Cemetery Mapping and Digital Data Analysis: A Case Study in Minnesota, USA
Bibliographic record
Abstract
This study examines how geospatial technologies can be used in the aid of local-level cemetery management with limited resources using a case study in Woodland Hills Memorial Park Cemetery, Minnesota, USA. The hard-copy records in a handwritten ledger were manually transferred into an Excel table. The spatial data of the gravesites were collected using a Trimble Geo 7X unit with a Zephyr antenna and a Laser Rangefinder sensor over the summer of 2017. A geodatabase was constructed by joining the Excel table with the GPS data in GIS. A procedure was also developed to map the spatial distributions of plots and analyze the demographic data. It was demonstrated that a very high locational accuracy could be achieved based on carefully designed GPS data collection strategies. In addition, the data analysis results revealed that there were 12,190 plots in total, approximately half of which were still available for purchase. Among the 5,906 inhabitants buried at the Woodland Hills, many were ethnically German and Scandinavian, of whom 9.7% were veterans and nearly half were from the Greatest Generation (born between 1901 and 1927). The birth, death, and age distributions are significantly different between the nonveteran and veteran groups. Clustered patterns were identified for the filled plots and all the Generation categories. Such results will be beneficial to local cemetery managers to plan for further development as well as to future historians or individuals interested in the local culture and history. The proposed methods can greatly facilitate local-level cemetery data collection, mapping, query, and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".