Web-based public participation GIS application : a case study on flood emergency management
Bibliographic record
Abstract
Scientific summary The increasing prevalence of natural disasters is driving people to pay more and more attention to emergency management. Progress in catastrophe analysis capabilities based on Geographical Information System (GIS) may allow the needs of public participation to be considered. Synchronous data sharing between citizens and emergency workers could effectively promote the process of decision making. This thesis introduces an interactive web-based application which mainly deals with flood risk management in Kamloops in Canada. The application is built for citizens and emergency workers using three layers: (1) the client side is developed in HTML and JavaScript; (2) the web server layer, which connects the users and the database, is implemented in PHP; and (3) the database contains PostgreSQL, GeoServer and OSM. Except the city map, PostgreSQL stores the spatial information with the support of OpenGIS. Generally, the application meets the initial objectives. Citizens can access present shelter information and register their own requirements for shelter, while emergency workers have the power to manage all the shelters and warehouses based on the available flood information and figure out the supply allocation solution based on the response from the public. On the other hand, the application also provides useful routing functions for both citizens and emergency workers, such as searching the available shortest path to a shelter, and computing the optimized allocation routes between all the shelters and warehouses. This practical study proved that Public Participation GIS (PPGIS), combined with IT knowledge, can provide very useful tools for decision making when facing a flood risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".