The DOI matches its Crossref record.
A simple positional accuracy measure for linear features
Abstract
In this paper we propose a simple technique for assessing the positional accuracy of digitized linear features. The approach relies on a comparison with a representation of higher accuracy, and estimates the percentage of the total length of the low accuracy representation that is within a specified distance of the high accuracy representation. The approach deals successfully with three deficiencies of other methods: it is statistically based; is relatively insensitive to extreme outliers; and does not require matching of points between representations. It can be implemented using standard functions and a standard scripting language in any raster or vector GIS. We present the results of a test using data from the Digital Chart of the World.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Improved Modeling of Elevation Error with Geostatistics
Fisher, P. (1998) · GeoInformatica · doi:10.1023/a:1009717704255 · Cited by 133
Güimil-Fariña, A., & Parcero-Oubiña, C. (2015) · Journal of Archaeological Science · doi:10.1016/j.jas.2014.11.030 · Cited by 95
Issues in replication and stability of least-cost path calculations
Herzog, I. (2022) · Studies in DIGITAL Heritage · doi:10.14434/sdh.v5i2.33796 · Cited by 23
Explaining known past routes, underdetermination, and the use of multiple cost functions
Lewis, J. (2024) · Journal of Archaeological Method and Theory · doi:10.1007/s10816-023-09621-w · Cited by 15
Lynch, J., & Parcero-Oubiña, C. (2017) · Journal of Archaeological Science: Reports · doi:10.1016/j.jasrep.2017.09.019 · Cited by 13
Metadata
- Authors
- GOODCHILD, M. F., & HUNTER, G. J.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 1997-04
- Volume
- 11
- Issue
- 3
- Pages
- 299-306
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2047185150
- Cited by
- 333
- References
- 0
- FWCI
- 27.40
- Type
- journal article
- Language
- en
- Open access
- closed
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: A simple positional accuracy measure for linear features. https://app.cochat.ai/papers/10.1080/136588197242419
- The paper
- GOODCHILD, M. F., & HUNTER, G. J. (1997). A simple positional accuracy measure for linear features. International Journal of Geographical Information Science. https://doi.org/10.1080/136588197242419
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.
