Geoprocessing Solutions Developed While Calculating the Mean Human Footprint™ for Federal and State Protected Areas at the Continent Scale
Bibliographic record
Abstract
We calculated the mean Human Footprint™ (HF) for 196,498 polygons representing state and federal administrated Protected Areas (e.g., National Forests, National Parks, State and/or Provincial Parks, etc.) of Canada, Mexico, and the Continental United States. Separate calculations were made for (1) the area in each protected area which ranged in size from less than one to over 11 million hectares and (2) the area outside and within 10 km of each protected area. We used “Last of the Wild’s” V. 2 (2005) for North America as the data source for Human Footprint™ values with spatial reference. This paper is about the technical problems we encountered using ArcGIS 9.3 and Spatial Analyst to accomplish this task in a timely manner. We wrote several scripts to automate processes and address overlapping polygons resulting from zone calculations of 10 km around each protected area (doughnut-shaped polygons defining the zones from which to calculate mean HF adjacent to protected areas). We learned that Spatial Analyst does not honor the object integrity of overlapping polygons when using them to define zones for calculating zonal statistics from raster data. We tried alternative solutions including the use of Hawth’s Analysis Tools v3.27 (Zonal Statistics [++]) and writing scripts in Visual Basic 6.0 to separate overlapping polygons and to calculate zonal statistics both as a table and output raster. One of the four scripts resulting from this project was written to calculate the 10 km zone around each Protected Area polygon. This script can be used to calculate a separate ‘doughnut’ polygon for any distance outside of any size polygon, even if it shares boundaries with other polygons. We also discovered that the zonal statistics function in Spatial Analyst does not calculate all of the zones in a large dataset even if the polygons do not overlap. Our solution for this problem is described in this paper as an iterative process ending with another custom script to define the raster value located under the ‘label point’ of each polygon in a dataset. Ultimately, we successfully calculated the mean Human Footprint™ from a spatially defined raster both inside and outside the nearly 200,000 polygons defining the boundaries of Protected Areas in North America (http://cec.org/atlas). MCFNS 2(2):138-144.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
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 source (direct Gemma or distilled Codex), 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".