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key-concepts: data, information, knowledge, and intelligence. The positivist epis-
temological perspective is reflected in statements such as: 'information is data
given meaning and structure' (Ratcliffe 2008a: 4). Although such definitions are
straightforward and deeply ingrained in policing practice, in day-to-day business
we observe that they tend to lead to discussions about format rather than inform-
ing, to unilateral processes of requesting and receiving information products
(rather than dialogue), and to data-driven rather than knowledge-driven explora-
tions. This data-driven focus often leads to something we call 'databesity', which
we define as an urge to collect data for the sake of assembling (large) data collec-
tions in which -potentially- informative patterns may be found (cf. Innes et al.
2005). Finding and not finding these patterns urges to extend the data collection
even more, either in depth or in breadth. Where the hunger of people with obesity,
however, cannot be satisfied by eating (as the true cause is of another nature), so
the data-hunger of organizations with databesity cannot be cured by collecting
more data. Instead, KBP from an interpretive-constructivist perspective urges
'old' knowledge-type of questions to be raised when dealing with 'new' knowl-
edge, such as: what is the problem to be solved? How can the criminal phenome-
non be recognized? How are the criminals organized? What would be a good
strategy to solve the problem? Who and what (including what data) are needed to
execute the strategy? Approaching problems from this perspective leads to more
focused data processing which is not limited to encoded data or computerized
analysis techniques, but involves on-going sense making and natural world feed-
back based on behavioral and other cues. Indeed, to achieve augmented reality,
ICT-affordances have to be integrated seamlessly into the construed reality of
natural persons (Feiner et al. 1993).
9.4.2 Augmented Reality: Real-Time Processing of Data-Streams
Augmenting reality is a way for police organizations to bring ILP as close to prac-
tice as it can possibly get. To this end, however, focus has to be shifted from (per-
sistent) databases to ephemeral data streams. The aim of creating and assessing
data-streams is not to collect evidence, but to augment reality with a data-layer
that complements the sentience and awareness of police officers, support sense
making and decision making processes, and thus, inform action. Modelling aug-
mented reality (i.e. configuring sensor-networks and data-layers) for such purpose
is a continuous effort, taking into account the characteristics of the police officers,
the phenomenon involved, its context, and its development through time.
Like shown in the grey box the creation of an augmented reality for fighting
drug-trafficking was achieved by explicating and sharing knowledge of police
workers, jointly developing hypotheses about the modus operandi of drug-
traffickers, instructing officers to look out for related (mostly behavioral) signs,
creating access to appropriate data-streams to identify generalized time-spatial be-
havior, and construct automated profiles to analyzing these live data-streams in
order to create real-time feed-back for the police officers conducting the control.
Little to none of this knowledge can be traced back to an existing database. But
even if such data-collection would have been present, its predictive value would
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