Assessment of Workforce Demands to Shape GIS&T Education

Transcription

1 Assessment of Workforce Demands to Shape GIS&T Education Gudrun Wallentin, Barbara Hofer, Christoph Traun University of Salzburg, Dept. of Geoinformatics Z_GIS, Austria

2 What should we know as GIS professionals? academia industry NCGIA Core Curriculum National Center for Geographic Information Analysis (NCGIA) GIS&T BoK University Consortium for GIS (UCGIS) Geospatial Technology Competency Model US Department of Labor BoK 2.0 UCGIS initiative EU Project GI-N2K

3 159 pages 10 Knowledge Areas 73 Units (25 core units) 329 Topics 1660 Learning objectives 3

4 Why BoK 2.0? Easy to use Higher Education curriculum design, accreditation Demanddriven GIS&T professional professional certification Workforce human resources professionals platform to manage and query GIS&T domain knowledge

5 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)

6 Assessment of Workforce Demand

7 Survey participation throughout Europe Survey (questionnaire) 435 questionnaires 33 countries In-depth interviews 21 leading experts 6 European regions

8 Do you know the GIS&T BoK? 14% 72% 14% aware & use aware not aware n=21

9 Rate the importance of..

10 Cartography and Visualisation 30.00% 25.00% preparing data for map production (e.g. classification, generalisation, map projection) 20.00% designing maps (e.g. symbology, typography, colour schemes) 15.00% 10.00% choosing adequate graphic representations (e.g. thematic maps, interactivity, web mapping) 5.00% producing maps (e.g. map reproduction, colour separation) 0.00% not relevant somehow relevant very relevant using and evaluating maps (e.g. map interpretation, usability evaluation)

11 Geospatial Data 35.00% working with land partitioning systems (e.g. cadastre) 30.00% using georeferencing systems (e.g. geographic coordinate systems, linear referencing) 25.00% specify geodetic datums (e.g. WGS84, vertical datums, NAP) understand map projections (e.g. projection classes, properties and parameters) 20.00% assess data quality (e.g. geometric or thematic accuracy and resolution) 15.00% 10.00% land surveying and GPS digitising (e.g. with tablet, on-screen or automated vectorisation) collecting field data (e.g. select sample size, field data technologies) 5.00% aerial imaging and photogrammetry (e.g. image interpretation, feature extraction) 0.00% not relevant somehow relevant very relevant remote sensing (e.g. applying algorithms and processing, accuracy assessment) metadata, standards and infrastructures (e.g. SDI, INSPIRE)

12 Analytical methods 35.00% apply query operations (e.g. SQL) 30.00% measure geometric properties (e.g. distance, area, connectivity) use basic analytical operations (e.g. buffer, overlay, map algebra) 25.00% analyse spatial data (e.g. point pattern analysis, multi-criteria evaluation) 20.00% analyse surfaces (e.g. viewsheds, cost surfaces, calculate slope) use spatial statistics (e.g. Morans I, spatial weights matrix) 15.00% use geostatistics (e.g. Kriging, semivariogram modelling) 10.00% use geostatistics (e.g. Kriging, semivariogram modelling) 5.00% apply spatial regression (e.g. geographically weighted regression) data mining (e.g. BigData handling, knowledge discovery) 0.00% not relevant somehow relevant very relevant analyse networks (e.g. graph theory, routing, utility networks) mathematical optimisation (e.g. operations research, linear programming, locationallocation)

13 Geocomputation 30.00% using advanced computational methods (e.g. neural networks, grid computing) 25.00% 20.00% using cellular automata (e.g. define transition and neighbourhood rules, apply CA) using heuristics (e.g. simulated annealing) apply genetic algorithms (e.g. location optimisation) 15.00% 10.00% developing agent based models (e.g. model specification, calibration, encoding) simulation modelling (e.g. Monte Carlo simulation) assessing uncertainty (e.g. error propagation, MAUP) 5.00% using fuzzy sets (e.g. fuzzify spatial decision making) 0.00% not relevant somehow relevant very relevant

14 Rating by educational level

15 Current and Future Tasks GIS data spatial

16 Diversity between sectors Which tasks do you frequently perform?

17 Gaps in the BoK Application development WebGIS SDI data acquisition Java, python, API html5, semantic web, restful, smartphone INSPIRE, harmonization, open data, big, data, VGI, UAV, Radar RS other hot topics augmented reality, City GML, OBIA

18 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

19 Is there a European GIS&T?

20 Future Trends Identified Câmara et al Geographical Information Engineering in the 21st Century: Technology side: Sensor networks Mobile devices Remote sensing Concepts side: Semantics Time Cognition

21 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?