So why use the raster method, if it is heavier than the vector method? Simply because this approach offers alternative but complementary possibilities in terms of data processing vis-à-vis the vector approach. ‘We privileged it for two reasons,’ explains Marie Trotta, co-author of the publication. ‘First of all because it allows the application of propagation algorithms. Propagation means that passing from one pixel to another will increase the value of the itinerary by a process of addition. As if each pixel had a value determined beforehand. To pass from one pixel to another, you have to ‘pay’.’ For example, in the context of this study, the resolution of each pixel corresponds to 20 metres by 20. On moving from one pixel to another next to it, one crosses a cost surface of 20 metres. The cumulated value of the second pixel is thus 40 metres. Normally, a propagation can be carried out in any direction starting from the original pixel. Yet the criminals had a car. To calculate their trajectory the researchers had to use the road network. In adding this network restriction to the logic of propagation it thus became possible to calculate the shortest cumulated distance between each pixel of the network and the different sites the crimes took place.

The raster method has also enabled math-algebra techniques to be used. ‘For the same mapped region, and depending on the data we wish to highlight in the pixels, we have to produce several images,’ illustrates Jean-Paul Kasprzyk. ‘One image, for example, will be made up of pixels which record the layout of the roads. Another will present the values according to relief, yet another according to the situation of the pixels in an urban or a rural environment, etc. The possibilities are infinite. What the raster method enables you to do is to attribute values to each of these pixels according to what they represent, and to add, multiply or subtract, for example, these values amongst themselves in a multi-criteria model.’ This allows one to demarcate the search zones according to several factors studies. Imagine, for example, that during an investigation, one reckons that the criminal being looked for is hiding in a forest next to a water course and a road. In adding together the rasterised images presenting the proximity to the road networks and the rivers, and the location of the forests, it would be possible to only extract the pixels close to the roads, close to the rivers, and located inside a forest, and as a result orient the search according to these results.

Developing different scenarios...

Once the maps had been digitalised the criminals’ journey needed to be imagined. All in all four scenarios were suggested. The first was a simple adding up of the distances covered between the criminal events. A loop between the thefts and the car being abandoned. This loop only measured 86 kilometres. This scenario did not suit, thus, as the expected 100km had not been covered. The second scenario integrated a withdrawal zone, with a round trip between this landmark and each of the crime scenes.