# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 2 Related Works  
### Categorical Data Comparison--Literature Review  
The literature on geospatial data quality is extremely rich, and those on the implementation of machine learning solutions as part of data quality improvement strategies are gaining popularity. This brief review will thus cover only one aspect, namely, the comparative analysis of maps and datasets containing categorical data. Categorical data typically result from mapping, classification, or modeling; hence most of the literature in the field of qualitative data comparison deals with land cover/land use or landscape data [11]. Differences between categorical maps can be characterized and measured in a variety of ways [12], from descriptive or inferential statistics to advanced data mining. However, two approaches dominate among comparative studies of categorical data, the first based on a cross-tabulation matrix to summarize the degree of data association, and the second using spatial and statistical analysis to determine data comparisons in locational and qualitative dimensions. The Cohen kappa coefficient of agreement derived from a cross-tabulation matrix, i.e., the relative rating of two or more classifications based on the proportion of correctly allocated cases, dominates most of the literature [13; 14]. The Kappa coefficient can also be used with missing data, as pointed out by [PERSON] et al. [15]. The authors analyzed three kappa coefficients: [PERSON]'s kappa, regular kappa, and listwise deletion kappa. They found that both [PERSON]'s kappa and listwise deletion kappa outperform regular category kappa in terms of bias, and generally have a very small mean squared error. It is even possible to use weighted kappa, introduced by [PERSON] in 1968 [16], which is of the utmost importance when disagreements between datasets are not equally important. [PERSON] and [PERSON] [17] remarked that, under certain conditions, \"the weighted kappa coefficient is equivalent to the product-moment correlation coefficient\". In 2000, [PERSON] [18] introduced some extension of kappa, namely, kappa with random change agreement and kappa for location (Klocation). The kappa coefficient, however, has some disadvantages. [PERSON] [13] pointed out that the sample used to evaluate maps should be non-dependent. However, in practice, this assumption is almost impossible, since the same sample of ground data sites is often used for each case of map elaboration. In conclusion, in [13], the author also suggests using a measure of the proportion of correctly assigned cases. A similar opinion is shared by [PERSON] and [PERSON] [18], who summed up more than a decade of research on the kappa coefficient and concluded that the simple measures of quantitative and allocation disagreement are even more useful in showing differences in categorical data. Moreover, [PERSON] [19] also suggests that in data agreement analysis, statistical measures of dispersion such as mean deviation, mean absolute deviation and correlation and slope are even more helpful in interpreting data dissimilarity than indices of agreement. In view of the aforementioned limitations of kappa, researchers use some qualitative-quantitative approaches that deserve attention due to their combination of evaluation and analysis. Multi-criteria analytical techniques predominantly rely on statistical methods or a combination with data mining techniques. [PERSON] and [PERSON] [20] quantified spatial heterogeneity in categorical maps using ANOVA statistics. Among scientists, a very popular method for comparing categorical data is clustering. However, as noted by [PERSON] et al. [21], the clustering of multi-dimension data can conceal some important relations between object classes, and the final results strongly depend on the algorithm used. Promising results were obtained by [PERSON] [22], using the fuzzy set theory, especially for ambiguities in determining the \"location of the category (fuzziness of location) and in the definition of the category (fuzziness of category)\".