The strategies of advanced local spatial data infrastructure for Seoul Metropolitan Government
Bibliographic record
Abstract
The LSDI (Local Spatial Data Infrastructure) of SMG (Seoul Metropolitan Government) began from 1996 and it entered the phase 5 in 2017. So far, the LSDI of SMG has been established by the influence of the NSDI (National Spatial Data Infrastructure) of the MOLIT (Ministry of Land, Infrastructure and Transport), which is a ministry of the central government and the ICT (Information & Communication Technology) plan of SMG. SMG is on the way of transforming to a smart city and IT (Information Technology) and services such as Network, Wi-Fi and Big data are in the world class. Even though the ICT infrastructure is excellent, the maturity of the LSDI of SMG is relatively insufficient. The aim of this study is to develop a strategy of advanced LSDI phase 5 of SMG. More strategic approach is required for the long term success and sustainability of the LSDI. For this purpose, with theoretical background of the LSDI, this study reviewed the cases of the USA and Germany on the LSDI assessment and the cost benefit analysis were reviewed. It was followed by the examination of the characteristics of the US local government where the LSDI developed the most, and York of Canada, a winner region of URISA (Urban and Regional Information Systems Association)’s ESIG (Exemplary Systems in Government). This study reviewed the development history, budget, laws and regulations and imminent issues of the LSDI of SMG. With the above cases and analysis, the study proposed 5 strategies for advanced LSDI of SMG which are human resources, organization, cost benefit analysis of the LSDI, governance and systematic LSDI plan development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".