Cemetery Archives for Burials Database

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Thomas Kiedrowski�The Pratt Institute�LIS 697�2018 May 5

A grave situation

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Archives may become:

• Underfunded
• Understaffed
• Incapable of offering records digitally or otherwise

A legal right

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• In many states, particularly with “municipal cemeteries, the burial record is government data and is classified as public data...this means the burial record must be accessible to the public for inspection”.
• Records are required by law to be registered with each state

Retrieved from: https://www.lmc.org/media/document/1/cemetery_regulations.pdf?inline=true

My inquiries

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My inquiries and pleas into archives & collections to offer database experience went unnoticed and unwanted.

What about a�universal database??

Sample data

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104 datasets were blended and reorganized.

NOT Green-Wood Cemetery Data

Note: This is a work of fiction. Names, characters, businesses, places, events, locales, and incidents are either the products of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

Concept

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Needed to write a set of business rules to understand user needs

Created a high-level conceptual data model

Entity Relationship Diagram (ERD)

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Logical data model showing entities, attributes, and relationships with Primary/Foreign Keys

Adheres to Crow’s Foot Notation Standards

MySQL

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Created database with MySQL Workbench

Created 16 tables that each contained information about each entity

User Tasks

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What are the needs of the user? and what might a user expect to find and learn from this information?

SELECT count(*) AS 'Count', section_code AS 'Cemetery Section', CASE WHEN DATEDIFF(date_death, date_birth)/365.25 < 10 THEN '09 and under' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 10 AND 20 THEN '10s (Denarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 20 AND 30 THEN '20s (Vicenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 30 AND 40 THEN '30s (Tricenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 40 AND 50 THEN '40s (Quadragenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 50 AND 60 THEN '50s (Quinquagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 60 AND 70 THEN '60s (Sexagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 70 AND 80 THEN '70s (Septuagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 80 AND 90 THEN '80s (Octogenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 90 AND 100 THEN '90s (Nonagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 100 AND 109 THEN '90s (Centagenarian)' ELSE 'Over 109 years (Supercentenarian)' END AS 'Age Brackets' FROM burial_record NATURAL JOIN plot NATURAL JOIN lot NATURAL JOIN section GROUP BY CASE WHEN DATEDIFF(date_death, date_birth)/365.25 < 10 THEN '09 and under' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 10 AND 20 THEN '10s (Denarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 20 AND 30 THEN '20s (Vicenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 30 AND 40 THEN '30s (Tricenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 40 AND 50 THEN '40s (Quadragenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 50 AND 60 THEN '50s (Quinquagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 60 AND 70 THEN '60s (Sexagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 70 AND 80 THEN '70s (Septuagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 80 AND 90 THEN '80s (Octogenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 90 AND 100 THEN '90s (Nonagenarian)' WHEN DATEDIFF(date_death, date_birth)/365.25 BETWEEN 100 AND 109 THEN '90s (Centagenarian)' ELSE 'Over 109 years (Supercentenarian)' END;

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Querying the database

�What section contained the most sexagenarians?

Myocarditis

�Also known as inflammatory cardiomyopathy, is inflammation of the heart muscle. Most often due to a viral infection.

Wikipedia entry : accessed May 2018

SELECT record_id AS 'Record', f_name AS 'First Name', m_name AS 'Middle Name', l_name AS 'Last Name' FROM burial_record WHERE person_id = ANY (SELECT person_id FROM person WHERE cause_of_death=’Myocarditis');

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Challenges

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• Different types of data
• Data organized differently
• Cemeteries not designed alike
• Many customs
• What are the user needs?

Next steps�

• Find data
• Test database to find out what queries would be most beneficial
• Rework the structure

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Thank you

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Images retrieved from Wikipedia except for database images and lucidchart diagram captures