rural-attractivness-service

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_f = ∑ ds_i / N_ds , where ds_i is a normalized value for dataset i and N_ds is a number of datasets in the factor.

For each region the aggregated index of attractiveness is then idx_a = (∑ (∑ ds_i * w_f)) / (∑ N_ds * w_f), where w_f is a weight of the factor.