Keynote

Presentation: Becoming a Geospatial Data Scientist 

Presentation Abstract: As geospatial professionals, we know what it means to ‘Think Spatially’ and have been doing so for over 50 years since the advent of GIS. During the COVID-19 pandemic, thinking spatially was never more relevant and mainstream, especially with how maps of cases have been poorly visualized (don’t forget to normalize visualizations: ‘cases per 1,000 people’ please!). But, as geospatial pros, we typically stay confined in our GIS silos and have trouble keeping up with those ‘other’ data scientists (you know…the ones who ignore spatial thinking!). Those ‘other’ data scientists use trendy but effective tools like GitHub, Jupyter Notebooks, R, Matplotlib, Tableau, and a myriad of other data analytics and programming tools. While the ‘others’ may be hip with the latest flashy data tools, they sometimes miss the mark on what GIS practitioners have known for decades: spatial data is special! But GIS pros may be missing the mark as those new, flashy, hip data science tools are becoming a new standard and changing the data science landscape. Are you keeping up? Probably not, and this has created a massive and important gap in knowledge for the GIS pro with the latest tools and methods available. Meanwhile, data scientists and software programmers continue seemingly ignore or be unaware of the nature of spatial data. But, if GIS pros stay in our silos, this knowledge gap will persist. As more data science tools become commonplace and integrated into GIS software (like Jupyter Notebooks embedded in ArcGIS Pro), the GIS professional needs to adapt and expand, and this needs to start at the academic level. So, we will explore what we can do as GIS professionals and instructors to expand the GIS data science toolbox and close the spatial thinking gap of those ‘other’ data scientists.