This repository supports the paper (***) and provides code and data for two illustrative examples. These examples demonstrate the application of spatial clustering methods, perimeter/area constraints, genetic data calculations, and risk assessments based on environmental data.
This example utilizes a subset of the data used in the paper to demonstrate the following:
- Spatial Clustering: Group data points based on spatial proximity.
- Perimeter Limitation: Apply constraints to limit the perimeter of clusters.
- Genetic Data Calculation: Compute genetic values (g-values) for clusters.
- Risk Assessment: Perform risk calculations based on vegetation data.
This example uses Atlas of Living Australia (ALA) records of koalas in New South Wales (NSW) from 2022-2025. It includes:
- High-Frequency Observation Areas: Identify areas with high observation densities.
- Area-Based Splitting: Use maximum area constraints instead of perimeter constraints to subdivide larger clusters.
This example highlights the versatility of the clustering method for ecological management.
- Clone this repository:
git clone https://github.com/eilishmcmaster/insitu_workflow.git
- Open the R scripts for each example and follow the instructions in the comments.
- Ensure the input data files are in the correct directory structure.
(***)
For questions or feedback, please contact ***.