Local Policing Dashboard: Mapping Vulnerable Communities

Rondalyn NorthamGIS Manager

Local Policing Dashboard: Mapping Vulnerable Communities

“Keeping communities safe from harm” is a key aim of the

organisation

Local Policing DashboardLocal Policing DashboardMust be relevant and current

Vulnerable Localities Index Recent crimes/incidents Demographics

Combine / cross query disparate data

Capture once – use many times

Starting Point…..• Requirement for web map

to support Local Policing• Jill Dando Institute VLI

methodology provided a starting point Review

Gap Analysis

Solution / Design

• Assess data• Assess methodology

• Iterative Process• Test > Review• Finalise

VLI methodology…..

Devise & test scoring system

Establish workflows

Automate

Marketing

Packaging

Data selection

Income Deprivation

Burglary in a dwelling

Criminal Damage

Anti-social behaviour

Common Assault

Violence/ Wounding

Hate tagged incidentsEmployment Deprivation

15-24 year olds

Educational attainment

Truancy rates Mental Health

Child ProtectionAcorn Segmentation

Families first

Customer confidence

Gloucestershire Constabulary VLIJill Dando Institute VLI

Criminal Damage

data…..• Demographics

Analysis: IMD vs commercial segmentation data

Deprived areas and high-rise flats; 1757; 22%

Young families in low cost private flats; 995; 12%

Struggling younger people in mixed tenure; 658; 8%Social rented flats, families and single

parents; 656; 8%First time buyers in small, modern homes; 555; 7%

Educated young people in flats and tenements; 506; 6%

Elderly people in social rented flats; 414; 5%

Singles and young families, some receiving benefits; 384;

5%

Low income large families in social rented semis; 288; 4%

Families in right-to-buy es-tates; 286; 4%

The interface…..

The interface…..

Scoring system…• Example of scoring: ASB

1 Year (rolling) ((ASB Rate per 1000 population)/(Community >0 Average))*100 Daily

3 Month Comparison

(LM = Last Month ASB Rate per 1000 population)(M2 = 2 Months Ago ASB Rate per 1000 population)(M3 = 3 Months Ago ASB Rate per 1000 population)If LM 5%> M2 AND M2 5% > M3, Score = 25If LM 5% > M2 AND M2 within or <5% M3, Score = 20If LM within 5% of M2 and within or <5% of M3 Score = 10If LM within or 5%< M2 and M2 5%> M3 15If LM 5%<M2 and M2 within or < 5% M3 Score = 5If LM 5%< M2 and M2 5%< M3 Score = 0

Monthly

Month to Date vs Same Period Previous Year

M2D = Month to Date ASB Rate per 1000 populationPY = Same period M2D for previous yearIf M2D 5%> PY Score = 20If M2D with 5% PY Score = 10If M2D 5%< PY Score = 0

Daily

Process flow…• GIS Database

Crime Data

Incidents Data

Demographic Data

Criminal Damage

Assault & Battery

Violence / Wounding

ASB

Harm

Hate

Selection Based on Date

Last Month 2 Months Ago

3 Months Ago

1 Year (rolling)

Month to Date

Month to Date for last Year

Count 1 Year

Count Month to

Date

Count Month to Date Last

Year

Count Last Month

Count Last Month

Count Last Month

Calculate Score 3

Month Trend

Calculate Score Trend Month to

Date

Calculate Score 1 Year

Postcode VLI Table

Community VLI Table

Postcode Boundaries

Community Boundaries

Families First

Truancy

Child Protection

Mental Health

AddressBase Premium Dwellings

Calculate Score: Demographics

Education

Calculate Score: Families First

Count of Child

Protection

Count of Mental Health

Calculate Score Child Protection

Calculate Score Mental Health

JoinAttribute Join

JoinAttribute Join

Join

Spatial Join

Attribute Join

Selection Based on LocationIterate through Communities or

Postcodes

Calculate Score: Truancy

Process flow…

Daily ASB: Community

1 Year Rolling

Month to Date

Compare

Temp ASB /Community

Data

05:45 10:15

05:45Script Start

Extract Relevant Records: Make Feature Layer Iteration: arcpy.da.SearchCursor

Select per Community & Count GetCount per Community & write to temp data

Community VLI Table

05:45 10:15

06:40Table Write

05:45 10:15

06:44VLI Calc Monthly

Monthly VLI Calculation

Daily VLI Calculation

05:45 10:15

06:47VLI Calc Daily

VLI Calc: Import numpy Sum ASB: arcpy.da.TableToNumPyArray Count Communities ASB >0: Select by Attribute, GetCount

Join: Temp table to VLI Calculate 1 Year:

((float(!VLI_ASB_Y1.Temp.COUNT!))*(1000/!Community.Population!)) / (" + str(sumASB) + "/" + str(YRASB_Count) + ")*100" Calculate Month to Date Comparison:

CalculateField: (Table, "Community_VLI.M2D_ASB", "qtrASB(float(!VLI_ASB_PY.TEMP.COUNT!*(1000/ !Community.Population! )), float(!VLI_ASB_M2D.TEMP.COUNT!*(1000 / !Community.Population! )))", "PYTHON_9.3", """def qtrASB(PY, M2D):\\n\\n if (M2D > ((PY /100) * 105)):\\n return 20\\n elif (M2D < ((PY /100) * 105) and M2D > ((PY /100) * 95)):\\n return 10\\n else:\\n return 0\\n""") Calculate VLI Total: Sum all categories

05:45 10:15Daily ASB: Postcode

1 Year Rolling

Month to Date

Compare

Temp ASB /Postcode

Data

07:30Start: Postcode

Extract Relevant Records: Make Feature Layer Tabulate Intersection & GetCount

05:45 10:15Daily:

Repeats

08:50Repeats

05:45 10:15

Daily VLI Calculation

Postcode VLI Table

Monthly ASB:

Community

3 Month Trend

Temp ASB /Community

Data

05:45 10:15

06:05Script Start (Monthly)

05:45 10:15

Monthly ASB:

Postcode

3 Month TrendTemp ASB /Postcode

Data

09:00Start: Postcode Monthly

05:45 10:15

Monthly VLI Calculation

10:05Calculate Postcode Monthly

VLI…..

VLI…..

VLI…..

Next Steps…

• Dashboard “Light”• Dashboard “Advanced”• Dashboard “Partner Agency”

Next Steps…

• Upgrade to 10.4 Server & Desktop• Migrate to Portal