There is a growing demand from different groups of users to make crime statistics accessible online for several good reasons: with online statistics, combining and analyzing data will become much easier and may give a better insight into certain phenomena. However, applying data mining or Web 2.0 technology, such as mash ups on crime data online, confronts us with undesired effects. These undesired effects are an increase of chances to misinterpret results with regard to crime and law enforcement, violation of the privacy law, and disclosure of the identity of groups of people. Moreover, bad management of crime statistics online may also lead to undesired effects such as breakpoints in series. In this paper, we focus on the potentials and undesired effects entailed by making crime statistics accessible on the web. In addition, we discuss two approaches - a data warehouse approach and a data space approach - to implement crime statistics online as such that the undesired effects mentioned above are minimized. We argue that a data space approach is better suited for the implementation of crime statistics online, since in this approach (highly) aggregated data are used instead of micro data and therefore the risk of violation of privacy is minimized. Moreover, in the data space approach relationships between databases are stored, which minimizes the chance of undesired effects.
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