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Welcome to the "Functionalities" section of the Puck 2.0 online help. Here, you can find a detailed description of all PUCK functional components. As showed on the image below, those can be firstly distinguished into three main components : the Menu Bar, the Main Window and the Partitions Bar.
This part of the guide is thus divided into sections that match with one of these Puck components. In order to navigate through the guide and find the functionality that you are looking for, you can identify the PUCK main component to which it is related to, and then navigate to the pertinent section of the guide by using the "Functionalities Menu" (located on the right side of this web page).
For instance, to know what is the "Additional data" frame (see below) and how to use it, click on the "Main Window" voice of the "Functionalities" menu, and then search the page running a Ctrl+F text query.
Be aware that, on the "Functionalities" menu, some entries appear even if they don't belong to the Puck main components showed below. It's because those entries are related to specific commands that the guide treates in the Menu Bar sections.
The File menu provides some of the most fundamental Puck functionalities such as creating, importing and exporting in a wide range of formats, fusing or updating kinship datasets. In addition, this menu has been recently enhanced with some kinship simulation functions.
In order to create a new kinship network, use the command File > New > Empty network.
It is possible to enter data directly into Puck, but you can also use the software of your choice : the program is compatible with most popular formats.
The submenu File > New > Random network allows creating several types of randomly produced kinship networks. This simulation technique can be useful in order to compare "real" kinship data - which represent actual social practices of a given population - with randomly generated ones.
In order to create a new random network, there are two possibilities :
- File > New > Random network > Classic
- File > New > Random network > Birth-Centered
As kinship simulation appears between the PUCK main functions, a specific section of this guide is dedicated to this two commands. If you want to keep reading about them, click here or follow the "Functionalities Menu".
Import a dataset by clicking File > Open... and choose your file. The data appears in the main window. It is possible to open a recently used file (File > Open recent) and to browse a recent folder (File > Open Recent Folder).
By default, Puck assumes that the dataset is a UTF-8 file. Anyway, it is possible to choose the encoding via the command : File > Open encoding.
***Open from Kinsources
The command File > Open from Kinsources allows downloading a corpus directly from the Kinsources project website. When executing the command, a dialog window called Kinsources Catalog Selector opens. You can then select a corpus and open it with PUCK.
***Reload / Revert
The command File > Reload re-establish a modified dataset to its original version (when opening the file). By doing so, every kind of modification (on individuals, families, ties, attributes, partitions...) is thus erased. Note that when a modification has been introduced into a corpus, the command name automatically switches to File > Revert. In order to avoid data loss, a dialog window automatically opens, asking to confirm the required action.
In order to fuse two datasets you can use the command : File > Merge. When executing the command, a dialog window opens requiring a “File to be joined” (the second dataset) and a concordance table. The latter, which enables the program to identify double entries, has to be provided if (and only if) some individuals appear both in the two datasets.
Warning : merging two datasets implies an automatic renumbering of individuals : individuals of the second corpus who have no doubles in the first corpus obtain a new Id number by adding the number of the last individual of the first corpus to their old number. Renumbering should remain a transitory exception while establishing a definite dataset. As a rule, individuals should have one unique identity number and belong to one unique corpus.
Update a Dataset
Genealogical corpuses can be updated and new supplementary information added to them. To update a corpus and add data from another dataset, Puck provides a specific command : File > Update. For safety, Puck will generate a new dataset in order to prevent the original dataset to be erased by mistake. The new dataset will then be stored in a new file, in the same format as the original one. A corpus update requires a file which fulfills the same format requirements as the files used to load a corpus.
Note : if you use a file in text format containing only supplementary information on individuals’ properties (so that the first block is empty), make sure that the file begins with two headlines (and not just one). The first headline (which may consist in a single letter) marks the presence of the first block, the second one indicates the switch to the second block. Otherwise Puck will read your supplementary information as basic genealogical information, and the update will fail. These problems do not happen when you use files in .tip format.
You have two choices: File > Update overwriting and File > Update appending.
In the first case, data are simply appended without overwriting existing data (in this case, certain data may not be added, for instance a father that does not correspond to the actual father). In the second case, data are used to overwrite existing data by new ones (in this case, an individual which appears in the update file as having no father will lose his father in the corpus).
***Save and Export Datasets
When executing the command File > Save (Ctrl+s) PUCK overwrites the recent changes on the original file. In order to secure this operation, a dialog window automatically opens asking to confirm the operation.
By the commands File > Save as and File > Save a Copy, it is possible to:
- Export the current dataset to a format of your choice : Gedcom, Pajek, Text, XLS, PUC, etc. (for a synoptic presentation of the different formats properties, click here). Generally, you save as much information as possible by exporting in .puc format
- Create a backup version of the dataset
- Save an updated version of a modified corpus (in which case you will have to change its name)
NOTE : on the current version of PUCK, the drop-down list of extensions does not work to actually change the file format. In order to do so, you must find the format in the "Files of type" list and then type the extension in the "File Name" field.
***Export to Pajek
The command File > Export to Pajek produces a file which can be opened with Pajek. This can be useful if you want the current kinship network to be drawn in a graph. When executing the command, the Export to Pajek Input window automatically opens. There you find the Graph Type frame, which contains a check-box list showing three possibilities (click on the file type to view its Glossary definition):
***The Partitions Label frame allows defining several properties (i.e., gender, birth place, patri-clan...) as partitions, that can be read and represented by Pajek.
Once those parameters set, you can then choose the destination file and create it by clicking on the Export button.
Close and Quit
To exit from PUCK there are two possibilities : the command File > Close (Ctrl+W) refers to the current dataset ; the command File > Quit refers both to the current dataset and the program. In both cases, Puck will automatically detect and notify unsaved changes on the current file.
Manage and Edit Data
Once the dataset is created, you can then begin entering new data by creating individuals, kinship relations, families, additional data (place of birth, occupation, religion, date of birth, etc.). In order to do so, you can use the following commands from the Edit Menu, the correspondent keyboard shortcuts, or the Main Window Bottom Toolbar (see here) :
||Ctrl + I|
||Ctrl + U|
||Ctrl + P|
||Ctrl + K|
||Ctrl + Maj + U|
||Ctrl + F
Note : The command Add Origin family create a family where Ego appears as a children. The command Add family creates a simple union tie, whose individuals must be defined.
The command Edit > Preferences allows choosing in which language Puck will run. The available languages are : English, French, German, Italian and Spanish.
*** In the Preferences dialog window, the Input settings frame allows defining how PUCK has to treat, by default, several special features. This can be useful, in particular, in order to prevent data input mistakes. There are three possibilities :
- None : the selected Special Feature is not reported
- Warning : the selected Special Feature is reported and a confirmation is asked
- Error : the selected Special Feature is denied
The Report menu contains several commands that can help organizing the dataset records, searching for potential errors and producing attribute statistics.
The sub-menu Reports > List (...) allows listing all the corpus individuals by choosing the most pertinent ordering criterion. After giving the chosen command, Puck put in order all the dataset individuals and produces an exportable report. You can then save the results in a .txt or .xls formats file, by clicking on the "Save" button (bottom right-hand corner of the results window).
The command Report > Homonyms allows detecting and list individuals who carry the same name, or a part of it (e.g., the first name). A dialog window opens when executing the command, asking to specify :
- in the Name parts field, the number of words (separated by a "/" symbol) that Puck has to consider as pertinent for regrouping homonyms.
- in the Minimal Number of names field, the minimal number of individuals who share a given name (or name part) that Puck will list.
E.g., the individuals who share their first name can be found by setting Name parts on "1" ; if you are looking for individuals whose name is shared by more than five persons, set the Minimal number of names to "5".
By the submenu Report > Controls, PUCK allows finding quickly Special Features (possible errors) that could be contained in the dataset (including input derived ones). The commands appearing in the submenu allow choosing the most appropriate errors and report it. The following list resumes the types of errors that PUCK recognises :
- Same-Sex spouses
- Female Fathers or Male Mothers
- Multiple Fathers or Mothers
- ***Cyclic descent cases - individuals for whom a descendant is, at the same time, an ascendant
- Unknown sex persons
- Unknown Sex Parents or Spouses
- Nameless Persons
- Parent-child marriages
- Inconsistent Dates
- Missing Dates
The command Reports > Controls > Special Features produces a full report of all these possible errors. Some them (such as persons lacking name or gender, or marriages between same-sex spouses or between parents and children) may actually be correct and wanted (depending from the fields and the sources), but they often are due to simple mistakes introduced during the data input. Puck indicates them in order to facilitate the researcher to check the dataset, but never automatically “corrects” possible mistakes.
When executing the command Reports > Controls > Special Features, a dialog window automatically opens. There, it is possible to define which kind of potential errors you want Puck to check for.
Otherwise, it is possible to run a step-by-step check-up, by selecting a single special feature in the submenu.
Note : even if they are true representations of a real kinship network, some of these irregularities may hinder certain functions of Puck from working correctly, which should lead to a reconsideration of analytical methods. Some errors can even cause PUCK crashes (for instance, cyclic descent cases cause infinite loops) ; some others will lead to erroneous results (for instance, the presence of male mothers or female fathers causes calculation errors in the matrimonial census).
The command Reports > Attribute Statistics allows obtaining basic statistics concerning the distribution of attributes. By default, these are classed as Not-set, Set blank, Filled and Set. In the Report Window, PUCK counts all the Corpus, Individuals and Families attributes, and sorts them by label. The results are exportable in .txt or .xls formats by clicking on the "Save" button placed in the bottom right-hand corner of the window.
The Transform menu features a number of commands allowing several systematic changes on the current dataset (duplication, anonymization, reduction, extraction, expansion, shrinking...). Some of these transformations concern the individuals names, their attributes, as well as the relations existing between them ; others can target the dataset as a whole or just some partitions of it.
The commandTransform > Duplicate creates an exact "live" copy of the original dataset. This can be useful if you want to test some transforming operations on a dataset, without affecting the original file with unwanted changes. Note that the duplicate wich PUCK thus produces is not automatically saved into a file.
The Anonymization commands are used for hiding individuals names. This can be useful, for example, if you work on a recent population and whish to publish some analytical results without revealing the individuals identity.
The following commands enable to choose the most appropriate form of anonimization :
- Transform > Anonymize by First Name ;
- Transform > Anonymize by Last Name ;
- Transform > Anonymize by Gender & ID.
Note : Numbered names are convenient if the corpus is exported in .paj format (Pajek files require renumbering of individuals in order to assure continuous vertex numbers. Numbered names thus serve to keep the original numbers). If a Pajek file with numbered names is imported to PUCK, numbers between parentheses are automatically re-converted into identity numbers.
The following commands allow to operate systematic changes on attributes. It can be useful to use them, as it avoids to operate such changes one by one through the entire dataset :
- Transform > Rename Attribute (acts on all Labels)
- Transform > Filter Exogenous Attribute (acts on some Values)
- Transform > Set Attribute Value (acts on all Values)
- Transform > Replace Attribute Value (acts on some Values)
- Transform > Valuate exo. Attribute
- Transform > Remove all Attributes (acts on all Label and Values)
- Transform > Transmit Attribute Value (act on all Values)
When executing each one of these commands, a dialog window automatically opens asking to specify which attribute has to be changed, and how. Thus, each command produces a specific dialog window, but some recurrences can be isolated (we will leave the rest of it implicit, as these functions are sufficiently intuitive). The Target field indicates the type of attribute concerned (i.e., All, Individual, Family...). The Label field indicates the "name" of the attribute concerned (i.e., BIRTH_DATE...). The Value field indicates the actual content of the attribute (i.e., "1955" for a birth date).
The command Transform > Marry Coparents associates to each fertile couple a matrimonial link. This can be useful, for instance, in order to make those unions visible in a matrimonial census. The command is effective on the entire dataset without taking into account partitioning and it doesn't change the family numbering.
The Transform > Re-number Ids sub-menu enables to change the whole dataset individuals Id numbering. The new numbering will start from 0 and will cover all individuals without gaps.
If you have a pre-existent corpus where the Id number is defined as an attribute, you can use the commands Transform > Renumber Ids from ID attr. and Transform > Renumber Ids from REFN attr. in order to make Puck recognize it as the actual individuals Id.
The sub-menu Transform > Reduce allows removing specific segment types from a kinship network. The reduction operations precisely concern segments that can be considered structurally irrelevant (unmarried people, structural children, etc.). Thus, these operations are meant to "clean" the corpus before proceeding to an analysis of its structure. This can be useful when preparing, for instance, a matrimonial circuit census, e.g. in order to refine an analysis of the datasets gender bias. The following list of commands gives the detail of each possible reduction :
- ***Transform > Reduce > Acyclic Segments : eliminates from the kinship network all the segments that do not contain cycles.
- Transform > Reduce > Marked doubles : eliminates all doubles. The individual with the lower identity number is considered as the original, the one with the higher ID number as the double. Doubles can be marked by substituting the original’s identity number for their name.
- Transform > Reduce > Structural children : eliminates all individuals who have neither spouses nor children (the structural children of the kinship network).
- Transform > Reduce > Unmarried : eliminates all individuals who are not married.
- Transform > Reduce > Virtual individuals : eliminates all virtual individuals. This reduction is recommended for the exploratory analysis of a corpus containing fictive individuals.
The sub-menu Transform > Extract enables to create a new dataset by selecting a specific sub-corpus of the original one. The new dataset will be thus composed only by the vertices of the selected sub-corpus. By the following commands, you can choose to extract different types of sub-corpuses :
- Transform > Extract > Current Segment
- Transform > Extract > Current Cluster (Ctrl+E)
- Transform > Extract > Kernel (maximal matrimonial bicomponent)
- Transform > Extract > Max. Bicomponent
- Transform > Extract > Core
- Transform > Extract > By cluster size / By cluster value : ***CORR*** reduces the kinship network to those vertices who have a positive cluster value in a chosen partition. For example, if “OCCU” (occupation) is chosen, then the network is reduced to the people whose occupation is known.
Note : Some of the commands in the list above presuppose a basic knowledge of the partitioning process. To read more about partitioning, click here or use the "Functionalities" menu (click on the voice : "Partitions Bar").
The sub-menu Transform > Expand current segment (...) allows creating a new dataset composed both of the selected partition members and of individuals somehow connected to them. This can be useful, for instance, when you want to operate a circuit census on a given partition of the dataset, without losing data about the ties that exist between its members (which could involve non-members of the partition).
Thus, the submenu allows expanding a partition to its connected non-members and, in addition, to operate a selection between them, based on the type of ties existing between the segment members and the "to be included" non-members. Such a selection can be operated by the following commands, which indicate different classes of connected individuals :
- Transform > Expand current segment > Special Features... : Includes individuals connected by special features
- Transform > Expand current segment > Universal : Includes individuals connected by all existing ties
- Transform > Expand current segment > All related : Includes all individuals connected by ties defined as a Relation Model
- Transform > Expand current segment > All Kin : Includes all individuals connected by marriage and filiation ties
- Transform > Expand current segment > Ascending : ***Includes all individuals connected by ascending ties
- Transform > Expand current segment > Ascending (Agnatic) : ***Includes all individuals connected by agnatic ascending ties
- Transform > Expand current segment > Ascending (Uterine) : ***Includes all individuals connected by uterine ascending ties
- Transform > Expand current segment > Descending : ***Includes all individuals connected by descending ties
- Transform > Expand current segment > Descending (Agnatic) : ***Includes all individuals connected by descending agnatic ties
- Transform > Expand current segment > Descending (Uterine) : ***Includes all individuals connected by descending uterine ties
- Transform > Expand current segment > Horizontal : ***Includes all individuals connected by marriage ties
The Shrink function allows regrouping the dataset individuals following a given criterion ; it also allows generating and analyzing the network of links existing between such groups. The results can be exported in .paj and .dat formats. Such networks can be thus represented as directed graphs, where both nodes and arcs have values. The nodes values will then quantify the partition size (number of individuals), the arcs values will quantify their weight (number of ties).
The Transform > Shrink > Alliance Network command produces a directed graph where nodes represent groups of individuals sharing a given endogenous/exogenous property and arcs represent the number of links between such groups. For instance, it can be used in order to analyze the matrimonial alliance network between different patri-lignages, or to study the transmission of professions through filiation.
In Puck, when executing the command, a dialog window automatically opens asking to set some criteria.
The Label field, allows defining which endogenous/exogenous property will actually regroup the dataset individuals.
The Alliance Type field allows defining as "alliance" relations three different types of ties : wife-husband, sister-brother and parent-child. Choosing one of these will produce, respectively, a matrimonial exchange network, a network of siblingship or a network of filiation ties.
The Weighted Arcs check-box allows choosing whether or not the arcs weight will appear in the results.
Finally, three fields allow to filter the resulting network depending on the Minimal number of : links (node degree), alliances per node (node strength), and alliances per link (link weight).
The results can be viewed (and managed) both as an autonomous Alliance Network Window (on the dialog window, click on the Launch button) and as a statistic report (on the dialog window, click on the Statistics button). For a description of the Alliance Network Window, see here.
The statistic report window is composed of six tabs :
- Alliance Network Report, which summaries the input criteria and allows exporting the results in the .paj, .paj (edge version) and .dat formats ;
- Analysis, which presents the results according to a number of indicators such as the number of nodes and arcs, the maximal weight (links per arc) and strength (links per node), the potential endogamic pairs, the distribution of circuits (etc.) (for more details see here) ;
- Matrix, which shows the network alliance matrix in a table and allows exporting it in .txt and .xls formats ;
- Couples, which lists in a table the linked cluster-to-cluster couples, as well as the composition of (directed) links connecting each couple. Every block then specifies the link weight and the individuals couples (their Id number, gender and Name) that are connected by marriage, siblingship or filiation tie (depending on the chosen criterion).
- Sortable List, which indicates : in the first column, the origin vertex (wife, sister or parent, depending on the chosen criterion) of each link : its Id number, Gender and Name ; in the second column, the destination vertex (husband, brother or child) its Id Number, Gender and Name ; in the third column, the cluster to which the origin vertex belongs ; in the fourth column, the cluster to which the destination vertex belongs ; in the fifth column, the link weight.
- ***GAP Sides, which lists [...]
The command Transform > Shrink > Flow Network allows producing, analyze and manage flow networks. Here, nodes represent segments that regroup the dataset individuals who share one (of two) given endogenous/exogenous properties. Concurrently, the network weighted arcs connect the segments that contain, each one, the same individual ; their weight correspond then to the number of individuals who share the two given properties, and they point from the first cluster to the other.
The command can be useful, for instance, for studying migration flows (from the birth place to the death place of the dataset individuals).
When executing the command, a dialog window automatically opens.
Here, the Source Label field allows defining the first property used for regrouping (i.e., BIRT_PLACE), and the Target Label field allows defining the second one (i.e., DEAT_PLACE).
***The Minimal number of links field allows excluding from the results network all the source nodes whose size doesn't reach a given number of individuals.
Unlike for alliance networks, the results are shown only as statistics. The Report Window is made up of five tabs:
- Flow Network Report, where one can find a review of the input criteria, as well as the possibility to export the network in .paj format ;
- Analysis, where appear specific statistics on the flow network ;
- Matrix, where the flow network matrix appears in the same form as an Alliance Matrix ;
- Flows, where are listed the couples of source > target nodes and, for each one of those, the individuals appearing in both segments.
- ***GAP Sortable List, where [...]
***GAP Simulation Tools
The command Transform > Reshuffling allows producing the network that results by randomizing the corpus marriages (and keeping the rest of it as it is). This simulation technique can be useful in order to understand to what extent specific matrimonial configurations depend from demographic and/or data collection biases.
When executing the command, a dialog window automatically opens, asking to specify :
- The Number of edge permutations per step - [...]
- The Maximum generational distance - [...]
- The Minimum shuffle percentage (stop condition) - [...]
- The Minimum stable iterations (stop condition) - [...]
The command Transform > Virtual Network allows simulating the biases introduced with data collection. It can be useful in order to know how the network morphology would change, if all informants came, for instance, from a small set of families.
When executing the command, a dialog window automatically opens, asking to specify :
- The Number of informants - [...]
- The Kin proximity - [...]
- The Kin degree - [...]
- The Near Kin weight - [...]
- The Memory - [...]
- The Acceptance of both a Male Informant and a Female Informant
- The Kin Recall rates of both Men's Kin (first degree) and Women's Kin (first degree).
The command Transform > Virtual Fieldwork Variations allows [...]
In addition to entering data and navigating through the corpus, Puck allows not only to explore the kinship environment of individuals, but also to run structural socio-centered analysis. The Analysis menu contains several tools whose functions concern such kind of analysis.
Pedigree and Progeniture
The commands Analysis > Pedigree and Analysis > Progeniture produce a complete list of Ego’s ascendants or descendants, up to a given degree. After executing each one of these commands, a dialog window opens asking to specify the maximal generational depth of the ascent/descent ties you're looking for. This will be done by entering a single number that indicates the generational limit of your search. For instance, entering “3” produces a tree structured report of Ego's known ascendants/descendants up to great-grand-parents/children.
The command Analysis > Relatives enables to obtain a complete list of Ego’s relatives of a given type. This is done by entering in the dialog window a structure formula in positional notation, just as in the case of a matrimonial or relational census (see infra). Note, however, that the present function is ego-centered : the first individual in the formula is the currently selected individual.
For instance, entering “XX(X)XX” will produce the list of all Ego's cousins (with their names, identity numbers, and exact kinship relation types).
The command Analysis > Kinship Chains enables to obtain an exhaustive list of the kinship chains connecting Ego to another individual. This is done by entering two numbers, where the first one is alter’s identity number, and the second the canonical degree (maximal genealogical depth). A third number can be entered which specifies the maximal order of the chain, that is the maximal number of marriages it may contain. Puck lists all tracks between ego and alter within specified bounds in the classification of your choice.
The command Analysis > Distances allows classifying ego's relatives depending on the genealogical distance existing between them. When executing the command, a dialog window opens asking to specify which kind of ego's relatives are to be taken into account. You can do so by selecting the wanted Filiation Type in the check-box list ; and than specify the the upper limit of your search into the Max Distance field.
When launching the count, PUCK automatically introduces a new attribute to all individuals. The attribute Label will be, i.e., "DIST 1" if ego Id number is "1", and its value will correspond to the genealogical distance between ego to alter (which is the minimal number of arcs connecting them).
The command Analysis > Basic information (CTRL+B) gives access to the basic information of a dataset, which is the starting point for the analysis of its structure.
It produces a report that contains many basic information such as :
- The number of individuals (differentiated by gender : men/women/unknown)
- The number of marriage relations and unions (differentiated by gender)
- The number of parent-child relations
- The number of fertile marriages (couples with children), in absolute terms and as a percentage of total marriages
- The number of co-spouse relations (relations between co-wives and between co-husbands).
- The number of components (maximal connected subnetworks), the size of the largest component, which is useful to evaluate the dataset cohesion (or disintegration).
- The mean share (size divided by total network size) of agnatic/uterine components and the share of the largest agnatic/uterine component, and the percentage of marriages involving a member of the largest agnatic/uterine component.
- Elementary Cycles : The cyclomatic number (number of independent cycles) of the network.
- The Density of the kinship network : The number of marriage and filial relations divided by the total number of possible relations between two different individuals.
- Maximal and Mean Depth : the mean genealogical depth is computed as an average of the mean generational depth of each individual’s pedigree, according to the formula of Cazes (Cazes & Cazes, 1996).
- Mean number of spouses (differentiated by gender).
- Mean fratry size (mean number of cognatic, agnatic and uterine groups of siblings).
After identifying the errors affecting the dataset, the researcher should take into account its limits and biases. This can be done by exploring the network morphology and it should be seen as a precondition for any analysis or matrimonial census. In order to do so, Puck offers a wide range of tools, accessible from the command : Analysis > Statistics (CTRL + G). When executing the command, the Statistics Input Window automatically opens. This is a fundamental tool for the dataset diagnostics, which constitute one of PUCK main functions. A specific section of this guide is thus dedicated to it. To move there, you can click here or use the "Functionalities" menu.
Note : the use of this command presume a basic knowledge of the partitioning process. To read more about partitioning, see here.
The command Analysis > Partition Statistics allows producing statistics concerning the distribution of given partitions on the dataset. When executing the command, a dialog window opens asking to set the Partitions Statistics Input criteria. This can be done, firstly, in the Partition Diagrams Criteria frame. Here you can set more than one partition criterion at the time. ***GAP Secondly, the Split Partition Criteria frame allows [...].
After launching the count, PUCK shows the results in a new tab, both as diagrams and as tables. In the diagrams, the abscissa indicates the size (number of members) of each resulting partition ; the ordinate indicates the number of existing partitions of each size.
One of the most important PUCK functions consists in running a circuit census of your kinship network. This can be done by executing the command Analysis > Circuit Census... (Ctrl+H), which automatically opens the dialog window called Census Reporter Inputs. A number of settings can be chosen from it, following your analysis needs.
A specific section of this guide is dedicated to the use of the Census Reporter Inputs. To move there, you can click here or use the "Functionalities" menu of this guide.
The command Analysis > Differential census allows conducting a segment-based census and provides several comparative means, which especially concern the relations between members of identical segments. The significant advantage over a global census is the possibility to consider several segments separately. By applying a differential census to all the dataset’s clusters configured by a certain segmentation, statistic results can be achieved concerning for instance uterine-agnatic relations within a household or kinship relations within a profession. The results of a Differential Census appear both as diagrams and as tables. A Differential census produces : relational statistics for each cluster ; global and mean percentages of relations ; distribution of relation percentages by cluster size.
In the diagrams, the abscissa orders the selected partitioning criterion (i.e., occupation) and the ordinate [...]
The Kinship calculator allows converting, transforming and analyzing kinship relations.
You can access to it by the command Tools > Calculator.
Kinship relations can be entered in any notation. The calculator contains three lines in order to allow unary and binary operations. These operations can act on fully specified relations or on a relational schema (without specification of gender).
The Standard button allows bringing the entered formula to its standard form. Then it will begin with the longest ascending and most "agnatic" chain (a chain is the more agnatic the more male members it contains and, in case of equality, the higher the position of these members).
You can change the ego/alter point of view by clicking on :
- Reflect : inverses ego and alter
- Rotate : replaces ego by the next married pivot (not married to ego). In consanguine relations, it is equivalent to identity.
You can perform some binary operations for composing and combine kinship relations :
- Compose : composes relation 3 by linking alter of relation 1 to ego of relation 2 (by marriage in the heterosexual case, identity in the homosexual case)
- Insert : calculates the relation 3 implied between ego and alter of relation 1 if ego's parents are in relation 2
- Switch : switches the selected relation from standard to positional notation, and vice-versa.
- ***GAP Develop : [...] all relations of a same type
- ***GAP Analyze : [...] a relation producing the analytic profile of the relation in the Report window
The Main Window provides both the navigation and the data management functions. From the Main Window it is possible to add, modify and delate individuals, families, additional data and relations. It can be used for data input, even for corpuses of large dimensions.
On the upper left side, there are the Navigation Tabs : Corpus, Individuals and Family. The Corpus tab contains general information on the dataset. The Individuals tab contains the list of all individuals and the general information related to them. The Family tab contains the list of all the families of the dataset.
- Ego's Identity Number. Each individual has a unique Id number which serves to identify it in all contexts (such as matrimonial circuits, or in the kinship section of another individual’s page). Ideally, this value should be assigned one and only one time. However, by clicking on it, it is possible to change it. If you re-assign an already used value, Puck notifies it in an error window.
- Ego's Gender. In the Individuals Tab, gender is represented by a circle for women, a triangle for men and squares for individuals whose gender is unknown. In every individuals frame, gender is represented by the commonly used symbols : ♂ for men, ♀ for women and ⚲ for unknown. Here, it is possible to change the gender of the selected individual by clicking on the symbol.
- The individual’s first name and last name. When importing or exporting kinship data, Puck identifies separate parts of names by a slash “/”. The name part before the first slash is identified as first name and all the other parts as the last name. However, any number of names can be distinguished by using the slash separator. The two name fields are auto-completing. In order to facilitate data entry, clicking on the name make a drop-down menu appear in which all first and last names of the corpus are displayed.
Ego's First Degree Relatives Frame
On the right side of the Identity Frame, a specific section is shows Ego's parents, who are designated by their gender, names and Id Numbers. Next to it, the yellow circles indicate their Union Status.
If Ego doesn't have a parent and you want to add one, click on the name field and either choose an existing individual or create a new one by typing a non-assigned number.
Under Ego's Identity frame, the first one is the Partners frame, which contains information about Ego's spouses ; the second one is the Children frame, which contains information about Ego's first degree descendants.
Additional Data Frame
The Additional data frame contains the individuals/family attributes (such as, i.e., events dates and places). All these attributes are exogenous properties (date and place of birth/marriage/death, occupation, etc.) and can be used to partition the corpus (see here).
Puck distinguishes three different categories of attributes and regroups them in separate sections. All attributes are characterized by a label (for which standard individual property codes should be used) and a value. For instance, a property with the label “OCCU” and the value “merchant” means that the individual’s occupation is that of a merchant. When importing gedcom files, relational properties are automatically transformed into properties of the concerned individuals with reciprocal alter specification. For a list of standard property codes and guidelines for personalized property codes click here.
In order to add an individual/family attribute : on the Additional Data frame headline, click on the "+" button and a line will then appear inside the frame. Click on the first cell from left and type the right property code (i.e., "BIRT_PLACE") ; then, in the second cell from the left, type the corresponding value (i.e., "New York") and press enter.
In order to add an existing individual/family attribute, in Ego's Additional Data frame headline, click on the "++" button. This will make all the existing properties appear, so that you won't have to type the property code again!
If you want to clear Ego's Additional Data frame, on the headline, click on the "c" button. This will erase all empty attributes.
In order to remove an an individual/family attribute : on the Additional Data frame, select the attribute to-be-removed, right-click and click on "delete".
In order to remove (or to make changes on) all attributes, see here.
Even an ego’s property with specification of alter and automatic attachment of a reciprocal property to alter with specification of ego still remains a combination of individual properties and is not a relation property. This is important when these properties are used to partition the corpus or to restrict a relational or matrimonial census to a subcorpus.
For instance, search results for matrimonial rings among individuals whose marriages lie within a certain period may well contain marriages outside that period, if both partners have been married before or after.
The last number of a date is automatically interpreted as year (important for partitioning). This must be taken into account when the specifying events occurred before, after, or around a certain year.
One and the same label cannot be used at the same time for a simple property and for an event property. For example, the code MARR cannot be used at the same time to indicate if a person is married (simple property) and when, where and whom he or she is married to (event property). “Notes” are simple properties with the only difference that they allow entering long texts and line breaks. The label of notes (“NOTE”) is not displayed and cannot be chosen or changed.
All attribute fields (with the exception of notes) contain drop-down menus which allow choosing among existing label, value, place, and date data and existing individuals (for alter). The drop-down menu for alter contains identity numbers and names of all individuals in the corpus, just as for kinship relation entries.
The Relation frame contains the list of Ego's alters of a specific relation (for its use, see the relation model paragraph). On the headline of each frame the number of the respective relatives (number of spouses or children) is indicated. By double-clicking on the name of a related individual in each frame, you can jump to its individual page. Jumping from one relative to another is a way to navigate through the corpus along kinship paths.
The Family tab looks just as the Individuals one, apart from the fact that records are families and not individuals. Families have Id numbers, they are listed in the left side box ; they have an Identity frame, as well as a Children and an Additional Data frames.
If you wish to change the Union Status of a single union (i.e., from "married" to "divorced") you can simply click on the Union Status symbol until reaching the right setting.
- adding an individual : by clicking on the "+" symbol ;
- removing an individual : by clicking on the "-" symbol ;
- sorting the individuals list by Id number, First Name, Last Name : by clicking on the "Sort" button ;
- navigating through the individuals list one by one : by clicking on "Previous" or "Next" buttons ;
- choosing the Id number assigning strategy : filling numbering gaps or always starting from the biggest one ;
- choosing the default Union Status (married, divorced, unmarried).
Remove a Kinship Relation
In order to remove a descent tie : move to the Family navigation tab, select the family where appear the tie-to-be-removed, in the Children frame select the right child/children, in the Bottom Toolbar click on "-".
In order to remove a union : on the Family navigation tab select the family to-be-removed, on the Bottom Toolbar click on "-". Then, move to the Individual navigation tab, select one individual from the couple to-be-removed and, in the Partner frame right-click on the partner and click on "Delete".
Search Dialog Box
A Search Dialog Box is located on the right-side of the Bottom Toolbar (bottom right-hand corner of the main window). It allows searching for individuals or families through the dataset. This can be done either by entering the name, a name part or the Id number of the searched individual(s). If several individuals fit the search criteria, successive “enter” clicks permit to pass from one selected individual to another, and thus to navigate through the corpus. This can be useful, for instance, when a family name has been entered, when the same individual appears in more than one family, or in cases of homonymy.
Note : For a brief introduction to the Statistics function, see here.
The Statistics Inputs window (Analysis > Statistics... Ctrl+G ) contains, first of all, the General frame. It allows obtaining information and diagrams about the current dataset. By selecting the query criteria from the checkbox list contained in the frame, you will obtain information about some dataset structural properties, such as :
- The corpus Gender bias (weight)
- The corpus Gender bias (net weight)
- The distribution of Components
- The corpus Genealogical Completeness
- The corpus Ancestor chains (“Ancestor types”, choosing degree)
- The Fratry Distribution
- Consanguine Chains
- Four Cousins Marriages
Partition Diagrams Criteria
The Partition Diagrams Criteria frame allows obtaining statistics and diagrams about partitions of the dataset. More than one partition can be analyzed at the time. The resulting diagrams will show, on the abscissa, the partitioning criterion and, on the ordinate, the clusters size.
***GAP The Split Partition Criteria frame allows [...]
***GAP The Mean Cluster Values checkbox allows [...]
After launching a query from the Statistics Inputs window, the results are displayed in a report window, both as graphs and tables. Graphs can be viewed individually (by clicking on them). Results can be saved in .txt or .xls formats (by clicking on the “Save” button and choosing the destination folder). The report window also provides information on the distribution of properties.
The first measure of the corpus gender bias is the Agnatic (Uterine) Weight. The following snapshot shows an example of Gender Bias (weight) report diagram, taken from M. Gasperoni's "Ebrei" corpus.
The other Gender Bias measure is the Agnatic (Uterine) Net Weight. The following snapshot shows an example of Gender Bias (net weight) report diagram, taken from M. Gasperoni's "Ebrei" corpus.
Both Gender Bias measures are useful indicators of the interdependence and interconnection of the genealogical knowledge : the more curves (uterine and agnatic) are close to one another, the higher is their interdependence ; the more they are apart, the more they become autonomous, that is to say that we know the agnatic or uterine unisexual lines. If curves are low, it means that there is interconnection.
Note : it is important to analyze the gender bias on partitions (e.g., depending on the generation or the age of birth of individuals). Partitioning is particularly useful for focusing on a part of networks.
Distribution of Components
The Components diagram shows the distribution of agnatic/uterine components (connected subnetworks made up entirely by paternal/maternal ties) according to their size : the abscissa of the diagram indicates the relative size of components (as a percentage of total network size, where size = number of individuals), the ordinate indicates the relative frequency of components of given size (as a percentage of the total number of components). The following snapshot shows an example of Components report diagram, taken from M. Gasperoni's "Ebrei" corpus.
The Genealogical Completeness of a kinship network corresponds to the percentage of known ascendants (agnatic, uterine and overall) by generation. The following snapshot shows an example of Genealogical Completeness report diagram, taken from M. Gasperoni's "Ebrei" corpus.
The Fratry Distribution is the distribution of agnatic and uterine fratries (sibling groups) according to their size. The following snapshot shows an example of Fratry Distribution report diagram, taken from M. Gasperoni's "Ebrei" corpus.
***First Cousin Marriage
The First Cousin Marriages diagram shows the occurrences of the four first cousins marriages: between cross/parallel patri-/matri-lateral cousins. The four cousins types are distributed on the abscissa axis and their respective number of occurrences is detailed on the ordinate. The following snapshot shows the First Cousin Marriages report diagram of M. Gasperoni's "Ebrei" corpus.
The Ancestor chains diagram shows the composition of ancestor chains, expressed in positional notation, depending on a given degree (your choice) and gender. It is very important to know the distribution of consanguine chains and this is an additional measure of bias (Barry & Gasperoni, 2008, p. 71‑77). The following snapshot shows an Ancestor Chains report diagram, taken from M. Gasperoni's "Ebrei" corpus.
***Distribution of properties
Using the Statistics Window, it is also possible to analyze the distribution of endogenous and exogenous properties, combining queries and property codes and exporting the results as partitions (to represent with other software like, for instance, Pajek). This allows knowing precisely the profile and composition of the dataset, not only for analyzing it, but also to improve and complete it thereafter. Puck generates information and statistics on all genealogical (number and distribution of known ascendants/descendants etc.) or exogenous data (occupation, place and date of birth, etc.). The results appear in the form of tables, diagrams or partitions.
The Census Reporter Inputs window (Analysis > Circuit Census (CTRL+H) ) enables to set and operate a circuit census on your dataset. It presents a number of parameters, which can be used depending on the analysis needs.
The Pattern field is a fundamental tool, which enables you to define the type of circuits that you wish to count. It allows you to define the type of census desired : consanguine marriages, two-groups and three-groups relinking. It can be used, for circuits counting, by resorting to two input methods.
- The first way to use the Pattern field is to specify the maximal dimensions of the matrimonial circuits to be searched for.
Matrimonial Circuits have two dimensions : their width (or order) and their maximal canonic degree of consanguine relations (or depth). The first number to type into the Pattern field indicates the maximal canonic degree of circuits with width 1 (i.e. incorporating 1 marriage arc, corresponding to consanguine unions) ; the second number indicates the maximal canonic degree of circuits with width 2 (i.e. incorporating 2 marriage arcs, corresponding to two-groups relinkings), the third number indicates the maximal canonic degree of circuits with width 3 (i.e. incorporating 3 marriage arcs, corresponding to three-group relinkings).
For instance, the code 3 2 1 sets the horizon of matrimonial circuit search to blood marriages between 2nd cousins (degree 3), marriage redoublings between pairs of 1st cousins (degree 2) and marriage retriplings between pairs of siblings (degree 1). All circuits/relations of lower dimensions (for instance, blood marriages between first cousins or marriage redoublings between pairs of siblings) are included in the search.
- The second way to use the Pattern field is to enter a structural schema expressed in positional notation (i.e. XX(X)XX will find all circuits corresponding to marriages between cousins). Note that, as a rule, a structure formula is read by Puck in a socio-centered manner. That is, the formula X(H)HX and XH(H)X designate one and the same type of relation. This is important if the census is run on a subcorpus, where ego may be in but alter may not. Without further indication, a structure formula limits search to circuits/relations that exactly fit this formula (unlike the first method which only sets an upper limit). We can, however, include all circuits/relations which lie within the limits of the formula by letting it precede the character “<”. For instance, the formula “<XXX(X)XX” limits the search to all blood marriage circuits within the limits of the 5th civil degree (including marriages between first cousins, between uncles and nieces etc.). Juxtaposition of several structure formula is interpreted as a combination by logical “or”. For instance, the formula X.X(X)X XX(X)XX limits circuit search to marriages between siblings in-law or 1st cousins.
After setting the Pattern field, a matrimonial census can then be refined according to specific criteria, i.e. : Filiation, Symmetry, Sibling, Circuit and Restriction types.
- Cognatic : all consanguinity relations are permitted ;
- Agnatic : only agnatic relations are permitted (unilinear census) ;
- Uterine : only uterine relations are permitted (unilinear census) ;
- Bilateral : only bilateral relations are permitted.
The Symmetry Type checkbox enables to decide on the relations permutability between ego and alter. For example, if kinship chains between co-residents are searched, the option “symmetry” has to be activated, for co-residence being a symmetric relation. Accordingly, the chains “father-son” and “son-father” will be counted as one single category. By contrast, if kinship chains between persons and their heirs are searched, the option “symmetry” has to be deactivated, for inheritance being an asymmetric relation. Accordingly, the chains “father-son” and “son-father” will be counted as different categories.
Note : the symmetry type choice is only relevant for a non-matrimonial circuit census. If matrimonial circuits or open kinship chains are searched, ego and alter are always considered as permutable (in the first case, male or female ego will be chosen according to the chosen option, in the second case, there is no criterion for the selection of ego or alter).
- 2 (None) : no siblings assimilated. Only paternal and maternal siblings are distinguished, full siblings are counted twice (once as paternal and once as maternal siblings). This method is recommended when half-sibling relations are frequent (e.g. because of high rates of polygamy) the agnatic or uterine relationship is more important than the sibling relationship as such.
- 3 (Full) : full siblings assimilated. True paternal and maternal half-siblings are distinguished from full siblings. This method provides the maximum of information.
- 1 (All ) : all siblings assimilated. Full and half siblings are not distinguished. This method is recommended when half sibling relations rarely occur or do not matter for marriage rules.
- Circuit : counts matrimonial circuits.
- Ring : does not consider circuits/relations that completely include shorter ones (matrimonial rings and not all matrimonial circuits).
- Minor : does not consider circuits/relations that intersect with shorter and narrower ones (minor matrimonial rings and not all matrimonial circuits).
- Minimal (Minimal rings only) : does not consider circuits/relations that intersect with shorter ones (minimal matrimonial rings and not all matrimonial circuits)
- All : all married individuals (matrimonial circuit pivots) must belong to the chosen cluster.
- Ego : ego (according to a chosen kinship schema) must belong to the chosen cluster (presupposes a search expressed by formula).
- Last married : the last married individual must belong to the chosen cluster (presupposes marriage dates, for the moment they were still treated as individual properties). With closing relation, it is possible to choose different types of censuses (matrimonial, relational, absence of closing relations) from endogenous or exogenous properties (occupation, residence etc.), extending the search criteria and producing diagrams.
- The Details and Diagrams frame allows [...] The Label column enables to choose [...] The Report and Diagram check-boxes allow to [...]
- The Couples only check-box [...]
- The Mark Individuals check-box allows adding the binary property of being in a circuit of a given type to each individual who forms a pivot of a circuit. The label of the property is the circuit type in standard notation, preceded by "CENSUS" (see the Individual Property Codes). The property includes indication of Alter and appears in the Additional Data frame.
Partitioning the corpus according to such a property permits to extract/expand the sub-corpus of all individuals that are part of a circuit of the given type.
Using this property for redefining spouses in order to effect a second relational or matrimonial census permits a complex matrimonial or relational census (multiple kinship relations or intersecting matrimonial circuits).
- The Circuits as Relations check-box [...]
- The Cross-sex chains only check-box [...]
- The List out-of-circuits pairs check-box [...]
- The List all perspectives check-box [...]
The Filter field allows excluding from a circuit census a relation type. You can define the relation to be filtered by typing it in positional notation.
***Open Chains Frequencies and Closure Rate
A Relational Census can be used both in order to count non-matrimonial relations and, which is more important for a kinship network structural analysis, to evaluate some of the dataset biases. For example, if in a kinship network the number of cross patrilateral cousins is much greater than the number of cross matrilateral ones, marriages between the former will automatically result more frequent. Thus, the prominence of a given marriage type does not necessarily indicate a social preference for that type of marriage. It can merely result from the higher frequency of that specific relation (not "closed" by a marriage tie) compared to others. Thus, to understand high frequencies of given matrimonial circuits as a direct sign of a social preference can reveal itself misleading.
The closure rate (Hamberger & Daillant, 2008, p. 27-28) is an indicator that has been conceived to prevent such mistakes. As it appears in the snapshot showed below, a calculation of the Closure Rate can be obtained by selecting, in the PUCK Census Reporter Inputs window, the Open Chains Frequencies check-box. Then, on the Results table, for each type of matrimonial circuit will both appear : the total Open Chains number and its Closure Rate.
It has to be set before launching a census ad it includes several possibilities :
- Circuits as network : exports all matrimonial circuits found by the census as separate networks (with nominal indication of individuals as vertex labels), as well as a partition to distinguish vertices that occupy spouse positions in the circuit.
- Circuit induced network : produces the matrimonial network corresponding to the matrimonial census (that is a network made up only of the links that form part of some matrimonial circuit), as well as a partition to distinguish vertices that occupy spouse positions in the matrimonial network.
- Circuit induced frame network : (consanguine chains reduced to lines) produces the matrimonial network frame corresponding to the matrimonial census, embedded in the total kinship network in which marriages are coded according to the circuit(s) they are part of. This may provide a synthetic representation in which certain connective features of the matrimonial network can be identified.
- Circuit intersection network : produces the circuit intersection network corresponding to the matrimonial census, as well as the circuit intersection matrix (Hamberger & Daillant, 2008, p. 22‑24).
***Relational to Complex Matrimonial Census
If the matrimonial circuit census counts the matrimonial circuits in a kinship network, a non-matrimonial circuit census counts relational circuits, which are kinship chains "closed" by a previously defined relation (for instance, co-residence, friendship, etc.). The Closing relation frame enables to run such a census, by selecting the wanted relation.
As shown in the example above, setting the Closing Relation to "Open" and selecting the Couples only checkbox leads to a census of open chains concerning married people.
A more complex relational or matrimonial census can be effectuated by combining two censuses, using the results of the first (stored as relational properties by the Mark Individuals function) in order to redefine spouses, and running the second census on the thus transformed corpus.
In this manner, one can search for MBD marriages that are at the same time ZD marriages, bilateral cross cousins, and so on.
Such a complex census is a useful analytical complement to the inspection of the circuit intersection network.
***CORR? Instead of generating relational data from a preliminary relational or matrimonial census, they can also be directly read from a file, for instance a list of ego-alter-pairs (in the form of a two-column text file). For the precise method see the entry Relational properties from text files.
Mixed Matrimonial and Connubial Circuits
Puck allows regrouping individuals according to a certain property (chosen from a drop-down menu by double clicking on the checkbox label) and effectuate a census of :
- Mixed Matrimonial Circuits containing the relation “belonging to the same cluster”. Puck distinguishes for the moment 9 types of mixed matrimonial circuits, according as the H, HF, or HM belongs to the same cluster as W, WF or WM.
- Connubial Circuits : Puck distinguishes endogamous circuits (1 group), redoubling or exchange circuits (2 groups, arrows pointing in the same or in inverse directions) and cycle circuits (3 groups, arrows consistently directed). For each circuit, the census lists the number of connubial circuits, the number of distinct cycles that may be formed from the marriages that constitute the connubial circuit, the weight of the circuit (the geometric mean of marriages joining two groups in the circuit) and the probability of the circuit, given the relative numbers of potential spouses in the constitutive groups.
After launching a circuit census, PUCK produces a report in which are indicated : the precise number and type of the searched circuits, as well as their classification (as individuals and couples). Each report can be saved in .txt or .xls formats, by clicking on the "Save" button placed in the bottom right-hand corner.
- Census : indicates the total number of circuits and the maximum height (the canonic degree), the number of different circuits, the number of individuals and couples involved, in absolute numbers and as percentages, both in total and on the circuits concerned.
- Circuits : lists, for each type of circuit/relation (indicated in standard notation), all relations found in the corpus, with nominal indication of their pivots (and their relations: “=” for marriage, “-” for consanguinity) and the complete chain in positional notation (individuals being indicated by their identity numbers).
- Couples : lists, for each couple or pair of relatives concerned by the census, all the circuits/relations that link them, both in standard notation of the type and in positional notation of the complete chain (individuals being indicated by their identity numbers).
- Sortable list : List all relations found in the census, with the index, standard and positional notation of the relation/ring type, nominal indication of their pivots and their relations (“=” for marriage, “-” for consanguinity) and the complete chain in positional notation (individuals being indicated by their identity numbers).
- ***Diagrams : currently, this function is not implemented.
Any individual/family property (for details on property codes see here) can be used to split he corpus into subcorpuses. A subcorpus (whose title indicates the parent corpus as well as the property label and value) has the appearance of an autonomous corpus (with its own corpus window and all dependant windows), and every operation on a corpus can also be effectuated on a subcorpus. The important difference is that a subcorpus remains linked to the parent corpus, and all relation and circuit search processes, while limiting results to individuals in the subcorpus, always run through the total corpus.
The partitioning commands are located on a specific bar, placed on the top of the PUCK Main Window.
Warning : a subcorpus that is saved and re-imported as a normal corpus loses this important subcorpus property. All links to individuals outside the subcorpus are cut, and external individuals can no longer act as intermediaries for chains between members of the subcorpus.
Here, the Model drop-down menu enables to choose between Individual or Family, which are the two models of partition criteria. The Label drop-down menu contains a list of endogenous and exogenous properties, that can be set as partitioning criteria. Note that PUCK automatically integrates into the list the attributes/additional data that you might have defined during the encoding phase.
For an explanatory list of the properties codes, see here.
The Parameter field allows using as partitioning criterion some properties, such as "PEDG" or "PROG", that require a specification to operate.
For example, to segment the dataset on the basis of the number of known ascendants of a given degree, the Model field must be set to "INDIVUDUAL" and the Label field to "PEDG". In the Parameter field, you will then define the maximal generational depth to which calculate the individuals pedigree. If you set the Parameter at "2" and the Type at "Raw", PUCK will create five clusters :
- "0" (composed by individuals whose grand-parents are all unknown) ;
- "1" (one grand-parent known) ;
- "2" (two grand-parents known) ;
- "3" (three grand-parents known) ;
- "4" (all grand-parents known).
The checkbox list called Type allows choosing, between several possibilities, the most pertinent way to regroup the clusters of a given partition. This choice depends on the partitioning criterion. Here is a brief description of how these operators work :
- Raw : produces a basic partition containing as much clusters as necessary. E.g. the Gender partitioning (Model : "INDIVIDUAL", Label : "GENDER") produces 3 clusters : a Null one, a Female and a Male ones. A last name partition (Model : "INDIVIDUAL", Label : "LASTN") as many clusters as the number of last names in the corpus (plus, eventually, a null cluster) ;
- Binarization : always produces 2 clusters, one corresponding to the Pattern and the other regrouping everything but the Pattern ;
- Free grouping : allows fixing irregular (or regular) date intervals ;
- Counted grouping : allows fixing the number of clusters in which you want to divide a given period (from start … to end) ;
- Sized grouping : allows fixing the duration of clusters in which you want to divide a given period (from start … to end).
Note : The dataset can be partitioned more than once, so you can superimpose different partitioning criteria. This can be done by running once again the Add Segment button (symbol "+"). So, the successive application of different properties as partition criteria permits to refine partitioning which can be necessary for your analysis process.
Navigate through partitions and clusters
After partitioning the dataset, you can navigate through the different partitions and clusters, by using the Partition / Cluster drop-down menus, or by clicking on the Up / Down one segment buttons, placed on the right side of the instruments bar ("Δ"/"∇" symbols on the bar). All the individuals and families appearing in the Individuals and Families navigation tabs, belong then to the selected cluster. However, Ego's relatives don't necessarily find themselves in his same cluster ; and the selected cluster changes if you double-click on an individual who doesn't belong to the previous cluster. So be careful : if you navigate through the corpus by clicking on individuals, you could jump out of the starting cluster without knowing it!
- Remove current segment (the "-" symbol on the bar), which erase the selected partition ;
- Clear all segments (the "broom" symbol on the bar), which erase all partitions.
Warning : partitions are hierarchically organized. If you erase the first partition (see the Partition drop-down menu) by using the command "-", the action will be effective on all other partitions too.
- Genealogical corpora can be partitioned on a first level according to Gender (Model: INDIVIDUAL, Label: SEX) and then, on a second level, one of the clusters can be partitioned to the Family Name (Model: INDIVIDUAL, Label: LASTN) and one of these clusters is to be partitioned according to the surname (Model : INDIVIDUAL, Label FIRSTN). Use the arrow buttons to move up and down to the different levels of partition. For each item from the « Partition » list, the correspondent clusters appear in the « Cluster » list (Fig. 26).
- Partitions the corpus according to the date of birth of individuals: Start = 1500 ; Size = 100 and End = 1900 divides individuals born in 1500 to 1900 onwards into sub-sets corresponding to 100 year intervals, adding another sub-set containing individuals born before 1500 and a still further sub-set containing individuals whose birth date is unknown. (Sized grouping) (Fig. 27)
Note : for a very brief introduction to kinship simulation techniques, click here.
When opening the submenu File > New > Random network, you find two possible types of random networks : Classic and Birth-Centered. In each one of these cases, after executing the command, a dialog window opens asking to input some statistic criteria. These will determinate the network dimensions and form.