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Enregistrement W2075264008 · doi:10.1179/0008704113z.00000000087

Fifty Years of Cartography: Some Personal Reflections

2013· article· en· W2075264008 sur OpenAlex

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Notice bibliographique

RevueThe Cartographic Journal · 2013
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueGeography Education and Pedagogy
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésOfficerGeographyScale (ratio)CartographyGovernment (linguistics)SuspectReading (process)Thematic mapSociologyPolitical scienceArchaeologyLaw

Résumé

récupéré en direct d'OpenAlex

When the first issue of The Cartographic Journal appeared 50 years ago, I was in Kenya working as an Education Officer for the Government of Kenya and at the same time conducting research for my Ph.D. thesis on rural development in Fort Hall District (renamed Muranga) in which I was working. In thinking about what cartography means to me I thought it would be interesting to compare what I was doing cartographically 50 years ago to what I am doing cartographically today. Cartography featured largely in my research on rural development, while I was in Kenya and formed a complete separate volume of my Ph.D. thesis. The technology I had available was very basic pen-ink and Letraset! Fortunately, there was one base map available at the unusual scale of 1 : 79 200. I never did discover the reason why the Survey of Kenya chose this scale, but I suspect that it was the size of the paper on which the whole District could be printed on one sheet. Using this base map I collected and mapped large amounts of thematic information, both qualitative and quantitative relating to economic and socio-cultural development in the District. I was an early proponent of what is now known as Volunteered Geographic Information. My high school students and their parents helped me collect information for my maps including, among other things, detailed rainfall statistics from a network of rain gauges in the network of primary schools, traditional periodic market information, qualitative surveys in questionnaire form on the effect of land consolidation, the location of village wells, the location of maize mills, transportation routes and costs and even the archaeological sites of the pit dwellings of the pre-Kikuyu people knows as the Gumba (Taylor, 1966). Education was a very important commodity to the people of the District and, as a result, cooperation and enthusiasm in providing and collecting the information I was seeking was never a problem. Much of the material was also integrated into the teaching of geography at the only secondary school which took 30 students a year from a primary school population of over 30,000 pupils. With a team of students, we could attend a market, record all of the prices for different products as well as where both buyers and sellers had come from. This allowed the mapping of market catchment areas. For transportation, we could stop all of the buses, including the minivans known as ‘matatus’, record what goods as well as people they were carrying, the fares being charged and the time taken to reach the main tarmac road to Nairobi. Depending on the season and the intensity of the rainy season, this could vary from 2 hours to days stuck in the mud! Mapping this original information was a major means of understanding the development challenges facing the District. Of special interest to me as a geographer were spatial relationships between and among different distributions and I attempted to do this by using hand drawn transparent overlays. This analytical methodology was very basic but nevertheless useful but after two or three overlays it was very difficult to see anything, especially since without electricity I could not construct even a primitive light table. In 1967, I became involved with the Harvard Laboratory for Computer Graphics where Howard Fisher and his colleagues were developing the early SYMAP computer programs as well as GRID, an overlay program based on a raster matrix. I could put in XY coordinates by hand at a distance manually using a SYMAP ruler and send these by mail to Harvard to be processed. Using GRID I could stack up as many as 20 variables which greatly increased my ability to identify spatial relationships. Harvard also developed early three-dimensional representation programs which were particularly interesting. By this time I had left Kenya and was working at Carleton University in Ottawa, Canada where I continued to analyse and map the large amounts of information I had collected in Kenya. Using the 3D programs, I was able to test hypotheses such as that on ‘growth centres’ for example. The theory argued that wealth would spread from an urban growth centre out into its rural hinterland. When I used a two-dimensional representation of average farm income to create a three dimensional representations of Muranga District, I found that the situation was exactly the opposite to that expected of the theory. The closer the small farmer was to the growth centre of Thika the poorer the average income was and this was shown very dramatically by the representation. The town of Thika is in the lower right hand corner of Figure 1 which shows average farm income. This was an early use of visualisation as an analytical tool. I bought my first digitizer in 1968. It was a custom made Autotrol and cost US$24,000 which was a very large sum The Cartographic Journal Vol. 50 No. 2 pp. 187–191 50th Anniversary Special Issue, May 2013 # The British Cartographic Society 2013

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,304
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,040
Tête enseignante GPT0,349
Écart entre enseignants0,309 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle