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En el instante 23 de junio de 2026, 16:13:50 UTC,
-
Modificado el valor del campo
spatial_coverage
a[{'bbox': '{"type": "Polygon", "coordinates": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}', 'centroid': '{"type": "Point", "coordinates": [-6.92, 35.715]}', 'text': 'España', 'uri': 'http://datos.gob.es/recurso/sector-publico/territorio/Pais/España'}]
en All wildboar-vehicle collisions respond to the same variables? Looking for patterns using self-organizing maps.
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| 80 | "notes": "Selecting the most efficient mitigation measures to reduce | 80 | "notes": "Selecting the most efficient mitigation measures to reduce | ||
| 81 | animal-vehicle collisions is difficult without knowledge of the | 81 | animal-vehicle collisions is difficult without knowledge of the | ||
| 82 | circumstances on each stretch of road. The identification of patterns, | 82 | circumstances on each stretch of road. The identification of patterns, | ||
| 83 | based on the variables that explain the spatial distribution of | 83 | based on the variables that explain the spatial distribution of | ||
| 84 | road-kills would be useful to improve decision-making. We used data | 84 | road-kills would be useful to improve decision-making. We used data | ||
| 85 | mining techniques to identify patterns within a dataset of wild | 85 | mining techniques to identify patterns within a dataset of wild | ||
| 86 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 86 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 87 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 87 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 88 | neural network of competitive learning. A 4x4 matrix was derived to | 88 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 89 | obtain 16 groups of collisions with similar properties in relation to | 89 | obtain 16 groups of collisions with similar properties in relation to | ||
| 90 | the traffic, the road and the surrounding environment. This number | 90 | the traffic, the road and the surrounding environment. This number | ||
| 91 | could vary to improve the adjustment between patterns and mitigation | 91 | could vary to improve the adjustment between patterns and mitigation | ||
| 92 | measures. We used logistic regressions to model each pattern. Knowing | 92 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 93 | what the more important variables in each collision are, we could | 93 | what the more important variables in each collision are, we could | ||
| 94 | choose the best type of mitigation measures in accordance with the | 94 | choose the best type of mitigation measures in accordance with the | ||
| 95 | road segment properties. Expensive wildlife passes and fences should | 95 | road segment properties. Expensive wildlife passes and fences should | ||
| 96 | be restricted to segments of highways with high traffic volumes and | 96 | be restricted to segments of highways with high traffic volumes and | ||
| 97 | hotspots, where the probability of successful crossing is very low. | 97 | hotspots, where the probability of successful crossing is very low. | ||
| 98 | For medium and low volumes of traffic the best solutions vary in | 98 | For medium and low volumes of traffic the best solutions vary in | ||
| 99 | relation to the characteristics of the surrounding landscape.", | 99 | relation to the characteristics of the surrounding landscape.", | ||
| 100 | "notes_translated": { | 100 | "notes_translated": { | ||
| 101 | "en": "Selecting the most efficient mitigation measures to reduce | 101 | "en": "Selecting the most efficient mitigation measures to reduce | ||
| 102 | animal-vehicle collisions is difficult without knowledge of the | 102 | animal-vehicle collisions is difficult without knowledge of the | ||
| 103 | circumstances on each stretch of road. The identification of patterns, | 103 | circumstances on each stretch of road. The identification of patterns, | ||
| 104 | based on the variables that explain the spatial distribution of | 104 | based on the variables that explain the spatial distribution of | ||
| 105 | road-kills would be useful to improve decision-making. We used data | 105 | road-kills would be useful to improve decision-making. We used data | ||
| 106 | mining techniques to identify patterns within a dataset of wild | 106 | mining techniques to identify patterns within a dataset of wild | ||
| 107 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 107 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 108 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 108 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 109 | neural network of competitive learning. A 4x4 matrix was derived to | 109 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 110 | obtain 16 groups of collisions with similar properties in relation to | 110 | obtain 16 groups of collisions with similar properties in relation to | ||
| 111 | the traffic, the road and the surrounding environment. This number | 111 | the traffic, the road and the surrounding environment. This number | ||
| 112 | could vary to improve the adjustment between patterns and mitigation | 112 | could vary to improve the adjustment between patterns and mitigation | ||
| 113 | measures. We used logistic regressions to model each pattern. Knowing | 113 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 114 | what the more important variables in each collision are, we could | 114 | what the more important variables in each collision are, we could | ||
| 115 | choose the best type of mitigation measures in accordance with the | 115 | choose the best type of mitigation measures in accordance with the | ||
| 116 | road segment properties. Expensive wildlife passes and fences should | 116 | road segment properties. Expensive wildlife passes and fences should | ||
| 117 | be restricted to segments of highways with high traffic volumes and | 117 | be restricted to segments of highways with high traffic volumes and | ||
| 118 | hotspots, where the probability of successful crossing is very low. | 118 | hotspots, where the probability of successful crossing is very low. | ||
| 119 | For medium and low volumes of traffic the best solutions vary in | 119 | For medium and low volumes of traffic the best solutions vary in | ||
| 120 | relation to the characteristics of the surrounding landscape.", | 120 | relation to the characteristics of the surrounding landscape.", | ||
| 121 | "es": "Selecting the most efficient mitigation measures to reduce | 121 | "es": "Selecting the most efficient mitigation measures to reduce | ||
| 122 | animal-vehicle collisions is difficult without knowledge of the | 122 | animal-vehicle collisions is difficult without knowledge of the | ||
| 123 | circumstances on each stretch of road. The identification of patterns, | 123 | circumstances on each stretch of road. The identification of patterns, | ||
| 124 | based on the variables that explain the spatial distribution of | 124 | based on the variables that explain the spatial distribution of | ||
| 125 | road-kills would be useful to improve decision-making. We used data | 125 | road-kills would be useful to improve decision-making. We used data | ||
| 126 | mining techniques to identify patterns within a dataset of wild | 126 | mining techniques to identify patterns within a dataset of wild | ||
| 127 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | 127 | boar-vehicle collisions in Castilla y Le\u00f3n, Spain. Collisions | ||
| 128 | were grouped using a Kohonen\u00b4s selforganizing map which is a | 128 | were grouped using a Kohonen\u00b4s selforganizing map which is a | ||
| 129 | neural network of competitive learning. A 4x4 matrix was derived to | 129 | neural network of competitive learning. A 4x4 matrix was derived to | ||
| 130 | obtain 16 groups of collisions with similar properties in relation to | 130 | obtain 16 groups of collisions with similar properties in relation to | ||
| 131 | the traffic, the road and the surrounding environment. This number | 131 | the traffic, the road and the surrounding environment. This number | ||
| 132 | could vary to improve the adjustment between patterns and mitigation | 132 | could vary to improve the adjustment between patterns and mitigation | ||
| 133 | measures. We used logistic regressions to model each pattern. Knowing | 133 | measures. We used logistic regressions to model each pattern. Knowing | ||
| 134 | what the more important variables in each collision are, we could | 134 | what the more important variables in each collision are, we could | ||
| 135 | choose the best type of mitigation measures in accordance with the | 135 | choose the best type of mitigation measures in accordance with the | ||
| 136 | road segment properties. Expensive wildlife passes and fences should | 136 | road segment properties. Expensive wildlife passes and fences should | ||
| 137 | be restricted to segments of highways with high traffic volumes and | 137 | be restricted to segments of highways with high traffic volumes and | ||
| 138 | hotspots, where the probability of successful crossing is very low. | 138 | hotspots, where the probability of successful crossing is very low. | ||
| 139 | For medium and low volumes of traffic the best solutions vary in | 139 | For medium and low volumes of traffic the best solutions vary in | ||
| 140 | relation to the characteristics of the surrounding landscape." | 140 | relation to the characteristics of the surrounding landscape." | ||
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| 278 | ttp://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a" | 249 | ttp://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a" | ||
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| 284 | "state": "active", | 255 | "state": "active", | ||
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| 289 | 260 | ||||
| 290 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | 261 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | ||
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| 292 | "tags": [ | 263 | "tags": [ | ||
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| 294 | "display_name": "accidentes-y-atropellos-de-fauna", | 265 | "display_name": "accidentes-y-atropellos-de-fauna", | ||
| 295 | "id": "629c6d17-b04f-404c-900a-8c31fae0ecc4", | 266 | "id": "629c6d17-b04f-404c-900a-8c31fae0ecc4", | ||
| 296 | "name": "accidentes-y-atropellos-de-fauna", | 267 | "name": "accidentes-y-atropellos-de-fauna", | ||
| 297 | "state": "active", | 268 | "state": "active", | ||
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| 301 | "display_name": "fragmentacion", | 272 | "display_name": "fragmentacion", | ||
| 302 | "id": "627f9c0a-a199-4c69-b563-0c8d626e3194", | 273 | "id": "627f9c0a-a199-4c69-b563-0c8d626e3194", | ||
| 303 | "name": "fragmentacion", | 274 | "name": "fragmentacion", | ||
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| 312 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | 283 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | ||
| 313 | ], | 284 | ], | ||
| 314 | "title": "All wildboar-vehicle collisions respond to the same | 285 | "title": "All wildboar-vehicle collisions respond to the same | ||
| 315 | variables? Looking for patterns using self-organizing maps.", | 286 | variables? Looking for patterns using self-organizing maps.", | ||
| 316 | "title_translated": { | 287 | "title_translated": { | ||
| 317 | "en": "", | 288 | "en": "", | ||
| 318 | "es": "All wildboar-vehicle collisions respond to the same | 289 | "es": "All wildboar-vehicle collisions respond to the same | ||
| 319 | variables? Looking for patterns using self-organizing maps." | 290 | variables? Looking for patterns using self-organizing maps." | ||
| 320 | }, | 291 | }, | ||
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| 322 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | 293 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | ||
| 323 | "type": "dataset", | 294 | "type": "dataset", | ||
| 324 | "url": | 295 | "url": | ||
| 325 | //iepnb.es:443/catalogo/dataset/0c4fc7ff-9834-5128-8a07-e1b182c3e351", | 296 | //iepnb.es:443/catalogo/dataset/0c4fc7ff-9834-5128-8a07-e1b182c3e351", | ||
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| 328 | "en": "", | 299 | "en": "", | ||
| 329 | "es": "" | 300 | "es": "" | ||
| 330 | } | 301 | } | ||
| 331 | } | 302 | } |