Testing least cost path (LCP) models for travel time and kilocalorie expenditure: Implications for landscape genomics

Kyle M Gowen,Timothy S De Smet,Song Gao

doi:10.1371/journal.pone.0239387

Abstract

Least Cost Path (LCP) analysis allows a user to define a cost parameter through which cost of movement can be assessed using Geographical Information Systems (GIS). These analyses are commonly used to construct theoretical movement through a landscape, which has been useful for creating hypotheses concerning prehistoric archaeology and landscape genomics. However, LCP analysis is commonly employed without testing the generated LCP(s), complicating its usefulness as a methodological tool. This paper proposes a model for analyzing movement in ArcGIS by using topography data to calculate slope. This slope data is then then used to calculate LCPs based on travel time and kilocalorie expenditure. LCPs were constructed in the Nature Preserve at Binghamton University, a 182-acre area that consists of wetland and mountainous terrain, and a Fitbit® Surge activity monitor was used to test the accuracy of the model’s predictions. Paired sample t-tests show a lack of significant difference between calculated and walked time in our analysis (p = .953), suggesting that our model can estimate travel time between two points based solely on slope of the region. Paired sample t-tests also show a lack of significant difference between calculated and observed kilocalorie expenditure (p = .930), suggesting that our model is also capable of estimating kilocalorie expenditure associated with movement between two points. Finally, paired sample t-tests confirm that straight line distances do not reflect real movement through a terrain (p = .009), highlighting the need for alternate measures of movement when analyzing the effects of local landscape on movement. Our current model shows strength in its estimations of travel time and kilocalorie expenditure based on topography of a region–future iterations of the model need to establish the statistical similarity between our model’s estimations and recorded values for walking time and kilocalorie expenditure.

Highlights

This paper proposes a model for analyzing movement through a region based on topography using ArcMap, a program in the ArcGIS suite
As a response to this, we constructed least cost path (LCP) utilizing an assessment of travel time as the cost parameter by using Tobler’s Hiking Function to assess movement speed in relation to slope [1]. These LCPs provided an estimation of the travel time between two villages in the highlands, a useful tool for comparing genetic distances, as the analysis revealed that several villages were more than a full walking day away
Before beginning LCP construction, we used ArcMap to measure the straight line distances sequentially between points of the Red Wing Trail (RW) and Ant Hill Trail (AH) trail to reflect the sequential nature of our model

Summary

Introduction

This paper proposes a model for analyzing movement through a region based on topography using ArcMap, a program in the ArcGIS suite. Our model uses topography data to calculate slopes throughout a region, which are used to calculate least cost paths (LCPs) based on time spent traveling and kilocalorie expenditure based on modified equations related to theoretical movement and energy expenditure [1,2,3]. Our methodology found no significant difference between its calculation of walked time vs actual walked time for the seven paths included in the model (p = .953), indicating that our model is capable of estimating walking time between two points if the slope of the region is known. Our model found significant difference between straight line distances and the walked trails included in the study (p = .009), indicating that straight line distances are limited in their usage as an analytical tool when considering movement and interaction through a landscape. A desire to understand the finer elements of population interaction and intermingling necessitates a consideration of the constraints that landscape places on biological organisms

Objectives

Methods

Results

Discussion

Conclusion