KlemenCE548/Fractal-dimension-of-line-features-Repo: Code for: An automated GIS model for calculating horizontal and vertical fractal dimensions of hiking trails

The repository contains eleven python scripts to calculate the horizontal and vertical fractal dimension of line features in ArcGIS Pro. The aim of the research team contributing these scripts was to study the horizontal and vertical fractal dimension of hiking trails. However, any type of linear geographic features can be investigated using these tools. Fractal dimension is a powerful approach to capture the cross-scale complexity of geometric features. Requirements: ArcGIS Pro 3.4 or later The following spatial data in an ArcGIS Pro geodatabase: a set of vector linear features a digital elevation model (DEM) covering the area of the linear features The eleven scripts are intended to be run in sequence to complete all processing and analytical steps. Minor modifications to fit a particular use case will be necessary (e.g., setting paths and file names). In addition, the ArcGIS Pro environment will need to be reloaded after some steps. Refer to the comments in each script for guidance. Background: Linear geographic features can be simple - a straight line - or highly complex. Fractal theory offers ways to characterize the complexity of these features across spatial scales. The higher above 1 the calculated fractal dimension of a feature, the less linear it is. The code in this repository implements box counting to estimate the fractal dimension of linear features. Box counting is a standard algorithm in fractal analysis. The approach works by placing a grid mesh over a feature and counting the number of grid cells that the feature intersects. This is done across a range of box sizes, and the counts are used to estimate fractal dimension via regression. In contrast to other implementations, this one enables the calculation of both the horizontal and vertical fractal dimensions separately. This enables the analyst to study the relationship between both dimensions in relation to surface topography and may help to better assess and predict characteristics of conditions along the linear feature, correlations with other geographic information known along the feature.