Industry Perspectives Regarding Tier 4 of the GTCM
Introduction
This study is intended to provide insight into geospatial practitioners' viewpoints toward the US Department of Labor, Employment and Training Administration (DOLETA) Geospatial Technology Competency Model (GTCM). These viewpoints are significant since business professionals use workplace competencies for job analysis, professional certification, and defining workforce requirements.
Problem Statement
Geospatial Information Science & Technology (GIST) is a rapidly evolving field. As the tools and capabilities rapidly expand in number, scope, and into a cloud-based environment, the diversity of people and disciplines involved with geospatial technology is expanding, the ways people acquire training and education are advancing, and societal needs are changing. These concurrent changes present challenges for those planning staffing changes for their organization, professors teaching with geospatial technology who seek to align their courses to workforce needs, and scholars who use GIS as a research tool.
Purpose Statement
The study aims to explore practitioners' viewpoints toward the US DOLETA's GTCM and why they hold these views. There are challenges to assuring competence within the geospatial field (Albrecht, 1998), but assessing viewpoints toward these competencies will enable a better understanding of the needs within the geospatial workforce. Also, identifying commonalities across viewpoints may reveal widely held beliefs within the field. Attempts to standardize the core competencies within the geospatial discipline are progressing, and developing a connection between the learning outcomes achieved in academia and the practical knowledge demonstrated in the workplace is a viable path to establishing competence (Mathews & Wikle, 2017).
Finding 1. The five shared perspectives developed from the analysis are Factor 1: Remote sensing is not central to our occupations (significant loadings range in value from 0.439 to 0.7391) explains 15% of the variance (Q-sorts 4, 9, 14, 16, 22, 29, 33, 36, 38, 45, 48, 49, 55, 61); Factor 2: We value automation of analysis (significant loadings range in value from 0.3428 to 0.6971) explains 9% of the variance (Q-sorts 3, 17, 31, 32, 35, 46, 56, 58); Factor 3: Cartography is king, but programming is not (significant loadings range in value from 0.5358 to 0.7738) explains 15% of the variance (Q-sorts 6, 8, 12, 19, 20, 24, 27, 28, 39, 47, 59); Factor 4: Grounded in tradition but always looking ahead (significant loadings range in value from 0.4521 to 0.6071) explains 6% of the variance (Q-sorts 5, 11, 50, 53); Factor 5: We are less concerned with modeling the Earth's shape than other tenets of GIS (significant loadings range in value from 0.3292 to 0.6382) explains 7% of the variance (Q-sorts 7, 25, 34, 54, 57). An asterisk in Table 4 represents the Q-sorts loading onto each factor for this study.
Finding 2. The second research question seeks to determine if the views of geospatial professionals aligned with the five identified factors are independent of the categorization of their employer, years of experience, industry sector, or GTCM Sector. The null hypothesis is that the participants will not reflect any differences in perception due to the employer category, years of experience, industry sector, or GISCI Certification. The researchers used Fisher's Exact Probability Test instead of the Chi-Square test to investigate if a relationship existed. The authors chose Fisher's Exact Probability Test because the data used in the study violated many of the conditions established by Swinscow (1997) for the Chi-Square Test of Independence. These results confirm the null hypothesis and the absence of a relationship between the employer, years of experience, industry sector, or GTCM Sector and a shared perspective (factor).
Finding 3. The authors found that the participants shared common perspectives, regardless of individual factor alignment. Table 13 contains the aggregated result of the sorting activity and provides additional data reflecting the geospatial community. Predictably, competencies anecdotally linked to foundational geospatial concepts appeared as some of the highest-ranked statements. The KSAs affiliated with capturing, managing, and analyzing spatial data dominated were positively viewed. Other areas of the geospatial field which scored well, as determined by their shared ranking in the sorting activity, were cartography (15), software applications (14), and professional activities (17).
Conclusion
Core competencies are seen as fundamental components within a competency model (Prahalad & Hamel, 1990) and help to define a worker's professional competence (Spencer & Spencer, 1993). This research study used workforce representatives to evaluate a conceptual model of competencies for the geospatial industry. It demonstrated that the GTCM continues to have relevance and applicability in reflecting the field of geospatial science. The results of this study provide commentary from GIS practitioners–who are also employers of those with GIS skills–regarding how the geospatial field views the core competencies in the GTCM. These results can provide educators with practical ways to increase the value of their instruction to future members of the geospatial workforce.
Geospatial science is fundamentally interdisciplinary, blending components from information science and geography (Tomaszewski & Holden, 2012). The fact that geospatial science touches all other sciences and is a standalone science is both an advantage to geospatial science (its applicability) and a disadvantage (a struggle to find its identity). Geospatial science distinguishes itself from simply the information technology behind it by applying spatial thinking (NRC, 2006). Communities within the geospatial field maintain specific knowledge domains outside or in addition to the generally recognized competencies. These sectors (e.g., photogrammetry, remote sensing, land surveying, geospatial intelligence, and others) contain fewer geospatial practitioners and may be viewed as supporting actors to the focus of geospatial analysis. The lack of equal representation from the different sectors affects the results and introduces bias.
This study is intended to provide insight into geospatial practitioners' viewpoints toward the US Department of Labor, Employment and Training Administration (DOLETA) Geospatial Technology Competency Model (GTCM). These viewpoints are significant since business professionals use workplace competencies for job analysis, professional certification, and defining workforce requirements.
Problem Statement
Geospatial Information Science & Technology (GIST) is a rapidly evolving field. As the tools and capabilities rapidly expand in number, scope, and into a cloud-based environment, the diversity of people and disciplines involved with geospatial technology is expanding, the ways people acquire training and education are advancing, and societal needs are changing. These concurrent changes present challenges for those planning staffing changes for their organization, professors teaching with geospatial technology who seek to align their courses to workforce needs, and scholars who use GIS as a research tool.
Purpose Statement
The study aims to explore practitioners' viewpoints toward the US DOLETA's GTCM and why they hold these views. There are challenges to assuring competence within the geospatial field (Albrecht, 1998), but assessing viewpoints toward these competencies will enable a better understanding of the needs within the geospatial workforce. Also, identifying commonalities across viewpoints may reveal widely held beliefs within the field. Attempts to standardize the core competencies within the geospatial discipline are progressing, and developing a connection between the learning outcomes achieved in academia and the practical knowledge demonstrated in the workplace is a viable path to establishing competence (Mathews & Wikle, 2017).
Finding 1. The five shared perspectives developed from the analysis are Factor 1: Remote sensing is not central to our occupations (significant loadings range in value from 0.439 to 0.7391) explains 15% of the variance (Q-sorts 4, 9, 14, 16, 22, 29, 33, 36, 38, 45, 48, 49, 55, 61); Factor 2: We value automation of analysis (significant loadings range in value from 0.3428 to 0.6971) explains 9% of the variance (Q-sorts 3, 17, 31, 32, 35, 46, 56, 58); Factor 3: Cartography is king, but programming is not (significant loadings range in value from 0.5358 to 0.7738) explains 15% of the variance (Q-sorts 6, 8, 12, 19, 20, 24, 27, 28, 39, 47, 59); Factor 4: Grounded in tradition but always looking ahead (significant loadings range in value from 0.4521 to 0.6071) explains 6% of the variance (Q-sorts 5, 11, 50, 53); Factor 5: We are less concerned with modeling the Earth's shape than other tenets of GIS (significant loadings range in value from 0.3292 to 0.6382) explains 7% of the variance (Q-sorts 7, 25, 34, 54, 57). An asterisk in Table 4 represents the Q-sorts loading onto each factor for this study.
Finding 2. The second research question seeks to determine if the views of geospatial professionals aligned with the five identified factors are independent of the categorization of their employer, years of experience, industry sector, or GTCM Sector. The null hypothesis is that the participants will not reflect any differences in perception due to the employer category, years of experience, industry sector, or GISCI Certification. The researchers used Fisher's Exact Probability Test instead of the Chi-Square test to investigate if a relationship existed. The authors chose Fisher's Exact Probability Test because the data used in the study violated many of the conditions established by Swinscow (1997) for the Chi-Square Test of Independence. These results confirm the null hypothesis and the absence of a relationship between the employer, years of experience, industry sector, or GTCM Sector and a shared perspective (factor).
Finding 3. The authors found that the participants shared common perspectives, regardless of individual factor alignment. Table 13 contains the aggregated result of the sorting activity and provides additional data reflecting the geospatial community. Predictably, competencies anecdotally linked to foundational geospatial concepts appeared as some of the highest-ranked statements. The KSAs affiliated with capturing, managing, and analyzing spatial data dominated were positively viewed. Other areas of the geospatial field which scored well, as determined by their shared ranking in the sorting activity, were cartography (15), software applications (14), and professional activities (17).
Conclusion
Core competencies are seen as fundamental components within a competency model (Prahalad & Hamel, 1990) and help to define a worker's professional competence (Spencer & Spencer, 1993). This research study used workforce representatives to evaluate a conceptual model of competencies for the geospatial industry. It demonstrated that the GTCM continues to have relevance and applicability in reflecting the field of geospatial science. The results of this study provide commentary from GIS practitioners–who are also employers of those with GIS skills–regarding how the geospatial field views the core competencies in the GTCM. These results can provide educators with practical ways to increase the value of their instruction to future members of the geospatial workforce.
Geospatial science is fundamentally interdisciplinary, blending components from information science and geography (Tomaszewski & Holden, 2012). The fact that geospatial science touches all other sciences and is a standalone science is both an advantage to geospatial science (its applicability) and a disadvantage (a struggle to find its identity). Geospatial science distinguishes itself from simply the information technology behind it by applying spatial thinking (NRC, 2006). Communities within the geospatial field maintain specific knowledge domains outside or in addition to the generally recognized competencies. These sectors (e.g., photogrammetry, remote sensing, land surveying, geospatial intelligence, and others) contain fewer geospatial practitioners and may be viewed as supporting actors to the focus of geospatial analysis. The lack of equal representation from the different sectors affects the results and introduces bias.
Recommendations
We recommend conducting rigorous and frequent research on how skills are gained and how the alignment of such skills connects to workforce needs. This examination is particularly relevant in a field where technology and methodology change as rapidly as is found within geographic information science and technology. We also recommend that researchers investigate relationships between specific categories of geospatial workers within an industry sector (e.g., geospatial technician, GIS analyst, geographic information systems scientist, and others). Comparison studies between geospatial DACUMs and Tier 4 of the US DOLETA's GTCM may reveal common themes relating to core geospatial tasks or KSAs. We recommend that future research target sub-populations within the geospatial industry and make recommendations about revisions to the GTCM. Sampling could be conducted by engaging individual sectors (e.g., photogrammetry, remote sensing, land surveying, geospatial intelligence, and others) or by selecting a specified number of participants from each population.
We recommend conducting rigorous and frequent research on how skills are gained and how the alignment of such skills connects to workforce needs. This examination is particularly relevant in a field where technology and methodology change as rapidly as is found within geographic information science and technology. We also recommend that researchers investigate relationships between specific categories of geospatial workers within an industry sector (e.g., geospatial technician, GIS analyst, geographic information systems scientist, and others). Comparison studies between geospatial DACUMs and Tier 4 of the US DOLETA's GTCM may reveal common themes relating to core geospatial tasks or KSAs. We recommend that future research target sub-populations within the geospatial industry and make recommendations about revisions to the GTCM. Sampling could be conducted by engaging individual sectors (e.g., photogrammetry, remote sensing, land surveying, geospatial intelligence, and others) or by selecting a specified number of participants from each population.
