What are the most common misconceptions about geospatial data careers?

2 Myth 2: You only work with maps

Another common misconception about geospatial data careers is that you only work with maps or map-making tools. While maps are a powerful and popular way of representing and communicating geospatial data, they are not the only or the main output of geospatial data work. Geospatial data careers involve working with various forms and sources of geospatial data, such as satellite imagery, GPS data, census data, social media data, or sensor data. Geospatial data careers also involve applying various methods and techniques to geospatial data, such as spatial statistics, machine learning, geoprocessing, web mapping, or geovisualization. Geospatial data careers can produce various outcomes and impacts, such as insights, recommendations, solutions, products, or services.

3 Myth 3: You only work in certain industries

A third common misconception about geospatial data careers is that you only work in certain industries or sectors that are traditionally associated with geospatial data, such as government, defense, natural resources, or transportation. While these are indeed important and prominent users and producers of geospatial data, they are not the only or the most innovative ones. Geospatial data careers can span across various industries and sectors that are increasingly adopting and leveraging geospatial data, such as health, education, retail, finance, or entertainment. Geospatial data careers can also involve working with various types of organizations and stakeholders, such as private companies, non-governmental organizations, academic institutions, or community groups.

4 Myth 4: You only work with one software

A fourth common misconception about geospatial data careers is that you only work with one software or platform that is specialized or dominant in geospatial data, such as ArcGIS, QGIS, or Google Maps. While these are indeed popular and powerful tools for geospatial data, they are not the only or the best ones for every geospatial data task or problem. Geospatial data careers require working with a variety of software and platforms that can offer different functionalities, features, or advantages for geospatial data, such as Python, R, SQL, Excel, Tableau, or Power BI. Geospatial data careers also require learning and adapting to new and emerging software and platforms that can provide new and improved capabilities for geospatial data, such as cloud computing, big data, or artificial intelligence.

5 Myth 5: You only work alone

A fifth common misconception about geospatial data careers is that you only work alone or in isolation from other people or disciplines. While geospatial data work can be independent and self-directed, it is not solitary or siloed. Geospatial data careers require working with and communicating with other people and disciplines that are involved or interested in geospatial data, such as clients, users, managers, colleagues, or partners. Geospatial data careers also require collaborating and integrating with other people and disciplines that can complement or enhance geospatial data, such as domain experts, data analysts, software developers, graphic designers, or storytellers.

6 Myth 6: You only work in one location

A sixth common misconception about geospatial data careers is that you only work in one location or in a fixed and rigid environment. While geospatial data work can be based or focused on a specific location or region, it is not limited or confined by it. Geospatial data careers require working with and exploring different locations and regions that are relevant or interesting for geospatial data, such as local, national, or global scales, urban or rural areas, or natural or human-made features. Geospatial data careers also require working in and adapting to different environments and situations that are conducive or challenging for geospatial data, such as office or field settings, online or offline modes, or stable or dynamic conditions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?