# An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness
## 5 Conclusions and Discussion  
### Conclusions  
Granular computing closeness was used to measure the similarities among spatial entities in this paper, and the land use change data in Guangxi was taken as an example. The three main conclusions are as follows:  
1. The similarity problem of spatial data attribute calculation in granular computing is introduced in this paper, and a similarity measure method for spatial entity type attributes based on granular computing closeness is proposed. The spatial information particles, atomic formulas, and formulas are defined and characterized. Based on fuzzy set theory, the similarity criteria of spatial entity attributes are discussed. The concept of distance between granular information and the similarity measure of granularity is proposed based on the principle of simple set membership degree and distance, so that the atomic formula can be used to granulate spatial entities and spatial attribute indexes to form different spatial entity granular sets. This method can measure the similarity between different types of spatial entities, which is a similarity problem between samples, and it can provide decision-making guidance for determining the similarities of socio-economic attributes between spatial entities as a basis for spatial classification. This study can also provide a unified theoretical framework for the study of spatial similarity in granular computing, enrich the spatial information science computing method and broaden the application field of granular computing.
2. This method of measurement uses the formula to divide different sets of indexes and overcomes the drawback that traditional fuzzy sets can only measure between the two types. The size of the method dimension does not depend on the number of spatial sets but instead depends on the number of indexes of the spatial evaluation object. Therefore, this method not only reflects the degree of similarity of spatial entity types at different indicator levels, but also reflects the comprehensive similarity between spatial entity types under the consideration of comprehensive indexes.
3. Taking the secondary land use types of the Guangxi Zhuang Autonomous Region in 2010 as an example, a land use change calculation framework based on granular computing closeness is established that also applies to index increases. In the example of the similarity measure of the socio-economic indexes of the land use types in Guangxi, the similarity coefficient between urban land and grassland was low, but the similarity coefficient between other land for construction and river types was higher. The similarity coefficient between forestland and agricultural land rural settlements was higher, while the similarity coefficient between grassland and wetland was lower. The similarity measure between these different land use types illustrates the proximity of different land use types in Guangxi to social and economic attributes. These laws are basically consistent with the actual situation in Guangxi and can provide reference for land planning and decision support in Guangxi. Land use is a function process determined by the coordination of land quality characteristics and social land demand, and is also an economic process that interactswith the economy and society. The land use types with high similarity values of measurement indexes have similar social and economic service values.