  Portuguese Labor force survey, from 4th quarter of 2014 onwards, started
geo-referencing the sampling units, namely the dwellings in which the surveys
are carried. This opens new possibilities in analysing and estimating
unemployment and its spatial distribution across any region. The labor force
survey choose, according to an preestablished sampling criteria, a certain
number of dwellings across the nation and survey the number of unemployed in
these dwellings. Based on this survey, the National Statistical Institute of
Portugal presently uses direct estimation methods to estimate the national
unemployment figures. Recently, there has been increased interest in estimating
these figures in smaller areas. Direct estimation methods, due to reduced
sampling sizes in small areas, tend to produce fairly large sampling variations
therefore model based methods, which tend to "borrow strength" from area to
area by making use of the areal dependence, should be favored. These model
based methods tend use areal counting processes as models and typically
introduce spatial dependence through the model parameters by a latent random
effect. In this paper, we suggest modeling the spatial distribution of
residential buildings across Portugal by a Log Gaussian Cox process and the
number of unemployed per residential unit as a mark attached to these random
points. Thus the main focus of the study is to model the spatial intensity
function of this marked point process. Number of unemployed in any region can
then be estimated using a proper functional of this marked point process. The
principal objective of this point referenced method for unemployment estimation
is to get reliable estimates at higher spatial resolutions and at the same time
incorporate in the model the auxiliary information available at residential
units such as average income or education level of individuals surveyed in
these units.
