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Mosquito Monitor

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The City of Winnipeg and City of Brandon have stopped conducting mosquito surveillance. This project will be active again in spring of 2025.

I developed an automated workflow and Shiny App that, in conjunction, collects mosquito trap data from government sources, stores historical data, and displays data in a digestable format. Historical data for the City of Brandon, City of Winnipeg and Western Manitoba will be displayed in addition to daily updates on City of Winnipeg and City of Brandon trap counts. When the data is updated, a Tweet is sent from the Mosquito Monitor Twitter Account.

Updates

Version 0.0.4

  • Identified a bug that causes the figure to be updated before the updated data is committed to the repository, resulting in old data being tweeted. This is likely due to how GitHub Actions handles triggers via commits
    • Added wait times such that the data is up-to-date by the time the scripts to update figures and Tweet runs
  • Added a workflow to include City of Brandon mosquito trap counts in both the Shiny application as well as the Twitter account notifications

Version 0.0.3

  • Changed y axis trans to log1p for the faceted Winnipeg plots to better display the poisson-like mosquito trap count distributions
  • Implemented a Twitter Bot using Python that sends a Tweet with the city map when the data has been updated

Version 0.0.2

  • Introduced a map of Winnipeg displaying the number of specimens caught in each zone separated by Forward Sortation Area
  • Introduced comparable weather data along with City of Winnipeg historical trapping data (e.g., temperature, precipitation)

Known Issues

  1. The R3C (Broadway / The Forks / Portage and Main) Forward Sortation Area (FSA) includes geometry for Northwest Winnipeg, out of city limits, with a land area of 143 square kilometres. If the R3C FSA is included in the colour scale, a large portion outside the city is also coloured and distorts the map. Thus, this FSA in the city map is not coloured. A ticket has been sent to Statistics Canada.

Citing This Repository

Baril, Cole. (2024). Mosquito Monitor: An Automated Workflow and Shiny App for Mosquito Trap Data Collection and Visualization [Repository]. GitHub. https://github.com/colebaril/Mosquito_Monitor

GitHub Actions

This repository and Shiny app relies on various automated GitHub Actions workflows:

  1. Scrape Data: Checks the City of Winnipeg Insect Control website once per hour for updates. If an update is found, the data is pushed to the main branch in this repository as mosquito_data.csv. This Shiny App mosquito_data.csv to display data. For the Brandon variant, it retrieves data from the City of Brandon API.

  2. Update Figure: When mosquito_data.csv is changed in the main branch, a new figure, wpg_mosquito_map_tmp.png, is pushed to the main repository in this branch. For the Brandon variant, a table containing the five trap counts is generated.

  3. Tweet Update: When wpg_mosquito_map_tmp.png is changed in the main branch, a Tweet is sent via Tweepy and tweet_mosquito_update.py by the Mosquito Monitor Twitter Account with the date the data was updated as well as the Forward Sortation Area (FSA) Boundary map of Winnipeg with FSAs filled with the number of mosquitoes collected. For the Brandon variant, the table is tweeted.

Shiny App

A shiny app reads the mosquito_data.csv (mosquito_data_bdn.csv for Brandon) file remotely and displays summary tables, figures and downloadable data. Weather data was obtained from Environment and Climate Change Canada's Winnipeg A CS Weather Station using the weathercan package. The map of Winnipeg was constructed using data obtained from Statistics Canada Boundary Files and the sf package.

Disclaimer

This application is not affiliated with, endorsed by, or sponsored by the City of Winnipeg, City of Brandon, or Government of Manitoba. All data utilized in this application is obtained from publicly available sources provided by the City of Winnipeg, City of Brandon and Manitoba Government. The creators of this application do not claim ownership of the data provided by the City of Winnipeg, City of Brandon or the Manitoba Government and do not assume responsibility for the accuracy or completeness of the data. Users of this application should verify any information obtained from this application with official sources.

Data

The City of Brandon and City of Winnipeg publishes their historical data annually on their websites. The Manitoba Government does not post any historical trapping data for provincial traps on their website, only the number of Culex tarsalis specimens identified. If you have any sources of data you wish to contribute, please email me at [email protected].

References

Baril, C., Pilling, B.G., Mikkelsen, M.J. et al. The influence of weather on the population dynamics of common mosquito vector species in the Canadian Prairies. Parasites Vectors 16, 153 (2023). https://doi.org/10.1186/s13071-023-05760-x.

City of Winnipeg (2024). Nuisance Mosquito Trap Counts. https://legacy.winnipeg.ca/publicworks/insectcontrol/mosquitoes/trapcounts.stm.

City of Brandon (2024). Mosquito Abatement Program. https://brandon.ca/mosquito-abatement/mosquito-abatement-program.

Environment and Climate Change Canada (2024). WINNIPEG A CS Weather Station. https://climate.weather.gc.ca/climate_data/.

R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Python Software Foundation. (2024). Python Language Reference, version 3.10. Available at https://www.python.org.

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Wickham H (2024). rvest: Easily Harvest (Scrape) Web Pages. R package version 1.0.4, https://CRAN.R-project.org/package=rvest.

Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2023). shiny: Web Application Framework for R. R package version 1.8.0, https://CRAN.R-project.org/package=shiny.

Pebesma E, Bivand R (2023). Spatial Data Science: With applications in R. Chapman and Hall/CRC. doi:10.1201/9780429459016 https://doi.org/10.1201/9780429459016, https://r-spatial.org/book/.

LaZerte S, Albers S (2018). “weathercan: Download and format weather data from Environment and Climate Change Canada.” The Journal of Open Source Software, 3(22), 571. https://joss.theoj.org/papers/10.21105/joss.00571.

Firke S (2023). janitor: Simple Tools for Examining and Cleaning Dirty Data. R package version 2.2.0, https://CRAN.R-project.org/package=janitor.

Grolemund G, Wickham H (2011). “Dates and Times Made Easy with lubridate.” Journal of Statistical Software, 40(3), 1-25. https://www.jstatsoft.org/v40/i03/.

Xie Y, Cheng J, Tan X (2023). DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.31, https://CRAN.R-project.org/package=DT.

Chang W (2021). shinythemes: Themes for Shiny. R package version 1.2.0, https://CRAN.R-project.org/package=shinythemes.

Roesslein, Joshua. (2024). Tweepy: Twitter for Python! Available at https://www.tweepy.org.

Reitz, Kenneth, & Chisom, Cory. (2024). Requests: HTTP for Humans [Software]. Available at https://docs.python-requests.org/en/latest/.