Valuable components of CyberGIS: Expert viewpoints through Q‐method interviews

Abstract

CyberGIS is an interdisciplinary field that merges components of cyber‐infrastructure, geographic information science, and spatial analysis. This fusion combines the technical capabilities of advanced cyber‐infrastructure with the spatial analysis capabilities of GIS. How expert GIS practitioners perceive, use, and value the various components of CyberGIS is unknown, making student preparation for CyberGIS competency difficult. To address this gap, we reviewed the CyberGIS literature to develop a set of 37 key CyberGIS concepts. Then, we used Q‐methodology to interview 20 expert GIS users to understand the components of CyberGIS they viewed as most applicable to their GIS practice. Three distinct conceptions of CyberGIS expertise emerged: the spatial analyst, the domain‐specific problem solver, and the CyberGIS‐enabled computer scientist. The CyberGIS‐enabled computer scientist emphasized the underlying technologies which allow spatial analysts to discover and implement advanced methods and processes in GIS, which are then utilized by problem solvers to address domain‐focused issues. These three contrasting perceptions of CyberGIS expertise raise questions about the nature of curriculum and instruction appropriate to develop competent CyberGIS practitioners. With the identified variety of expert practices concerning CyberGIS, confusing practices and contrasting visions of the nature of CyberGIS reveal a need for instructional organization.

Course syllabi in GIS programming: Trends and patterns in the integration of computer science and programming

“…Indeed, the three major themes explored here—Python scripting, web enabling, and geodatabases—form a basis for at least three GIS programming courses in the curriculum. When considering the variant viewpoints of instructors on how to handle those courses (see Bowlick et al 2018), courses could be handled from different viewpoints and contain entirely different content. What belongs in the modern GIS curriculum?…”

Applications of geospatial big data in the Internet of Things

“…) to support the extraction of geospatial knowledge from structured and unstructured IoT data, with due consideration of issues of data quality, volume, and structure (Breunig et al, 2020). CyberGIS analytics is also increasingly used to deal with the complexity and massiveness of geospatial big data (Armstrong et al, 2019;Bowlick, Goldberg, & Bednarz, 2018). It is a distributed computing model that applies advanced cyberinfrastructure, GIS, spatial analysis, and e-science in geographical information science and systems.…”

“…He argues that the "black box" and complex nature of artificial neural networks hampers their application in complex geographical problems despite their high performance and reliable methods. CyberGIS tools, like the CyberGIS-Jupyter notebook (Yin et al, 2019), have also been increasingly applied in the access, analysis, and synthesis of geospatial massive data (Bowlick et al, 2018;Wang et al, 2021;Yin et al, 2017) and can offer many possibilities if embedded with the IoT management tools.…”