SENSOR: First Year Findings and Development
TRUDY LOWEResearch Fellow
Universities’ Police Science Institute
Email: [email protected]
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 20071. Groups of
youths1. 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. Speeding3. 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.
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