Mapping – CityGeographics

Tracking Gentrification in London and Manchester Using the 2021 Census Occupational Class Data

December 15, 2022December 15, 2022Posted in City Planning, Housing, London, MappingTagged Census 2021, gentrification, Housing, London, Manchester, Mapping, PlanningLeave a comment

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.

Pandemic Geographies and Challenges with the 2021 England & Wales Census Results

August 16, 2022December 15, 2022Posted in City Planning, Housing, London, MappingTagged census, Census 2021, comparative urbanism, demographics, Great Britain, Housing, LondonLeave a comment

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

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
Camden	220338	279516	210100	27.0	-4.6	-24.8
Westminster	219396	269848	204300	22.9	-6.9	-24.3
City of London	7375	10938	8600	47.6	16.6	-21.4
Islington	206125	248115	216600	20.3	5.1	-12.7
Coventry	316960	379387	345300	19.7	8.9	-9.0
Kensington and Chelsea	158649	156864	143400	-0.9	-9.6	-8.6
Hackney	246270	280941	259200	13.7	5.3	-7.7
Richmondshire	51965	53732	49700	0.8	-4.4	-7.5
Tower Hamlets	254096	331969	310300	29.7	22.1	-6.5
Kingston upon Thames	160060	179142	168000	11.7	5.0	-6.2
Gwynedd	121874	125171	117400	3.0	-3.7	-6.2
Isles of Scilly	2203	2226	2100	0.1	-4.7	-5.7
Canterbury	151145	166762	157400	10.7	4.1	-5.6
Sheffield	552698	589214	556500	6.8	0.7	-5.6
Brighton and Hove	273369	291738	277200	6.9	1.4	-5.0
Newcastle-under-Lyme	123871	129610	123300	4.6	-0.5	-4.9
Guildford	137183	150352	143600	9.3	4.7	-4.5
Blaenau Gwent	69814	70020	66900	0.3	-4.2	-4.5
Nottingham	305680	337098	323700	10.9	5.9	-4.0

New Book – Gilded City: Tour Medieval and Renaissance London

June 27, 2022September 22, 2022Posted in Books, London, MappingTagged architecture, Books, City of London, Gilded City, history, London, Mapping, photography, UnicornLeave a comment

Have you ever wondered how London began? Or how London grew to become such an influential world city for business, politics and culture? You might be interested in Gilded City, a new book recently published (available on Amazon). 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

Gilded City is published by Unicorn Publishers, and is available to buy online and in bookshops around London-
Gilded City on Amazon
Waterstones
Unicorn Publishers
Bookshop.org
London Review Bookshop
Blackwells
WHSmith
Stanfords

Hopefully it will inspire more people to explore more of London, and connect the city today to its fascinating and complex history.

Global Data Visualisations by CASA MSc students

June 10, 2022June 21, 2022Posted in Mapping, Uncategorized, VisualisationTagged CASA, global cities, Sustainability, Teaching, VisualisationLeave a comment

Each year MSc students at CASA demonstrate their spatial data visualisation skills with a group project. The theme this year was ‘Global to Local’, and the class of 2022 has produced some particularly excellent work, experimenting with a range of visualisation tools and techniques.

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

Global Flooding Events Map by Group 1 (Nina Fabsikova, Sangbin Lee, Murray Chapman, Xinyi Huang, Henry Song)

Global Temperature Change and the Wine Industry by Group 5 (Andras Gelanyi, Ruby Johnson, Haofu Wang, Shuyi Zheng, Kameliya Staneva)

Arctic Sea Ice and Climate Change by Group 3 (Nayomi Kasthuri Arachchi, Haisu Chen, Daphne Badounas, Tianyi Li)

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

The Silk Road and Chinese global cultural diffusion by Group 4 (Rongrong Xue, Yuning Jiang, Zhonghao Li, Ce Hou)

The World to your Kitchen, global food study by Group 7 (Zicheng Fan, Jingran Ma, Hangjin Cai, Haotian Meng, Liyuan Dong)

Global food imports to the UK by Group 10 (Shengwei Deng, Zhenlei Gu, Jing Liu, Yujie Hu, Xinwei Kang)

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

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

Global energy consumption by type by Group 2 (Jiani Gong, Jeonghwa Kang, Linhan Cao, Seren Shi)

House price affordability comparison in global cities by Group 8 (Mengjiao Luo, Jieqi Tan, Wei Wang, Siyi Cai, Yanpu Huang)

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

Global Spread of Covid-19 by Group 9 (Ian Liu, Peizhao Wang, Shirao Zhang, Miaomiao Pan, Lin Sheng)

Global Covid-19 infections by Group 11 (Jikai Song, Lucia Zhang, Jianqiang Li)

Global Digital Divides
Finally, another interesting take was to think about online communities as interactions between global and local, including the changing geography of internet access and the division of the world into different online platforms by language and political and economic divides.

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

Here is the full list of project groups and websites-

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

A Compact City for the Wealthy? Continuing Inner London Gentrification and Impacts on Accessibility Inequalities

June 29, 2020July 2, 2020Posted in City Planning, London, Mapping, SustainabilityTagged accessibility, gentrification, London, public transport, Publications, Sustainability, transportLeave a comment

We have a new paper out in the Journal of Transport Geography- “A compact city for the wealthy? Employment accessibility inequalities between occupational classes in the London metropolitan region 2011“. The paper explores how the increasingly affluent nature of Inner London has improved sustainable travel opportunities for more affluent professional and management classes, while less affluent groups have increasingly been priced out to lower accessibility Outer London locations.

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.

Graduate Mobility and Closing the Productivity Gap for UK Cities

April 11, 2016April 11, 2016Posted in Economy, MappingTagged economy, Flows, Great Britain, higher education, PolicyLeave a comment

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) — 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.

LondonPropFlows_02 — 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.

ManchesterPropFlows_01 — Data HESA Destination of Leavers Survey 2013/2014

WestYorkshirePropFlows_01 — Data HESA Destination of Leavers Survey 2013/2014

WestMidlandsPropFlows_01 — 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.

For more details on the Foresight research, read the report here, and it is also worthwhile exploring the wider Foresight Future of Cities page.

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

April 4, 2016January 3, 2020Posted in Mapping, VisualisationTagged global cities, Publications, VisualisationLeave a comment

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

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

New Paper- Online Interactive Mapping: Applications and Techniques for Socio-Economic Research

February 23, 2016March 11, 2016Posted in MappingTagged CASA, comparative urbanism, interactive mapping, open data, Visualisation, web mappingLeave a comment

I have a new paper published in Computers Environment and Urban Systems- Online interactive thematic mapping: applications and techniques for socio-economic research. The paper reviews workflows for creating online thematic maps, and describes how several leading interactive mapping sites were created. The paper is open access so you can download the pdf for free.

Figure_04 — Global Metro Monitor by Brookings- http://www.brookings.edu/research/reports2/2015/01/22-global-metro-monitor

The paper features web mapping sites by Oliver O’Brien (http://www.datashine.org.uk), Kiln (http://www.carbonmap.org) and Alec Friedoff at Brookings (http://www.brookings.edu/research/reports2/2015/01/22-global-metro-monitor). Many thanks to these cartographers for agreeing for their work to be included in the paper, particularly Ollie O’Brien who also kindly provided comments on the paper draft. Also many thanks to Steven Gray at CASA who set up the hosting for the LuminoCity3D site.

Here’s the paper abstract-

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-

Datashine by Oliver O’Brien and James Cheshire- http://www.datashine.org.uk

Luminocity — LuminoCity3D by Duncan Smith- http://luminocity3d.org

Figure_06 — The Carbon Map by Kiln- http://www.carbonmap.org

Exploring the Users of Interactive Mapping Platforms

November 20, 2015March 11, 2016Posted in Mapping, VisualisationTagged Great Britain, interactive mapping, research impact, VisualisationLeave a comment

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.

Full Tables of Twitter Sharers

DataShine Twitter Sharers Classification

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

LuminoCity Twitter Sharers Classification

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

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

September 27, 2014October 20, 2015Posted in City Planning, Mapping, VisualisationTagged Great Britain, LuminoCity3D, Planning, Urban Form, VisualisationLeave a comment

Urban policy is currently riding high on the UK political agenda. A combination of the desire to rebalance the UK economy away from financial services; debates over massive high-speed rail investment; the worsening housing crisis in the South-East; and city devolution demands following the Scottish referendum, all point to major reform. As we move towards the 2015 general election, addressing city concerns is going to be a key, perhaps even decisive, election debate.

It is therefore a good time to take stock of recent urban growth and change in Great Britain, assess policy successes and failures, and consider how better outcomes might be achieved in the coming decades. This post draws on map visualisations from the LuminoCity3D.org website.

London and the South-East: Global Boom Region to Elite Island?
London’s recent growth has been phenomenal, gaining over a million residents (+13%) between 2001 and 2011. As we can see in the figure below, population growth has occurred across all of Greater London (except Kensington & Chelsea), with the strongest concentrations in Inner London and East London, reflecting the priorities of successive London Plans. This spectacular growth has not been confined to Greater London either, but is found across the South East region. The fastest growing UK towns and cities are nearly all in London’s orbit, including Milton Keynes with 20% growth, Ipswich with 15% growth, Cambridge with 16% growth and Ashford with 21% growth. This shared growth clearly illustrates that the South East is a closely integrated region, as further demonstrated by extensive commuting flows.

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

Inevitably it is strong economic growth that underpins this rise in population. London gained 650,000 jobs (+15%) between 2001-2011, strongly focussed in Inner London and Canary Wharf. Employment growth is much more unevenly spread across the South East, and arguably booming Inner London is taking jobs away from other centres, or pressuring some into becoming dormitory suburbs through soaring demand for housing. This is most clearly seen in Outer London in centres such as Croydon and Bromley where employment has fallen, while resident population has risen.

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

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

So with so many success stories, you be forgiven for thinking everything looking rosy for London and the South East. Unfortunately this is not the case. Soaring population growth has in no way been matched by new housing construction. What was previously a housing affordability problem in the South East is now an outright crisis that threatens to put the brakes on the entire region. Mean house prices just passed the incredible figure of £500,000 in July of this year, and a recent survey placed London as the most expensive city in the world to live and work. This is a looming disaster for future growth prospects. The crisis is not limited to London either, as shown below, with median prices above £300k for much of the South East, and the most popular cities experiencing similar extremes to London.

LondonSE_HousePrices copy — House prices 2013 in the South East region.

Soaring prices may seem like great news for property owners, but ultimately cities rely on their ability to attract talent and new businesses. And as London’s competitiveness falls, growth will go elsewhere. What has traditionally been a region of opportunity risks becoming a closed-shop for the wealthy.

And the situation is in danger of getting worse before it gets better. The current UK government did not create the housing shortage, but have overseen a period of historically low house building, with 2014 rumoured to hit rock-bottom. Mapping new-built housing sales leaves a sea of white, largely because there have been so few new houses constructed to sell. The recession presented an ideal opportunity for investing in housing and addressing unemployment, but this opportunity was missed. Trumpeted planning reforms have achieved very little, while right-to-buy policies have simply further increased prices.

Solving the housing crisis requires reform on a number of fronts. More power for local authorities to borrow money and make compulsory land purchases would certainly help. Linked to this is a desperate need for property tax reform to encourage housing to be used efficiently. Currently a £300k house pays the same council tax as a £10 million house, while empty housing is not discouraged, leaving many houses in Inner London as empty or underused investment vehicles. Similar arguments are made in favour of a land value tax to encourage land to be used efficiently and stop land banking.

Perhaps the most controversial issue is whether the green-belt can be retained in its current form. Calls from the eminent Richard Rogers that all new development can still be on brownfield frankly look out of touch with the reality in the South East. The debate really needs to switch towards how a controlled release of green belt land can be managed to avoid car-based sprawl and develop sustainable urban areas. Mapping rail infrastructure and urban density in the South East as shown below indicates that there are many potential locations with rail stations and room for growth. This approach would only however create more commuter towns, and ultimately there needs to be stronger planning for the entire South East region, likely with big urban extensions for successful cities such as Milton Keynes, Cambridge and Brighton. It is interesting that recent entries for the Wolfson prize were focussed on this approach.

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

Northern Evolution: an Emerging Hierarchy of Urban Centres?
While the South East is in danger of overheating, the majority of the UK’s city-regions have been focussed on post-industrial regeneration and stimulating growth. And in the last decade there has been significant change for many northern cities. Starting in the North West and Yorkshire we can see rising populations in all the major city centres. Greater Manchester in particular has experienced high levels of growth, gaining 200,000 residents (+8%) and 100,000 jobs (+10%) between 2001 and 2011. By the regional definitions used in LuminoCity3D.org, Greater Manchester has overtaken the West Midlands to become the second largest city-region in the country with 2.6 million residents. Manchester city centre has also experienced high rates of employment growth and is the primary centre in the North West, with positive signs in the business services and science & engineering sectors.

The Leeds and West Yorkshire region is also growing quickly, gaining 120,000 residents (+8%) and 50,000 jobs (+6.6%). Population growth is greatest in Leeds city centre, but is evident across the region, particularly in Bradford and Huddersfield. Similar to Manchester, employment growth is focussed strongly on the largest centre, Leeds, with a concentration in financial and business services. Despite West Yorkshire and Greater Manchester being two of the most dynamic northern regions, there is very little travel interactions between them due to poor transport links, and this surely needs to be a policy priority.

Sheffield also displays significant city centre led growth, gaining 45,000 (+6.3%) residents and 21,000 jobs (+6.7%), as does Liverpool although there has been some population decline in the suburbs. Liverpool’s figures are a gain of 21,000 residents (1.8%) and a more impressive 44,000 jobs (10%).

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

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

The house prices map for the north-west and Yorkshire makes a very interesting comparison to London. The dramatic gentrification that has transformed Inner London towards increasing affluence and polarisation has not (yet?) occurred. The wealthy areas are mainly suburban in the north-west, often where large cities merge with national parks such as the Peak District and the Yorkshire Dales. There are some signs that wealthier South Manchester is beginning to move towards the city-centre, but this is still in earlier stages of city-centre transformation.

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

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

Midlands_PopChange — Population change 2001-2011 in the Midlands region

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

Similar to the North West and Yorkshire, city centre housing markets are relatively inexpensive in the Midlands, with wealthier areas in the suburbs, particularly between Birmingham, Coventry and Warwick/Leamington Spa. There are signs that wealthier groups to the south of Birmingham are moving further into the city centre.

Midlands_HousePrices — House prices 2013 in the Midlands region.

Will Growth Transfer from the South East to the North?
With the South East struggling to accommodate growth and northern regions trying to attract more growth, the answer seems obvious- transfer growth to the north. Unfortunately urban economics is seldom that straightforward. London is a global leader in a range of service sectors, and it does not automatically follow that existing firms and new firms would choose northern cities over the South East. There are however many encouraging signs in cities such as Manchester, Leeds and Birmingham with growth in a range of knowledge-economy sectors. The gap with the South East still remains extensive, and this essentially is the crux of the debates about city devolution and infrastructure investment: whether or not these policies can enable northern cities to bridge this gap. London currently has great advantages in terms of public money invested in infrastructure like public transport, and also in terms of political power to plan and manage growth through the Mayor and Greater London Authority. The argument in favour of empowering northern cities looks increasingly convincing, and we shall see in the coming months whether politicians are brave enough to instigate this process.

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

September 22, 2014June 5, 2017Posted in City Planning, Mapping, VisualisationTagged Density, Great Britain, LuminoCity3D, Planning, Urban Form, Visualisation3 Comments

Recent urban growth in the UK has further emphasised the role of cities in influencing economic prosperity, quality of life and sustainability. If we are to meet 21st century social and economic challenges then we need to plan and run our cities better. Data analysis can play a useful role in this task by helping understand current patterns and trends, and identifying successful cities for sharing best practice.

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

Taking for example employment density change in northern English cities as shown below. Current growth is mainly in ‘knowledge-economy’ services that generally favour being clustered together in city centres, generally reinforcing a select few larger centres rather than many smaller centres. There is clear growth in Manchester, Leeds and Liverpool city centres, particularly Manchester which displays the biggest increase in employment density of any location in GB. But around these success stories there is a much more mixed picture of growth and decline for many other centres that are finding it more difficult to compete for firms and jobs.

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

Interactive City Statistics

City statistics are available to make more precise comparisons between urban areas. Statistics can be viewed on LuminoCity3D.org by moving your mouse pointer over a city of interest, or by hovering/clicking on the GB Overview Chart at the bottom left of the screen. The graphs and statistics change depending on the map indicator selected, so that the LuminoCity maps and statistics are interactively integrated.

The example below shows public transport travel, a key sustainability indicator that also has important economic and equity implications. Greater London is by far the public transport centre of the UK with nearly 50% of commuting by public transport. Without the investment and historic advantages of London, city-regions like Manchester and Birmingham do not even manage 20% PT commuting. But we can see that it is not essential to be as gigantic as London to achieve more sustainable travel. Edinburgh, with a compact form and extensive publicly owned bus network, achieves 36% PT commuting.

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

Indicator Themes

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

LuminoCity3D_HousePPSE — House prices 2013 in the South East of England.

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

LuminoCity3D Credits

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

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

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

All the datasets used are government open data. Websites such as LuminoCity would not be possible without recent open data initiatives and the release of considerable government data into the public domain. Links to the specific datasets used in each map are provided to the bottom right of the page under “Source Data”.