Approaches to compositional data analysis ompositional data are vectors, called compositions, whose components are all positive, it satisfies the sum equal one and has a Simplex space. The sum constraint induces the correlation between the components and this requires that the statistical methods for the analysis of datasets consider this fact. The theory for compositional data was developed mainly by Aitchison in the 1980s, and since then, several techniques and methods have been developed for compositional data modelling. This work presents the main approaches for the statistical analysis of independent compositional data, such as Dirichlet regression (natural distribution to compositional data) or the use of transformations log-ratios that aim to leave the simplex space for to Euclidean space. Also describes the methods for cases where the assumption of independence cannot be satisfied, for example, spatial dependence compositional data. For these cases, there are in the literature methods of analysis based on the theories developed for univariate geostatistics analysis or use of logratios transformations with the inclusion of the spatial dependence generated by the distance between the points. In addition, to revisiting the already diffused methods, this work propose the use of the Generalized Estimation Equation (GEE) method as an alternative for the analysis of independent compositional data and with spatial dependence. The GEE only requires the specification of functions that describe the mean and correlation matrix (covariance structure, therefore, it is not necessary to assign a probability distribution to the data or transformations. The application of the GEE method for independent compositional data presented results as efficient as Dirichlet regression or log-ratios transformation. Compositional data with spatial dependence, log-ratios transformations presented predicted values close to the real values. GEE method was more effective than the traditional geostatistical approach, however, compared with the other methods, It was the one that presented the high residual values.