Name
readme.csv
Soubor
readme_0.csv
| README |
|---|
| 1) Dataset: |
| Mining Pits at Písečný vrch |
| Pit Inventory for Recency Index Modelling at Písečný vrch, Bečov, Czech Republic |
| Description: |
| This dataset contains tabular data used to classify mining pits at the Písečný vrch site. The dataset was used in a MaxEnt-based modelling workflow to estimate the relative probability that individual pits represent recent mining features (“Recency Index”). |
| Coordinate reference system: |
| S-JTSK / Krovak East North (EPSG:5514) |
| 2) data_table_with_predictions.csv |
| ID: |
| Unique identifier of each mapped pit. |
| x_5514: |
| X coordinate in S-JTSK / EPSG:5514. |
| y_5514: |
| Y coordinate in S-JTSK / EPSG:5514. |
| z_5514: |
| Elevation value in S-JTSK / EPSG:5514. |
| r: |
| Pit diameter in metres measured in the field. |
| deep: |
| Binary variable in metres expressing pit depth: |
| 1 = deep pit (>0.5 m) |
| 0 = shallow pit (<0.5 m) |
| quartz: |
| Visible quartzite in the pit wall: |
| 1 = present |
| 0 = absent |
| historical_land_use: |
| Category of historical land use derived from georeferenced Stable Cadastre maps: |
| arable_land, common_pasture, pasture, pasture_with_fruit_trees |
| r1958: |
| Feature identified at the pit location in the 1958 aerial photograph: |
| n = no visible feature |
| j = pit visible |
| k = quartzite / quarry trace visible |
| v = vegetation visible |
| r1963: |
| Feature identified at the pit location in the 1963 aerial photograph. |
| n = no visible feature |
| j = pit visible |
| k = quartzite / quarry trace visible |
| v = vegetation visible |
| r1975: |
| Feature identified at the pit location in the 1975 aerial photograph. |
| n = no visible feature |
| j = pit visible |
| k = quartzite / quarry trace visible |
| v = vegetation visible |
| recent_prosp: |
| Recent pit identified only during field survey: |
| 1 = pit with visible recent traces recorded during field survey |
| 0 = no visible recent traces recorded during field survey |
| slope: |
| Slope value trend at the pit location(degree). |
| surface_category: |
| Interpretation of recent terrain disturbance based on DEM analysis: |
| 1 = undisturbed terrain |
| 0 = recently disturbed terrain |
| nearest_neighbour: |
| Distance in metres to the nearest neighbouring pit. |
| difference_raster_value: |
| Value extracted from the DEM of Difference (DoD), expressing local topographic change in metres. |
| recent: |
| Final binary classification of recentness based on field survey and interpretation of historical aerial photographs. |
| 1 = recent pit |
| 0 = potentially older / non-recent pit |
| prediction: |
| Fitted Recency Index from the full model (all pits), on a 0-1 logistic scale. Higher values indicate a greater estimated likelihood of recent (20th-century) origin. |
| 1 = pits with recency index above treshold (>0.441) |
| 0 = pits with recency index under treshold (<0.441) |
| prediction_category: |
| Binary classification (1 = recent, 0 = non-recent) obtained by applying the Youden-optimal threshold, derived from leave-one-out cross-validated predictions, to prediction. |
| pred_lwr: |
| Lower bound of the 95% bootstrap confidence interval for the Recency Index, based on the 2.5th percentile of predictions across 500 bootstrap-refitted models. |
| pred_upr: |
| Upper bound of the 95% bootstrap confidence interval for the Recency Index, based on the 97.5th percentile of predictions across 500 bootstrap-refitted models. |
| pred_ci_width: |
| Width of the 95% bootstrap confidence interval (pred_upr minus pred_lwr). Larger values indicate greater uncertainty in the predicted Recency Index for that pit. |
| pred_stability: |
| Proportion of the 500 bootstrap replicate models whose classification (recent / non-recent, using the Youden-optimal threshold) agrees with the classification obtained from the full model for that pit. Values near 1 indicate a classification that is reproduced consistently under resampling; values near 0.5 indicate a classification that is essentially arbitrary under resampling and should be treated with caution. |
| loocv_prediction: |
| Predicted Recency Index for the pit obtained under leave-one-out cross-validation, i.e., from a model refit with that pit excluded from the training data. Comparing this value to prediction indicates how much a pit's own inclusion in the training data influences its predicted score; large discrepancies flag potentially influential or unstable observations. |
| 3) maxent_analysis.R |
| R script containing the full analysis pipeline used for Recency Index modelling. The script loads the input pit inventory table, defines the predictor set, assesses multicollinearity using Spearman correlations and VIF, performs MaxEnt hyperparameter tuning using AICc, fits the selected MaxEnt presence-background model, generates Recency Index predictions, evaluates model performance using AUC and leave-one-out cross-validation, determines the Youden Index threshold, calculates predictor importance and response curves, quantifies prediction uncertainty using 500 bootstrap replicates, and exports the final table with predictions and uncertainty metrics. |
| Notes: |
| This repository contains only derived tabular data used in the published analysis. Raw aerial photographs, lidar point clouds, and source cartographic data are not redistributed because they are subject to third-party rights. |
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