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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".