SENSOR: First Year Findings and Development

TRUDY LOWE

Research Fellow

Universities’ Police Science Institute

Email: lowet@cardiff.ac.uk

With the financial support of the Prevention of and Fight against Crime ProgrammeEuropean Commission Directorate – General Home Affairs

This project has been funded with the support of the European Commission. this communication reflects the views only of the author and the European Commission cannot be held responsible for any use which may be made of the information contained therein

Intelligence-orientated Neighbourhood Security Interviews (i-NSI)

• Public engagement and community intelligence gathering

methodology

• Based upon the Signal Crimes Perspective (Innes, 2004 )

• Developed academically, used operationally by NPT officers

• Conducted annually in London Borough of Sutton since 2007

+/- 610 respondents across 18 wards each year

• Systematic: combined demographic and geographic

sampling frame ‘widens of the radar’ to include those

‘harder to hear’ or less naturally inclined to give their views

• ‘Proactive’: requires police staff to actively seek public

views rather than just inviting them

• Integrated: designed to be used by Neighbourhood Police

Teams as an integral part of their role

Intelligence-orientated Neighbourhood Security Interviews (i-NSI)

Development under the TaRDiS Project:SENSOR

• i-NSI methodology currently utilises PC – based data capture and analysis software packages

• The TaRDiS project incorporates funding for the development of a mobile application to simplify and improve the data capture process and subsequent data quality: SENSOR

• Year 1 saw the development of SENSOR V1.0 for pilot during the 2013 i-NSI data collection sweep

Development under the TaRDiS Project:SENSOR

• Fourteen (14) wards in the London Borough of Sutton collected data as normal using the laptop based i-NSI Capture programme

• The remaining 4 wards used the SENSOR Application on i-Pads for comparison

SENSOR vs. i-NSI Capture

i-NSI Capture SENSOR

User Interface Requires IT skill and considerable training

More intuitive

Data Entry Linearity Forced down a data entry path

More intuitive, flexible data entry

Mapping Manually loaded GPS - based

Data download Manual at the end of sweep from each laptop

Real time at the end of each interview to central server

Application Updates / bug fixes

Manually on each machine

Over-air

Portability Heavy laptop Light i-Pad/Tablet

SENSOR: User Survey

is easier to navigate

is easier to carry around

is more intu-itive

is quicker to use

is easier to learn

crashed less often

0

1

2

3

4

5

6

Compared with the laptop application, the iPad Application ....

Strongly Agree Agree

Neither Agree nor Disagree Disagree

Strongly Disagree

SENSOR: User Survey

understand the i-NSI interview structure better

can concen-trate better on listening to the

respondent

understand bet-ter what to do next in an in-

terview

am more confi-dent my data is

safe

enjoy doing i-NSI interviews

more

0

1

2

3

4

5

6

Compared with using the laptop application, with the iPad Application I ....

Strongly Agree Agree

Neither Agree nor Disagree Disagree

Strongly Disagree

SENSOR trial: Operational Lessons from the Pilot

• Data Capture Linearity: flexibility allowed some elements of data capture to be inadvertently missed by users

• Quality of 3G/4G signal: mapping had to be manually loaded

• Data download to one location: difficult to separate individual data records into ward areas

• Data Analysis: manual analysis time consuming

SENSOR V2.0

• Re-introduces Linearity: to provide structure for operational ease and to minimise training

• Additional fields: to better separate out records on central server

• Data Analysis: automatic analysis algorithms being developed

• Ready for use across the Borough in May 2014

London Borough of Sutton Findings 2013

Top Signals

• Broad consistency year on year• Domestic Burglary entered the top 5 signals for the first time

2013 2011 2010 2009 2008 2007

1. Groups of youths

1. Groups of youths

1. Groups of youths

1. Groups of youths

1. Groups of youths

1. Groups of youths

2. Speeding 2. Speeding 2. Speeding 2. Speeding 2. Speeding 2. Speeding

3. Dog mess 3. Inconsiderate parking

3. Inconsiderate parking

3. Inconsiderate parking

3. Inconsiderate parking

3. Graffiti

4. Burglary (domestic)

4. Dog Mess 4. Litter 4. Undesirable groups

4. Graffiti 4. Litter

5. Inconsiderate parking

5. Litter 5. Dog mess 5. Litter 5. Litter 5. Public drinking

Effects Profile

• Impact of signals is important to understand dissatisfaction and

disaffection

Effect Signal Coh.

ANGER Litter 157

Groups of Youths 118

Dog Mess 115

Inconsiderate Parking 101

Effects Profile

Conclusions

• SENSOR application generally liked by users and lessons from the pilot have been useful in informing the second phase of development

• LBS signal profile in 2013 shows overall signal counts are decreasing but key problems functioning as drivers of insecurity have remained broadly the same.

• However there is widespread concern about domestic burglary, putting this signal type in the top five signals across the Borough for the first time.