Bannister, Jon

Brunt-So, Ben

Ellison, Mark

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Ellison, Mark and Bannister, Jon and Brunt-So, Ben (2019) Insights from datascience: Understanding demand and vulnerability. In: Excellence in Policing,16 September 2019 - 17 September 2019, College of Policing, Ryton-on-Dunsmore , UK.Downloaded from: http://e-space.mmu.ac.uk/624291/Version: PresentationPlease cite the published versionhttps://e-space.mmu.ac.ukExcellence in PolicingInsights from data science: Understanding demand and vulnerabilityGreater Manchester PoliceCrime & Well-being Big Data Centre, Manchester Metropolitan University Structure of the session• Overview of aims, approach and individual projects.• Data collection and integration.• Case studies of projects.• Mental Ill Health• Domestic Abuse• Missing Persons• Knife Crime• Please make this session interactive…• Do these findings match your experience? • Has anyone undertaken similar work / research?Aims• To ensure that the ‘Operational Analytics Partnership’ serves to complement and enhance existing GMP demand analysis.• To deliver research outside the capability and capacity of GMP in-house analytical services – ‘Big Data’ analytics.• To improve the understanding of vulnerability (demand and response). • To found the partnership on a co-production approach – ‘better research and impactful strategic and operational products’.Approach and Projects• Co-production approach: an appreciation of GMP administrative data and existing analyses; GMP (policing and civilian) staff interviews and subject matter expert workshops providing ‘expertise by experience’;  an iterative approach – from research design to the interpretation of results.• Project 1: Data collection, rebuild and integration, enabling analysis.• Project 6: Understanding vulnerability – (demand and response):  Mental Ill-health; Domestic Abuse; and, Missing Persons.• The extent and nature of Knife CrimeData collection and integration (1)• Data collection, rebuild and integration, enabling analysis.• Infrastructure – ISAs, BDC secure data facility (physical / virtual), vetting of researchers / support staff.• Data Collection.• Police - Crimes, Incidents, Incident text narrative, public protection incidents / DASH, Missing from Homes, Duty Status, Airwave / GPS.• Socio-economic data / Census, ACORN.• Spatial datasets - OS POI / topography. • Partner datasets - TfGM inc. mobile phone data.• Published datasets.• Data Dictionary.Data collection and integration (2)• Data Rebuild and Integration.• Relational database(s). • Spatial /  Temporal / Categorical referencing.• Integration across datasets.• Integration with other datasets.• Development of research dataset(s).• Research Datasets Enabling Analysis.• Overarching dataset. • Theme specific datasets.• Scale of data requires data science techniques. Mental Ill-health (1)• Wider Policy literature and GMP officers / staff perceive that between 20% and 40% of police time is spent dealing with Mental Ill-health incidents.• But, only 2% of incidents in GMP are flagged as Mental Ill-health.• Developed a novel automated text mining computational approach to quantify and qualify mental ill-health demand utilising a six year dataset (Jan 2012 to Sept 2017 – 5.4m records) of incident logs and deployment (Airwave) data (Oct 16 to Sept 17).• Incidents coded by researchers and police officer.Mental Ill-health (2)• 9-10% of all incidents entail Mental Ill-health.• Utilising the text mining dataset identified different spatial and temporal patterns, enhancing our understanding of vulnerability.• Text mining of keywords identified the qualities of Mental Ill-health incidents (i.e. the types, (limited) characteristics), other agency involvement and conclusions.• 20% of deployed resource is associated with Mental Ill-health.Mental Ill-health (3)• An underestimate? Not a statutory responsibility, lack of training and back office costs.• Systemic Failure - Approximately 1 in 5 logs identified as mental ill-health related include a closing code in the text.• Operational implications: Changing recording practices (automated alert system), capability and partnership working.Domestic Abuse (1)• Domestic abuse as a proportion of all calls for service (in Greater Manchester) has increased between 2011 and 2017 from 6% to 8%.• The police deploy the ‘Domestic Abuse, Stalking, Harassment and Honour Based Violence (DASH)’ assessment tool (27 questions) to identify risk and prioritise resource allocation. But, there is no guidance as to how to interpret responses to DASH questions nor evidence of its efficacy as a risk assessment tool.• Developed a machine learning and deep learning (i.e. artificial neural networks) classification model to evaluate DASH and to identify key questions.Domestic Abuse (2)• 380,000 DASH assessments completed over six years.• Data linkage – integrating PPI / DASH, Incident, Crimes data to develop a victim and episode dataset to understand: victim types; ‘dyad’ relationships; location / time and escalation of risk.• Informed by co-production approach including stakeholder workshops.• Responses to DASH are not interpreted in a consistent manner in the risk assessment process.• Reliance upon DASH varies by victim characteristics, location and time.Domestic Abuse (3)• Not all questions are of equal importance and key questions hold a greater influence on risk categorisation and outcomes. • Research raises concerns regarding the efficacy and efficiency of DASH.• DASH is lengthy and there are redundant questions leading to significant Waste / ‘Costs’ to GMP (approx. 190 police officers per year).• Operational implications: Develop refined DASH assessment for gender / family groups; shorten DASH to support faster prioritisation, linking responses to risk classification.Missing Persons• Increasing demand – of which (83%) is being driven those repeatedly missing from home (esp. under 18s). • Most people (around 80%) are found / turn up within 1 day and the majority (approx. 98%) are found safe.• Particular groups have an elevated risk (e.g., first time missing teenage girls / when patterns are out of the norm!).• Approx. 6% of deployed resource – doesn’t capture back office functions.• Operational implications: Need to improve data capture / rethink risk assessment – not fit for purpose! Key issue: Risk aversion vs. reducing demand on service.Next Steps: DASH and Missing Persons• Redesign assessment tools and their application – to improve their effectiveness in determining risk and to deliver efficiencies through creating savings in the time required to answer them.• To this end:• There is the need to revise the set of DASH questions in a more intelligent way; • Some DASH questions need to be pre-populated with answers;• Need to link the patterns of questions with outcomes;• Remove duplication (paper and computer); and• Make better use of technologies (iOPS / mobile technology) to enable this.Knife Crime : Research Questions and Approach• RQ1: What is the current extent of, and longer-term extent in, knife crime?• RQ2: What is the nature of knife crime and is this changing? • Developed a novel automated context-based text mining computational approach to quantify and qualify knife crime / officer verification.• Approximately 1.6m crime records, spanning January 2013 to mid-December 2018.• MO text (plus: Instruments, Means and Weapons fields) as opposed to assessment of weapon field (traditional ADR counting mechanism).The Extent of Knife Crime in GM RQ1: Conclusions and Implications• Why did knife crime reporting diverge from the national trend in 2016? (implication of mobile devices, etc. – important lessons to be learnt).• When corrected the extent of Knife Crime in GM reflects the national trend.• The research confirms the validity of the post December 2017 changes to GMP knife crime recording practices.• Automated text mining algorithms can be used to back-fill GMP data on knife crime• A context-based text mining algorithm can be deployed – going forward – to meet GMP knife crime reporting requirements.• The efficacy of the algorithm can be improved via improved recording practices (MO text) and (potentially) via reference to incident text logs and other datasets.RQ2: The Nature of Knife Crime• Offence types:• ADR only covers specific groups (Some Violence, Robbery & Sexual Offences). • To understand the extent of knife crime it is sensible to consider all offences including: Homicides and Possession offences.• This allows for a more nuanced investigation incorporating: Use, Threat and Possession (plus Robbery / Sexual Offences where it is more difficult to determine use / threat).Types of Knife Crime Offences12mths to Nov. 2015 (n=3,278) 12mths to Nov. 2018 (n=6,196)• Knife crime offences almost doubled between 2015 and 2018. However, the proportion of violence offences have fallen (40% to 30%), with a corresponding increase in the proportion of possession offences (33% to 41%).Known Offenders• Offenders are predominately male (88%), with the 15-19 age group accounting for 22% of offenders. • The proportion of the 15-19 age group has increased from 20% to 25% over the last three years.• Across GM a disproportionate proportion of young offenders (15-19) are BAME*.• Over two thirds (67%) of offenders live in the 20% most deprived communities.• OCG: 98% Male, 35% 15-19; 62% 10-24.• 86% of offenders have offended on only one occasion.Population Pyramid of all Known Offenders (all years)Note*: Ethnicity Utilising Officer Classification on Custody Record.RQ2: Conclusions• More of the same by type, however: o Disproportionate increases in robbery and possession offences.o Lower increases in violence offences.o Domestic abuse (at least a third of ‘violence / possession / sexual related offences’).o This is not an increase in organised crime – there is not a disproportionate increase in OCG flags – these have remained approx. 10% over time.o Homicide very similar over time.• More of the same by location, however:o A concentration of crime in Manchester local authority (with city centre hotspots), which is also increasing faster than other LAs.o More crimes are taking place in open public spaces (street etc.).o Three-fifths of KC occurs in the most deprived areas (1/5th) – this ratio has also stayed constant over time.o Increases in offences at Prisons and Schools.RQ2: Conclusions• The severity of offences (2015 -) is not necessarily increasing.• The count of ‘Sanction outcomes’ is similar over time. However the  proportion of sanction outcomes is falling. This is a positive story  (police budgets)?• Age – Increases in younger age groups (esp. 15 – 19), different distributions for Possessions, Robbery and Violence.• Ethnicity – higher proportions of BAME (especially for Robbery).Challenges and benefits of approach• Key Challenges • New ways of working (agile). • Capacity / joined-up nature of organisations.• Data and Data Quality.• Language / translation.• Knowledge mobilisation.• Benefits (and Emergent Priorities)• Co-production approach has led to better research and impactful strategic and operational products.• Better recording and measurement.• Better evidence of the problem.• Better policing (policy and practice).• Making better use of data – to address priorities and partnership.Questions?Mark EllisonDeputy Director: Data & External RelationsManchester Metropolitan University Crime & Well-Being Big Data Centrem.ellison@mmu.ac.uk | 0161 247 4640Sgt. Ben Brunt-SoBusiness Change Manager – Data ScienceGMP Operational Analytics ProjectBen.Brunt-So@gmp.police.uk | 07469 357436Professor Jon Bannister | Jon.Bannister@mmu.ac.uk | 0161 247 3083Key Insights• Location • Offences occurring outside in public places increased by 126% in three years.  (Robbery of personal property 173% increase; Possession of weapons, 134% increase; Violence, 76% increase• Increases in Schools (108%) and Prisons.• Knife crime occurs disproportionately in the most deprived areas (41% in top decile, 60% in top quintile).• Hotspots concentrating in particular areas.• Seasonal and temporal distribution varies by type.• Overall, 10% of knife crimes are flagged as OCG. – these proportions have remained consistent over time.• The count of ‘Sanction outcomes’ have remained stable over time. However the  proportion of sanctioned outcomes is falling. This is a positive story  (police budgets)?Known Offenders (2)• Known Offenders between Dec 16 and Nov 18 (last two years)Use (Violence) Possession (exc. Prisons)79% Male, 17% 15-19, 37% 10-24 91% Male, 26% 15-19, 51% 10-24*Ethnicity Utilising Officer Classification on Custody Record Robbery95% Male, 44% 15-19, 65% 10-24Victims• Victims are predominately male (74%), with the 15-19 age group accounting for 22% of victims.• The proportion of 15-19 victims has increased from 20% to 25% over the last three years.• 60% of victims live in the 20% most deprived communities.Population Pyramid of all Victims

Insights from data science: Understanding demand and vulnerability

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Insights from data science: Understanding demand and vulnerability

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