PLS 2014: Early Results of the LANTERNS Project

The LANTERNS project is funded by the NIHR Public Health ResearchProgramme. The views expressed here are those of the authors anddo not necessarily reflect those of NIHR or the Department ofHealth

Early results of the LANTERNS project

What are the public health

impacts of reduced street

lighting at night?

• To collate information on street lighting adaptation schemes nationally

• Statistically examine whether reduced lighting has any effects on traffic crashes or crime

• Explore local public opinion on street lighting provision, and potential for reducing levels

• Investigate whether street lighting adaptation schemes offer value for money

LANTERNS Project Aims

Does:

Switch off

Part night

Dimming

Trimming

White light

..or combinations

of these?

Cause any changes in:

ROAD CASUALTIES

- Car occupant

- Pedestrian

- Motorcyclist

- Cyclists

CRIME

- Burglary

- Criminal Damage & Arson

- Vehicle Crime

- Violence & Sexual Offences

- Robbery

All local authorities in England and Wales have been invited to participate…


67 local authorities have contributed data


67 local authorities have contributed data

Street lighting data from 60 local authorities will be used in the analysis

Part Night Lighting27/6045%

Trimming29/6048%

Dimming/variable lighting37/6062%

White light46/6077%

Switch off3/605%

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Jan

-00

Jun

-00

No

v-0

0

Ap

r-0

1

Sep

-01

Feb

-02

Jul-

02

Dec

-02

May

-03

Oct

-03

Mar

-04

Au

g-0

4

Jan

-05

Jun

-05

No

v-0

5

Ap

r-0

6

Sep

-06

Feb

-07

Jul-

07

Dec

-07

May

-08

Oct

-08

Mar

-09

Au

g-0

9

Jan

-10

Jun

-10

No

v-1

0

Ap

r-1

1

Sep

-11

Feb

-12

Jul-

12

Dec

-12

May

-13

Oct

-13

Kilometres of road with lighting interventions

Part-night lighting Dimming Trimming White Light Switch off

Road traffic injury data

Stats19

– Easting and Northing of collision

– Time of day of collision

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Non-day casualties

A roads B roads Minor roads

0

5000

10000

15000

20000

25000

30000

35000

40000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Day time casualties

A roads B roads Minor roads

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Jan

-00

Jun

-00

No

v-0

0

Ap

r-0

1

Sep

-01

Feb

-02

Jul-

02

Dec

-02

May

-03

Oct

-03

Mar

-04

Au

g-0

4

Jan

-05

Jun

-05

No

v-0

5

Ap

r-0

6

Sep

-06

Feb

-07

Jul-

07

Dec

-07

May

-08

Oct

-08

Mar

-09

Au

g-0

9

Jan

-10

Jun

-10

No

v-1

0

Ap

r-1

1

Sep

-11

Feb

-12

Jul-

12

Dec

-12

May

-13

Oct

-13

Kilometres of road with lighting interventions

Part-night lighting Dimming Trimming White Light Switch off

0

0.01

0.02

0.03

0.04

2009 2010 2011 2012 2013

Non-day time casualties per km

Part-night lighting Dimming Trimming

White Light Switch off Non-intervention

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

2009 2010 2011 2012 2013

Day time casualties per km

Part-night lighting Dimming Trimming

White Light Switch off Non-intervention

slope pre-

Injury and crime count

Time

slope post-

step change associated with the intervention

Lighting ‘interventions’ included:

Switch off TrimmingPart-night lighting DimmingWhite light LEDs

Controlled interrupted time series model

number of casualties & crimes in road segment ‘s’ in year ‘t’

𝑌𝑠,𝑡~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝜇𝑠,𝑡

log 𝜇𝑠,𝑡 = 𝛼𝑠 + 𝑆 𝑡, 𝑧𝑠 + 𝛃. 𝐱𝑠,𝑡

𝑦𝑠,𝑡 =

𝛼𝑠 = road segment effect

𝑆 𝑡, 𝑧𝑠 = function of year to allow for nationwide trends, dependent on road segment characteristics

𝑧𝑠 = road segment characteristics

𝐱𝑠,𝑡 = vector of indicator (0,1) variables identifying road segments affected

𝛃 = vector of coefficients of effect of lighting change on casualties and crimes

Crime data

Police.uk (national coverage)

– Monthly counts of ASB, criminal damage, burglary, vehicle crime, robbery

West Midlands Police Force recorded crime data

– Type of crime

– Date and time (from and to)

– Location

Police.uk data

National coverage (2011-)

• Month of crime (not day or time of the day)

• Type of crime*

• Location*

Which Crimes?

British Crime Survey

0

10

20

30

40

50

60

70

80

90

2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/092 2009/103

% c

rim

es

du

rin

g e

ven

ing/

nig

ht

BCS year

All Violence Theft from Person Vehicle related theft All burglary

Which crimes?

Police recorded crime - Aoristic

Profiles

• For many crimes the time of the offence is unknown

Mid

nig

ht

No

on

6p

m

6am

Mid

nig

ht

crime

0.00

0.02

0.04

0.06

0.08

5 10 15 20

Hour of the day

Pro

bab

ility

cri

me

occurs

in

ho

ur

t

Aoristic graph for Criminal Damage, n = 44,966

Which crimes?

Police recorded crime - Aoristic Profiles

0.000

0.025

0.050

0.075

0.100

5 10 15 20

Hour of the day

Pro

bab

ility

cri

me

occurs

in

ho

ur

t

Aoristic graph for Robbery, n = 19,397

0.00

0.01

0.02

0.03

0.04

0.05

0.06

5 10 15 20

Hour of the day

Pro

bab

ility

cri

me

occurs

in

ho

ur

t

Aoristic graph for Vehicle Crime, n = 69,860

0.00

0.01

0.02

0.03

0.04

0.05

5 10 15 20

Hour of the day

Pro

bab

ility

cri

me

occurs

in

ho

ur

t

Aoristic graph for Burglary, n = 79,108

Which crimes?

Police recorded crime - Aoristic Profiles

Police.uk - Geomasking

• “Each dot marks the approximate location of an incident of crime or anti-social behaviour and it will usually appear on a street with 8 or more postal addresses. We have purposely used radar-style icons to demonstrate this and also clearly state at each dot that the incident is ‘on or near.’”

(http://www.police.uk/help?)

Testing the Spatial Accuracy

of police.uk data

Tompson, L., Johnson, S.D., Ashby, M., Perkins, C., and Edwards, P. (2014). UK open source crime data: Accuracy and possibilities for research.

MSOA level

0.0 0.2 0.4 0.6 0.8 1.0

MSOA

Index of Similarity (S)

Robbery 2013

Robbery 2012

Robbery 2011

Robbery Total

Crim Dam 2013

Crim Dam 2012

Crim Dam 2011

Crim Dam Total

ASB 2013

ASB 2012

ASB 2011

ASB Total

Vehicle 2013

Vehicle 2012

Vehicle 2011

Vehicle Total

Burglary 2013

Burglary 2012

Burglary 2011

Burglary Total

PoliceTotal

OpenTotal

79104

29038

25883

24183

69860

25371

22222

22267

262793

106969

80363

75461

47492

7879

20083

19530

19396

8213

5775

5408

78392

28743

25658

23991

72746

26516

23120

23110

274336

118852

80287

75197

60255

9857

25569

24829

19192

8108

5707

5377


Postcode level

0.0 0.2 0.4 0.6 0.8 1.0

PC

Index of Similarity (S)

Robbery 2013

Robbery 2012

Robbery 2011

Robbery Total

Crim Dam 2013

Crim Dam 2012

Crim Dam 2011

Crim Dam Total

ASB 2013

ASB 2012

ASB 2011

ASB Total

Vehicle 2013

Vehicle 2012

Vehicle 2011

Vehicle Total

Burglary 2013

Burglary 2012

Burglary 2011

Burglary Total

PoliceTotal

OpenTotal

79113

29043

25885

24185

69861

25372

22222

22267

262803

106972

80367

75464

47454

7879

20084

19491

19398

8213

5776

5409

78395

28745

25660

23990

72638

26464

23063

23111

274341

118853

80290

75198

60184

9849

25504

24831

19188

8108

5703

5377


Outcome Analysis

• Better thought of as reflecting crime on and/or near a street

• Area level analysis

• Spatial accuracy improved substantially by modeling the error in the data

• Case study analyses with police recorded crime data

• To collate information on street lighting adaptation schemes nationally

• Statistically examine whether reduced lighting has any effects on traffic crashes or crime

• Explore local public opinion on street lighting provision, and potential for reducing levels

• Investigate whether street lighting adaptation schemes offer value for money

LANTERNS Project Aims

Rapid appraisal

• Survey of web and public sources• LA consultations• Local media

• Key informant interviews

• Ethnographic interviews and observations

• Focus groups and in depth interviews

• Household survey in one LA comparing affected and unaffected streets (N=1000)

Sample of 8 contrasting Local Authorities

Expressed public views

I pay my tax, I now have to walk home in the pitch black […] I expect a basic service

I am over the moon […] to look up at the night sky and be able to see the stars on a clear night

Am I alone in thinking it’s a fantastic idea? … the money saved … can be ploughed back into schools and lollipop ladies…. Sleeping in the pitch black will have amazing health benefits for the people of this town. Not to mention the impact on the environment

Interaction and deliberation

• You wouldn’t want them to just totally switch things off...

• I don’t know, because it’s, on estates, because I know what Lucy was saying, since she got burgled, because it’s so well lit where she is. She says, the, the policeman said if, if it hadn’t had been as well lit and they hadn’t been able to see your house, your garage ...

• [...]

• I suppose you’re never going to please any, everybody are you?

• No.

• [laughter]

• You’re never going to please everybody.

Private views: wonder, and

fear

I know this sounds weird, but I got quite obsessed by it when they first started turning them off at midnight, I started to stay up to watch them going out – just to see how dark it was. It was so strange watching the lights going out – an odd thing to happen.

(Herts resident, informal conversation)

I don’t like to walk in the dark. I could carry a torch – but that shows you up, If there’s a street light, people can say ‘I saw a lady walk past us...’ If I’m carrying a torch, all they can see is the torch. (Herts resident, informal conversation)

Private views: ‘Going

backwards’

you know, you take it for granted but the whole streetlight thing seemed to me to be a big step forwards in the quality of life 150 years ago now probably or whatever. But the thought of actually going backwards seems to me to be quite appalling, you know, so I’d, I’d feel quite strongly about it in that sense. (Bucks resident, interview)

Lighting ‘in the right place’:

the modern city

I used to live out in [small village], it’s very rural there, it’s completely different. When I was there I didn’t go out as much at night, I didn’t feel as secure. Here I go out to the theatre, cinema, I feel safe. (Wakefield, interview)

The city people want street lights - there is a new build estate, and its second generation people coming from London, with different expectations – they want them. It is a divide – people do talk about it as something people come to the country and then expect it to be like the city (Herts, informal interview)

The silent majority: lights,

what lights?

• I don’t notice to be honest (informal interview, Shrewsbury)

• I don’t notice really as I don’t go out after dark (informal interview, Swansea)

• To be honest, I haven’t got an opinion, you just get used to it. (Informal interview, Swansea)

• I’m not going to lie, when you first, um, suggested it to me I was like, eh, street lights, that’s a random topic. (Hackney, in depth interview)

Survey of residents

Part Night Unaffected OR p

3Thinking about the spring and autumn periods, how often do you usually walk alone in your neighbourhood after dark?

At least once a week 73 79 0.804 0.266

Other 177 154

4Thinking about the spring and autumn periods, how safe do you feel walking alone in your neighbourhood after dark?

Very safe 39 61 0.521 0.004

Other 211 172

5Thinking about the spring and autumn periods, how safe do you feel driving home after dark?

Very safe 115 117 0.845 0.354

Other 135 116

6How worried are you about having your car stolen or broken into after dark in your neighbourhood?

Very worried 7 7 0.93 0.894

Other 243 226

Next Steps

• Data collection is finished!

• Finalize quantitative models

• Use results from quantitative and qualitative aims to inform cost-benefit analysis

• Final Results due January 2015

PLS 2014: Early Results of the LANTERNS Project

Data & Analytics

Transcript of PLS 2014: Early Results of the LANTERNS Project