Gloucester Constabulary - Rondalyn Northam - Local Policing Dashboard - Mapping Vulnerable...
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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