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Making Public Spaces Safer Dr B. Nasa, Prof J. Binner Dr M. A. Ferrario, Dr W. Simm, Prof J. Whittle, Dr B. Lam; Sheffield Management School Lancaster.

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Presentation on theme: "Making Public Spaces Safer Dr B. Nasa, Prof J. Binner Dr M. A. Ferrario, Dr W. Simm, Prof J. Whittle, Dr B. Lam; Sheffield Management School Lancaster."— Presentation transcript:

1 Making Public Spaces Safer Dr B. Nasa, Prof J. Binner Dr M. A. Ferrario, Dr W. Simm, Prof J. Whittle, Dr B. Lam; Sheffield Management School Lancaster University/ Brunel University Towards Policing Perception Maps automatically extracting quantitative analysis of qualitative survey data

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3 Background: Derry/NI HOW TO build trust in the police after 30+ years of Conflict (The Troubles)? SELF POLICING & FREE DERRY: 1960s on going clashes between Protestants and Catholics. By the end of 1971, barricades prevented access to Free Derry (Bog Side Area). No police forces allowed. BLOODY SUNDAY: 30 th January 1972, 27 civilians were shot by the British Army Parachute Regiment during a Civil Rights march. 13 died on the scene.

4 Derry District Policing Partnership Derry DPP is one of the 26 DPPs in NI Responsibilities: Monitoring local police performance Giving a voice to community views on policing Gaining the public's cooperation to prevent crime. DDPP Household survey 2009 Confidence and SatisfactionDDPP Household survey 2009 Confidence and Satisfaction

5 DDPP Household Satisfaction & Confidence Survey’09 All-household survey to inform next year local policing plan (2010/11) First-ever DPP blanket consultation by Neighbourhood 46,000 households: 465 responses received to the survey (1% response rate)

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7 Verbatim vs Tick-Box

8 The survey contained questions with multiple choice answers and the opportunity for the respondent to expand upon their selection in free text form. Traditionally, this free text is difficult and time consuming to analyse by theme and sentiment. Automatically extracting ‘quantitative summaries’ could help identify themes which are not part of the multiple choice options and would allow comparison across results from different districts. Making Sense of Qualitative Data

9 Factual vs Perception Mapping Map of actual crime statistics Perceptions maps

10 Challenge: How do people feel... 36% of respondents** answered ‘don’t know’ to the satisfaction question => How can the police ‘win’ the undecided? Understanding ‘Why’ people are undecided may help… ** diagram and figures from DDPP survey

11 Data Set: Original Source 1 2 3 4 5 6 DDPP ID LOCALE CONFIDENCE LEVEL CONFIDENCE VERBATIM SATISFACTION LEVEL SATISFACTION VERBATIM 1 2 3 4 5 6

12 Extracted – Full Data Set First Step: 449 Records in total Second Step: Sentence Level Split + Clean Data+ Remove Blank Verbatim = 379 Sentence level comments (Satisfaction) = 485 sentence level comment (Confidence)

13 Sample Data Set – 95% CL; CI 5% 200 comments (Satisfaction) 221 comments (Confidence) Manual analysis (three researchers) Theme, Actionability, Sentiment (TAS) Automated Theme Extraction – Accuracy Testing VYV Bayes Classifier

14 TOP THEMES (Satisfaction & Confidence) Approach: 1.Initial Identification of themes (Cross-checked with DDPP) 2.Independent Theme Review (by three researchers) 3.Up to 4 themes per comments 4.Calibration and Compilation of final theme List Emerging Results: SatisfactionFrequency%ConfidenceFrequency% General Impression 6117.6 Response Time and Quality 7018.4 Presence and Visibility4813.9Police Behaviour/Political Policing6416.8 Police Behaviour/Political Policing4613.3Presence and Visibility6116.0 Offence and Crime Level4011.6General Impression3810.0 Response Time and Quality329.2Resources incl. Admin318.1 Other298.4Offence and Crime Level236.0 Resources incl. Admin216.1People behaviour and Attitude215.5 People behaviour and Attitude174.9Other174.5 Community Policing144.0Community Policing153.9 Specific Events/Location144.0Specific Events/Location153.9 Complexity of Situation102.9Complexity of Situation133.4 Authority/Law enforcement102.9Perceived Safety71.8 Perceived Safety41.2Authority/Law enforcement61.6 Total – themes 346 100Total 381 100%

15 THEMES BY LOCATION Second Step: Match Themes to Neighbourhoods Neighbourhood analysis of Satisfaction with Policing in Derry Themes (aggregated)Foyle City Centre City NorthFoyle City West Waterside Rural Waterside Urban Total by Themes Freq.% General Impression8178 116117.6 Presence and Visibility21391684813.9 Police Behaviour/political policing4137 94613.3 Offences and Crime Level31481144011.6 Response Time and Quality1261112329.2 Other534107298.4 Resources Incl. Admin23547216.1 People Behaviour37223174.9 Community Policing-5333144.0 Specific Events/Location-4253144.0 Complexity of Situation32-41102.9 Authority/Law Enforcement-5113102.9 Perceived Safety---4-41.2 Neighbourhood Freq. 31885510171346100.0 % 9.025.415.929.220.5100.0

16 3-WAY ANALYSIS: themes v. sentiments v. location City CentreCity NorthCity WestWaterside RuralWaterside Urban Sentiment level Negative Neutral Positive Total Negative Neutral Positive Total Negative Neutral Positive Total Negative Neutral Positive Total Negative Neutral Positive Total Themes General Impression 31013262611190951544917141015 Presence and Visibility 0033200250511704 701017 Police Behaviour/political policing 33061500 51101610331681111 Offences and Crime Level 303620135409220464010 Response Time and Quality 0661383315110213100313417 Other 00002306402511131114 Resources Incl. Admin 333106208400420020314 People Behaviour 360102002000011240101 Community Policing 63010141116132015920113306 Specific Events/Location 3130160123052718110180 Complexity of Situation 00000000000020240000 Authority/Law Enforcement 00002115400440153104 Perceived Safety 00006006020210013104 Total by sentiment 2645291006118201005133161005321261003831 100

17 SATISFACTION v. SENTIMENTS

18 Is stated level of satisfaction reflected in verbatim Satisfaction Level Sentiment derived from verbatim NegativeNeutralPositiveTotal No 109140123 32.15%4.13%0%36.28% Don’t Know 4640692 13.57%11.8%1.77%27.14% Yes 143278124 4.13%9.44%23.01%36.58% Total 1698684339 49.85%25.37%24.78%100% Null: SL is independent of VS Chi square statistics : χ2 = 211.04 (0.000 ) Reject null, so SL is associated with VS

19 Example VERBATIM ANSWERS** Do you think the PSNI are doing a good job in the Foyle area? Don’t know – PSNI take too long to respond to a reported incident. Don’t Know - They certainly don’t appear to be able to curb the problems in my area of Culmore. Don’t Know -Because they let the young joy riders straight out again also they know all the drug dealers in the one area still they turn a blind eye.

20 Automated Theme Extraction VYV -Bayes Classifier

21 Accuracy of Automated VYV Theme Extraction Approach : 1.VYV automatically assigned (two) themes 2.Accuracy assessed on a 3-point Likert scale 3.Independent assessment by 2 reviewers, followed by a ‘calibration exercise’ to address differences 4.69-72% of the themes were acceptable or accurate for both sets 5.Reviewers agreed on both themes 93-94% of the time

22 Noun picked from POS = “Staff” sem tag I3.1 Secondary sem tag S2 staff “Very helpful and friendly staff”

23 VYV tags vs Manual (Satisfaction) VYV identified a total of 90 unique themes Manual tagging identified 17 themes Some Interesting Parallels and Challenges

24 Theme Assignment Alternative Classifier methods can be used to classify texts: 1.The classifier is trained using manual data 2.A number of exclusive classifications are defined 3.As subset of data is selected and manually classified 4.This subset is used to train the classifier on the features (in this case the words) that make up each classification 5.New comment are classified by the classifier based on features

25 Bayes Classifier applied to DDPP Data Naive Bayes Classifier was applied to “Confidence” data: Training subset used previous 83 manually tagged comments classifier accuracy increases with number of training data 20 further comments were extracted at random and classified by the Bayes classifier to test accuracy. Preliminary Results: In a manual review by 2 researchers (method described previously) the classifier returned an “Acceptable” or “Accurate” classification for 60% of the 20 comments.

26 Towards Perception Maps

27 Interactive Map Display Plot the information derived from automatic analysis of survey verbatim on an interactive map Allows interaction with, and evaluation of the data by stakeholders and possibly public Safer Streets

28 Implementation Design Storyboard by Information Architecture student Zoe Zhao (Brunel University) Development Progress using web technologies Google maps Web 2.0 technology including Ajax and JQuery

29 1.Frontpage, which contains: i.Map of the whole area ii.Overall impression – e.g. Themes, Number of People Reporting, etc StoryBoard A

30 StoryBoard B 2.Click on the map to select the area that you want more information 3.The selected area will be highlighted 4.The information of the specific area replaces the overall impression

31 StoryBoard C 5.Select the information that you want to explore further 6.Place the cursor on the theme for actual figures 7.You also can rate your satisfaction level in this category 8.The sentiment charts will be updated automatically

32 StoryBoard D 9.Click on the themes for actual comments 10.You will see both positive and negative comments under this theme 11.You can enter your comments or respond to other people’s comments 12.You will see your comment appears on the list straightaway

33 DDPP Survey Data 2009 Challenges: Database design Google map plots Ajax for realtime updates Jquery interface animation

34 Conclusion and Next Step Empirical Analysis Test specific hypothesis such as whether there is more crime/offences where police are sectarian Is response related with police behaviour Automatic TAS Train bayes classifier using larger dataset with fewer categories Interactive Map Put the plans into action

35 Partners & Collaborators www.voiceyourview.com


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