The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people and the environment in which we live. Such datasets are often high dimensional and heterogeneous in nature, with complex geospatial relations. Analysing such data can be challenging, especially when there is a need to maintain spatial awareness as the non-spatial attributes are studied. Geo-Coordinated Parallel Coordinates (GCPC) is a geovisual analytics approach designed to support exploration and analysis within complex geospatial environmental data. Parallel coordinates are tightly coupled with a geospatial representation and an investigative scatterplot, all of which can be used to show, reorganize, filter, and highlight the high dimensional, heterogeneous, and geospatial aspects of the data. Two sets of field trials were conducted with expert data analysts to validate the real-world benefits of the approach for studying environmental data. The results of these evaluations were positive, providing real-world evidence and new insights regarding the value of using GCPC to explore among environmental datasets when there is a need to remain aware of the geospatial aspects of the data as the non-spatial elements are studied.