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Collecting accurate data at an urban level

As the UN’s Millennium Goals are replaced with globally applicable Sustainable Development Goals this autumn, a new urban goal is being included.We tested it out in Greater Manchester and found that 9 – 20% of Greater Manchester’s urban population lives in ‘slums or informal housing’, while 63% of the population has mid to high levels of public transport accessibility and 97% of our local authority collected waste is well-managed. UN definitions for slums, accessible transport and disasters don’t readily translate into data collected in the UK and collating accurate data on housing and transport affordability is wrought with complexities.Local authorities and the government won’t fund additional data gathering. So, what is useful and relevant to Greater Manchester from a new UN urban sustainable development goal?

It’s taken several years to negotiate and develop indicators for the seven targets within the urban goal, covering themes of housing, transport, land-use, cultural and natural heritage, disasters, environmental impacts and public space.

The goal is likely to be approved by the UN before the intricacies of data collection are finally signed off by governments. Agreeing indicators that are relevant to cities around the globe, drive future sustainable development and for which data can actually be collected and reported in practice is very challenging.

We all know how difficult it can be to agree meaningful indicators for a project, let alone the whole world’s cities that house half its population. 

In March 2015, Mistra Urban Futures asked the Local Interaction Platforms in Greater Manchester, Cape Town (South Africa), Kisumu (Kenya) and Gothenburg (Sweden) and the Indian Institute for Human Settlements in Bangalore, to test data collection for the proposed indicators.

This rapid pilot project aimed to understand whether locally collected data could be used to report on the goal, and what it meant to each city. We reported back to an international workshop in Sweden in early June. This report can be viewed in our document library here.

With the support of AGMA, TfGM and local authorities, we prioritised the targets then researched and collated a vast amount of data.

Despite the quantities of data we could not provide everything that was needed for each indicator.

Some data is simply not available disaggregated to a Greater Manchester level, and was available for England, or the North West. Other data is collected at a local authority level and can be aggregated for GM.

For indicators such as cultural and natural heritage, the indicators did not apply to the target, and were simply not practical or relevant to collect.


Transport for Greater Manchester (TfGM) has data at its fingertips for the whole city and is blessed with an enthusiastic and helpful intelligence officer, Ian Davies. GM can  consequently provide the UN with its 40% figure for share of trips by walking, bicycling and public transport.

TfGM’s Greater Manchester Accessibility Levels (GMAL) exceed the blunt indicator of % of urban population within 0.5km of public transit running every 20 minutes. It provides a more nuanced picture of accessibility, including walk access time, waiting time and reliability.

Using this data we can provide the UN with GM’s 63% of the urban population having mid to high-level public transport accessibility. If other cities can provide similar data there is an interest in benchmarking GM. But experience from European transport benchmarking is that the data collected is not comparable so it adds little learning to the city.

Interestingly PTEG (Passenger Transport Executive Group) enables the comparison of transport data for our big cities, and has been formed to fill the gap in reporting duties to the Department of Transport and provide evidence and a strong joint voice.


We can provide CO2 emissions and most of the waste and air pollution data requested under this goal, because they are collected centrally and reported for EU obligations.

Despite this, our commercial waste goes unreported below the national level. GM’s 48% emissions reduction target is already ahead of many other cities, including the much lauded #OneNYC plan.


Where there is a statutory duty to report data, it can be obtained, however, its reliability is variable. For example, housing condition data is available annually, but its quality is being eroded by budget cuts. Regularly conducted housing stock condition surveys are carried out less often because they are so costly. 

Three GM local authorities subscribe to BRE modelling which is dependent on a range of national surveys such as the English Housing Survey (which was subject to consultation at the time of research) and the Census. As such, consolidated housing data is not available for the city-scale.

We found that applying UN Habitat criteria for slums and informal housing (a slum household lacks one or more of five elements including improved water, improved sanitation, security of tenure, durability of housing and sufficient living space) gave rise to the surprising figure of between 9 – 20% of GM’s population being defined as slum households.

This was through lacking security of tenure (households with assured shorthold tenancies) and inadequate housing condition (Category 1 Hazards). But politically, we cannot say that such a high percentage of GM households are slums, it is simply not comparable with cities such as Kisumu, who by the same definition had to state that 100% of their households fell into this category.

What was useful for housing was the idea that this indicator could ‘help get the message upstairs’ to politicians.

Housing officers told us that with the erosion of quality data on housing condition it is hard to make a case for the area-based investment that can prevent areas falling into neglect and disrepair.

For GM’s sustainable urban future, reliable stock condition data will be needed to inform retrofit schemes. The team at AGMA’s Low Carbon Hub are currently identifying metrics for housing energy efficiency for the climate change implementation plan.


Affordability of both housing and transport was very difficult to produce at a GM level. Data on affordable housing is available only for new housing, and is used in the planning process.

Producing city-wide data for the indicator on how many households are spending 30% of their income on accommodation cannot be achieved. It is also not a useful statistic without income levels.

AGMA works on a smaller scale to identify affordability and housing needs for different segments of the population, so researching the urban goal housing affordability indicator does not enable progress in actually tackling the problem in the city.  

TfGM was able to come up with some data on share of income spent on transport, but the indicator misses the point that those on the lowest incomes may simply walk. So it does not really tell us anything useful. 


A tangible outcome of the pilot was to connect the Low Carbon Hub, tasked with climate change adaptation, with GM’s Civil Contingencies and Resilience Unit, to enable early consideration of slow onset climate change impacts.

GM is already a Role Model for Total Resilience in the UN Office for Disaster Risk Reduction’s Making Cities Resilient campaign, and has risk reduction strategies and resilience plans firmly embedded. But despite already leading on resilience, the unit’s head, Kathy Oldham, was happy to meet and look at issues raised by the target and indicators, leading to consideration of how a city might report on risks without causing alarm or putting off potential investors.

We also considered how multiple risks to areas of population might be mapped to account for low level risks from different types of flooding or industrial hazards.

Who collects and owns the data?

Locating data for an urban area rather than a nation is complicated. Different information is collected across a range of organisations and some of it cannot be disaggregated down to the city level.

The Office for National Statistics is yet to catch up with governance changes from regions to city-regions or LEP areas and the North West Regional Intelligence Unit is no more.

We found a growing dependence on private companies to model data for public sector use, for example, waste and air quality data. Experian and Acorn provide modelling on population segments and behaviour at the postcode level to enable tailored service design by local authorities, whilst BRE provides housing stock condition modelling that enables local authorities to report back to government. Underpinning much of this modelling is public data collection, such as the English Housing Survey and the Census.

We also found a circular effect of data reported to DCLG by local authorities, that is then collated and published in national datasets, yet we know that local authorities are facing cut backs and do not have resources to report accurately.

The effect of this can be that it is hard to gather the ‘right data’ to build a robust business case or evidence base to target expenditure and support at the areas and people that need it most.

The government’s reduction of statutory reporting requirements on local authorities in a bid to reduce costs and cut red-tape is having the effect of eroding the evidence base to inform policy and action.

What is relevant to GM?

Applying the proposed targets and indicators for the UN’s urban sustainable goal at a city level posed questions about why and how we collect data and what we do with it. It gave rise to a growing sense of fragmentation and politicisation of data, knowledge and intelligence nationally and locally.

Will the urban Sustainable Development Goal change anything for Greater Manchester?

Probably not. The city region already has sustainability within many of its policies. AGMA and Manchester a Certain Future are identifying metrics for measuring progress and have used this project to check against their indicators, some of which overlap.

Will it change the way we collect data?

Not really, although we have identified that housing affordability and e-waste measures can be improved.

Devolution is likely to provide an incentive to review the indicators for the city and perhaps to collect data in new ways. There might be an opportunity to collect more people-based data, through citizen participation, which could be useful for cultural indicators in particular.

A GM Mayor will certainly provide a figurehead to sign up to the urban Sustainable Development Goal and represent Greater Manchester at the UN alongside cities like New York and London.

To read our report, 'Urban Sustainable Development Goal Pilot Project – Greater Manchester,' be sure to visit our document library.


Main image 'Sustainable Energy from Mother Earth' by Flickr user Intel Free Press.