Culture&Territories#3

Neighbourhood & City
Data collection
The statistical information comes from the 2010 Population and Housing Census
conducted by the National Institute of Statistics and Geography of Mexico. The space
of analysis is composed by 1,661 Basic Geostatistics Areas (AGEBs by its acronym
in Spanish) which corresponds to the subdivision of the municipal areas of the MMA.
74 AGEBs with data gaps are excluded. Eighteen socioeconomic indicators were
estimated in 1,587 AGEBs. The indicators of access to and use of ICTs were
integrated in the dimension social environment. Finally, a matrix of 16 columns
(indicators) by 1,587 rows (AGEBs) was produced from which the MQOLI was
calculated.
Construction of the MQOLI
The index is constructed by a four-phase process revealed in Fig. 2. The core is
the configuration of an algorithmic model that leads to the reduction of data by
synthesizing the main independent variables of the phenomenon. The selected
software is the statistical package for social sciences SPSS 24.0 of IBM (2016).
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Fig. 2: Elaboration process for the MQOLI. Source: Own elaboration based on CONAPO, 2010.
Geostatistical analysis
The approach is quantitative and the test to check the feasibility of the factor
analysis is the Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity test (BST). The
data is accepted only when KMO is greater than 0.5 and the level of significance of
the Bartlett test are less than 0.1. The method to be used is Principal Component
Analysis (PCA). The PCA is a technique that extracts and prioritizes information
from large data sets and determines the number of underlying dimensions contained
in a system of observed variables, known as “components,” with the first component
accounting for most of the variation in the data, Leva (2005). The VARIMAX
Rotation was included for the ranking of significant variables and construction of
a synthetic index of measurement. Verification is sought through the sedimentation