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time
1 - 1 / 1

Site Network Connected by Hollow-ways in Turkey

Authors:
|
John Wainwright
|
|
Laura Turnbull
|
|
|
Formats
csv, other
Nodes
1010
Edges
1432
Isolates
528
Years
-3000–-600
Access:
|
Added:
2025-12-02
Site Network Connected by Hollow-ways in Turkey

"Networks are increasingly used to describe and analyse complex archaeological data in terms of nodes (archaeological sites or places) and edges (representing relationships or connections between each pair of nodes). Network analysis can then be…

Tags
hollow-ways
•
prediction
•
sites
Modern Countries and Continents
Iraq
•
Syria
•
Türkiye
Structure
Directionality
directed and undirected
Weighted
yes
Hypergraph
no
Longitudinal
-
Multigraph
no
Multilayer
no
Multipartile
-
Probabilistic
yes
Self Loops
no
Signed
no
Spatial
yes
Data Url
https://github.com/dpriss/Filling_the_gaps
Canonical Citation
License
Creative Commons Attribution 4.0 International
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 859937.
Data Publisher
Journal of Archaeological Method and Theory, 2024.
Related Publications
Priß, D., Wainwright, J., Lawrence, D. et al. Filling the Gaps—Computational Approaches to Incomplete Archaeological Networks. J Archaeol Method Theory 32, 19 (2025). https://doi.org/10.1007/s10816-024-09688-z
Network Topics
connectivity
•
roads
Node Topics
settlement
•
site
Edge Topics
hollow-ways
•
road
Node Attributes
latitude
•
longitude
•
name
•
size
•
time-period
Edge Attributes
-
Uncertainties
Nodes
no
Edges
no
Node Attributes
no
Edge Attributes
no
Statistics
Avg. Clustering Coefficient
-
Avg. In Degree
-
Avg. Out Degree
-
Construction

"The script for the site algorithm needs to be run first to predict potential missing sites that can then be included in the hollow way algorithm. All files should be loaded into one folder. For those scripts, MatLab Version R2019b is required. All the .m files should be opened in one MatLab project. Main_programm.m is the main script which draws from the additional .m scripts and can be run either as a whole or in batches as desired. The scripts for the hollow way algorithm are numbered in their chronological order, i.e. “1.Site_data_preparation” should be run first and “4.Path_analysis” should be run last. “5.Descriptive_network_analysis” contains optional code to analyse the resulting data as presented in the paper but can be omitted or adjusted.

The input data for the individual scripts is provided in the folder “Data” in this repository. Input for the scripts “3.Network_extraction_from_shapefile” and “4.Path_analysis” is created in the respective previous script.

In script “1.Site data preparation”, the available site data from three distinctive sources are formatted and adjusted so that they can be merged into one coherent file. Fur this purpose, attributes are aligned, duplicates removed and as much information for individual sites drawn into one file.

In script “2.Connecting_hollow_ways”, the resulting site data from the previous step are used together with the original hollow way data to connect the hollow way segments into longer routes. In this process, sites and adjacent hollow ways are connected as well to form the basis for the network graphs. The hollow ways are stored as polylines.

In “3.Network_extraction_from_shapefile”, the networks created from connecting the hollow ways and the hollow ways and sites are converted from shapefiles to network graphs. This step is necessary to allow further analysis with R packages for network analysis, such as igraph or network.

In “4.Path_analysis”, an inherent issue of the networks is solved: as the computational procedure for the connection of the hollow ways leads to a high amount of superfluous connections (see paper), the result is messy and the analysis of the data becomes time- and resource-intensive. To clean the hollow way data set, a procedure was therefore developed to extract only the simple paths between all sites of the network, thereby reducing the hollow way data set significantly.

In “5.Descriptive_network_analysis”, the code for the analysis conducted in the paper is presented. However, depending on the requirements of the study and the research questions, this analysis can be either omitted, reduced or altered.

The Site Algorithm (site prediction) was developed in MatLab 2019b (The MathWorks Inc., 2019) with the toolboxes Image Processing 11.0 and Statistics and Machine Learning 11.6.

Data and scripts are available on GitHub (https://github.com/dpriss/Filling_the_gaps) and on Zenodo (https://zenodo.org/records/13825544)."

Sources

https://github.com/dpriss/Filling_the_gaps

Source Types
publication
•
repository

Pedersen, O. (2012). Ancient near East on Google Earth: Problems, preliminary results, and prospects. In Proceedings of the 7th International Congress on the Archaeology of the Ancient Near East: 12 April - 16 April 2010, the British Museum and UCL, London, Fielworks and recent research (pp. 385–393). https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-172607

Source Types
publication

https://zenodo.org/records/13825544

Source Types
publication
•
repository
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