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Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy. 

The growth of the geospatial services industry is increasing the demand for graduates with training in both geography and computational thinking (geocomputational thinking). The limited availability of learning pathways towards geocomputationally intensive jobs requires employers across the public and private sectors to choose between hiring a geographer or a computer science graduate. This collaboration of authors will initiate the formation of a researcher-practitioner partnership (RPP) in Southern California, as a new strategy to addresses the lack of geocomputational learning pathways.