An Approach to Developing a Spatio-Temporal Composite Measure of Climate Change-Related Human Health Impacts in Urban Environments
Notice bibliographique
Résumé
Introduction: \nRapid population growth along with an increase in the frequency and intensity of climate change-related impacts in costal urban environments emphasize the need for the development of new tools to help disaster planners and policy makers select and prioritize mitigation and adaptation measures. Using the concept of the resilience of a community, which is a measure of how rapidly the community can recover to its previous level of functionality following a disruptive event is still a relatively new concept for many engineers, planners and policy makers, but is becoming recognized as an increasingly important and some would argue, essential component for the development and subsequent assessment of adaptation plans being considered for communities at risk of climate change-related events. The holistic approach which is the cornerstone of resilience is designed to integrate physical, economic, health, social and organizational impacts of climate change in urban environments. This research presents a methodology for the development of a quantitative spatial and temporal composite measure for assessing climate change-related health impacts in urban environments.\nMethods:\nThe proposed method is capable of considering spatial and temporal data from multiple inputs, relating to both physical and social parameters. This approach uses inputs such as the total population density and densities of various demographics, burden of diseases conditions, flood inundation mapping, and land use change for both historical and current conditions. The research has demonstrated that the methodology presented generates sufficiently accurate information to be useful for planning adaptive strategies. To assemble all inputs into a single measure of health impacts, a weighting system was assigned to apply various priorities to the spatio-temporal data sources. Weights may be varied to assess how they impact the final results. Finally, using spatio-temporal extrapolation methods the future behavior of the same key spatial variables can be projected.\nAlthough this method was developed for application to any coastal mega-city, this thesis demonstrates the results obtained for Metro Vancouver, British Columbia, Canada. The data was collected for the years 1981, 1986, 1991, 1996, 2001, 2006 and 2011, as information was readily available for these years. Fine resolution spatial data for these years was used in order to give a dynamic simulation of possible health impacts for future projections. Linear and auto-regressive spatio-temporal extrapolations were used for projecting a 2050’s Metro Vancouver health impact map (HIM).\nConclusion:\nResults of this work show that the approach provides a more fully integrated view of the resilience of the city which incorporates aspects of population health. The approach would be useful in the development of more targeted adaptation and risk reduction strategies at a local level. In addition, this methodology can be used to generate inputs for further resilience simulations. The overall value of this approach is that it allows for a more integrated assessment of the city vulnerability and could lead to more effective adaptive strategies.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».