Time’s wingèd chariot…

I’ve written before (here) about the genuinely fascinating (no – really) attempt by the GLA to pool planning application data for London on the Planning London Datahub. Or get it from the horse’s mouth here. There’s even now an API available so the data can be interrogated directly, in addition to using some of the dashboards the GLA has helpfully made, or the Kibana interface (which, to be honest, is not that easy).

Why is that of any interest whatsoever? (Or indeed related in any way to a 17th century love poem?)

Well, Simon Ricketts (yes, I’ve written another entry in response to Simon’s blog) has given us a very succinct, if depressing, account of the procedural Horlix that is being made of amending planning permissions. S73s, S73(B)s, S9As, Hillside, drop-ins, minor material vs non-material vs substantially similar, etc. One gets the picture.

He makes the point that the longer it takes to get permission, the greater the likelihood that changes are needed, but suggests that the Government has no understanding of this, drawing as it does (false) comfort from the fact that 86% of major applications are determined within 13 weeks “or the agreed timescale” according to Government statistics.

Anyone who has ever dealt with a major application knows that one has little choice but to agree extensions of time, unless one wants a summary refusal. Which makes me suspect that those stats are about as reliable as Southeastern Trains on a snowy Monday morning during an RMT strike.

Simon asks whether it is possible to find the average timescale for determination of major applications. The Datahub perhaps lets us get close for London at least. I’ve pulled out the stats available for applications going to committee over the last couple of years and the average timescales involved:

BoroughAverage Determination Period (weeks)Number of applications found
City of London4944
Hammersmith & Fulham4151

Not quite “deserts of vast eternity” – but considerably over 13 weeks nonetheless.


Now, as my long-suffering colleagues are used to hearing me complain, I’m just a town planner with a history degree, and computer coding isn’t my strong suit, so I need to put a few health warnings around this:

  • My code could well be dodgy – if there’s anyone reading this who also knows how to write Elasticsearch queries and would like to check my coding do contact me.
  • These are committee schemes, rather than Majors – the data hub doesn’t identify majors, but I assume that most majors would go to committee. If anything, I suspect looking at all committee schemes rather than just majors probably reduces the average timescale.
  • Not all London boroughs are covered, so if your favourite isn’t on the list – sorry. I’m certain that not all applications are on here, either, nor am I convinced that all committee schemes are correctly flagged (I’m sure Redbridge has had more than 5…).
  • This covers the period 2020 – 2022. You know – COVID-19 etc – which did cause more than a touch of disruption.

Nevertheless, this is the best dataset of which I’m aware.

I would also endorse the point that Simon, and many others, make about planning department resourcing, which is at the heart of this. The demands and complexities of the system increase constantly. If anything, the speed with which new issues are layered onto the system is getting faster. In the last couple of years in London we’ve had fire safety, urban greening, and embodied carbon layered onto the system’s already creaking chassis, with more to come in the form of biodiversity net gain and the growing confusion around Part L 2021, to name just two. Housing gets every more politicised, even as our ability to plan for it declines (see pretty much every planning blog, so I’m not going to go there).

The only thing that doesn’t increase is the resourcing for the departments that have to grapple with all these issues.

So these statistics are no surprise. Frankly, that they aren’t a lot worse is the result of Stakhanovite work by many planning officers. But the data hub does illustrate what we all really know to be the case about the timescales of applications of any complexity, Government stats notwithstanding. Right – now let’s see where that train has got to…

Time’s wingèd chariot hurrying near; 

And yonder all before us lie 

Deserts of vast eternity. 

Andrew Marvell, To His Coy Mistress

(Feature image generated by AI, specifically the amazing-but-slightly-terrifying DALL-E.)

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