Calculating economic indexes per household and censal section from official Spanish databases

Abstract

In the competitive environments, in which all sorts of organisations move it is of utmost importance to have information about clients. Public databases offer information about households and families. However, the non-crossed and nongeoreferenced format of these databases often makes it difficult to extract typologies and information. There are only two public databases from which to get information at the household or family level in Spain: Population and Housing Censuses, which provide aggregated and georeferenced information, and the Family Expenditure Surveys, which provide information on household consumption, both published by the National Statistics Institute. The two databases cannot be directly crossreferenced, because the Family Expenditure Surveys offer a detailed description of the families, whereas the Census provides the same data but aggregated without cross-references. In this paper, we define a procedure for cross-referencing these DBs and calculating the economic household indexes for Spanish censal sections that define the average quarterly economic behaviour of the households located in each censal section. The necessary Symbolic Data Analysis procedure is based on neural networks and provides an estimate of the trend in these indexes over a series of years. The procedure can be easily extrapolated to similar problems with official data sources from other countries.

Extending UML for Modeling Data Mining Projects (DM-UML)

“…There is also a new attribute, "income", which is added and gathered from a new data source provided by the Census Bureau, namely, the Population and Housing Censuses and Household Expenditure Surveys. After processing, this attribute offers an estimate of the average income per household in the area related to a zip code [43]. The income attribute is established by merging the customer table and the censusderived tables through the zip code attribute.…”

Lag‐ℓ forecasting and machine‐learning algorithms

“…ANN has gained popularity in a variety of applications (Burrell & Folarin, 1997;Zhang et al, 1998;Oh & Kim, 2002;Frutos et al, 2003;Oh et al, 2006;Kim et al, 2009). In particular, ANN is a universal-function approximator that can map a non-linear function better than most other statistical machine-learning methods so that it is less sensitive to error-term assumptions and can tolerate noise, chaotic characteristics and heavy tails (White, 1989;Kaastra & Boyd, 1996).…”