Online Interactive Mapping: Using the Panoramic Maps Collection
Notice bibliographique
Résumé
Remember where the train station used to be? Turn left at that corner. Often, when giving directions, we assume familiarity with historical landscapes. We expect that the listener can adjust layers of mental from the past and present to imagine a specific location. By developing students' knowledge of historical landscapes, we deepen their abilities to understand our changing physical and social geography. Our students will help plan and build the landscapes of the future.They need to value and understand the land's past if they are to create socially and environmentally sustainable communities. Many state curricula require the teaching of local history in elementary and middle grades. We would like to introduce teachers to a wonderful online resource for displaying images of local communities as they may have looked more than 100 years ago. We also suggest ways to analyze changes to the landscape by comparing these historical maps with current cartographic images. By comparing images at MapQuest with those at the Panoramic Maps Collection at a Library of Congress website, students are moving to higher analytical skills beyond simple location and magnification. When students look at landscapes around them and ask, Why is that there? they must use critical and analytical thinking skills to evaluate spatial information. History, geography, science, math, and language arts are all used to understand and interpret the information presented in these maps. Panoramic Maps Collection From the late 1840s until the early 1900s, artists drew bird's-eye view landscapes of hundreds of American cities and towns. These popular provide a detailed, if somewhat idealized, image of American communities from a past era. As the nation grew and aerial photography became possible, these maps were no longer fashionable, but today they are a teaching treasure. A map of your community (or at least one from your state) may well be in the Panoramic Map Collection, 1847-1929. landscapes were drawn as if viewed from a hot air balloon, which was the only aerial transport until 1903. Most of the research for the maps, however, was done on foot and with the use of existing maps. drawings were painstakingly rendered and made into lithographs for mass production and sales. Five artists produced most of the maps in this collection. Local chambers of commerce often funded these panoramic maps, with specific businesses paying for a featured image in the map's border art. Wealthy individuals, whose mansions might be shown in a detailed inset, underwrote some of the maps. Real estate agents and chambers of commerce used the maps with prospective buyers of homes and business properties. Some maps showed not only the existing city, but areas planned for development. People would hang framed panoramic maps of their towns prominently in their homes. In the collection, there are maps from each of the 48 contiguous states, some of which have only one map (Arizona, Delaware, and Idaho) while others have hundreds (Pennsylvania has 208). There are also maps of communities in the Canadian provinces. New York and Pennsylvania were the two most populous states between the years 1870 and 1890. Pennsylvania was a central rail hub, and many travelers passing through might have wanted a souvenir of their visit--a panoramic map! activity described below involves maps of Philadelphia, Pennsylvania, from different decades in the 1800s. Teachers may also visit The Learning Page at the Library of Congress to glean other teaching suggestions for exploiting the Panoramic Maps Collection, at memory.loc.gov/ ammem/ndlpedu/collections/pmap/. Today, the fine details on these maps can be enjoyed online. Students can zoom into a map to locate streets, neighborhoods, and specific physical features. If there is a map of your city or town, it will be fun to search for familiar places to see how they appeared more than 100 years ago. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,030 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».