DyME-CHH (DyME - Climate, Heat and Health) is a project at the Alan Turing Institute, building upon models and tools developed as part of previous work in the AI for Science and Government (ASG) programme.
Our interaction with the natural environment plays a crucial role in all aspects of society: our health, wealth, safety and future prosperity. Climate change will bring fundamental changes to our environment that have the potential to pose significant threats to people’s health. Estimating the risks associated with higher temperatures in epidemiological studies and exposures used in health impact analyses are almost exclusively based on aggregate measures of heat (e.g. averages of measurements in an urban area or of model outputs) with the assumption that all members of the population experience the same temperatures. In reality, different members of the population will spend different amounts of time in different locations, i.e. outdoors and indoors in different types of building stock. The ability to produce high quality, disaggregated information on heat exposures experienced by different population groups would provide a step-change in our understanding of the adverse health effects associated with higher temperatures and the ability to generate information on the exposures experienced by different population groups will be essential in developing adaptation measures that ensure that everyone will have homes, and access to locations, that provide cooler, healthier, temperatures in times of extreme heat.
This project will create an impact case study on urban heat and health, connecting a climate modelling and projections tool with policymakers at local authorities in the United Kingdom.
Inspired by Cookie Cutter Data Science
├── LICENSE
├── README.md <- The top-level README for users of this project.
├── CODE_OF_CONDUCT.md <- Guidelines for users and contributors of the project.
├── CONTRIBUTING.md <- Information on how to contribute to the project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── project_management <- Meeting notes and other project planning resources
│
├── src <- Source code for use in this project.
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualisation <- Scripts to create exploratory and results oriented visualisations
│ └── visualise.py
└──
Maintainers
This repository is jointly developed and maintained by Open Research Community Building (led by Dr. Malvika Sharan) and Research Application Management (led by Dr. Aida Mehonic) teams under the Tools, Practices and Systems Research Programme at The Alan Turing Institute.
Please create an issue to share references or ideas related to the development of this project.
TBD.
TBD.
This work is licensed under the MIT license (code) and Creative Commons Attribution 4.0 International license (for documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use, and with no additional restrictions.
Thanks goes to these wonderful people (emoji key):
 Ruth Bowyer 🐮 📖 |
 Fernando Benitez 🌎 📖 |
 Jennifer Ding 🐏 📖 |
 Camila Rangel Smith 🌎 📖 |
 Aoife Hughes 🌎 📖 |
This project follows the all-contributors specification. Contributions of any kind welcome!