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

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Esri UK
Esri UK

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

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Local Policing Dashboard: Mapping Vulnerable Communities Rondalyn Northam GIS Manager
Local Policing Dashboard: Mapping Vulnerable Communities “Keeping communities safe from harm” is a key aim of the organisation
Local Policing Dashboard Local Policing Dashboard Must 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 estates, 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 = 25 If LM 5% > M2 AND M2 within or <5% M3, Score = 20 If LM within 5% of M2 and within or <5% of M3 Score = 10 If LM within or 5%< M2 and M2 5%> M3 15 If LM 5%<M2 and M2 within or < 5% M3 Score = 5 If 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 population PY = Same period M2D for previous year If M2D 5%> PY Score = 20 If M2D with 5% PY Score = 10 If 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 onDate 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 FamiliesFirst 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 Join Attribute Join Join Attribute Join Join Spatial Join Attribute Join Selection Based onLocation Iterate 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 05:45 Script 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 06:40 Table Write 05:45 10:15 06:44 VLI Calc Monthly Monthly VLI Calculation Daily VLI Calculation 05:45 10:15 06:47 VLI 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 10:15 Daily ASB: Postcode 1 Year Rolling Month to Date Compare Temp ASB / Postcode Data 07:30 Start: Postcode  Extract Relevant Records: Make Feature Layer  Tabulate Intersection & GetCount 05:45 10:15 Daily: Repeats 08:50 Repeats 05:45 10:15 Daily VLI Calculation Postcode VLI Table Monthly ASB: Community 3 Month Trend Temp ASB / Community Data 05:45 10:15 06:05 Script Start (Monthly) 05:45 10:15 Monthly ASB: Postcode 3 Month Trend Temp ASB / Postcode Data 09:00 Start: Postcode Monthly 05:45 10:15 Monthly VLI Calculation 10:05 Calculate 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

More Related Content

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

  • 1. Local Policing Dashboard: Mapping Vulnerable Communities Rondalyn Northam GIS Manager
  • 2. Local Policing Dashboard: Mapping Vulnerable Communities “Keeping communities safe from harm” is a key aim of the organisation
  • 3. Local Policing Dashboard Local Policing Dashboard Must be relevant and current Vulnerable Localities Index Recent crimes/incidents Demographics Combine / cross query disparate data Capture once – use many times
  • 4. 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
  • 5. VLI methodology….. Devise & test scoring system Establish workflows Automate Marketing Packaging
  • 6. 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
  • 7. 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 estates, 286, 4%
  • 10. 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 = 25 If LM 5% > M2 AND M2 within or <5% M3, Score = 20 If LM within 5% of M2 and within or <5% of M3 Score = 10 If LM within or 5%< M2 and M2 5%> M3 15 If LM 5%<M2 and M2 within or < 5% M3 Score = 5 If 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 population PY = Same period M2D for previous year If M2D 5%> PY Score = 20 If M2D with 5% PY Score = 10 If M2D 5%< PY Score = 0 Daily
  • 11. Process flow… • GIS Database Crime Data Incidents Data Demographic Data Criminal Damage Assault & Battery Violence / Wounding ASB Harm Hate Selection Based onDate 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 FamiliesFirst 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 Join Attribute Join Join Attribute Join Join Spatial Join Attribute Join Selection Based onLocation Iterate through Communities or Postcodes Calculate Score: Truancy
  • 12. Process flow… Daily ASB: Community 1 Year Rolling Month to Date Compare Temp ASB / Community Data 05:45 10:15 05:45 Script 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 06:40 Table Write 05:45 10:15 06:44 VLI Calc Monthly Monthly VLI Calculation Daily VLI Calculation 05:45 10:15 06:47 VLI 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 10:15 Daily ASB: Postcode 1 Year Rolling Month to Date Compare Temp ASB / Postcode Data 07:30 Start: Postcode  Extract Relevant Records: Make Feature Layer  Tabulate Intersection & GetCount 05:45 10:15 Daily: Repeats 08:50 Repeats 05:45 10:15 Daily VLI Calculation Postcode VLI Table Monthly ASB: Community 3 Month Trend Temp ASB / Community Data 05:45 10:15 06:05 Script Start (Monthly) 05:45 10:15 Monthly ASB: Postcode 3 Month Trend Temp ASB / Postcode Data 09:00 Start: Postcode Monthly 05:45 10:15 Monthly VLI Calculation 10:05 Calculate Postcode Monthly
  • 16. Next Steps… • Dashboard “Light” • Dashboard “Advanced” • Dashboard “Partner Agency”
  • 17. Next Steps… • Upgrade to 10.4 Server & Desktop • Migrate to Portal

Editor's Notes

  1. As a police force, keeping our communities safe from harm is one of our main objectives. The Dashboard aims to make best use of data in order to support Local Policing
  2. Understanding vulnerability within our communities is key, and GIS is an effective way to get to grips with the layers of complexity Provide a platform to support local policing: Relevance Making sure that only relevant data are included – don’t muddy the waters… Currency - It’s key to ensure the currency of the data if the Dashboard is going to be effective in supporting decision making, and to ensure currency, automating data processing is essential to ensure currency but consistency. Identifying vulnerable communities & social incohesion: Calculate a Vulnerable Localities Index (VLI) Recent crimes / incidents (specific types) Demographics & behavioural Make best use of available data: Capture once, use many times… Combine / cross query disparate data
  3. The methodology set out by the Jill Dando Institute provided a sound basis for the work, but it’s important to question the methodology in order to; improve and update, make relevant for the organisation A review of the recommendations was made together with gap analysis, with a resolution & design
  4. Design, set up steering group, revisit/scrutinise design/ideas, demo – iterative process Automate: Python and scheduled tasks
  5. JDI uses IMD to LSOA. Need to assess which segmentation groups best fit in with sentiments of the JDI study. Step 1. Look for correlations geographically 2. Identify segmentation types for use in the VLI, 3. Apply the types across the county, - greater granularity, increased currency
  6. Talk through interface
  7. Talk through interface
  8. Additional scoring to compare last 3 months and same period for last year, Same process applied to other crime and incident data Scoring applied to the other data sets; truancy, families first, etc
  9. Data flow
  10. Talk through interface
  11. Talk through interface
  12. Talk through interface
  13. Major infrastructure upgrade