ZanZeMap

Mosquito map developed for Fondazione Edmund Mach, displayed on https://www.zanzemap.it/. See LICENSE.md for licensing conditions.

The light version with basic controls only:

The full version with all features:

Data updates with Python scripts

1. Update the file

Clone this repo with git clone https://github.com/do-me/zanzemap.git or simply download it via the browser.

Just overwrite this file data_processing/data/in_updatable/albo_alpine_ML_Standardised_MeanSpatPred.nc with the new one. Keep the same name.

2. Run the script

• Install uv: https://docs.astral.sh/uv/getting-started/installation/#installing-uv.

• Change the directory in your terminal from the root directory to data_processing with: cd data_processing

• Then run this command to execute the script:

uv run --python 3.12 --with-requirements requirements.txt process_netcdf_layers.py

First run will take a minute depending on your internet speed. Following runs will be much faster (10-20s) as the dependencies are cached.

3. Copy outputs

Copy these files

data/out/EU_NUTS3_01M_2025.fgb
data/out/IT_comuni_2025.fgb
data/out/model_output_trentino_2025.fgb

to https://zanzemap.it/. Done. You should see the update after a site refresh in the browser.

Misc

• process_geojson_layers_5_years_mean.ipynb is how I preprocessed all the 5 years mean layers. This notebook should not be run again.
• utilities.py includes utility functions called in the notebooks
• I converted the jupater notebook to plain python with jupyter nbconvert process_netcdf_layers.ipynb --to python

---

Appendix

1 Run script as notebook

For debugging the notebook can be run with these steps:

Create kernel: Install anaconda or miniconda, then run these commands once to create a virtual environment and install all dependencies:

cd data_processing
conda create --name zanzemap python=3.12
conda activate zanzemap
pip install -r requirements.txt

Optionally if you have uv installed replace pip by uv pip for faster setup.

Simply head over to your favorite notebook IDE (like VS Code), open the notebook and select the kernel zanzemap and run all cells.

2 Netcdf file specifics

Can be checked in python with

import xarray as xr
ds = xr.open_dataset("data/in_updatable/albo_alpine_ML_Standardised_MeanSpatPred.nc")
ds

1. Dimensions: Axes

• Z1 (size: 32):
• latitude (size: 76):
• longitude (size: 151):

2. Coordinates:

• longitude: An array of 151 floating-point numbers, ranging from 2.15 to 17.15.
• latitude: An array of 76 floating-point numbers, ranging from 50.55 down to 43.05.
• Z1: An array of 32 floating-point numbers from 1.0 to 32.0.

3. Data Variables: These are the actual data arrays.

• Std_alboeggs: A 3-dimensional array with the shape (32, 76, 151), corresponding to the Z1, latitude, and longitude dimensions. It contains 367,232 data points of type float32. Standard deviation of mosquito (specifically Aedes albopictus) egg counts.
• crs: Coordinate Reference System (CRS) of the data.

3 Run and build webapp locally

Local run and build

Install dependencies (assumes node.js is installed)

npm install 

For local development start server with

npm run dev 

Build with

npm run build

If you want to check the distribution files after build, cd into dist folder and run a web server

npx server

This serves all the generated static files locally. Do not use python3 -m http.server 8080 as it doesnt include all relevant mime types for wasm, relevant for maplibre! So the app will appear broken even though it isn't.

The password zanze2025 for staticrypt is defined in the build script in package.json.