# Rural Attractivness back-end service

After the update of source data, refresh the service cache

GET https://publish.lesprojekt.cz/nodejs/refresh


List of all the datasets

GET https://publish.lesprojekt.cz/nodejs/datasets

Attractivity metadata information

GET https://publish.lesprojekt.cz/nodejs/ontology


Attractivity data for the region with ID equal to the 'nuts' parameter

GET https://publish.lesprojekt.cz/nodejs/scores/{nuts}/


Attractivity data for all the regions in source CSV data

GET https://publish.lesprojekt.cz/nodejs/scores


Computes and returns attractivity data for all the NUTS regions based on the incomming datasets and factor weights

POST https://publish.lesprojekt.cz/nodejs/scores


Computes and returns clusters based on attractivity data for all the NUTS regions and based on the incoming datasets and factor weights

POST https://publish.lesprojekt.cz/nodejs/clusters


## Start the system service
sudo systemctl start fz-node-rural_attractiveness

## Install

Install R-project >= 3.6

(On Windows: Add R.exe, Rterm.exe and Rscript.exe to PATH)

`Rterm`
`install.packages("needs")`
`install.packages("jsonlite")`
`install.packages("cluster")`
`q()`

Install NodeJS + npm

`npm i`
`npm start`


# About the attractiveness calculation

For each region and each factor, the index of attractiveness is _idx<sub>f</sub> = &sum; ds<sub>i</sub> / N<sub>ds</sub>_ , where _ds<sub>i</sub>_ is a normalized value for dataset _i_ and _N<sub>ds</sub> is a number of datasets in the factor.

For each region the aggregated index of attractiveness is then _idx<sub>a</sub> = (&sum; (&sum; ds<sub>i</sub> * w<sub>f</sub>)) / (&sum; N<sub>ds</sub> * w<sub>f</sub>)_, where _w<sub>f</sub>_ is a weight of the factor.