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Record W247449427

THE EFFECTS OF GIS ARCHITECTURE, DATA MODELS AND DATA SOURCES ON THE ACCURACY OF DIGITAL MAPS: AN EXAMPLE OF DIGITAL TERRAIN MODEL OF IBADAN REGION

2002· article· en· W247449427 on OpenAlexaboutno aff
Fabiyi O.Oluseyi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTerrainDigital elevation modelCartographyDigital dataGeographyRemote sensingComputer scienceGeologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Maps have been widely accepted as a veritable instrument in national development. They are graphic representations that facilitate a spatial understanding of things, concepts, conditions, processes, or events in the human world. Map making; which is an important means of graphically communicating information has been greatly influenced by modern technological developments including digital technology and development in geographic information systems However different type of maps exist and at differing scales. The usefulness of any map is heavily dependent on the level of accuracy of the map. The issue of map accuracy becomes prominent in developing countries where substantial parts of the countries are only partially surveyed. With the advent of GIS (Geographic Information Systems) the procedures for production of basic maps and derived maps have been revolutionized accordingly. While the conventional methods for data capture in the production of analogue maps utilized intuition and experience of the cartographer, the automated mapping functions of GIS depend on data algorithms, software techniques and architecture for carrying out data conversion, transformation and spatial analysis in map making. The paper discuses different types of errors in digital map making and their It identifies the unique problems of analogue-Digital map conversion in Nigeria, using the specific example of l production of digital terrain model of Ibadan region. The paper compares different transformation errors and the accuracy levels of digital maps. It also highlights different sources of errors in digital map-making and suggests techniques of minimizing errors in digital maps. INTRODUCTION Maps have been widely accepted as a veritable instrument in national development. Since the first world map compilation by Idrisi and his team, the issues of mapping and accuracy levels of maps have been of serious consideration in Geography , cartography, surveying and many other land based disciplines. However different type of maps exist and at differing scales. The usefulness of any map is heavily dependent on the level of accuracy of the map. The issue of map accuracy becomes important in developing countries where substantial parts of the countries are unmapped or mapped with error prone methods. With the advent of GIS (Geographic Information Systems) and cartographic packages the procedures for production of basic maps and derived maps have been revolutionized accordingly. While the conventional methods for data capture for producing analogue maps (often called map generalization) utilized intuition and experience of the cartographer, the automated mapping functions of GIS depends of Symposium on Geospatial Theory, Processing and Applications, Symposium sur la theorie, les traitements et les applications des donnees Geospatiales, Ottawa 2002 2 data algorithms, software techniques and architecture for carrying out data conversion, classification, transformation and spatial analysis. The contribution of GIS to map making will not be of significant value if the procedure does not remove or rather ameliorate the numerous problems associated with precision and accuracy in conventional map making. The improvements of digital map-making over the conventional method is documented in Fabiyi (1996). GIS makes use of data from different platform and sources for the purpose of producing digital maps. Each of these data sets needs to be transformed to a common platform for the purpose of digital map making. The procedures for digital map-making adopted by different software relates to the architecture of the package. The software architecture relates to the structure of data storage, analytical procedures and spatial analysis algorithms. CONCEPT OF ACCURACY AND PRECISION IN DIGITAL MAP MAKING. Digital map-making is the process of utilizing computer technology to produce map. A map is a twodimensional scale model of a part of the surface of the earth. This model is a systematic description or representation of the part of the earth, generally using symbols to represent certain objects and phenomena. Maps are effective ways of presenting a great deal of information about objects and the spatial relationship of objects. Maps are of different categories for example, topographic maps, planimetric maps/ cadastral maps, thematic maps, cartograms among others. Most earth related disciplines benefits immensely from cartography and revolution in the procedures and techniques of cartography is obviously affecting the processes of utilization of cartographic products as well as the revolution taking place in deferent disciplines based on the discoveries of digital/ automated cartography. With the advent of electronic computers and the development of graphic cards in modern computers, efforts were directed towards the use of computer to do some graphic jobs that were manually handled. Map Accuracy Map accuracy have been variously observed and defined. Some have differentiated accuracy from precision by indicating that accuracy is relative while precision is absolute. In this case accuracy is the relative correctness of the position of objects on a map from the real position of the object on the earth surface, Precision is the level of exactness of the object position. Aronof (1989) gave a broader consideration of Accuracy. The direction of research in Digital map making is in the area of absolute precision of data capture and map presentation. The accuracy of a digital map is not dependent on the map's scale. Instead, it depends on the accuracy of the original data used to compile the map, how accurately is this source data has been transferred onto the map, the algorithms of transformations, and the resolution at which the map is printed or displayed. In the case of analogue-digital map conversion the digital map cannot be more accurate than the original sources (paper map). However some software allow minor corrections through the input of ground measurements (ground truth) For example digital map in Arc/info format can be transformed using the ground coordinates (at least 3 point) obtained through GPs, this will adjust the digital map in accordance with the in put co-ordinates. In this case the resulting map is more accurate and has better topology than the source map. To create the spatial database for the purpose of digital map making, existing maps or manuscripts may have been digitized or scanned, and other original data, such as survey reports, aerial photographs and images, and data from third parties may also have been used. The final map will therefore reflect the accuracy of these original sources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.160
GPT teacher head0.305
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Published2002
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