The need for addressing geoprivacy in location based services has increased the offer of mechanisms that protect location information, however, these algorithms are not always developed to ensure the usability of the data and therefore, their adoption is not wide. In this work, a framework is presented to evaluate the effects of geoprivacy mechanisms on the quality of geodata to provide insights into how the data is affected for geospatial analysis. For this purpose, a toolkit of indices was developed to evaluate different characteristics of the data before and after a geoprivacy mechanism is implemented, providing a criterion to select one of them. The indices measure the changes in the presence of clusters through the quantification of hotspots in hotspot analysis and the difference observed in heatmaps of the concentration of the geodata. Variations in global indices like the Nearest Neighbor Index (NNI) and the orientation of the standard deviational ellipse are also measured. For demonstration, the data of crime arrests in New York was used for the month of January in 2017 and 2018. Five mechanisms were tested with different settings, resulting with the NRand-K algorithm producing fewer alterations to the reference data, preserving its initial characteristics better than the other mechanisms.