INTEGRATION OF AUTHORITATIVE AND VOLUNTEERED GEOGRAPHIC INFORMATION FOR UPDATING URBAN MAPPING: CHALLENGES AND POTENTIALS
Bibliographic record
Abstract
Abstract. This paper provides a bibliometric review between integration of authoritative and volunteered geographic information for the purpose of cartographic updating of urban mappings. The adopted methodology was through a bibliometric survey of the literature published by Web of Science and Science Direct. The period was evaluated from 2005 to 2020 and the keywords used were: integration of authoritative data, volunteered geographic information, VGI, large scale topographic mapping, Authoritative urban mapping. The number of publications found was small for the topic that deals with this integration, totalizing 14 articles at Web of Science and 23 at Science Direct. 38% of them were published in the International Journal of Geo Information (ISPRS), 16% in the International Journal of Geographical Information Science. 5% were published in the Cartography and Geographic Information Science and the Computer Geosciences respectively. The other 36% is shown in several other journals, approximately 3% each. Regarding the origin of publications, 25% are in Germany (University of Heidelberg), 14% in the UK (New Castle University), 13% in China (Wuhan University), 11% in Canada (Calgary University), and other countries show percentages between 3% and 5%. Among the research, areas are physical geography, remote sensing, computer science, information science, engineering, and public administration. Among themes addressed in the articles, potentials can be pointed out as existence of models which institutions can implement management of information received collaboratively, existence of several methodologies for quality control of this information so that they can be integrated into authoritative data that are called as data conflation. Methodologies for handling big data and semantic interoperability, as well as automation of processes. This data potential is not only on platforms such as OpenStreetMap, but also on data collected through scraping from social networks such as twitter, sites, and others. Among the challenges, there are still somethings to investigate regarding consideration of temporal, historic, political, and economic aspects, as well as the consideration of legal aspects. The integration of this volunteered geographic information is necessary, mainly in cities with economic and cultural difficulties to maintain their mapping up to date, as well as the difficulty of accessing information that allows access to authoritative data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.052 | 0.097 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".