Lessons from spatial transcriptomics and computational geography in mapping the transcriptome

Spatial data, data with some form of location attached, are the norm: all data are spatial now. However spatial data requires consideration of three critical characteristics, observation spatial auto-correlated, process spatially non-stationarity and the effect of the MAUP. Geographers are familiar with these and have tools, rubrics and workflows to accommodate them and understand their impacts on statical inference, understanding and prediction. However, increasingly researchers in non geographical domains, with no experience of, or exposure to quantitative geography or GIScience are undertaking analyses of such data without full or any understanding of the impacts of these spatial data properties. This short paper describes recent interactions and work with research in gene analysis and Spatial Transcriptomics, and highlight the opportunities for GIScience to inform and steer the many new users of spatial data.