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An analysis about the accuracy of geographic profiling in relation to the number of observations and the buffer zone

Author

Listed:
  • Ugo Santosuosso

    (University of Florence)

  • Alessio Papini

    (University of Florence)

Abstract

Geographic Profiling (GP) attempts to reconstruct the spreading centre of a series of events due to the same cause. The result of the analysis provides an approximated localization of the spreading centre within an area (often represented as a red red), where the probability of finding it is higher than a given threshold (typically 95%). The analysis has as an assumption that the events will be likely to occur at very low probability around the spreading centre, in a ring-shaped zone called the buffer zone. Obvious examples are series of crimes perpetrated by an offender (unwilling to perpetrate offences close to home), or the localities of spread of an invasive species, where the buffer zone, if present, depends on the biological features of the species. Our first aim was to show how the addition of new events may change the preliminary approximate localization of the spreading centre. The analyses of the simulated data showed that if B, the parameter used to represent the radius of the buffer zone, varies within a range of 10% from the real value, after a low number of events (7–8), the method yields converging results in terms of distance between the barycentre of the red zone and the “real” user provided spreading centre of a simulated data set. The convergence occurs more slowly with the increase in inaccuracy of B. These results provide further validity to the method of the GP, showing that even an approximate choice of the B value can be sufficient for an accurate location of the spreading centre. The results allow also to quantify how many samples are needed in relation to the uncertainty of the chosen parameters, to obtain feasible results.

Suggested Citation

  • Ugo Santosuosso & Alessio Papini, 2022. "An analysis about the accuracy of geographic profiling in relation to the number of observations and the buffer zone," Journal of Geographical Systems, Springer, vol. 24(4), pages 641-656, October.
  • Handle: RePEc:kap:jgeosy:v:24:y:2022:i:4:d:10.1007_s10109-022-00379-5
    DOI: 10.1007/s10109-022-00379-5
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    References listed on IDEAS

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    1. E. Baker & M. J. Jeger & J. D. Mumford & N. Brown, 2019. "Enhancing plant biosecurity with citizen science monitoring: comparing methodologies using reports of acute oak decline," Journal of Geographical Systems, Springer, vol. 21(1), pages 111-131, March.
    2. F. H. C. Marriott, 1979. "Barnard's Monte Carlo Tests: How Many Simulations?," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 75-77, March.
    3. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
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    More about this item

    Keywords

    Geographic profiling; Criminal geographic targeting algorithm; Modelling; Centre of origin; Buffer zone; Crimes mapping;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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