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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 Programme European Commission Directorate – General Home Affairs
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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 Programme European 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
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 Intelligence-orientated Neighbourhood Security Interviews (i-NSI)
Intelligence-orientated Neighbourhood Security Interviews (i-NSI) 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
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
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
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 trial: Operational Lessons from the Pilot
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 SENSOR V2.0
Broad consistency year on year Domestic Burglary entered the top 5 signals for the first time Top Signals
Impact of signals is important to understand dissatisfaction and disaffection Effects Profile
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. Conclusions