Exploring the spatial pattern of heatwaves in the city
Based on challenge #06:
Visualizing urban heat wave patterns
In this project we created a Data Package and basic data science environment to visualize the locations of sensors, create a heat map using open source tools, and begin to address the more technical part of the challenge: Develop a transfer function ... to correct for the estimated radiative bias of each station of the monitoring network.
Heat islands in Bern
Data Package 🌐 www 🌐 json
- data_harmonized_3 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■
- Met_Zoll_HW192 ■ ■ ■ ■ ■ ■
- Metadata_Logger ■ ■ ■ ■ ■ ■
The Data Package contains data from the three heatwaves that was published by the researchers, plus additional specifications of the locations of the sensors, and meterological observations.
We have collected useful references and sketched a concept application that could be built with this data - see Project Notes.
👌 Results 💁
- We have collected are lots of links to similar data and applications around the world, and sketched a basic design for an app inspired by our research into related projects. You can find these links and sketch in our Slack.
- There is a basic suggested set up for doing open data science in Python to explore this data further. A basic Jupyter notebook (nbviewer preview) was started for further work in exploring the data. Please see README above for instructions on getting the code running.
- A Data Package can be used for sharing this data further with the scientific and citizen scientist community. This is the basis of the GitHub repository (Source link below)
- We collected some data of our own in Bern during the hackathon using the Logair.io open hardware sensor, just to have as a basis for comparison in regards to the data model.
Watch video of our presentation:
🚧 Under construction 🚧
- Determine the roadmap and put together a more specific concept for this application.
- Work out the design into a workframe that makes this data accessible and interesting to lay users.
- Upload and compare data from the LogAir mobile sensor.
- Discuss further Data Management needs with research groups on this topic.
🎍 Contributions welcome 😍
- You are welcome to connect via GitHub (Source) and Slack (Contact) if you would like to continue this project.
We are trying to reproduce and interpret research of climate scientists into mapping and modelling of heat spots, or urban heat islands in the city using DIY (low cost and accessible-build) temperature sensors.
This is a project started at the #EnergyClimateHack hackathon on August 31, 2021. For more background visit the challenge page here, and contact us via GitHub issues or the Slack channel if you have questions.
See also NOTES.md for a sketch of our app idea, additional links and references.
This repository contains data collected during three heat waves in Berne, Switzerland, during June 2018, June 2019, and July 2019.
The dataset was created and published by the Climatology Group of the Institute of Geography (Oeschger Centre for Climate Change Research, University of Bern), available as open access at Burger, Gubler, Heinimann, Brönnimann 2021.
We made an effort to "harmonize" the timestamp fields in the data, i.e. put them all in the same UTC format (some of them were only with AM/PM or vorm./nachm. indication). Both the original (
1-s2*.csv) and adjusted (
data_harmonized*.csv) data is in the
data subfolder, along with the locations of the sensors (
This is complemented by meteorological variables (Radiation, Precipitation, Wind speed, Wind direction & Temperature) from the official measurement station in Zollikofen during the same three heatwaves. See
Met_Zoll*.csv. Caution! During hw18 wind speed was measured in m/s, during hw191 and hw192 it was measured in km/h.
We have ensured all data is in UTF-8 and a Simple Data Format (CSV), and created a Data Package containing all the readings and control measures. This can be validated and explored with Frictionless Data tools.
This repository includes a Jupyter data science notebook which you can preview here (nbviewer) to facilitate the sharing and further analysis and development using the latest open data sharing standards.
To start a cloud-hosted notebook that you can run and modify, use Binder or Colab.
Slightly slower start
To get this running on your local machine, we suggest:
- Install Anaconda and open a shell in the root folder of this project.
- Create a new environment using
conda env create -n heatspots -f=environment.yml
- Run the command
jupyter notebookto start Jupyter
You will see an initial Heatmap visualization. Click "Run all".
This project is open source using the MIT License
Joined the team
Initial data science notebook and instructions
Joined the team
added harmonized csv files (@matthme)
Added conda env and demo notebook
Add files via upload
The source of these data files is the Open Access publication https://www.sciencedirect.com/science/article/pii/S2212095521001152?via%3Dihub#s0160 https://doi.org/10.1016/j.uclim.2021.100885 Available under a Creative Commons licens
Initial commit (@matthme)