The strategies of advanced local spatial data infrastructure for Seoul Metropolitan Government
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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 it