Date published 21 August 2019
Last reviewed 12 December 2023

We appreciate your interest in the dashboard. Your questions will assist other users to interrogate and interpret the data. 

If you are having problems with, or are finding it difficult to interpret, the data dashboard, we recommend that you start by referring to our support resources.  

  • The glossary of terms will assist users to understand key terms used in the dataset. Subsequent queries can be directed to [email protected].

The dashboard starts by displaying the data for all allegations received by the CCC. The data can then be filtered by selecting any aspect, such as sector (top row), alleged conduct (categories from top left bar graph or top centre treemap), the activity related to the alleged conduct (right bar graph), the Queensland Government Department that the allegation is relevant to (bottom right table), the rank of the police officer that the allegation is about (bottom centre table), the position within local government that the allegation is about (bottom left table) or the quarter in which the allegation was received by the CCC (centre line graph).

The CCC has decided to show numbers less than 5 with the “<5” symbol. Any interpretation of changes or differences in small numbers (e.g. from one month to the next or between two departments) is not advised. Small numbers of allegations could reflect a single incident as well as inflate the magnitude of change. For example, consider the changes from (a) 1 to 2, (b) 100 to 101, and (c) 1000 to 1001. Although all three consist of 1 unit of change, the percentage change depends on their size, (a) 100% increase, (b) 1% increase, and (c) 0.1% increase. As can be seen, the smaller the number, the greater the relative change.

Data is filtered when an item is selected and unfiltered when that item is selected again. To “drill down” into the data, you can select more than one item and the data will continue to be filtered. To undo or reset the page click on the bottom right hand side buttons that read “undo” and “reset”.

The three tables are only relevant to one sector each. Selecting an item (and thus filtering the data) will only reveal information that is relevant. For example, clicking on Public Service Departments reveals the departments table only as Queensland police rank and local government position do not apply. If you cannot see a table then there is no data to display in that table. To see the table select undo or reset until the table reappears.

It is important to understand that the allegations have not been assessed or investigated. This means that you cannot make inferences or draw conclusions about the nature of corruption in Queensland or the merit of individual allegations.

The dataset reflects suspected corrupt conduct, not proved corrupt conduct. In addition, this is raw data and does not take into consideration the size of the department or the number of people at a certain rank or position. This means that comparisons between departments, ranks or positions cannot be based on this data alone.

The CCC has published this data in the interests of transparency and to assist public sector agencies better understand corruption risks.

It is important to understand that the allegations aggregated via the dashboard have not been assessed or investigated; for that reason there is no outcome data provided. This means that no inferences or conclusions can be drawn from the data dashboard about the nature of corruption in Queensland or the veracity of individual allegations.

Allegations may nonetheless provide a useful barometer of corrupt conduct.

The dashboard does not provide any visualisation for geographic location, because doing so may enable a person to identify an individual who is the subject of an allegation, or an individual who lodged a complaint. However, if you want specific information about alleged conduct in a given location, you may request data from the CCC by contacting us on [email protected]. In making a decision about each specific request, the CCC will determine whether the data is potentially identifiable.

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Corruption prevention
Local government
Public sector
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