## Local Datavores Workshop Lga - July 13

Sarah Henry - GM Connect and Head of Intelligence and Performance - Manchester City Council s.henry@manchester.gov.uk

## A Families Database - From An Individual To A Holistic View

People, and especially children, are affected - positively, and sometimes negatively - by their family circumstances and the people around them But what we collectively know - the data - sits in systems about individuals so we need:

- To link individuals to 'families' and 'households'
- Intelligent linkages between individuals e.g. family 
relationship, mention on benefit claims
- Linkages to organisations such as schools - Linkages to and tracking of interventions, services 
and outcomes, recognising the engagement with the family is not static

## Our System -

•
Replaces and complements manual processes 
to find out what others know about the family –
MASH, Panels and conferences, desk-top work etc
•
Visualises the family and the issues
•
Creates data that can be analysed over time
•
Brings together 18 datasets including offending, ASB, absence from school, low attendance at school, various benefits (including out of work), debt, social care, 
children missing education, NEET, poverty 
indicators, substance misuse, mental ill health etc.

## Ibase Data Schema April'15 1. Find The Case / Family 2. Direct Links To Case 3. Immediate Family 4. Extended Family 5. Exploring The Issues 6. Looking For Links 7. Expanding The Links 8. Finding The Connection Challenges

•
Information Governance
•
Technical - selection of a system
•
Information Governance
•
Developing and agreeing rules and processes 
- data architecture
•
Information Governance

## Data Protection And Security

•
Underpinned by data sharing agreements and 
a Privacy Impact Assessment
•
Consent, where needed and appropriate
•
Removal of the decision to share or not to share away from individual workers –
consistency and corporate accountability 
•
Who should access? - Restrictions, training and access control
•
Policies and procedures - including security, 
retention, destruction 
•
Footprints

## Legal Gateways

•
Human Rights Act - right to life is absolute
•
Right to privacy - conditional
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Duty to safeguard vulnerable people
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Local Government Act - general power of competence
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Duty to prevent crime (and fraud)
•
Regulations designed specifically for programmes like Troubled Families
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And more…

## Critical Success Factors

•
Reasonable budget (but it didn't break the 
bank!)
•
Senior strategic sponsor
•
Clarity of purpose - but agile enough to build in new things
•
It wasn't an IT project!

## Research And Insight

- Understanding the impact on the system 
(so it can be planned)
- Understanding the causal factors behind 
the outcomes (so we provide the right 
service)
- Predictive modelling
- Ultimately leading to decision making 
tools

## Data Mining & Testing

There are numerous data mining techniques that could be used in an analysis of this large dataset The analysis has been based on 326,486 records, representing 21,584 individuals over a 5-year period Five techniques have been used in this project:
Data included:

- Decision Tree
- Cluster Analysis
- Regression Analysis - Sequence Analysis - Spatial Analysis
- Child in Need episodes
- Domestic Abuse incidents
- School Absence issues - School Exclusions - Anti-Social Behaviour 
incidents & legal actions
- Offences

## Decision Trees Reviewing Recent History And Using The Common Decisions To Predict A Likely Future Decision

The most important attribute to predicting TF was the number of MiCARE CIN events associated with a family. Troubled Families in general have far more MiCARE events associated with them - an average of 5.68, compared to 0.31 for the non-TF School absence, number of ASBs and PRU also were deemed consistently important. Number of adult offences was consistently the least important.  

## Cluster Analysis Reviewing The Current Distribution To Identify Common Groups / Characteristics Cluster Analysis Was Performed On 2,028 Families, Where There Had Been A Referral Made To The Frs And Where There Was At Least 2-Years Worth Of Event Data Four Clusters Were Identified. No Completely Distinct Groups But There Were Still Patterns In The Data

Cluster 1 - 689 families - No MiCARE CIN events 
but a quarter of all DA events in this cluster. Just 
under a third of families had absence, a quarter had 
exclusions, and 22% had offences but none 
involving minors.
Cluster 2 - 724 families  - All had MiCARE CIN 
events. No school absence, but 13% had 
exclusions. 18% had offences, none involving 
minors.
Cluster 3 - 308 families - All had MiCARE CIN 
events.  All had absence, 37% have exclusions.  
17% had offences but none involving minors.
Cluster 4 - 307 families - Half had MiCARE CIN 
events. Half had absence, 62% have exclusions.  
All families had offences, 73% of these offences 
involving minors.

## Regression Analysis

Variables in the Equation
 
B
S.E.
Wald
df
Sig.
Exp(B)
direction
Step 1a
Previous CIN referral
1.418
.071
401.379
1
.000
4.127
+
Age
-.054
.009
33.884
1
.000
.948
-
Absence periods (0,1,2,3)
.227
.076
8.959
1
.003
1.255
+
Previous absence
-.096
.033
8.245
1
.004
.909
-
Exclusions periods (0,1,2,3)
.084
.093
.814
1
.367
1.087
Previous exclusions
.012
.035
.110
1
.740
1.012
Person Offending (0,1,2,3)
-.133
.185
.516
1
.473
.876
Previous Person Offending
.072
.071
1.038
1
.308
1.075
Family Offending (0,1,2,3)
.292
.064
20.586
1
.000
1.339
+
Previous Family Offending
-.163
.030
30.670
1
.000
.849
-
Constant
-3.454
.115
903.863
1
.000
.032

a. V
ariable(s) entered on step 1: Pre_CIN_R, Age, x_periods, total_abs, Exc, Exc_Pre, P_Off, P_Off_Pre, F_Off, F_Off_Per. 

## Provides An Insight Into A Child'S Short-Term Life History

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Having a **previous Child in Need (CIN) referral** is a strong predictor of a new 
Child in Need referral
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An increase in the *frequency* **of Unauthorised Absence** (in previous 3 halfterms) is a predictor of CIN
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Exclusions and Personal offending are not significant
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An **increase in the frequency of Family offending** (in previous 3 half-terms) 
is a predictor of CIN
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The likelihood of a CIN referral is less when the young person gets older 

## Sequence Analysis Spatial Analysis

Location of Troubled Families These areas are similar with regards of Median Income, Types of problems the TFUs have, Deprivation indices But they differ in: Transience, Predominant tenure Density of Troubled Families TF scattered across all the city. Very few areas with no TFUs (Didsbury). Number of TFU never exceeds 5% of all households in any LSOA Substantial clusters: Harpurhey, Gorton / Belle Vue, Whythenshawe

## Predictive Modelling

The techniques used in this project culminate to aid with Predictive Modelling, this would include:

- Using data mining to identify your clusters / cohorts - Testing cluster / cohorts characteristics to identify the 
significant factors
- Apply the significant factors to the whole population to 
identify scale
- Use decision trees and sequence analysis type tools to 
test likely impact of decision for this broader cohort
- Use this to inform the business planning

Review the 

Inform 

| Identify a    |
|---------------|
| Existing      |

Business 

| cohort / issue   |
|------------------|
| Evidence         |

Planning