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Fig. 1 | Crime Science

Fig. 1

From: Overlapped Bayesian spatio-temporal models to detect crime spots and their possible risk factors based on the Opole Province, Poland, in the years 2015–2019

Fig. 1

Spatio-temporal models of relative risks (A) and growth rates (B) of total crime in Opole, Poland, from 2015 to 2019. The maps are thematically represented by the arbitrary equidistant ranges of crime risk levels to show their spatial risk diversity (left map) and changes over times (right map), respectively. If the RR or GR of observed/expected value is significantly smaller (p < 0.05) than 1.0 = 100% (horizontally hatched units), then there is said to be a “moderate risk”, whereas if RR/GR > 1.0 (vertically hatched areas), then we are dealing with an “excessed risk” of crime. The overlapping of both of the same type of hatched surfaces reveals cold and hot-spots of space and time crime risks documented in the next thematic maps in Fig. 2

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