Combined data and code package.

Geospatial analyses of human-environment interactions are challenged by the multi-scale, multi-dimensional nature of human-environment systems. Research in such contexts must often rely on integrating multiple, independently produced data sources, which presents heterogenous data qualities and interoperability challenges. Understanding data quality and transparency becomes increasingly important in these contexts, and multi‐granularity and context specific spatial data quality indicators are needed. We develop a data pedigree system that accounts for multiple data quality aspects, geospatial ambiguities that may hinder interoperability, and the fitness-for-use of each data source for indicating causal linkages between human activities and environmental change. We demonstrate its application to a particularly challenging and data sparse case study of identifying the location and timing of transnational cocaine trafficking, or ‘narco-trafficking’, in Central America with five spatial and temporal data quality indicators: geographic clarity, geographic interpretation, provenance, temporal specificity, and narco-trafficking certainty. The proposed data pedigree system provides a systematic and coherent analytical framework for interoperability, comparison, and corroboration of fragmented and incomplete data, which are needed to support advanced geospatial analyses, such as causal inference techniques. The study demonstrates the transferability and operationalization of the data pedigree system for examining complex human-environment interactions, especially those influenced by illicit economies.