Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

References

• Alper, P., et al., 2015. Labelflow: exploiting workflow provenance to surface scientific data provenance. In: B. Ludaescher and B. Plale, eds. Provenance and annotation of data and processes. 5th. International Provenance and Annotation Workshop Springer, 84–96.
   Google Scholar
• Ballatore, A., Scheider, S., and Lemmens, R., 2018. Patterns of consumption and connectedness in GIS web sources. In: The Annual International Conference on Geographic Information Science Springer, 129–148.
   Google Scholar
• Bernard, L., et al., 2014. Scientific geodata infrastructures: challenges, approaches and directions. International Journal of Digital Earth, 7 (7), 613–633. doi:10.1080/17538947.2013.781244
   Web of Science ®Google Scholar
• Bertin, J., Barbut, M. and Bonin, S. ... (1967). Sémiologie graphique: les diagrammes, les réseaux, les cartes. Paris/La Haye: Gauthier-Villars/Mouton
   Google Scholar
• Bizer, C., Heath, T., and Berners-Lee, T., 2009. Linked data-the story so far. International Journal on Semantic Web and Information Systems, 5 (3), 1–22.
   Web of Science ®Google Scholar
• Bobat, A., 2015. Thermal pollution caused by hydropower plants. In: A.N. Bilge, A.Ö. Toy, and M.E. Günay, eds. Energy systems and management. Cham: Springer International Publishing, 19–32.
   Google Scholar
• Buchanan, I. and Lambert, G., 2005. Deleuze and space. Toronto: University of Toronto Press.
   Google Scholar
• Burrough, P.A., 1986. Principles of geographical information systems for land resources assessment. New York: Oxford University Press.
   Google Scholar
• Canagaratna, S.G., 1992. Intensive and extensive: underused concepts. Journal Chemical Education, 69 (12), 957. doi:10.1021/ed069p957
   Web of Science ®Google Scholar
• Carral, D., et al., 2013. An ontology design pattern for cartographic map scaling. In: Extended Semantic Web Conference Berlin, Heidelberg : Springer, 76–93.
   Google Scholar
• Casati, R. and Varzi, A.C., 1999. Parts and places: the structures of spatial representation. Cambridge, MA: MIT Press.
   Google Scholar
• CBS, 2014. Kerncijfers wijken en buurten 2014. [online]. Available from: https://www.cbs.nl/nl-nl/maatwerk/2015/48/kerncijfers-wijken-en-buurten-2014 [Accessed May 2017].
   Google Scholar
• Chrisman, N., 1997. Exploring geographic information systems. Washington: Jon Wiley & Sons.
   Google Scholar
• Cohen, E.R., 2007. Quantities, units and symbols in physical chemistry. Cambridge, UK: Royal Society of Chemistry.
   Google Scholar
• De Smith, M.J., Goodchild, M.F., and Longley, P., 2007. Geospatial analysis: a comprehensive guide to principles, techniques and software tools. Leicester, UK: Troubador Publishing Ltd.
   Google Scholar
• Deleuze, G., 1994. Difference and repetition. New York: Columbia University Press.
   Google Scholar
• Egenhofer, M.J., 2002. Toward the semantic geospatial web. In: Proceedings of the 10th ACM international symposium on Advances in geographic information systems, 1–4. doi:10.1044/1059-0889(2002/er01)
   Google Scholar
• Evans, I.S., 1977. The selection of class intervals. Transactions of the Institute of British Geographers, 2, 98–124. doi:10.2307/622195
   Web of Science ®Google Scholar
• Flowerdew, R. and Green, M., 1993. Developments in areal interpolation methods and GIS. In: M.M. Fischer and P. Nijkamp, eds. Geographic information systems, spatial modelling and policy evaluation. Berlin, DE: Springer, 73–84.
   Google Scholar
• Fotheringham, A.S. and Wong, D.W., 1991. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23 (7), 1025–1044. doi:10.1068/a231025
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T., and Tibshirani, R., 2001. The elements of statistical learning. Vol. 1, New York,NY: Springer series in statistics.
   Google Scholar
• Gangemi, A. and Presutti, V., 2010. Ontology design patterns. In: S. Staab and R. Studer, eds. Handbook on ontologies. Dordrecht: Springer Science & Business Media.
   Google Scholar
• Goodchild, M.F. and Lam, N.S.N., 1980. Areal interpolation: a variant of the traditional spatial problem. London, ON, Canada: Department of Geography, University of Western Ontario.
   Google Scholar
• Goodchild, M.F., Yuan, M., and Cova, T.J., 2007. Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21 (3), 239–260. doi:10.1080/13658810600965271
   Web of Science ®Google Scholar
• Gorenstein, M. and Gadzicki, M., 2011. Strongly intensive quantities. Physical Review C, 84 (1), 1–5. doi:10.1103/PhysRevC.84.014904
   Web of Science ®Google Scholar
• Hatsopoulos, G.N. and Keenan, J.H., 1965. Principles of general thermodynamics. Vol. 398, New York,NL: Wiley.
   Google Scholar
• Hofer, B., et al. 2017. Towards a knowledge base to support geoprocessing workflow development. International Journal of Geographical Information Science, 31 (4), 694–716. doi:10.1080/13658816.2016.1227441
   Web of Science ®Google Scholar
• Huang, W., et al. 2018. Synchronising geometric representations for map mashups using relative positioning and linked data. International Journal of Geographical Information Science, 32 (6), 1117–1137. doi:10.1080/13658816.2018.1441416
   Web of Science ®Google Scholar
• Jaeger, J.A., 2000. Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landscape Ecology, 15 (2), 115–130. doi:10.1023/A:1008129329289
   Web of Science ®Google Scholar
• Janowicz, K., Scheider, S., and Adams, B., 2013. A geo-semantics flyby. In: S. Rudolph, et al., eds. Reasoning web. semantic technologies for intelligent data access. Vol. 8067. Dordrecht: Springer, 230–250.
   Google Scholar
• Jelinski, D.E. and Wu, J., 1996. The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology, 11 (3), 129–140. doi:10.1007/BF02447512
   Web of Science ®Google Scholar
• Jones, C.B., 2014. Geographical information systems and computer cartography. London: Routledge.
   Google Scholar
• Kemp, K., 2007. Encyclopedia of geographic information science.  Los Angeles: SAGE publications.
   Google Scholar
• Kraak, M.J. and Ormeling, F., 2011. Cartography: visualization of spatial data. New York: Guilford Press.
   Google Scholar
• Krivoruchko, K., Gribov, A., and Krause, E., 2011. Multivariate areal interpolation for continuous and count data. Procedia Environmental Sciences, 3, 14–19. doi:10.1016/j.proenv.2011.02.004
   Google Scholar
• Kuhn, W., 2012. Core concepts of spatial information for transdisciplinary research. International Journal of Geographical Information Science, 26 (12), 2267–2276. doi:10.1080/13658816.2012.722637
   Web of Science ®Google Scholar
• Kyriakidis, P., 2017. Aggregate data: geostatistical solutions for reconstructing attribute surfaces. In: X.Z. Shashi Shekhar and H. Xiong, eds. Encyclopedia of GIS. chap. 7. New York,NY: Springer, 57–67.
   Google Scholar
• Lamprecht, A.L., 2013. User-level workflow design. Lecture Notes in Computer Science, 8311. Berlin: Springer.
   Google Scholar
• Learn, S., 2017. SVM documentation. [online]. Available from: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html [Accessed May 2017].
   Google Scholar
• Lin, J., Hanink, D.M., and Cromley, R.G., 2017. A cartographic modeling approach to isopleth mapping. International Journal of Geographical Information Science, 31 (5), 849–866. doi:10.1080/13658816.2016.1207776
   Web of Science ®Google Scholar
• Lobato, J. and Thanheiser, E., 1999. Re-thinking slope from quantitative and phenomenological perspectives. In: Proceedings of the 21st Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, Vol. 1, 291–297.
   Google Scholar
• Longley, P.A., et al., 2005. Geographic information systems and science. Chichester, UK: John Wiley & Sons.
   Google Scholar
• McNaught, A.D. and McNaught, A.D., 1997. Compendium of chemical terminology. Vol. 1669. Oxford, UK: Blackwell Science Oxford.
   Google Scholar
• Mijnarends, R., et al., 2015. Advanced data-driven performance analysis for mature waterfloods. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
   Google Scholar
• Monmonier, M., 2014. How to lie with maps. Chicago: University of Chicago Press.
   Google Scholar
• Müller, J.C., Lagrange, L.P., and Weibel, R., 1995. GIS and generalization: methodology and practice. London: Taylor & Francis.
   Google Scholar
• Openshaw, S., 1984a. Ecological fallacies and the analysis of areal census data. Environment and Planning A, 16 (1), 17–31. doi:10.1068/a160017
   PubMed Web of Science ®Google Scholar
• Openshaw, S., 1984b. The modifiable areal unit problem. In: No. 38 concepts and techniques in modern geography. Norwich, UK: Geo Books.
   Google Scholar
• Pedregosa, F., et al. 2011. Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Redlich, O., 1970. Intensive and extensive properties. Journal Chemical Education, 47 (2), 154. doi:10.1021/ed047p154.2
   Web of Science ®Google Scholar
• Scheider, S. and Ballatore, A., 2017. Semantic typing of linked geoprocessing workflows. International Journal of Digital Earth, 11 (1), 113–138.
   Web of Science ®Google Scholar
• Scheider, S., Ostermann, F.O., and Adams, B., 2017. Why good data analysts need to be critical synthesists. determining the role of semantics in data analysis. Future Generation Computer Systems, 72, 11–22. doi:10.1016/j.future.2017.02.046
   Web of Science ®Google Scholar
• Scheider, S. and Tomko, M., 2016. Knowing whether spatio-temporal analysis procedures are applicable to datasets. In: R. Ferrario and W. Kuhn, eds. Formal ontology in information systems. Amsterdam: Frontiers in Artificial Intelligence and Applications IOS Press, 67–80.
   Google Scholar
• She, B., Duque, J.C., and Ye, X., 2017. The network-Max-P-regions model. International Journal of Geographical Information Science, 31 (5), 962–981. doi:10.1080/13658816.2016.1252987
   Web of Science ®Google Scholar
• Stasch, C., et al., 2014. Meaningful spatial prediction and aggregation. Environmental Modelling & Software, 51, 149–165. doi:10.1016/j.envsoft.2013.09.006
   Web of Science ®Google Scholar
• Stern, C. and Sester, M., 2013. Deriving constraints for the integration and generalization of detailed environmental spatial data in maps of small scales. In: Proceedings of the 16th ICA Workshop on Generalisation and Multiple Representation, jointly organised with the ICA Commission on Map production and Geo-Business, 23–24.
   Google Scholar
• Stevens, J., 2016. Earth Observatory images by Joshua Stevens, using Suomi NPP VIIRS data from Miguel Romàn, NASA’s Goddard Space Flight Center. [online]. NASA, Available from: https://earthobservatory.nasa.gov/Features/NightLights/ [Accessed January 2017].
   Google Scholar
• Suppes, P. and Zinnes, J.L., 1963. Basic measurement theory. In: D. Luce et al, eds. Handbook of mathematical psychology, Volume I. New York: John Wiley & Sons, 1–76.
   Google Scholar
• Tolman, R., 1917. The measurable quantities of physics. Physical Review, 9 (3), 237–253.
   Google Scholar
• Tomko, M., et al., 2012. The design of a flexible web-based analytical platform for urban research. In: I. C. et al., ed. Proceedings of the 20th international conference on advances in geographic information systems, 369–375.
   Google Scholar
• Tooamnian, A., et al., 2013. Automatic integration of spatial data in viewing services. Journal of Spatial Information Science, 2013 (6), 43–58.
   Google Scholar
• US Census Bureau, 2017. Annual estimates of the resident population for the United States, regions, states, and puerto rico: april 1, 2010 to July 1, 2016. [online]. Available from: https://www2.census.gov/programs-surveys/popest/datasets/2010-2016/national/totals/nst-est2016-alldata.csv [Accessed June 2017].
   Google Scholar