Use of a Neural Network to Measure the Impact of Social Distribution and Access to Infrastructure on the HDI of the Municipalities of Mexico

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.