Analysis and design of a GIS-enabled virtual public meeting space using UML for participatory municipal planning
Bibliographic record
Abstract
The main aim of this research is to develop and test Web-based Public Participation Geographical Information Systems (WebPPGIS) to enable public involvement and participation in municipal planning and decision making. This objective is based on the belief that by providing citizens with access to information and data in the form of maps and visualisations they can make better informed decisions and it can immerse them into the spatial decision making process. This thesis presents a prototype implementation serving for spatially related discussions which is based on the GeoVPMS (GIS-based Virtual Public Meeting Space) model introduced by (Li et al., 2007). Moreover, a prototype has been analysed, designed and implemented using UML (Unified Model Language) approach to demonstrate a Web GIS-based architecture with utilization of various open source GIS and other OSS (Open Source Software) tools. In addition, it depicts a cost effective model of n-tier (multi-tier) Web integrated application prototype that can facilitate online public participation in municipal planning and development processes. Its components include online GIS-based participation forum as well as notification system enhance communication during spatially-related discussions in municipal planning and manage all kinds of notice among members as well as general public participants. Furthermore, the spatial data handling components used in this prototype is designed to help the public to explore the spatial contexts related to the issues under planning with and without addressing the form, whereas this contribution makes the protytope more effective and successful. In addition, the prototype is demonstrated with a scenario of public participation in spatial planning using Region of Peel's 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".