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technological sensors. These are potential candidates for augmenting reality. Ex-
amples include sensors for measuring weight, heat, speed, direction or route, or
sensors which can be used to recognize texts on license plates, voices or faces. As
policing takes place in public space, sensors used to augment reality need to be ei-
ther integrated in the personal gear or equipment of police officers, or made avail-
able through a web of geographically distributed and interconnected sensors. In an
ideal situation these sensors are seamlessly integrated in our natural environment,
thus becoming transparent to natural persons (also known as ubiquitous computing
(Weiser 1993)).
To facilitate the contextual presentation and interpretation of the real-time data-
streams being produced by this sensor-network, knowledge-based systems are
used (based on models) that can be customized to personal roles and contexts (us-
ing profiles) (cf. Feiner et al. 1993). The latter is of utmost importance to manage
the volume of data being processed. For effectively augmenting reality, only those
data have to be processed that are related to a particular officer who is working at
a given location, at a given time, in a given context. Rather than storing all 'obser-
vations' of all sensors in perennial databases for offline analysis, for augmented
reality only a selection of the ephemeral data-streams have to be analyzed in real-
time. Based on explicated profiles these data-streams are used to assess how close
a person or event comes to a predetermined characterization or model of infraction
(Marx and Reichman 1984:4), thus rigorously endorsing the principle of 'select
before you collect' (Jacobs 2005). Fitting a profile oftentimes does not provide
sufficient legal grounds for treating someone as a criminal suspect. Like natural
observations, however, synthesized observations just aid officers 'to select cases
worth inspection' (Holgersson and Gottschalk 2008).
Augmenting reality in such real-time real-life environment is complex. This
complexity is caused by several factors, including technical, financial, organiza-
tional, cultural, and not in the least of juridical and ethical factors. To name a few,
networks of distributed sensors have to be integrated in the (fortified) ICT-
network of the police, while the location and configuration of the sensors need to
be attuned to the (fluid) criminal phenomenon under study; Police officers have to
learn how to mentally integrate the augmented part of reality with their own ob-
servations and common sense, while the organization has to learn how to contex-
tualize signals from their sensor-network and organize a response. Last but not
least, the application of profiles has to be incorporated in the legal framework of
policing. This includes the organization of mandates to act upon synthetic obser-
vations, defining the legal status of the data being processed, and the justification
for breaching privacy due to the processing of person-related data. These particu-
larities and the potential value of augmented reality in policing practice are illus-
trated in the grey box below.
Large numbers of drug-seeking tourists have been causing major problems in
the public domain in the Dutch city of Maastricht for years. Traveling up and down
the nearby borders of Germany and Belgium they flooded the city, visiting Dutch
coffee shops, looking for drugs. These large numbers of customers attract criminal
groups like drugs-traffickers and hard-drug retailers. As coffee shops, selling small
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