TITLE:
Use of a Neural Network to Measure the Impact of Social Distribution and Access to Infrastructure on the HDI of the Municipalities of Mexico
AUTHORS:
Fernando I. Becerra López, Ricardo Pérez Ramírez
KEYWORDS:
Multilayer Perceptron, Human Development Index, K-Means, Non-Linear Correlation
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.4,
November
15,
2023
ABSTRACT: The
Human Development Index (HDI) was created by the United Nations (UN) and is the
basis for many other indicators, as well as being the origin of many public
policies worldwide. It is a summary measure of life expectancy, education, and
per capita income. These components, in addition to being global measures, show
difficulty in being impacted and, with this, advancing in the level of human
development. This work shows a model that relates variables of social
distribution and access to infrastructure in Mexico, with the HDI. These
variables were chosen through a statistical analysis based on a set of
indicators measured by the National Institute of Statistics and Geography
(INEGI) periodically at the municipal level. The statistical analysis shows
that there is no simple correlation between these variables and the HDI, so
that a supervised learning model based on a neural network was used, therefore
proposing a classification technique based on the distribution of data in the
underlying metric space. In addition, an attempt was made to find the simplest
possible model to reduce the computational cost and in turn obtain information
on the variables with the greatest impact on the HDI, with the aim of
facilitating the creation of public policies that impact it.