Visualizing Dutch Church Records in East Jersey!

First of all, I really enjoyed this exercise and becoming familiar with the many options for organizing, contrasting, categorizing, and combining data in Tableau Public. However, one problem that I encountered in this process underscored the dangers of digital data storage. Long story short, in updating my software to prepare for the new program, my computer struggled to retrieve many of the files, bookmarks, and notes I had accumulated over the past week (including the ones later used here). Before beginning this post about the exciting possibilities of data visualization, the issue of first and foremost protecting previous data is an important reminder. Because what is data visualization without the data to begin with?

To experiment with data visualization, I used Tableau Public to display the earliest burial records from Bergen, New Jersey’s Reformed Protestant Dutch Church. With the goal to use data visualization tools to understand the population size, geographic spread, and social structure of the largest Dutch community in the first decade of British East Jersey, this project focused on the burial records of church members between the years 1666-1680.

Example 1: Graphing Names & Identities

This first graph (above) demonstrates the ability to visualize the language reflected in early American church records. These lists have always struck me as inconsistent in whether or not they name the individual, which seems like an important detail in books documenting death. To create a better understanding of why these entries are so different, I organized the data to reflect both who was identified and unidentified and what family descriptors were being used. After mapping these first fifty data points, it became quickly apparent that adult men were always named and never described in terms of family relations in these early Dutch documents, while ¬†children were rarely mentioned by name, and even adult women, described always as either “married” or “widowed” to a named man, were less consistently identified. The graph presents an interesting burial trend that can tell historians much about gender and age relations in East Jersey’s Dutch communities.

Example 2: Graphing Populations

This second graph, taken from the exact same data entries used above tells a very different story based on these early burial records of Bergen’s Reformed Protestant Dutch Church. Created also on Tableau Public, it calculates the number of burials based on date and places of residence rather than on the individuals. In the end, it provides insight into the number of burials recorded each year as well as the spread of Dutch church members across geographic locations in the area. Some more striking estimates and changes in the the population of Dutch persons in late seventeenth century East Jersey could become even more apparent with the expansion of this data visualization into vital records of ¬†later decades.

While I invested the bulk of my time on this assignment testing out the different features of the program to understand all of the ways in which I could manipulate the extensive church data, the fifty entries that I included are immensely helpful in revealing some general trends from the period that are often difficult to read in their original formats and in demonstrating the possibilities for further analyzing such information.