# Tools: check connection of families

This is an analysis tool used to check the number of fully interrelated families. The tool starts from a person and then processes all persons that have any relation with the initial person—regardless of relation degree. All inspected persons are combined into one group. All persons interrelated with each other, but not with the first group, are combined into other groups.

You can develop several unrelated families in one database. This tool gives you ability to visually manage their composition. Also, you can use this tool as an integrity checking procedure. For example, for cases when you removed a person from a family but did not add that person to some other family. In such cases, this tool shows the person as disjointed from all groups.

Double-click on a person in a group list to open this person in the main working list of the database. This may be helpful when you want to make some changes.

There is a database fragmentation diagram under the list of group contents. For better visual perception the diagram has a logarithmic scale, therefore the size of a segment is not directly proportional to the number of persons in the relative group. This decreases the size of large segments yet keeps small fragments clearly visible. Hovering the mouse over a fragment displays the fragment/group number and its size.

The "Data Quality" tab displays a second view of data fragmentation. This view combines two approaches: a graphical map ("TreeMap") and a heat map ("HeatMap"). Each fragment of the database is represented by a rectangle, the size of which is proportional to the size of the fragment in personal records. The color of the rectangles is determined by the "quality" of the data, determined based on the certainty index from dark green for extremely reliable and confirmed data, to dark red for highly unreliable data.

By right-clicking on the diagram fragments, you can select to filter the main list of persons by the selected fragment - this will allow you to study the fragment in isolation from other data and make a decision on its further researching (or deletion).

See also: Certainty Index.