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https://github.com/mrc-ide/arbomap

Web application displaying dengue indicators
https://github.com/mrc-ide/arbomap

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Web application displaying dengue indicators

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# ArboMap



Web application for displaying Dengue (and eventually other arbovirus) indicators on choropleth maps, on desktop and mobile. 



The application is built using [Vue](https://vuejs.org/), [Vuetify](https://vuetifyjs.com/) and [Pinia](https://pinia.vuejs.org/), and is deployed to Github Pages here: [https://mrc-ide.github.io/arbomap/](https://mrc-ide.github.io/arbomap/)



## Development



Install dependencies: `npm ci`



Build: `npm run build`



Run in development mode, with hot reloading: `npm run dev`



Lint: `npm run lint` or `npm run lint-fix`



Run unit tests: `npm run test:unit`



Run e2e tests: `npm run test:e2e`



## Data and Scripts



We work with two sets of data:

- indicator values: these are provided by the researchers in the form of xlsx files, and can be found in the `data/raw` 

directory, with `Adm1_` and `Adm2_` prefixes. These provide indicator values (force of infection, seroprevalence at age 9,

and age-stratified hospitalisations) for admin1 and admin2 regions, using GADM's region IDs. 

- geographical data defining the shape and locations of regions: we use GADM's geojson data, which has been simplified

using MapShaper. This data can be found in `public/dengue/may24/resources/geojson`.



When the app runs, all data is loaded from `public/dengue/may24/resources`. Some of this data has been further processed from the 

datasets described above. The data files are:

- `countryNames.json`: a handy mapping of country IDs to names, used for Excel download and country selection. Generated by the

script `generate_country_names_from_adm0_geojson.cjs` using data in `geojson/admin0`.

- `geojson/all_adm1_0_5_pc_by_iso.json`: a single json file containing simplified admin1 level geojson for all countries - this

is in the form of a dictionary where the country IDs are the keys, and is generated by the script `transform_adm1_geojson_to_dict.cjs`

using the file `data/raw/all_adm1_0_5pc.json`, which contains all features in the form of a single FeatureCollection. We load this

when the app first loads, to display the global view with admin1 data from all countries.

- `geojson/admin0`: country-level geojson, one file per country. We use these to draw an outline around the selected country,

- `geojson/admin1`: admin1-level geojson, one file per country. We keep this only for the small set of countries which have no admin2 regions (`countriesWithoutAdmin2`

in config.json). This geojson is more detailed than the simplified regions defined in the `all_adm1..` file. When one of these

countries is selected we load and display from these files.

- `geojson/admin2`: admin2-level geojson, one file per country. When a country is selected, we load and display from these files. 

- `indicators/admin1`: a single file containing all admin1 level indicator values. We load this when the app first loads, to display

the global view. Generated from the xlsx file in `data/raw` by the script `process-data.R`

- `indicators/admin2`: admin2-level indicator values, one file per country, loaded when the country is selected. Generated 

from the xlsx file in `data/raw` by the script `process-data.R`



We also pre-generate the global Excel files available to download through the app. These can be found in the `excel`

sub-folder of the resources folder. 



All public data is committed to github, and is not currently automatically updated using the scripts. So you need to 

remember to run scripts manually when required:



- if indicator values have changed: run `process-data.R`

- if admin1 geojson has changed: run `transform_adm_geojson_to_dict.cjs`

- if there are any changes to the list of countries, or country names: run `generate_country_names_from_adm0_geojson.cjs`



Remember to copy resulting data from the `data/processed` folder to the relevant part of `public/dengue/may24/resources`.



- After copying new indicator values to the resources folder, run `npm run generate:global-excel` to regenerate the 

global Excel files.