Ancient roads; Archaeological Mobility; Comparative Analysis; Spatial Uncertainty; GIS


Curriculum Vitae | CV
Email | josephlewis1992@gmail.com
Linkedin | https://uk.linkedin.com/in/josephlewis1992
Github | https://github.com/josephlewis


PhD (Probationary) in Archaeology (2020-2024)
University of Cambridge


MSc Geographical Information Science | Distinction - 78%
University of Leicester

Dissertation - 80% | The Suitability of Using Least Cost Path Analysis in the Prediction of Roman Roads in the Highland and Lowland Zones of Roman Britain

Introduction to GIS - 75%; Programming in R - 87%; Earth Observation and Remote Sensing - 84%; Spatial Information Science - 75%; Geographical Visualisation - 67%; GIS Research Methods in the Field - 73%


BSc (Hons) Applied Geology | Upper Second Class -
University of Plymouth


Interested in Computational Archaeology; Geographic Data Science; Spatial Modelling; Spatial Uncertainty; Bayesian Data Analysis

Proficient in R; Python; QGIS; ArcGIS; SQL


Awards


Open-Oxford-Cambridge AHRC DTP - Judy and Nigel Weiss Studentships at Robinson College Match-funded Studentship - Selected for full scholarship based upon academic merit alone.

Vice-Chancellor’s & King’s College Scholarship (now Honorary) - Selected for a full scholarship on the recommendation of the University’s selection committee for Cambridge International and King’s College Scholarships. Awarded to 250 highest ranked students, irrespective of nationality.

CASA Prize for best paper on Spatial Analysis - GISRUK 2018 | Paper ; Presentation

2nd Prize GISCRG Dissertation Prize 2017

Royal Institution of Chartered Surveyors Prize 2017 - Best Dissertation


Published and Peer-reviewed Papers


Lewis, J., 2020. Probabilistic Modelling using Monte Carlo Simulation for Incorporating Uncertainty in Least Cost Path Results: a Postdictive Roman Road Case Study. SocArXiv, mxas2, ver. 17 peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.31235/osf.io/mxas2

Lewis, J., 2020. Visibility of the Gask Ridge road from simulated Watchtowers: A Monte Carlo testing approach. Journal of Archaeological Science: Reports 33, 102482. https://doi.org/10.1016/j.jasrep.2020.102482


Software


Author and Creator of the R package leastcostpath | CRAN status CRAN Downloads Total

Easy calculation of Least Cost Paths and Least Cost Path networks using multiple cost functions. Allows for the incorporation of barriers that inhibit movement, as well as the propagation of uncertainty through the use of probabilistic LCPs.


Pre-prints


Lewis, J., 2020. Probabilistic Modelling using Monte Carlo Simulation for Incorporating Uncertainty in Least Cost Path Results: a Roman Road Case Study. https://doi.org/10.31235/osf.io/mxas2

Lewis, J., 2020. Visibility of the Gask Ridge Road from Simulated Watchtowers: a Monte Carlo Testing Approach. https://doi.org/10.31235/osf.io/ebt9n


Presentations


ArcheoFOSS 2020 - 15-17/10/2020, Online (originally Viterbo, Italy) |
Leastcostpath: Modelling Pathways and Movement Potential Within a Landscape ; Presentation

Computer Applications and Quantitative Methods in Archaeology (CAA) 2020 - 14-17/04/2020, Oxford (UK) |
Cancelled due to Covid-19 (https://caa-international.org/2020/06/11/caa-2020-cancelled/)

The Archaeologies of Roads - 07-08/11/2019, Florence (Italy) |
Seeing While Moving: Direction-Dependent Visibility of Bronze Age Monuments Along a Prehistoric Ridgeway in Cumbria, England

Geographical Information Science Research Group - 2018, Leicester (United Kingdom) |
The Suitability of Using Least Cost Path Analysis in the Prediction of Roman Roads in the Highland and Lowland Zones of Roman Britain


Discussions


Reflections on Modelling Movement Potential using Least Cost Paths |
A reflection on the concepts of mobility and movement when applying Least Cost Path analysis to archaeological problems.


Notes from Books


Spatial Simulation: Exploring Pattern and Process by David O’Sullivan and George L.W. Perry


Statistical Rethinking by Richard McElreath


Complexity in Landscape Ecology by Green et al. (2020)


Statistics and Probability