reaching £580k and Outer London £420k in 2016 (see chart below). The median house price to income ratio for Inner London soared from 9.9 in 2008 to 15.1 in 2016; for Outer London the ratio increased from 8.2 in 2008 to 11.8. In addition to high prices, first-time buyers have also been hit with record mortgage deposit requirements, with average deposits reaching £148,000 for Greater London, compared to around £10,000 in the late 1990s (Greater London Authority, 2022). Owner occupation is now effectively impossible in Inner, and much of Outer, London for low and moderate income buyers.
There have also been substantial increases in prices across the London region. The map below shows prices per square metre in the South East showing four radial corridors of high prices extending beyond Greater London into the Green Belt. East London is increasingly mirroring West London with two radial corridors of higher prices extending north-east and south-east from Inner East London. These are the primary areas of gentrification in London in the last decade (discussed in previous blog post), squeezing out what was the largest area of affordable market housing. There is also a distinct spatial alignment between London’s Green Belt boundary and higher prices, which is evidence of regional housing market integration, and that Green Belt restrictions are pushing up prices.
New Build Housing Delivery in the London Region Greater London has struggled to meet its housing targets in the last decade. The current London Plan target is for 52k annual completions, which, as can be seen in the graph below, London is significantly short of. The 52k annual target has been criticised as being too low, with other estimates of housing need calculating that 66k or even 90k houses per year are needed (LHDG, 2021). Given the extremely high prices, affordable housing tenures are needed more than ever, yet affordable housing delivery has fallen in the 2010s (although note there has been progress in affordable housing starts in the last two years). Finally, the recent impacts of the pandemic and high interest rates have hit market housing activity, meaning that London will very likely continue to miss its overall housing targets for the next 2-3 years.
We can look in more detail at the geography of housing delivery at local authority level in the scatterplot below. There is high development in most of Inner London, and some Outer London boroughs. These boroughs contain Opportunity Areas (major development sites in the London Plan): Canary Wharf in Tower Hamlets; the Olympic Park in Newham; Battersea Power Station in Wandsworth; Hendon-Colindale in Barnet; Wembley in Brent; Old Oak Common-Park Royal in Ealing; and Croydon town centre. Given that there are only a few Opportunity Areas in Outer London, this leads to relatively low delivery in most Outer London boroughs, and points to the need for a wider strategy for Outer London development.
Meanwhile, there is low development activity in nearly all Green Belt local authorities, much lower than London boroughs and also below the average for the rest of the South East. Green Belt restrictions affect both local authorities in the commuter belt and also Outer London boroughs as well (e.g. Enfield, Bromley) with 27% of Outer London consisting of Green Belt land. We can confirm how rigidly Green Belt restrictions are being applied using the official statistics, which calculate that the London region Green Belt land area was 5,160km2 in 2011 and 5,085km2 in 2022 (DLUHC, 2023). Therefore, only 74km2 or 1.4% of Green Belt land was released over the decade (this figure is for all development uses, not only housing), which is strong evidence of minimal change.
One final impact of the Green Belt can be seen by mapping development in the last decade as shown below. In addition to the patterns of high development in Opportunity Area sites, and generally low development in the Green Belt, there is a ring of high development activity just beyond the Green Belt boundary. This ring includes dispersed car-dependent development in semi-rural areas, and the expansion of medium-sized towns and cities such as Milton Keynes and Reading. This pattern looks very much like Green Belt restrictions are pushing development beyond the Green Belt boundary, creating sprawl-type patterns in several authorities. One important caveat is that several South East cities have strong economies in their own right, particularly technology industries in the Oxford-Milton Keynes-Cambridge arc, creating local development demands in addition to London-linked demand.
Potential for Green Belt Reform With Greater London consistently falling short of housing targets, reform of the Green Belt has been cited as a promising solution (see for example Mace, 2017; Cheshire and Buyuklieva, 2019). The release of Green Belt land could greatly boost development and ease prices. Green Belt reform could also be a substantial source of revenue for austerity-hit local authorities, if authorities are given the powers to purchase Green Belt land at current use value and benefit from the land value uplift (this is part of the Labour proposals).
Traditional objections to Green Belt development focus on rural land preservation. Yet the Green Belt is massive in scale – 12.5% of all the land in England is Green Belt. London’s Green Belt is 5,085km2, or three times bigger than Greater London. Medium density housing development would take up a small proportion of this land. For example, building 100k dwellings at a gross density of 40 dwellings per hectare would add up to 25km2, or less than 0.5% of the London region’s Green Belt. Appropriate Green Belt reform could simultaneously allow for a moderate increase in development and improve environmental aspects of the Green Belt – the current environmental record of the Green Belt is mediocre on key measures such as biodiversity – through green infrastructure funding and principles of Net Biodiversity Gain. The land preservation arguments against Green Belt development do appear to be solvable. There are however further sustainability impacts from housing development to consider, including transportation and housing energy impacts, as discussed below.
Sustainability Impacts- Travel Transport is the largest source of GHG emissions in the UK – 26% of all emissions in the latest 2021 data (DBEIS, 2023). The route to Net Zero requires both the electrification of transport systems and a significant mode shift from private cars to public transport, walking and cycling (HM Government, 2021). Greater London is a UK leader in sustainable travel, but this is not the case for the wider London region, much of which is car dependent. The analysis here uses car ownership and commuting mode choice data from the 2021 census to create a Travel Sustainability Index, as shown in the table below, which classifies Greater South East residents into 6 travel classes of around 4 million people. The South East covers a very wide range of travel behaviours, from an average of 20% commuting by car and 62% zero car households in the most sustainable class 1; to as high as 87% car commuting and 6% zero car households in the most car-dependent class 6.
Travel Sustainability Classes Average Statistics (2021 Census data)
Travel Sustainability Class
Travel Sustain. Index
Car Commute %
Public Transport Commute %
Walk & Cycle Commute %
Car Owning Households %
Residential Net Density (pp/km2)
Total Pop. in South East
1
45-82
20.3
48.5
26.4
38.3
51.5k
3.56m
2
30-45
41.6
33.2
20.9
61.5
32.1k
4.03m
3
21-30
60.6
18.1
17.6
74.7
25.0k
4.03m
4
15-21
71.6
10.9
14.2
83.3
20.2k
4.16m
5
10-15
80.0
6.5
10.9
89.4
16.4k
4.34m
6
1-10
87.3
3.6
6.7
94.1
11.1k
4.29m
Mapping the travel sustainability classes highlights the stark travel behaviour differences between Greater London and the wider region. The Inner London population-weighted average travel sustainability score is 51.6 (class 1), and Outer London is 32.1 (class 2). The Green Belt is overwhelmingly in car dependent classes 4 and 5, with an overall population-weighted average of 16.4 (class 4). The Rest of the South East has a population-weighted average score nearly identical to the Green Belt at 16.5, emphasising the disappointing levels of car dependence in the Green Belt despite its rail infrastructure and proximity to London.
The patterns shown in the above map clearly present a challenge for Green Belt development, as new housing in the wider region risks extending patterns of car dependence. Car dependent areas include some locations next to rail stations (proximity to rail stations has been advocated as a criteria for prioritising Green Belt land for housing). We can directly measure the travel sustainability of housing development from the last ten years by matching the output areas locations of new housing to the Travel Sustainability Index scores. This is shown in the scatterplot below, where Inner London boroughs score highly with this measure, followed by Outer London. Much of the housing development in the wider region scores poorly in terms of travel sustainability, including in areas with high housing development such as Bedfordshire and Milton Keynes.
Although travel sustainability is generally low in the wider region, there are trends identifiable in the above results that can be used as basis for guiding more sustainable development. Several towns and cities show moderately sustainable travel outcomes, including the Green Belt towns Luton, Watford, Guildford and Southend, and wider South East towns and cities Brighton, Reading, Oxford, Cambridge, Portsmouth, Norwich and Southampton. Generally, development in existing towns and cities is likely to be more sustainable than developing smaller settlements and more dispersed rural areas. There are also noticeably better results in active travel-oriented cities such as Brighton and Cambridge. Overall, if we want Green Belt housing development to minimise travel sustainability impacts, then it would be most realistic to achieve this by extending existing towns and cities, both within the Green Belt and in the wider South East. Promoting development in Outer London boroughs also looks to be an efficient strategy given generally good travel sustainability levels in Outer London, and that Outer London is 27% Green Belt land.
Sustainability Impacts- Energy Another important sustainability impact of new build is energy use and carbon emissions resulting from space and water heating, which we can estimate from the Energy Performance Certificate data as shown below. CO2 emissions per dwelling are considerably lower in Inner and Outer London, with overall London emissions per dwelling around two thirds of the value for the Green Belt and Rest of the South East. This is only partly due to smaller dwelling sizes, as CO2 emissions per square metre in London are significantly lower as well. The lower emissions in London housing can be explained by the much higher proportion of flats and also the use of community/district heating, with three quarters of all new build in Inner London and 47% of new build in Outer London connected to community heating networks. The community heating approach is only efficient for high density developments. For medium and lower density developments, air and ground source heat pump technologies are a key technology for improving energy efficiency and replacing gas boilers. The statistics from 2011-22 are very disappointing on this front, at 4% of new build with heat pumps in the Green Belt and 6% in the Wider South East.
New Build Annual Average CO2 Emissions and Energy Summary 2011-2022 (Data: EPC 2023)
Subregion
CO2 per Dwelling (tonnes)
CO2 per m2 (kg)
Energy Consumption (kWh/m2)
Community Heating %
Heat Pump % (air + ground)
Inner London
0.93
12.9
72.9
75.2
2.7
Outer London
1.04
15.3
87.2
46.9
2.8
Green Belt
1.60
18.7
106.9
7.9
3.5
Rest of South East
1.53
17.2
97.7
5.7
5.9
All Subregions
1.34
16.3
92.5
27.0
4.3
The average annual CO2 emissions by dwelling are summarised at the local authority level in Figure 19 (note y axis starts at 0.5). Similar to the travel sustainability results, London boroughs have considerably more sustainable results. Town centres in the South East again are the best performing outside of London, including Cambridge, Southampton, Eastleigh, Reading, Luton, Watford, Woking and Dartford. As the chart shows average CO2 per dwelling, there is a connection between affluence and dwelling size, with higher income boroughs such as Richmond Upon Thames and particularly Kensington and Chelsea, having high emissions. Overall however, energy efficiency is much better in London boroughs and this is a further challenge for the sustainability of Green Belt development. Similar to the travel sustainability analysis, the results point to the extension of existing towns and cities, and Outer London development, as the most sustainable development strategies.
Summary There is a widespread consensus that London needs to build more housing to meet demand and try to reduce record levels of unaffordability. Yet London has been consistently short of meeting housing targets for the last decade, despite substantial growth in Inner London. Green Belt restrictions do appear to have played a major role in constraining development, with low levels of new build in Green Belt local authorities, and in Outer London boroughs with extensive Green Belt land. There is also a significant price premium in Green Belt areas compared to the wider South East.
This analysis agrees with research advocating Green Belt reform. Travel sustainability conditions are needed to avoid this reform producing highly car dependent housing, such as has been occurring in Central Bedfordshire and Milton Keynes (where the East-West should have been built much earlier). Pedestrian access to rail stations is a sensible starting point for prioritising Green Belt land for housing, but it is not sufficient to produce sustainable travel outcomes in the Green Belt. The aim should be for new housing to have local access to a range of services (e.g. retail, schools), providing sustainable travel options for multiple trip types. Another related issue is the need for more sustainable energy efficiency measures in medium density new build housing. There is little evidence in the EPC data for adoption of key housing technologies such as heat-pumps and solar PV. Widespread adoption of these technologies is needed for sustainable development at scale in the Green Belt. Other studies have also identified poor design and planning in new build housing in the UK (see Carmona et al., 2020), and this needs to change as part of any plan to increase the volume of new housing.
Green Belt reform would have to come from national government, changing the very restrictive current National Planning Policy Framework to allow authorities with housing shortages to develop Green Belt land of low environmental quality near services, and to use land value uplift to fund services and affordable housing. It would be logical to give powers to the GLA (and other combined authorities) for the strategic coordination of this development within their boundaries, given the GLA’s strong track record on sustainable housing delivery. It is difficult however to envisage large scale change happening in the South East without national government also organising improved regional coordination and planning. This analysis identifies better travel sustainability outcomes for new build in larger towns and cities in the South East, and supports the urban extension model for development in the Green Belt. There are many candidate towns in London’s Green Belt for urban extensions, including Luton, Guildford, Watford, Maidenhead, Hemel Hempstead, Chelmsford, Basildon, Reigate and Harlow. This larger scale solution is politically more challenging, and would again require leadership and coordination from national government.
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 Name
Population Weighted Density 2020 (pp/km2)
1
Singapore
30.9k
2
Republic of Congo
25.2k
3
Somalia
24.1k
4
Egypt
21.8k
5
Comoros
17.4k
6
Djibouti
17.2k
7
Iran
16.8k
8
Yemen
16.7k
9
Jordan
15.8k
10
North Korea
14.8k
11
Democratic Republic of the Congo
14.2k
12
Bahrain
13.9k
13
Colombia
13.5k
14
Equatorial Guinea
13.5k
15
Turkey
13.5k
16
Morocco
13.4k
17
Bangladesh
13.3k
18
Taiwan
12.9k
19
South Korea
12.7k
20
Western Sahara
12.6k
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 Name
Population 1980
Population 2000
Population 2020
Pop. Weighted Density 2020 (pp/km2)
1
Guangzhou-Shenzhen-Dongguan-Jiangmen
5.8m
30.9m
43.8m
20k
2
Jakarta
16.1m
26.3m
38.7m
13.4k
3
Tokyo
27m
31.3m
34.1m
10.2k
4
Delhi
8.3m
19.1m
30.3m
29k
5
Shanghai
6.7m
15.1m
27.8m
27.9k
6
Dhaka
6.2m
14.8m
26.8m
47.9k
7
Kolkata
16.3m
22.9m
26.7m
36.4k
8
Manila
11.3m
18.3m
24.8m
27.1k
9
Cairo
9.8m
16.6m
24.5m
44.9k
10
Mumbai
11.3m
18.4m
22.9m
52.4k
11
Seoul
13.3m
19.7m
22.7m
19.8k
12
São Paulo
13.7m
17.4m
19.7m
14.5k
13
Beijing
7.2m
11.6m
19m
20.2k
14
Karachi
5.8m
10.9m
18.7m
48.8k
15
Mexico City
13.8m
17.9m
17.8m
13.2k
16
Bangkok
5.4m
9.2m
17.4m
11.6k
17
Osaka
17.2m
16.8m
15.6m
8.1k
18
Moscow
9.9m
11.9m
14.9m
16.7k
19
Los Angeles
10m
13.1m
14.5m
4.6k
20
Istanbul
6.1m
10.4m
14.3m
25.2k
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.
The Office for National Statistics have started to publish the more detailed tables from the new 2021 census. Of particular interest for my research are the variables related to gentrification. In this post I look at the occupational class data (Standard Occupational Class) to identify areas of London and Manchester with the biggest social changes.
The Changing Geography of London Given that gentrification has been transforming Inner London for over half a century, some researchers had speculated these processes will start to slow with fewer and fewer working class districts left in Inner London that have not already been transformed. The 2021 census data shows however that gentrification has continued at pace between 2011-2021, with substantial changes in the geography of occupational classes, particularly in East and South-East London.
The map below shows the proportion of residents in the three most affluent occupational classes: Managers, Professionals and Associate Professionals in 2021. This is a useful overall indicator of gentrification (though note there are differences between these classes, and deprived populations can also be present in gentrifying areas). The map shows the long established structure of affluent Londoners clustered in Inner and West London, with radial corridors extending South West and North West through historic wealthy areas such as Richmond and Hampstead. The most dramatic changes with the 2021 data are in East London, and South East London, in areas such as Stratford, Walthamstow, Deptford and Greenwich, fitting with widely discussed social trends of these areas in the last ten years. The changes in East London are so substantial that we can identify new radial corridors of relative affluence forming in North East and South East London, mirroring the radial sectors in West London and forming a big red ‘X’ shape on the map. The traditional divisions between East and West are eroding over time with gentrification processes and the substantial transport and planning interventions in East London in recent decades. On the other hand, many of these areas in East and South East London still include high levels of deprivation alongside these gentrification processes, leading to a complex picture.
Below the 2011 and 2021 data are shown side by side with a slider. Gentrification is almost always a spatially clustered process, with newly gentrifying neighbourhoods forming next to existing affluent areas. You can see this very clearly in the maps below as the red areas follow a pattern of organic growth as they expand and strengthen from historic clusters-
We can also map change directly, as shown below. This highlights just how concentrated gentrification is in East and South East London. Some major development sites are picked out, such as the Olympic Park at Stratford, and the developments at North Greenwich. New build gentrification is clearly playing a significant role. But many of the changes are spread across districts such as Walthamstow, Leyton, Bow, Peckham and Deptford, and these changes will be through turnover in the historic housing stock alongside new build housing.
An important difference in 2021 compared the 1990s and early 2000s appears to be more social change in Outer London, at the urban fringe and extending into the more rural Home Counties. This is particularly noticeable in South East London (Bromley, Orpington, Bexley), as well as other parts of Outer London (Barnet, Ruislip, Romford, Coulsdon). This will likely be linked to the extreme unaffordability of Inner London, but the 2021 census may also be picking up some of the demand for larger houses with gardens in 2020 and early 2021 following the pandemic.
On the topic of whether gentrification processes are displacing lower income populations in London, it is not possible to answer with this data. A useful overall perspective comes from looking at the profile of all occupational classes for Greater London in 2011 and 2021. We can clearly observe in the graph below a very large increase in the Professional class, and a big increase in the Management class (Associate Professional is static- this is on average the youngest class, and is probably a sign that this group has been more affected by the price/rent increases). The remaining classes do not however show a decline, they are largely static (except for Administrative, on average the oldest class). This implies that the overall process of increased professionalisation in the last decade has been more about new populations moving to London rather than lower income classes being further displaced, but this conclusion is speculative and migration data would be needed to investigate this. Note also that the professionalisation pattern is clearly visible in the occupational class profile of England and Wales, showing that the growth in knowledge jobs is affecting the entire economy, not just big cities like London.
The Wider Picture Across England The scale and economic dynamism of London typically makes the capital an outlier compared to other UK cities in terms of processes of urban change. However, devolved governments in city regions such as Greater Manchester and the West Midlands have brought substantial new investment and developments in their cities over the last decade, and we would anticipate some similar processes of social change to be happening. If we look at percentage point change in professional classes for England and Wales (the same measure as the change map above, but this time at local authority scale), we can see the top ranked local authorities are mainly in East and South East London. A crucial difference however is the prominence of Greater Manchester boroughs, including Trafford and Salford. This indicates that major social change is occurring in Manchester. Additionally the West Midlands is also picked up within the top 25 local authorities. Note the national average change in Professional classes is +5.4 percentage points between 2011-2021, so the change towards professional and service jobs is occurring across the economy as a whole.
Local Authorities Ranked by Percentage Point Change in Profess. Classes 2011-2021-
Rank
Local Authority
Region
Prof. % 2011
Prof. % 2021
Percentage Point Change 11-21
1
Waltham Forest
London (East)
39.2
50.5
11.3
2
Trafford
Grt. Manchester
48.1
58.4
10.3
3
Newham
London (East)
32.0
41.5
9.5
4
Salford
Grt. Manchester
36.1
45.5
9.4
5
Dartford
London (OMA)
37.3
46.6
9.3
6
Bromley
London (SE)
50.6
59.8
9.1
7
Bexley
London (SE)
36.7
45.8
9.1
8
Greenwich
London (SE)
44.2
53.1
8.9
9
Lewisham
London (SE)
48.8
57.4
8.5
10
S. Cambridgeshire
East of England
52.3
60.8
8.5
11
Stockport
Grt. Manchester
44.3
52.6
8.4
12
Havering
London (East)
36.9
45.3
8.3
13
Warwick
West Midlands
51.9
60.1
8.2
14
North Tyneside
Tyne & Wear
38.3
46.3
8.0
15
Southwark
London (Inner)
53.8
61.8
8.0
16
Broxbourne
London (OMA)
35.2
43.2
8.0
17
Epsom and Ewell
London (OMA)
51.4
59.2
7.9
18
Sutton
London (SW)
43.9
51.7
7.8
19
Hackney
London (Inner)
56.5
64.2
7.8
20
Mid Sussex
South East
48.0
55.7
7.7
21
Tonbrg. & Malling
South East
44.1
51.7
7.7
22
Exeter
South West
39.2
46.8
7.6
23
Bromsgrove
West Midlands
48.2
55.7
7.5
24
Solihull
West Midlands
45.4
52.9
7.4
25
N. Hertfordshire
London (OMA)
49.6
57.0
7.4
26
Sevenoaks
London (OMA)
48.1
55.4
7.3
27
Epping Forest
London (OMA)
45.8
53.1
7.3
28
Bristol, City of
West of England
44.9
52.1
7.2
29
Ribble Valley
North West
45.2
52.4
7.2
30
Cambridge
East of England
57.9
65.1
7.2
Occupational Class Change in Greater Manchester It is clear from the table above that alongside London, there are considerable changes in the occupational class geography of Greater Manchester ongoing. The borough of Trafford ranks second in the above table, while Salford is at four and Stockport at eleven. Repeating the mapping exercise below, we can see that occupational class changes are widespread across Greater Manchester, particularly to the west and south of the city centre. In economic terms this is a positive sign that Manchester’s knowledge economy is growing and attracting skilled workers, an important trend given generally low productivity for many UK cities beyond London. On the flip side however, Manchester residents will be less impressed if house prices and levels of inequality start to resemble the extreme situation in the capital.
We can repeat the change map again for Greater Manchester. Some big development sites appear to be picked up in Salford, Ancoats and Sale (the prominent development site of Salford Quays appears to have already gentrified before the 2011 census). But the growth in more affluent occupational classes is not confined to these development sites, it is occurring across most of Greater Manchester and, similar to London, includes many suburban areas. It is only really in Bolton, Rochdale and Oldham where there are few signs of change identifiable.
Summary The occupational class data from the 2021 census shows that evolution towards knowledge economy and service jobs continues at pace, with Professional and Management classes showing by far the highest growth levels across England and Wales. London has continued to experience significant gentrification levels, with the spatial focus in the last decade concentrated in East and South East London. London centres such as Walthamstow, Stratford, Deptford and Lewisham have changed dramatically, so much so that we can see two new radial corridors of more affluent populations forming in North East and South East London, mirroring the long established structure of West London.
Gentrification is by no means confined to London, and indeed Greater Manchester has several of fastest gentrifying local authorities in England and Wales, particularly the boroughs of Salford and Trafford. This is largely reinforcing the structure of wealthier populations being concentrated to the south and west of Greater Manchester, and in the city centre.
This analysis has looked only at the new occupational class data, which does provide some interesting insights. To look into these issues more deeply however we would need to add analysis on housing markets, tenure, deprivation, age and migration data.
The Census is the most comprehensive demographic survey in the UK, providing detailed data for government and researchers in many fields, from health and education, to planning and transport. The 2021 Census has a unique context, as the 2021 census day (21st March 2021) occurred when the UK was still in the 3rd national lockdown which began on the 6th of January 2021. The lockdown will likely have various impacts on the census results, particularly on groups who may have changed their residence during lockdown, such as students (many of whom were studying remotely) and employees in the hardest hit sectors, such as retail, arts and hospitality.
The issue is not that the census will be inaccurate per se (indeed the Census has a very thorough survey methodology) but rather that the period in time captured of March 2021 will have aspects unique to the pandemic. These aspects are likely to be temporary as society returns to something more like normality in 2022 and beyond. While Scotland chose to delay its 2021 census for a year (which may prove to be a sensible decision), researchers in England and Wales will need to be make the most of the 2021 results and be made aware of any unusual aspects.
At present only the early population results have been released for the 2021 Census, so more detailed breakdowns of population groups will have to wait for further releases later this year. The following analysis compares the Census 2021 local authority totals to the ONS mid-year population estimates for 2020 to check how the census population results compare to the next most recent population estimate.
The differences between the 2020 data and 2021 Census are likely to reflect several factors-
The higher accuracy of the census methodology. The ONS mid-year estimates can have some errors due to limited data on some groups, such as international migrants, which are better represented in the census. Potentially Brexit could have increased the degree of error in the mid-year estimates, given changes in international migration.
Temporary pandemic changes to places of residence. These could include for example students working remotely from home during term time (including international students not coming to the UK), younger populations returning to live with parents as jobs furloughed/ended/changed to remote working, and wealthier residents choosing to live in second homes.
Longer term pandemic changes to residential preferences. This could reflect changing residential preferences towards larger houses with more space/gardens following a dramatic rise in remote working during the pandemic.
Right now the extent of these different factors is not known, and it is very difficult to separate them without more analysis and data. So the following discussion is speculative in nature.
Comparing the Census 2021 Populations to the 2020 ONS Mid-Year Estimates The map below shows the percentage differences between the 2020 mid-year population estimates, and the 2021 Census. Blue areas show where the census 2021 population is lower than the 2020 estimates, and red areas where the census 2021 population is higher than the 2020 estimates. The differences are substantial. In South East England there is a strong geographical pattern with Inner London populations down dramatically (Camden and Westminster both have 24% lower populations in the census results). London as a whole has a population of 8.8 million in the 2021 census, which is 200k lower than the estimated 2020 total. In contrast, commuter towns and the home counties surrounding London have distinctly higher populations of around 5-10%. This pattern very much looks like a pandemic geography of Inner London residents leaving during the lockdown. Analysis by the GLA using PAYE income data confirms this general conclusion, and also points to this population drop being concentrated in young adults (note also the GLA analysis shows this population largely returning to Inner London by 2022). It is possible however that other factors such as post-Brexit emigration and very high rents are also reducing Inner London populations, and could have produced errors in the 2020 mid-year estimate data.
It is not just in the South East where there are differences between the 2020 and 2021 data. The South West and the Midlands are also areas where generally 2021 Census populations are higher than the 2020 data. The higher populations are mainly in more rural authorities, as well as some urban areas including Leicester, Lincoln, Derby, Worcester and Swindon, while Coventry and Nottingham have lower 2021 populations (university related?). There is no simple pattern here, and there are likely some 2020 mid-year population errors here in addition to any pandemic related changes. There also appear to be higher populations in areas within 1-2 hour journey times to London, possibly linked to changing residential preferences following the rise in flexible working.
North-West England has a mixed pattern with higher census populations in Cheshire to the South and in Burnley, but not in central Manchester or Liverpool. In Yorkshire and Humberside, Leeds and Hull have higher populations in the 2021 Census, while Sheffield is lower. The North East and Wales generally have a much closer alignment between the 2020 and 2021 data. The higher than expected populations in many rural and smaller town authorities fits with pandemic related patterns, but the mixed picture for many cities implies that the situation is complex, and may include both pandemic changes and errors in the 2020 data.
London and the South East As mentioned above, the 2021 Census data for London and the South East does look to have been significantly influenced by the pandemic, with much lower than expected populations in Inner London, and higher populations in towns surrounding Greater London, and those with longer distance rail connections, such as Peterborough, Milton Keynes and Reading. We can look in more detail at some of these patterns.
As well as higher populations in commuter towns surrounding London, there are also higher population results recorded in the Outer West London boroughs of Ealing and Hounslow. This is quite an outlier compared to the rest of Greater London, and it is not clear why pandemic or mid-year population error factors would affect these boroughs in particular. In relation to the student population argument, it is interesting that both Oxford and Cambridge have higher than expected 2021 Census populations, likely because Oxbridge colleges insisted on students being on campus in 2021, and likely because the 2020 data has underpredicted wider population increases.
The big question on the geography of the South East is to what extent these pandemic related changes are a temporary lockdown phenomena, or may relate to longer term trends in residential preferences. The analysis by the GLA using PAYE data pointed to the population decreases in Inner London being a short term trend for younger adults, which in turn could have pushed up populations in the wider South East in 2021. However, an argument can also be made that some of the patterns observed fit trends of households looking for more spacious residences, and adapting to flexible working patterns that do not require daily attendance at the office. Areas beyond 1 hour travel to London with more affordable housing become much more attractive in this context (and have seen big house price increases). The map above shows a ring of local authorities surrounding London with higher than expected populations in 2021 that stretches beyond the South East into the Midlands and South West. We will need to wait for more data to see whether this is a trend beyond the immediate residential changes during the pandemic.
Age Profile Comparison, 2011 and 2021 In the comparison above, it is very difficult to separate out errors in the mid-year estimates from genuine population changes. Another approach is to look at the age profiles in 2011 and 2021 for those areas with significant population differences in the 2021 census. Firstly for Inner London boroughs with lower than expected populations, you can see very clearly in the charts for Westminster and Camden that the lower populations are focussed on younger adults, 20-40. This fits with the temporary pandemic residential changes argument. There are however other factors aside from the pandemic, such as increased rents and post-Brexit visa issues, that could also lower the population of younger adults.
For London as a whole, there is a modest drop in the population in their 20s, and increase in nearly all other age groups, with the average age increasing overall. The comparison between the 2020 population data with the 2021 census above did not pick up unexpectedly lower populations in other large English cities apart from London. Looking at other English cities in terms of age profiles, generally there does not appear to be this fall in the proportion of younger adults. Leeds is a fairly typical example shown below. Manchester on the other hand has a pattern a bit more like London, and perhaps this signals more pandemic related changes here, or maybe more similarity to London in terms of international migration.
Turning to those cities with higher than expected population increases in the 2021 census, we can also look at their age profiles. The examples below of Peterborough and Milton Keynes show really big increases in populations in their 30s and to a lesser extent 40s. Many of these households will have kids, and so there are similar jumps in the population of young children (though this does not appear in the age 0-4 group). This pattern looks very much like these towns are attracting families looking for more affordable housing, and the 2020 data has underestimated this trend. It is possible the pandemic has further encouraged this, but it looks overall like a longer term trend. Note that other towns growing rapidly in the South East such as Reading and Bedford has similar age profile charts (as does Ealing in London). The big outlier is Cambridge, where the population increase is geared more towards adults in their 20s.
Summary This analysis has found some significant differences between the new 2021 census data, and mid-year population estimate data from 2020. It is very difficult to know whether this is due to errors in the 2020 data, or alternatively pandemic factors affecting the population in March 2021. Some of the biggest differences are in London, and it does appear that London experienced a drop in the younger adult population during the pandemic, particularly in Inner London. Manchester also has signs of a similar trend. GLA analysis indicates this drop was temporary in London, though there are longer term factors such as high rents which could also be playing a role.
Another big difference between the 2020 and 2021 data is much faster growth in many towns and small cities in the South East. Places like Milton Keynes and Bedford have growth of around 17% between 2011 and 2021. The age profile data shows this is driven mainly by adults in their 30s and 40s, often with children. The population differences look more like errors in the 2020 data here, though it is possible that the pandemic has accelerated families moving to more affordable towns to purchase larger housing.
Overall is not straightforward to separate out errors in the ONS mid-year estimates from pandemic changes, or to separate temporary pandemic changes from any longer term trends that are emerging. When the full data is released it will likely be possible to filter out certain demographics (e.g. students, younger populations) more affected by the pandemic. But it does look like the census 2021 data is going to be less certain than usual, particularly for London, and maybe for other large cities. Given that the census is traditionally used as a basis for investment in public services, more caution will be needed when using the 2021 census results (indeed London Councils have already responded that the 2021 census is underpredicting London’s population).
Table of Local Authorities with Greatest Increases Between 2020 ONS and 2021 Census Populations
Name
2011 Census Population
2020 Mid-year Population
2021 Census Population
Population Change 2011-2020
Population Change 2011-2021
Difference Between 2020 ONS and 2021 Census
Cambridge
123867
125063
145700
1.9
17.6
16.5
Reading
155698
160337
174200
3.2
11.9
8.6
Ealing
338449
340341
367100
0.3
8.5
7.9
Oxford
151906
151584
162100
0.9
6.7
6.9
Harlow
81944
87280
93300
6.2
13.9
6.9
Peterborough
183631
202626
215700
9.8
17.5
6.5
Milton Keynes
248821
270203
287000
8.1
15.3
6.2
Bedford
157479
174687
185300
10.7
17.7
6.1
Hounslow
253957
271767
288200
6.6
13.5
6.0
Cherwell
141868
151846
161000
6.7
13.5
6.0
Burnley
87059
89344
94700
2.7
8.8
6.0
Slough
140205
149577
158500
6.3
13.0
6.0
Watford
90301
96623
102300
6.6
13.3
5.9
Rushmoor
93807
94387
99800
0.0
6.4
5.7
Luton
203201
213528
225300
4.9
10.9
5.5
Crawley
106597
112474
118500
5.1
11.2
5.4
Swindon
209156
222881
233400
6.3
11.6
4.7
West Northampton.
375101
406733
425700
8.2
13.5
4.7
Merton
199693
206453
215200
2.9
7.8
4.2
Basingstoke and Deane
167799
177760
185200
5.5
10.4
4.2
Leicester
329839
354036
368600
7.4
11.8
4.1
Pendle
89452
92145
95800
2.9
7.1
4.0
Table of Local Authorities with Greatest Decreases Between 2020 ONS and 2021 Census Populations
Have you ever wondered how London began? Or how London grew to become such an influential world city for business, politics and culture? You can find out how in Gilded City, a new book recently published (available on Amazon), shortlisted on the Architectural Books of the Year 2023. Gilded City tells the story of London by touring its most fascinating historic districts and buildings, and describing how the emergence of social groups during the medieval and early modern periods – such as the livery companies, religious orders, scholars and writers – helped shape both London and modern society more generally.
Gilded City tells London’s history visually, with extensive colour photography and mapping. Readers can see how the different ages of London have left their mark in the built-environment, and you can follow nine walking tours to explore these sites, including both famous historic landmarks and more secluded historic locations away from the main tourist trail.
Each chapter follows an influential social class in London’s history. Chapter 4 above covers the religious orders and shows St John’s Gate in Clerkenwell.
Given my background in cartography, lots of new maps have been created for this book. Each of the nine tours is mapped in detail with the architectural form of historic buildings illustrated. The maps are intended to show the important buildings that are still standing today, as well as the site of the many historic buildings lost over time in the Great Fire and other destructive events. These help to show the geography of London during different historical periods, and how the character of different parts of London – such as the financial quarter, Inns of Court and Whitehall – were first established.
Each tour is mapped in detail showing the historic buildings and sites of important features no longer present
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.
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.
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-
The Continuing Gentrification of Inner London
The gentrification of Inner London was first recorded by Ruth Glass back in the 1960s, with middle class residents moving into largely working class neighbourhoods as London’s economy began its long evolution from manufacturing towards service jobs. This process has continued for decades, ultimately transforming most of Inner London. In the 21st century, some researchers have argued that gentrification has stalled (perhaps because there are few neighbourhoods left to gentrify) or has entered a different phase (e.g. processes such as super-gentrification and new-build gentrification as discussed by Davidson and Lees).
This research uses the Standard Occupational Class data as the basis of measuring social class. This classification was found to correspond to differences in income, as well as to a distinct residential geography. In particular, the three most affluent groups (Management, Professional and Associate Professional) cluster together, resulting in the social geography we can see in the map below using the 2011 Census data. There is a clear clustering of professional classes in Inner-West London, with two prominent radial corridors extending northwards through Camden, Islington and Hampstead; and south-westwards through Kensington, Wandsworth and Richmond. Concentrations of non-professional groups are mainly in Outer London to the east, north-east and west, with only smaller pockets remaining in Inner London. This analysis largely matches the description of Inner London now being dominated by professional classes, with lower income groups increasingly in Outer London (with some exceptions remaining in Inner East and South-East London).
Professional Classes (Manag., Prof. & Assoc. Prof.) Residential Percentage 2011. Data: Census 2011 (Office for National Statistics, 2016).
As well as mapping the 2011 geography of occupational class, we looked at more recent changes to see if gentrification is continuing or has slowed, using the ONS Annual Population Survey. Analysing changes between 2006 and 2016, we found had substantial gentrification had continued in Inner London, as shown in the table below. The Management, Professional and Associate Professional groups all grew as a proportion of the Inner London population, while all other occupational classes fell proportionally (green cells are above the average for the metro region, and orange cells are below the average). Interestingly, the biggest growth was in the Management and Professional classes, rather than the younger Associate Professional class, arguably more in line with super-gentrification processes. In contrast, there are proportional increases in several lower income classes in Outer London.
Sub-Regional Occupational Class Percentage Point Change by Residence 2006–2016 (final 2016 sub-regional percentages in brackets)
We can also explore these changes at the more detailed level of local authorities, and show that even more dramatic changes are occurring at the local level. In the chart below, each Local Authority is shown as a trajectory connecting its position in 2006 to its position in 2016 in relation to the percentage of professional classes and the total working population. Generally, Inner Greater London Authority (GLA) boroughs experience high working population growth combined with large increases in the proportion of professional classes. Boroughs with a long history of gentrification, such as Camden and Islington, are higher up in the chart reaching 70% professional classes, while more recent gentrifiers, such as Lewisham and Southwark, are rapidly gentrifying from a lower base. Outer GLA boroughs also show substantial population growth but with lower levels of change in professional classes, and decline in some cases. The exceptions are mainly in South and South-West London, with Croydon, Sutton and Richmond all gentrifying. Outer Metropolitan Area (OMA) local authorities have a mixed picture, with some increases in professional occupational classes with minimal working population growth; while some lower income towns such as Luton and Harlow are not gentrifying.
Local Authority Trajectories for Combined Percentage of Professional Occupational Classes and Total Working Population for 2006 and 2016. Data Source: Annual Population Survey 2005–2017.
What Impacts Does Inner London Gentrification have on Accessibility to Jobs?
We would expect that the dominance of more affluent classes in Inner London translates into accessibility advantages for these classes, as Inner London has substantially better accessibility opportunities by public transport, walking and cycling. We were particularly interested in accessibility by more affordable travel modes in this research. Bus travel is in general considerably cheaper than other public transport options in London. This is reflected in higher rates of more affordable bus and walking trips by lower income classes in the 2011 Census data. We can see in the table below that the three lowest income classes (6, 7 & 9) have around three times higher rates of bus travel and two times higher rates of walking than the most affluent three classes (1, 2 & 3)-
We used network analysis to analyse accessibility differences (see working paper on accessibility model). The analysis was carried out using the 2011 census data. The box plot below shows the cumulative accessibility to jobs for 60 minutes travel by Car, Public Transport (all modes) and Bus Only for the occupational classes. We can see differences between classes, particularly for public transport and bus trips, though there is also much variation within each class.
GLA 60mins Cumulative Accessibility to Employment by SOC Groups: Absolute Results
The accessibility differences between occupational classes can be more clearly seen by plotting differences between how the average accessibility for each group varies from the average accessibility for the entire working population, as shown below. Note in this chart the accessibility differences are normalised by travel mode, so the differences between travel modes in the chart above are normalised in the chart below. We can see clear consistent accessibility advantages for the top three occupational classes, particularly for more affordable slower modes- walking and cycling. The remaining occupational classes have below average accessibility to jobs, particularly for the more car oriented Skilled Trades and Process groups.
Greater London Authority 60mins Cumulative Accessibility to Employment by SOC Groups: Relative Differences in Occupational Class Mode Means and Mode Means for Total Population
The results for bus and walking modes is a particular accessibility challenge. Accessibility by these more affordable modes is generally low in absolute terms outside of Inner London. For the bus mode, less than half the number of jobs are reachable at typical commute times compared to the full public transport network. Given that lower income groups are the most frequent bus and walking commuters, and that these classes are increasingly being priced out of Inner London, these limitations are a significant accessibility challenge going forward.
What Policies Can Planner follow to Mitigate this? In terms of transport policy, this research supports efforts to improve the affordability and connectivity of public transport for lower income populations. This is indeed a priority of the current London Mayor Sadiq Khan, who has committed to freezing public transport fares, and has reformed ticketing to allow multiple bus journeys on a single fare. These measures help offset travel costs for lower income residents in Outer London.
The main policy conclusion is the importance of housing policy in influencing accessibility outcomes in the study area. Low and moderate income groups are being priced out of public transport accessible areas. Without a step-change in the delivery of genuinely affordable housing in accessible locations, the increasing dominance of Inner London by professional classes will continue, resulting in greater accessibility inequalities, and likely increased travel costs for lower income classes.
Note on Covid-19 and Travel Inequalities This research was completed in 2019, before the recent COVID-19 pandemic. The pandemic has in the short term shut down public transport networks, and greatly disadvantaged millions of city residents around the world. Longer term it is possible that the pandemic will reduce the attraction of inner city areas such as Inner London, due to perceived risk of future pandemics as well as the acceleration of telecommuting and home-working trends. The overall effect could be to slow gentrification processes, although this is difficult to predict. The alternative view is that London will recover and adapt as it has done following many crises in the past. East Asian metropolises offer a good model of how to built resilience following their response to the earlier SARS and MERS outbreaks.
The wider economic impacts are clearly also important. Certainly we are in line for a very large recession, hitting important sectors such as tourism and hospitality. More specifically in London, the recession may hit development viability for affordable housing, and is a real headache for public transport operators. Transport for London was in financial trouble before the crisis, and is currently dependent on government bailouts to keep running. This will likely curtail the ability of the Mayor to maintain lower public transport fares, and so impact the kind of transport accessibility inequalities this paper discusses.
There has been much discussion in recent years about the UK ‘productivity puzzle’: the shortfall in productivity between the UK and comparable EU states like Germany and France, with this gap widening in the last decade. One important perspective for understanding productivity relates to skills and education, and how well graduate skills are integrated with businesses and are helping to expand knowledge economy industries. This is where the UK has a distinct advantage due to the high number of world leading universities across the country. Yet this strong higher education base is not currently translating into sufficient numbers of productive graduate jobs in the UK.
The Foresight Government Office for Science has been investigating this topic, and recently published the Future of Cities: Graduate Mobility and Productivity report. I contributed to the report with data analysis on graduate flows from higher education institutions to workplaces using HESA data from 2013/2014.
There are several interesting aspects of the Foresight report. Firstly there is a strong city focus, which is vital when you see that productivity is highly city dependent, and has close links with regional patterns such as the north-south divide in the UK.
Productivity (Gross Value Added(GVA) per person employed) across British cities, 1981 and 2011 (Source Martin, Gardiner and Tyler 2014)
The productivity gap at the city level is further linked to graduate flows. London dominates the UK as a graduate employer, both in absolute terms and in proportional flows from higher education institutions to workplaces. The scale of the labour market and graduate recruitment programs in London, as well as its reputation as an ‘escalator region’, all add to this huge reach.
Data HESA Destination of Leavers Survey 2013/2014
That is not to say however that other large city-regions do not also have significant national graduate flows. Birmingham, Leeds and Manchester all draw significant numbers of graduates, with respective strengths in industries such as advanced manufacturing, creative industries and financial services (note HESA data is at county level, with Birmingham part of West Midlands and Leeds part of West Yorkshire). This is the foundation on which future growth will build.
Data HESA Destination of Leavers Survey 2013/2014
Data HESA Destination of Leavers Survey 2013/2014
Data HESA Destination of Leavers Survey 2013/2014
A second interesting aspect of the Foresight report is that it has been produced in collaboration with regional and local government agencies in Birmingham, Manchester, Leeds, Liverpool, Bristol and Cardiff. There are a number of initiatives in development to address key aspects of graduate employment, including:
• The Skills Engine being developed in Birmingham brings together a network of key players from the local area in order to improve the matching of demand for and supply of talent in the local economy.
• FASTTRACK is an initiative being tested by Leeds University to attract and assist graduate integration into small and medium-sized businesses in the region through placements and specially designed induction and training programmes.
• The Graduate Business Lounge builds on Bristol’s existing engagement in student enterprise to integrate existing graduate enterprise service providers and platforms to foster greater student entrepreneurship.
• New Economy Hubs in Birmingham, Liverpool and Manchester will take a multi-sector approach to understanding key economic growth areas at the city regional level.
• The GRAData Project, working with Leeds City Council and Leeds Institute for Data Analytics, aims to improve university and council use of national graduate data. The hope is that this will improve local careers support for students, and illuminate graduate mobility to enable the development of regional talent strategies.
These cities are well aware of the challenges in graduate skills and recruitment, and recent devolution processes are providing opportunities for improving graduate employment offers and addressing regional economy issues more generally. Data analysis and policy support are important is this role, with organisations set up such as New Economy Manchester and the University of Birmingham City REDI institute expanding.
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-
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.
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.
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.
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.
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.
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.
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%).
Population change 2001-2011 in the North West and West Yorkshire regions.
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.
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.
Population change 2001-2011 in the Midlands region
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.
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.
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.
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”.
CityGeographics 