Geo-linkage, examining near-repeat & hot spot analyses as indicators of serial offenders.
21
Transcript of Geo-linkage, examining near-repeat & hot spot analyses as indicators of serial offenders.
Two perspectives on the use patterns of serial offending
I. POP or intelligence led Policing • Hot Spots
– Surveillance – Problem-solving Policing
• Near repeat policing – Quick surveillance – Temporary prevention
strategies
2 J.J. van der Kemp©
Two perspectives on the use patterns of serial offending
II. Modus via approach of Geographical
offender profiling • Assigning weights • Geographical
scenario’s
• Example: – Crime distribution
Backcloth
3 J.J. van der Kemp©
Recognizing linkage? • Recognizing property crimes that are linked:
– Few forensic traces (i.e. prints, biological, tool marks) – Very little crime characteristics:
• > Much behavioral similarity • > Little differentiation between offenders • > Sensitive for situational factors
– Spatially and temporally ‘close’
• Many property (burglaries) crimes committed – Only partly by serial offenders (however few solved) – Hot spots due to opportunity structure
4 J.J. van der Kemp©
Geo-linkage Scanning
• In what way would spatial cluster analyses be useful to identify potential series of crimes by the same offender(s) for further investigation?
• What is close? – Distinguishing serial clusters in/from hot spots – Using Repeat and Near-repeat phenomenon
• Assumptions about serial offender • Near-repeat or ‘run’?
5 J.J. van der Kemp©
Burglary data • Tilburg – Mid size town • Police registrations of burglaries 2006-mid 2011
– 8221 burglaries > 6961 separate addresses
Table 1: Repeat victimization at exact address level
Victimization % burglaries on exact address level
Once burgled 85%
Twice burgled 11%
More than two times burgled 4%
Total number of different dwelings 6961 (100%)
J.J. van der Kemp© 6
Hot Spots
7 J.J. van der Kemp©
Hot Spots
8 J.J. van der Kemp©
Kernel Density
Hot Spots
9 J.J. van der Kemp©
Hierarchical Nearest Neighbour
Hot Spots
10 J.J. van der Kemp©
Kernel Density Hierarchical Nearest Neighbour
Near-repeat analysis methodology
• Spatial and temporal bandwidth • Monte Carlo Simulations
J.J. van der Kemp© 11
Near repeat results
Table 2: Near-repeat pattern 2006- may 2011 (n=8221) 0 - 1 days 2 days 3 days 4 days 5 days 6 days 7 days > 7 days
Same target 26.18 7.26 3.63 2.78 2.42 2.42 2.18 0.97
1-50 m. 3.80 1.95 1.23 1.25 0.67 1.48 0.66 1.00
51-100 m. 3.62 2.23 1.25 1.44 1.19 1.39 1.37 1.00
101-150 m. 2.39 1.86 1.67 1.11 1.16 1.03 1.06 1.00
151-200 m. 1.99 1.54 1.57 1.06 1.35 1.16 1.09 1.00
201-250 m. 1.64 1.06 1.35 1.17 1.18 1.23 1.06 1.00
251-300 m. 1.49 1.28 1.17 1.01 1.29 1.33 1.22 1.00
301-350 m. 1.29 1.04 1.00 1.08 1.08 1.26 1.14 1.00
351-400 m. 1.48 1.14 1.13 1.02 1.15 1.11 0.97 1.00
401-450 m. 1.37 1.18 1.31 .095 0.98 1.03 1.03 1.00
451-500 m. 1.14 1.06 1.23 1.23 1.23 1.1 1.13 1.00
> 500 m. 0.97 0.99 0.99 1.00 0.99 0.99 1.00 1.00
Bold results are significant at > 0.05
J.J. van der Kemp© 12
Small distances bands & small temporal bands
Near repeat clusters
J.J. van der Kemp© 13
Near repeat clusters
J.J. van der Kemp© 14
Near repeat clusters
J.J. van der Kemp© 15
Burglary times… Table 3: Bandwidth of uncertainty time of burglary ( n=8919)
Time frame N burglaries Percentage Cumulative Percentage
Immediatly 1517 17,0 17,0 Within half hour 1051 11,8 28,8 Between half and 1 hour 339 3,8 32,6 Between 1 and 2 hours 367 4,1 36,7 Between 2 and 3 hours 370 4,1 40,8 Between 3 and 4 hours 373 4,2 45,0 Between 4 and 6 hours 697 7,8 52,8 Between 6 and 12 hours 1740 19,5 72,3 Between 12 and 18 hours 540 6,1 78,4 Between 18 and 24 hours 349 3,9 82,3 Within 2 days 515 5,8 88,1 Within 1 week 650 7,3 95,4 Between 1 and 2 weeks 168 1,9 97,3 Over 2 weeks 243 2,7 100,0
RUN = Short distance < 500m < within 90min time frame
J.J. van der Kemp© 16
Clustering patterns
J.J. van der Kemp© 17
• 2006- mid 2011 (N=8920) – 5148 within 1st level nearest neighbour cluster – 7391 within > 1 SD Hot Spot Density value – Runs N = 442 (5%)
Clustering patterns
J.J. van der Kemp© 18
• Within 2010 (N=1465) – 179 within 1st level
nearest neighbor cluster – 332 within 2nd level
nearest neighbor cluster – 798 ‘Contagious’
• 394 Originators – 404 near repeats
– 40 Runs
Discussion Geo-linkage • When are crimes spatially and temporally close?
– If they are a ‘run’ of crimes • Offenders targeting a apartment building or a ‘mobile group’ targeting
a street or neighbourhood within a certain time frame
– If crimes are contagious and clustered • Offender returning to the same area
– But (possibly) also when located in Hot Spots...
• New Routes > – Examining varying parameters in spatial clusters:
• Which time frames? > within what time is an offender returning? • Which spatial bands? • Clusters within Hot Spots? • …?....
J.J. van der Kemp© 19
References
J.J. van der Kemp© 20
• Goodwill, A.M., Kemp, J.J. van der & Winter, J.M. (2013). Applied Geographical Profiling. In G.J.N. Bruinsma & D.L.
Weisburd (Eds.), Encyclopedia of Criminology and Criminal Justice (pp. 86-99). New York: Springer.
• Kemp, J.J. van der & Koppen, P.J. van (2007). Fine-Tuning Geographical Profiling. In R.N. Kocsis (Ed.), Criminal Profiling: International Perspectives on theory, practice and research (pp. 347-364). Totowa, NJ: Humana Press.
• Kemp, J.J. van der (2012, april 11). Modus via. Moving beyond the journey-to-crime to fine-tune geographical offender profiling. Nicosia, Cyprus, 21st Conference of the European Association of Psychology and Law
• Kemp, van der, J.J. (2014). Modus Via. Verfijning van geografische daderprofilering. Boxpress uitgevers.
• Schaaik, J.G.J. & Kemp, J.J. van der (2009). Real Crimes on Virtual Maps, The Application of Geography and GIS in Criminology. In H.J. Scholten, R.J. van de Velde & N. van Manen (Eds.), Geospatial Technology and the Role of Location in Science (GeoJournal Library, 96) (pp. 217-237). New York: Springer.
• Peeters, M.P., Beijers, W.M.E.H. & Kemp, J.J. van der (2013). Waarom is besmettelijkheid geen besmettelijkheid? In S. de Ruiter, W. Bernasco, W. Huisman & G.J.N. Bruinsma (Eds.), Eenvoud en verscheidenheid. Liber amicorum voor Henk Elffers (pp. 261-279). Amsterdam: Nederlands Studiecentrum voor Criminaliteit en Rechtshandhaving (NSCR) & Afdeling Strafrecht en Criminologie , Vrije Universiteit Amsterdam.
• Wouters - Peeters, M.P., Kemp, J.J. van der, Beijers, W.M.E.H. & Elffers, H. (2012). Het effect van intensief surveilleren vlakbij en vlak na een eerdere inbraak. Tijdschrift voor Criminologie, 54(4), 336-349.
• Wouters - Peeters, M.P., Beijers, W.M.E.H. & Kemp, J.J. van der (2012). Besmettelijkheid van woninginbraak. Analyse van woninginbraken in Tilburg. (Reek Criminologie 8). Amsterdam: Criminologie VU.
Jasper J. van der Kemp [email protected]
www.facebook.com/VUschoolofcriminology
www.twitter.com/VU_Criminologie
