Point pattern and spatial analyses using archaeological and environmental data – A case study from the Neolithic Carpathian Basin

Kempf, Michael; Günther, Gerrit

doi:10.1016/j.jasrep.2022.103747

Cited by 6 publications

(8 citation statements)

References 61 publications

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“…On the other hand, second-order properties describe inherent spatial dependency within the point pattern [ 47 ], such as attraction or repulsion, and occur when a point’s location is influenced by the presence or absence of other points [ 48 ]. Ecological studies commonly use point pattern analyses [ 48 , 49 ], and these methods have increasingly been applied in archaeological research [ 2 , 6 , 35 , 36 , 50 , 51 ]. Point pattern analysis provides a clear terminological frame that naturally highlights issues of interaction with the environment (first order properties) and with other sites (second order properties) (see Fig 2 ).…”

Section: Methods and Datamentioning

confidence: 99%

Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany

Bilotti,

Kempf,

Oksanen

et al. 2024

PLoS ONE

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Point Pattern Analysis (PPA) has gained momentum in archaeological research, particularly in site distribution pattern recognition compared to supra-regional environmental variables. While PPA is now a statistically well-established method, most of the data necessary for the analyses are not freely accessible, complicating reproducibility and transparency. In this article, we present a fully reproducible methodical framework to PPA using an open access database of archaeological sites located in south-west Germany and open source explanatory covariates to understand site location processes and patterning. The workflow and research question are tailored to a regional case study, but the code underlying the analysis is provided as an R Markdown file and can be adjusted and manipulated to fit any archaeological database across the globe. The Early Iron Age north of the Alps and particularly in south-west Germany is marked by numerous social and cultural changes that reflect the use and inhabitation of the landscape. In this work we show that the use of quantitative methods in the study of site distribution processes is essential for a more complete understanding of archaeological and environmental dynamics. Furthermore, the use of a completely transparent and easily adaptable approach can fuel the understanding of large-scale site location preferences and catchment compositions in archaeological, geographical and ecological research.

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Section: Methods and Datamentioning

confidence: 99%

Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany

Bilotti,

Kempf,

Oksanen

et al. 2024

PLoS ONE

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“…(2) Ripley's L-function, or the K-function, counts the average number of points that are on average close to each other. It assesses whether point occurrences show clustering or dispersion, or if they are distributed uniformly (Clark & Evans, 1954;Baddeley et al, 2012;Carrero-Pazos, 2019;Kempf & Günther, 2023):…”

Section: The F G K and L-functionsmentioning

confidence: 99%

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

Sa’i,

Aleem,

Musa

et al. 2024

Preprint

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Climate change monitoring and analysis is a critical aspect that involves the consideration of both spatial and temporal dimensions. Improved spatial distribution of the Global Navigation Satellite System (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS Radio Occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley's F, G, K, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran's P-value of 0.001 and a Moran's I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide, Hence, this study suggests the utilization of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution.

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“…All formal analyses in this paper have been conducted using R software. The codes underlying the analysis of this paper were adapted from Kempf and Günther (2023) and are available from https://zenodo.org/record/7307543 (KemPf and Günther, 2023). The rbias package can be downloaded from https:// zenodo.org/record/7071418 (Günther et al, 2022).…”

Section: Formal Analysesmentioning

confidence: 99%

“…Quantitative, digital statistics, and spatial analyses are common tools in archaeological research that focuses on the recognition of patterns in past societies' behavior and particularly settlement distribution and land-use (Brandolini and Carrer, 2021;Carrero-Pazos et al, 2019;GillinGs et al ., 2020;KemPf, 2020dKemPf, , 2021KemPf and Günther, 2023;VerhaGen and Whitley, 2012). In this context, the use of point pattern analysis has become increasingly important to understand the spatiotemporal components behind the socio-cultural, political, and ecological driving factors that control individual and group decision-making processes (BeVan, 2012;Crema et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Focusing on the transitions from pre-Roman to the Roman period and to post-Roman chronology, we assemble a large number of archaeological sites of various origin to measure environmental landscape transformation processes as well as the impact of modern land-use and landcover change. We build on the statistical methods described in and ( 2021) and Kempf and Günther (2023) and test the infrastructural bias by applying the recently developed R-package rbias, which ultimately is designed for this purpose (Günther et al, 2022;KemPf and Günther, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Modelling Bias and Environmental Preferences in Archaeological Spatial Analysis

Kempf

2023

VEG

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Point pattern analysis (PPA) has gained momentum in archaeological research that models large-scale distributions of sites and explanatory covariates. As such, there has been increased interest in the bias of archaeological distributions, which mostly have an impact due to modern land-use change. These interactions, however, have not yet been fully explored. In order to better understand archaeological point patterns as functions of explanatory covariates, we offer three different approaches: (i) environmental preference modelling of archaeological records in different chronological phases; (ii) a custom bias surface that represents the variability of the regional landscape; (iii) an R-package (rbias) allowing the generation of a fuzzified bias surface based on Open Street Map (OSM) data.

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Point pattern and spatial analyses using archaeological and environmental data – A case study from the Neolithic Carpathian Basin

Cited by 6 publications

References 61 publications

Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany

Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany

An Investigation of GNSS RO Data Pattern for Climate Change Monitoring and Analysis over Africa

Modelling Bias and Environmental Preferences in Archaeological Spatial Analysis

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