map_it

An sf object including a map of Italian NUTS-3 provinces

datasets

Estimation and inference of spatial and spatio-temporal
semiparametric models including spatial or spatio-temporal non-parametric
trends, parametric and non-parametric covariates and, possibly, a spatial
lag for the dependent variable and temporal correlation in the noise.
The spatio-temporal trend can be decomposed in ANOVA way including main and
interaction functional terms. Use of SAP algorithm to estimate the spatial
or spatio-temporal trend and non-parametric covariates. The methodology of
these models can be found in next references
Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
particularly referred to the focus of the package, Minguez, R.,
Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Roman Minguez

pspatreg

Spatial and Spatio-Temporal Semiparametric Regression Models
with Spatial Lags

Roberto Basile

Maria Durban

Gonzalo Espana-Heredia

map_it function

An sf object with 103 rows and 2 columns:<dl>
 <dt>COD_PRO</dt>
<dd>province (NUTS-3) coded as a number.</dd> <dt>geometry</dt>
<dd>geometry (polygons) of the sf object.</dd> 
</dl>

Format

map of Italian provinces — map_it

An sf object with 103 rows and 2 columns:<dl>
 <dt>COD_PRO</dt>
<dd>province (NUTS-3) coded as a number.</dd>

 <dt>geometry</dt>
<dd>geometry (polygons) of the sf object.</dd>

 
</dl>

State of Data and AI Literacy Report 2025

map_it: map of Italian provinces

Description

Usage

Arguments

Format