Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 18

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Predictive Spatial Modelling

Verhagen, Philip, Whitley, Thomas G.·2020·Archaeological Spatial Analysis

Abstract

Predictive modelling is a set of techniques, used since the 1970s, to predict the location of archaeological sites in uninvestigated areas as an aid to spatial planning, for example, in Cultural Resource Management. Predictive modelling is also used to develop and test scientific models of human locational behaviour, as it is based on either statistical extrapolation from known archaeological data, or on explanatory models of site location preference. In practice, a number of methods can be used in predictive modelling, and the resulting maps of predicted site locations or density can vary in accuracy. The main difficulties in producing accurate and precise predictive models are coupled to the resolution and representativeness of the archaeological and non-archaeological datasets used, the theoretical frameworks underlying the models, and the nature, or lack, of model testing. Nonetheless, predictive models are often found useful to provide basic protection to areas of high sensitivity, and can save costs for development projects or archaeological investigations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.4324/9781351243858-13

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Metadata

Authors
Verhagen, P., & Whitley, T. G.
Venue
Archaeological Spatial Analysis
Publisher
Routledge
Published
2020-01-16
Pages
231-246
Cited by
18
References
1
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Predictive Spatial Modelling. https://app.cochat.ai/papers/10.4324/9781351243858-13
The paper
Verhagen, P., & Whitley, T. G. (2020). Predictive Spatial Modelling. Archaeological Spatial Analysis. https://doi.org/10.4324/9781351243858-13

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.