Software Open Access

Spatial Humanities: the application of Neyman-Scott Cluster Process in landscape archaeology

Filippo Brandolini

Data collector(s)
Stefano Costanzo
Research group(s)
Habab Idriss Ahmed; Andrea Zerboni
Andrea Manzo

The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: "Costanzo S., Brandolini F., Ahmed H., Zerboni A., Manzo A - Creating the funerary landscape in eastern Sudan", PLOS ONE 16(7): e0253511.

The original dataset was compiled by S. Costanzo (Università di Napoli “L’Orientale”, Italy) as part  of his PhD project within the frame of the IAEES - Italian Archaeological Expedition to the Eastern Sudan (Università di Napoli “L’Orientale”, Italy). The raster products (GeoTiff) were elaborated by dr. F. Brandolini with GRASS GIS and SAGA GIS software. This research was supervised by prof. Andrea Zerboni (Università degli Studi di Milano, Italy) and prof. Andrea Manzo (Università di Napoli “L’Orientale”, Italy).


Funeral landscapes are a specific example of archaeological landscapes that embed the relationship between environment, burials and human behaviour. In this study, we employ the Point Pattern Analysis (PPA) technique to explore the choice of location of a desk-created dataset of several thousands stone-built medieval Islamic funerary monuments (qubbas),  found along the foothills of isolated rocky outcrops in the remote semi-arid Kassala province of Eastern Sudan. In the region a comprehensive geomorphological and geoarchaeological survey (Costanzo et al. 2021) was carried out in the field to assess the main archaeological features, highlighting the existence of an impressive number of archaic and modern qubbas, whose spatial distribution can be interpreted using specific tools, including, for the first time in archaeology, of the Neyman-Scott Cluster Process.

Archaeological Sites Dataset Creation

The dataset explored in this research has been compiled adopting a remote sensing approach. In particular, free-open access satellite imagery collections were manually explored through the plugin QuickMapServices (NextGIS, 2019) in the GIS software QGIS 3.4 (QGIS Development Team, 2019). A 5 x 5 km grid was superimposed to the study area, which hereafter will be referred to as ROI (Region of Interest), to guarantee an orderly examination, gradually marking the completed squares. Adequate symbology was used for different kinds of archaeological evidence, manually and individually pinpointing all the recognizable features. Terrain surveys have been conducted in sampled areas of the ROI to validate the archaeological features detected through remote sensing observation.

Point Pattern Analysis (PPA)

PPA (Baddeley, A., Rubak, E., Turner, R., 2015) has been performed using the package spatstat (Baddeley and Turner, 2005) within the software system for statistical computing R (R Core Team, 2019) through the visual interface of Rstudio (RStudio Team, 2019).

List of files included in

  1. R_script_code named "PPA_qubbas" in .rmd and .html formats.
  2. Output folder: png and .txt products of the R script code
  3. GeoTiff folder (.TIFF file format):
    • Elevation
    •  Slope
    • Aspect
    • Geomorphons
    • Topographic Position Index (TPI) 100, 500 and 1000
    • Convergence Index (CI)
    • Euclidean distances from Igneous rocks outcrops (I.R.Dist)
    • Euclidean distances from Metamorphic rocks outcrops (I.M.Dist)
    • Euclidean distances from streams network (S.Dist)
    • Tumuli Cumulative Viewshed Analysis (CVA)
  4. EsriSHP folder (.shp file format):
    • qubbas folder: qubbas arcaheological sites (qubbas.shp)
    • tumuli folder: tumuli arcaheological sites (tumuli.shp)
    • ROI folder: Region Of Interest (ROI.shp)

Corresponding Authors

  • R script code: dr. Filippo Brandolini (McCord Centre for Landscape - School of History, Classics and Archaeology, Newcastle University, Armstrong Building, Newcastle upon Tyne - NE1 7RU)
  • GeoTiff products: dr. Filippo Brandolini (McCord Centre for Landscape - School of History, Classics and Archaeology, Newcastle University, Armstrong Building, Newcastle upon Tyne - NE1 7RU)
  • Archaeological sites datasets: Stefano Costanzo (Dipartimento Asia, Africa e Mediterraneo, Università degli Studi di Napoli "L'Orientale", Napoli, Italy)


  • dr. F. Brandolini:
  • S.Costanzo:


This research was carried out as part of S.C.’s PhD project within the frame of the IAEES - Italian Archaeological Expedition to the Eastern Sudan. The IAEES is funded by the Università di Napoli “L’Orientale”, ISMEO - Associazione Internazionale di Studi sul Mediterraneo e l’Oriente, and the Italian Ministry of Foreign Affairs. The Expedition is also generously supported by the Regional Government of the Kassala State. Additional financial support was provided by Italian Ministry of Education, University, and Research (MIUR) through the project ‘Dipartimenti di Eccellenza 2018–2022’ (WP4—Risorse del Patrimonio Culturale) awarded to the Dipartimento di Scienze della Terra ‘A. Desio’ (University of Milan, Italy). All authors wish to thank the archaeological authorities of the Republic of the Sudan, the National Corporation for Antiquities and Museums, the Ministry division in Kassala, their welcoming staff and the skilled drivers who made the field surveys possible.  Finally, the authors thank Prof. Francesco Carrer (Newcastle University, Newcastle upon Tyne, UK) for his comments on the R script code, and Prof. Isaac Ullah (San Diego State University, San Diego, CA - USA) for his suggestions on the use of the Cumulative Viewshed Analysis Grass GIS module.

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  • Baddeley, A., Turner, R., 2005. spatstat: An R Package for Analyzing Spatial Point Patterns. Journal of Statistical Software, Articles 12, 1–42.

  • Baddeley, A., Rubak, E., Turner, R., 2015. Spatial Point Patterns: Methodology and Applications with R. CRC Press.

  • Costanzo, S. et al. 2021. Geomorphology and palaeohydrography of the Southern Atbai Plain and western Eritrean Highlands (Eastern Sudan/Western Eritrea). J. Maps (2021) doi:10.1080/17445647.2020.1869112

  • NextGIS, 2019. QuickMapServices

  • QGIS Development Team, 2019. QGIS Geographic Information System. Open Source Geospatial Foundation Project.

  • R Core Team, 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.

  • RStudio Team, 2019. RStudio: Integrated Development for R. RStudio, Inc., Boston, MA.

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