1. A Clean Sweep Delivering cleaner streets in diverse neighbourhoods Nigel Tyrrell London Borough of Lewisham

2. A Clean Sweep? Narrowing the gap between deprived and better off neighbourhoods Annette HastingsGlen Bramley Nick Bailey Rob Croudace David Watkins University of GlasgowHeriot-Watt University

3. Research aims To understand more about how different neighbourhood contexts predict environmental problems; To explore the organisational challenges and financial costs involved of meeting different kinds and levels of need ; To examine different approaches to narrowing the gap; To provide ideas, strategies and tools which local authorities can use to design policy and practices capable of narrowing the gap with relation to street cleanliness.

4. Research methods Three case-study local authorities – contrasting urban locations Street-level analysis Quantitative and qualitative methods Assessment of distribution of service inputs and cleanliness outcomes by area deprivation Test hypotheses from previous research on neighbourhood context and environmental challenges Research funded by Joseph Rowntree Foundation

5. Why try to narrow the gap? Different neighbourhoods present distinctive challenges – one size doesn’t fit all Need to show ‘continuous improvement’ Moral imperative – consistency or ‘justice’ 2009 Social Mobility White Paper : “tackling socio-economic disadvantage and narrowing gaps in outcomes for people from different backgrounds is a core function of key public services”

6. Outcomes: the big picture All three case studies: meet or exceed national benchmarks have shown improvement over time where is there scope for further improvement? But what about the distribution of outcomes?

7. Outcomes by deprivation

8. Percent of transects falling below acceptable litter threshold by street deprivation 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Least23456789Most Street deprivation (estd.) Fife Lewisham Leeds

9. Percent of transects gaining an A grade by street deprivation 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Least23456789Most Street deprivation (estd.) Fife Lewisham Leeds

10. Deprivation profile of the case studies

11. Relationship between high grade average and inequality of outcomes LAs with high average grades tend to have more unequal outcomes LAs with low average grades more equal outcomes Need to avoid a ‘leveling down’ Should we aim to be in the middle, therefore?

12. How does neighbourhood context affect outcomes? Physical environment: - Flats and non-traditional forms of housing; small properties –Open spaces; Physical regeneration –On-street parking –Alleys; unfenced gardens –Housing density Social environment –Economic inactivity; Poverty –Overcrowding; Population density –Child/youth density; Lone parent density –Density of vulnerable households –Turnover

13. Evidence on environmental difficulty: the first case study Using national data sets density, overcrowding, flats and lone parents associated with litter, rubbish, weed growth non-employment, lone parents associated with graffiti and vandalism (n=1030, range of correlations at the 10% significance level) Detailed bespoke survey Hedged gardens, open areas, wind tunnels, derelict sites associated with litter, rubbish and weeds Grass verges, planted beds, alleys, on street parking, street furniture, bus shelters, open areas, building sites associated with flytipping, flyposting and graffiti ( n=52, range of correlations at 10% significance level)

14. Deprivation and environmental difficulty: the first case study Deprived streets have much higher incidence of the factors thought to be associated with more challenging service context N=1030, using national data sets

15. Achieving a Clean Sweep: three pathways Pathway one: topping up standardised services dedicated ‘beat’ sweepers, topped up with responsive mobile squads Informal targeting of problem areas by operatives “I can get away with giving (place A) only one sweep, but would give (place B) three.. Don’t tell my supervisor, but he probably knows anyway”

16. Standardised programmed services??

17. Programmed services topped up in deprived areas Overall, positive skew in expenditure Programmed services skewed towards less deprived streets Responsive services skewed to more deprived streets

18. Achieving a Clean Sweep: three pathways Pathway two: using non-mainstream resources and services May be based on standardised or targeted programmed service Our example: targeted mechanical and manual sweeping Topped up by ‘additional’ resources in the most disadvantaged areas Additional resources involved new ways of working

19. Additional resources associated with narrowing the gap

20. Targeting of programmed services?

21. Achieving a Clean Sweep: three pathways Pathway Three: Programmed adjustments to standardised services Core services engineered to target need May involve dedicated ‘beat’ operatives (squads or individuals) May involve a ‘cover up’ of the extent of targeting –All streets swept twice weekly –But sweepers with more challenging areas, given smaller workload –Operatives in affluent areas work smartly

22. Inputs – beat size by deprivation (Fig 4.2)

23. Working smartly in affluent areas In an affluent and high workload (in street length) beat, Clive contends with ‘moany people’ as well as comments like “don’t you come down here anymore”. He consciously leaves his barrow on show in prominent places to show that he is around and tries to work “in ways to keep people happy”, which include doing “extras” (unpaid). NB Recall the lack of ‘A’s in this case study

24. Conclusions: Cross cutting issues Absolute levels of resources as well as distribution Requires sufficient basic service in most needy areas – otherwise doomed to fail May mean less ‘A’ grades elsewhere Different modes of working may suit particular neighbourhood contexts Know your area – multiple sources of intelligence? Challenges of achieving a ‘win win’ situation

25. Further information Final report to be published by Joseph Rowntree summer/autumn 2009 as A Clean Sweep: Narrowing the gap between deprived and better off neighbourhoods Download for free a www.jrf.org.uk or email A.Hastings@lbss.gla.ac.ukwww.jrf.org.uk A.Hastings@lbss.gla.ac.uk Previous report : Cleaning up Neighbourhoods: Environmental problems and service provision in deprived areas free download www.jrf.org.uk www.jrf.org.uk

26. Nigel Tyrell LB Lewisham

27. Resident Satisfaction

28. Key Factors Staff management – Performance – Sickness – Work planning Resident Engagement – Love Lewisham etc.

29. Budget £3.92 Million – £25 per resident PA

30. Activity All streets swept at least once a week – over 700 miles of roads (135 beats) 10,000 tonnes of fly tips cleared 6,000 tonnes of litter Filled 900,000 blue bags 2,500 litter bins emptied 364 days a year

31. Staff 144 Street Sweepers 22 mobile team workers 26 mobile estate sweepers 4 mechanical broom/mini RCV drivers I Scarab driver 14 managers

32. Sickness 4.9 days per operative p.a. – Lower than the Council ‘office worker average’ Key element of our approach to efficiency

33. Everyone on-board Love Lewisham, staff & community engagement

34. Community Engagement Clean & Green Schools Programme Business Environmental Excellence Streetleaders NEAT’s Graffiti Busters River Clean Up’s Awareness Raising Community Groups Letters to markets traders

38. Councillor involvement

40. Love Lewisham Ability to show "before" and "after" pictures Staff can demonstrate what has been achieved Councillors and citizens can see that a problem has been dealt with

41. Love Lewisham Benefits Environmental issues are now far easier to report. – More staff report problems - fewer issues to irritate residents Open resident/Member engagement – Our response is public and accountable

42. Practical Partnerships

43. Cross-service collaboration

44. Love Lewisham Results Dramatic improvement in the time taken to remove graffiti – From 2.78 days in 2003 (before ‘Love Lewisham’) to 0.50 days to complete now

45. Love Lewisham Results Reports of graffiti have fallen by about 30% this year. – Metres removed and the number of jobs has also fallen. Less graffiti has been observed from our own monitoring, down from 18% (05/06) to 9.43% this year

46. Love Lewisham Results In the same period the number of graffiti removal jobs reported has trebled. – The resources to do the job stayed the same.

47. Love Clean Streets Building on Love Lewisham

48. EnCams Survey Data. Live

52. Blogging the Borough SEND LINK EMBED

53. Blogging the Borough

54. http://lovelewisham.wordpress.com/

55. Resident Satisfaction