Data-centric science, data-empowered society, and policymaking based on data can suffer from flawed conclusions if data are representative, biased, or unavailable. This paper focuses on missingness for which the common mitigation and handling strategies is a deletion or single imputation. However, understanding the reasons causing the missingness can help to understand phenomena better. Distinguishing the different types of missingness help us to develop and implement new imputation approaches, sampling strategies and output uncertainty quantification. In this paper, using missing data mechanism and structure a new taxonomy has been created to classify the causalities of missing geospatial data.