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2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.19659242 |
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| _version_ | 1866901270931963904 |
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| author | Osmanagich, Sam |
| author_facet | Osmanagich, Sam |
| contents | <p>Geometric patterns identified in spatial data after inspection are difficult to evaluate statistically. <br>When hypotheses are formulated a posteriori, conventional tests can overestimate significance <br>because exploratory choices are not accounted for. This problem is pronounced in small-N spatial <br>point sets, where model flexibility and feature selection strongly influence outcomes.A constrained <br>evaluation framework is applied to assess a posteriori geometric hypotheses in spatial data. The <br>approach limits the geometric degrees of freedom and conditions tests on a fixed set of candidate <br>points. It is intended for situations in which a geometric pattern is first observed and then formally <br>assessed. Point-to-curve deviations are used to compare the observed configuration with alternative <br>spatial and geometric arrangements subject to specified constraints.The framework is demonstrated <br>using a summit landscape in Central Bosnia, where a constrained logarithmic curve pattern has been <br>proposed to link a small set of named summit locations derived from LiDAR data. The observed <br>configuration occupies an extreme position relative to alternative constrained configurations within <br>the defined summit set.The analysis is limited to spatial geometry and does not address origin or <br>interpretation. The contribution is a transparent method for evaluating a posteriori geometric <br>hypotheses in small-N spatial datasets.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19659242 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | EVALUATING A POSTERIORI GEOMETRIC HYPOTHESES IN SPATIAL DATA: CONSTRAINED LOGARITHMIC CURVE PATTERNS IN A SUMMIT LANDSCAPE Osmanagich, Sam Spatial Data Analysis A Posteriori Geometric Hypotheses; Constrained Logarithmic Curves; small-N Point Sets; Summit Landscape Analysis <p>Geometric patterns identified in spatial data after inspection are difficult to evaluate statistically. <br>When hypotheses are formulated a posteriori, conventional tests can overestimate significance <br>because exploratory choices are not accounted for. This problem is pronounced in small-N spatial <br>point sets, where model flexibility and feature selection strongly influence outcomes.A constrained <br>evaluation framework is applied to assess a posteriori geometric hypotheses in spatial data. The <br>approach limits the geometric degrees of freedom and conditions tests on a fixed set of candidate <br>points. It is intended for situations in which a geometric pattern is first observed and then formally <br>assessed. Point-to-curve deviations are used to compare the observed configuration with alternative <br>spatial and geometric arrangements subject to specified constraints.The framework is demonstrated <br>using a summit landscape in Central Bosnia, where a constrained logarithmic curve pattern has been <br>proposed to link a small set of named summit locations derived from LiDAR data. The observed <br>configuration occupies an extreme position relative to alternative constrained configurations within <br>the defined summit set.The analysis is limited to spatial geometry and does not address origin or <br>interpretation. The contribution is a transparent method for evaluating a posteriori geometric <br>hypotheses in small-N spatial datasets.</p> |
| title | EVALUATING A POSTERIORI GEOMETRIC HYPOTHESES IN SPATIAL DATA: CONSTRAINED LOGARITHMIC CURVE PATTERNS IN A SUMMIT LANDSCAPE |
| topic | Spatial Data Analysis A Posteriori Geometric Hypotheses; Constrained Logarithmic Curves; small-N Point Sets; Summit Landscape Analysis |
| url | https://doi.org/10.5281/zenodo.19659242 |