Sorry, we're only accepting searches for within Greenwich at the moment.
Sorry, you haven't entered a valid postcode
Your StreetScore is
Your StreetScore is
Do you agree with the score we've given your postcode or street?
If you were to score it differently, how would you rate your street (from 1 to 100, where higher is better)
Do you live here?
If so, are you happy living here?
If you'd like to share your email address with us, please enter it here. We promise not to spam you or share it with 3rd parties, but might send you the occasional email about StreetScore
is a web-based tool that uses open data to characterise objectively and consistently physical qualities of the built environment (and air quality). It is a way of proving a consistent measure of the quality of a place based on multiple urban form, heritage, connectivity and other data-points and in a way which can be compared to outcomes. It is a 'learning' tool and we are continuing to refine and improve the underlying algorithm based on our ongoing research into the associations between urban form with wellbeing, health and happiness and also by analysing the preferences provided by the tool's feedback system.
Cities are complex places. And so is the data that underpins them. That means that although we can be quite confident about the types of place that tend to make for better places, this won't be true 100% of the time.
won't always get it right – at least not yet! The tool is 'learning' (see below).
How does it work?
You enter a precise postcode or street name in Greenwich or move to a location in a map.
then goes into Google Maps and our data banks and computes a score for the selected location. We collect data on things such as street trees, population density, connectivity of streets, building height, amount of greenery, amount of unbuilt land and many more. In total, we are using 36 data sets.
Using the academic research and our own analysis of correlations between things such as density, amount of greenery, types of buildings and connectivity we then 'score' those places under 11 key criteria: greenery, homes, height, connectivity, land use, block type, space, beauty, facades, density and air quality.
This is where the judgement and some art as well as science comes in. We weight these based on what the wider research tends to show and then add them up to get a score out of one hundred. Higher scores are better places. Lower scores are less good places. The average score is 53. In total, we are using 24 measures and interactions between measures in a total of 11 categories.
What sort of places tend to get a high
The types of places that tend to score well have the advantages both of suburbia (greenery and private space) and of a more tightly-formed city (you can walk to work or school). They are characterised by a structure of human scale lots, blocks and streets that clearly define the public and private realm and permit residents to express themselves both as individuals and as members of the wider community. They rely on a network of beautiful streets, civic structures, and public spaces to establish a well-defined civic realm, 'a place which residents identify with and can think of as "their" block, street, or development.'
What sort of places tend to get a low
The places that tend to get low scores will tend to be extremes: either very low density, wider, faster roads or large blocks of flats in wider, poorly observed or managed open space. Or perhaps an overdeveloped street with very high buildings and little light. Conversely a cul-de-sac which is hard to get to, and from which you need to drive everywhere could also score rather poorly. That it is not say that these will always be poor places or that some people won't enjoy living in them or passing through them. But on the whole, these types of place will tend to work less well for most people most of the time - either to live in or to use.
How do you know
Well we certainly don't claim it's always right but we think it generally is. We have tested
in two ways.
Firstly, during the process of creating
, we have run a range of correlation analyses of its findings with sales values, indices of multiple deprivation, childhood obesity and longevity. We are able to use
successfully to predict certain portions of these outputs. The correlation is not perfect but that is because some metrics that we know are quite powerful in predicting health outcomes are less powerful in predicting values (and vice versa). Because we are building a tool objectively and consistently to characterise quality in the built environment we are quite keen to 'hold' this tension within the tool.
Secondly, we have asked a range of friends and experts who know Greenwich to review actual places they know and comment on the results they have found. We have used this to 'fine tune' the way the scoring works.
We are encouraging all users to rate each score for places they know by how useful and true they found it. We are also encouraging all users to score how much they actually like being in a place on a scale of 0 (hate it) to 10 (love it). Once we've got enough user-entered data, we'll be using this user-rating to further improve our own indices but also to provide a user-rating score. For the moment, we don't have enough data to do this robustly. But watch this space...
Why have we set up
David Halpern (Director of the Cabinet Office Behavioural Insight Unit) once said that: 'architecture and planning does not have an empirical, evidence-based tradition in the sense that ...sciences would understand.' He's right. There is no generally accepted 'measure' of the quality of a place. Indeed, many planners and architects argue that quality is subjective. This is incorrect. While preferences may be subjective at the individual level correlations between urban form with outcomes are more objective at the city-level. Peer-reviewed work over the last 30 years and our own research is finding that what is popular and links between elements of urban form and good mental and physical health or value can, with some margin of error, be predicted.
Getting these correlations out of academic papers and our reports and presentations and into usable tools seems to us existentially important. We have set up
to give policy-makers, developers, planners, designers, home-buyers and indeed anyone interested in the places they live in a comparable perspective on the types of place that tend to be correlated with higher and lower wellbeing.
We hope that you find it useful. If you can take the time to fill in the user-data to help us improve the tool in the months and years to come, we'd be incredibly grateful. Thank you!