Assessment of Workforce Demands to

Shape GIS&T EducationGudrun Wallentin, Barbara Hofer, Christoph Traun

gudrun.wallentin@sbg.ac.atUniversity of Salzburg, Dept. of Geoinformatics – Z_GIS, Austria

www.gi-n2k.eu

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BoK 2.0

UCGIS initiative

EU Project ‚GI-N2K‘

What should we know as GIS professionals?

NCGIA Core Curriculum

National Center for Geographic

Information Analysis (NCGIA)

GIS&T BoK

University Consortium for

GIS (UCGIS)

GeospatialTechnology

CompetencyModel

US Department of Labor

acad

emia

indu

stry

3

159 pages

10 Knowledge Areas

73 Units (25 core units)

329 Topics

1660 Learning objectives

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Why BoK 2.0?

Higher Education

curriculum design, accreditation

Workforcehuman

resourcesprofessionals

GIS&T professional

professional certification

Demand-driven

Easy to use

platform to manage and query GIS&T domain knowledge

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GI-N2K project

Milestones Lead partner

Danny Vandenbroucke, Univ. Leuven (BE)

1 Analysis of demand and supply

Oct 2013 –Jun 2014

Wageningen Univ. (NL)

Univ. Salzburg(AT)

2 Content-revision of the BoK Sept 2015 AGILE Univ. of Muenster (DE)

3 The Virtual Lab for the bok: VirLaBok Sept 2015 Nova Univ. of Lisbon

(PT)

Univ. of the Bundeswehr

(DE)

4 Testing & Validation Sept 2016 Univ. West-Hungary (HU)

Univ. of Girona (ES)

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Assessment of Workforce Demand

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Survey participation throughout Europe

Survey (questionnaire)

435 questionnaires33 countries

In-depth interviews

21 leading experts6 European regions

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Do you know the GIS&T BoK?

14%

14%

72%

aware & useawarenot aware

n=21

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Rate the importance of..

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0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

not relevant somehowrelevant

very relevant

preparing data for map production (e.g.classification, generalisation, map projection)

designing maps (e.g. symbology,typography, colour schemes)

choosing adequate graphic representations(e.g. thematic maps, interactivity, webmapping)

producing maps (e.g. map reproduction,colour separation)

using and evaluating maps (e.g. mapinterpretation, usability evaluation)

Cartography and Visualisation

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0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

not relevant somehowrelevant

very relevant

working with land partitioning systems (e.g.cadastre)

using georeferencing systems (e.g. geographiccoordinate systems, linear referencing)

specify geodetic datums (e.g. WGS84, verticaldatums, NAP)

understand map projections (e.g. projectionclasses, properties and parameters)

assess data quality (e.g. geometric or thematicaccuracy and resolution)

land surveying and GPS

digitising (e.g. with tablet, on-screen orautomated vectorisation)

collecting field data (e.g. select sample size,field data technologies)

aerial imaging and photogrammetry (e.g. imageinterpretation, feature extraction)

remote sensing (e.g. applying algorithms andprocessing, accuracy assessment)

metadata, standards and infrastructures (e.g.SDI, INSPIRE)

Geospatial Data

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0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

not relevant somehowrelevant

very relevant

apply query operations (e.g. SQL)

measure geometric properties (e.g. distance,area, connectivity)

use basic analytical operations (e.g. buffer,overlay, map algebra)

analyse spatial data (e.g. point patternanalysis, multi-criteria evaluation)

analyse surfaces (e.g. viewsheds, costsurfaces, calculate slope)

use spatial statistics (e.g. Morans I, spatialweights matrix)

use geostatistics (e.g. Kriging, semivariogrammodelling)

use geostatistics (e.g. Kriging, semivariogrammodelling)

apply spatial regression (e.g. geographicallyweighted regression)

data mining (e.g. BigData handling, knowledgediscovery)

analyse networks (e.g. graph theory, routing,utility networks)

mathematical optimisation (e.g. operationsresearch, linear programming, location-allocation)

Analytical methods

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0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

not relevant somehow relevant very relevant

using advanced computational methods (e.g.neural networks, grid computing)

using cellular automata (e.g. define transitionand neighbourhood rules, apply CA)

using heuristics (e.g. simulated annealing)

apply genetic algorithms (e.g. locationoptimisation)

developing agent based models (e.g. modelspecification, calibration, encoding)

simulation modelling (e.g. Monte Carlosimulation)

assessing uncertainty (e.g. error propagation,MAUP)

using fuzzy sets (e.g. fuzzify spatial decisionmaking)

Geocomputation

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Rating by educational level

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Current and Future Tasks

GISdata

spatial

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Diversity between sectors

Which tasks do you frequently perform?

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Java, python, API

html5, semantic web, restful, smartphone

INSPIRE, harmonization, 19109

open data, big, data, VGI, UAV, Radar RS

augmented reality, City GML, OBIA

Application development

Java, python, API

WebGIS html5, semantic web, restful, smartphone

SDI INSPIRE, harmonization, 19109

data acquisition open data, big, data, VGI, UAV, Radar RS

other ‘hot’ topics augmented reality, City GML, OBIA

Gaps in the BoK

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Complementary: Qualitative Interviews

Three major deficits repeatedly mentioned: – IT skills– applying theoretical knowledge to real-world

problems – soft skills: (English) language and team working.

Variation in the GIS&T job market– ‘no problem to find employees’– ‘slight oversupply features the market’– ‘industry needs more good graduates’– ‘it is difficult to find GIS&T experts’

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Is there a European GIS&T?

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Future Trends Identified

Câmara et al. 2009 – Geographical Information Engineering in the 21st Century:

Technology side: Sensor networks Mobile devices Remote sensing

Concepts side: Semantics Time Cognition

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Conclusions and open questions

– GIS&T trends towards IT

– Variety between sectors >> between continents

– Where are disciplinary boundaries of GIS&T?

– Should workforce demands drive higher education?