Abstract
Anselin and Getis argue in their 1992 paper “Spatial statistical analysis and GIS” that the development of a toolbox of spatial analytic techniques should be directed by the scientists whose work defines the need for such a toolbox. The field of GIS has changed fundamentally since 1992 as a result of new technical developments, including a general move away from the map as the defining metaphor, the influence of the Internet and the World Wide Web, and changes in the practice of software engineering. Science as a whole has also changed, towards a more collaborative model that is more dependent on computational infrastructure. The impacts of space on the methodology of science are also better understood. The Anselin and Getis paper was remarkably prescient in its identification of the major issues that continue to affect the relationship between spatial analysis and GIS. Institutional issues continue to frame the relationship between GIS and spatial analysis, and are best addressed through partnerships.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abler RF (1987) The National Science Foundation National Center for Geographic Information and Analysis. Int J Geogr Inf Syst 1:303–336
Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27:93–115
Anselin L, Getis A (1992) Spatial statistical analysis and geographic information systems. Ann Reg Sci 26:19–33
Arctur D, Zeiler M (2004) Designing geodatabases: case studies in GIS data modeling. ESRI, Redlands, CA
Atkins DE, Droegemeier KK, Feldman SI, Garcia-Molina H, Klein ML, Messerschmitt DG, Messina P, Ostriker JP, Wright MH (2003) Revolutionizing science and engeineering through cyberinfrastructure. Working paper, National Science Foundation
Burrough PA (1990) Methods of spatial analysis in GIS. Int J Geogr Inf Syst 4:221–223
Cowen DJ (1988) GIS versus CAD versus DBMS: what are the differences. Photogramm Eng Remote Sens 54:1551–1555
Crosier SJ, Goodchild MF, Hill LL, Smith TR (2003) Developing an infrastructure for sharing environmental models. Environ Plann B Plann Des 30:487–501
Ding Y, Fotheringham AS (1992) The integration of spatial analysis and GIS. Comput Environ Urban Syst 16:3–19
Fischer MM (1997) Computational neural networks: a new paradigm for spatial analysis. Environ Plan A 30:1873–1891
Fischer MM, Leung Y (1998) A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction dataNeural network for modelling spatial interaction data. Ann Reg Sci 32:437–458
Fotheringham AS, Rogerson P (1994) Spatial analysis and GIS. Taylor & Francis, London
Fotheringham AS, Brunsdon C, Charlton ME (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York
Goodchild MF (1987) A spatial analytical perspective on geographical information systems. Int J Geogr Inf Sci 1:327–334
Goodchild MF, Gopal S (1989) The accuracy of spatial databases. CRC, Boca Raton
Goodchild MF, Haining RP, Wise S (1992) Integrating GIS and spatial data analysis: problems and possibilities. Int J Geogr Inf Sci 6:407–423
Longley PA, Goodchild MF, Maguire DJ, Rhind DW (1999) Geographical information systems, 2nd edn. Wiley, New York
Maguire DJ (1991) An overview and definition of GIS. In: Longley PA, Goodchild MF, Maguire DJ, Rhind DW (eds) Geographical information systems: principles and applications. Longman Scientific and Technical, Harlow, pp 9–20
Maguire DJ, Dangermond J (1991) The functionality of GIS. In: Maguire DJ, Goodchild MF, Rhind DW (eds) Geographical information systems: principles and applications, vol 1. Longman Scientific & Technical, Harlow, pp 319–335
Maguire DJ, Michael B, Goodchild MF (2005) GIS, spatial analysis and modelling. ESRI, Redlands, CA
Maling DH (1989) Measurements from maps: principles and methods of cartometry. Pergamon, New York
Mitchell A (1999) The ESRI guide to GIS analysis. ESRI, Redlands, CA
Nebert D (1993) Implementation of wide area information server (WAIS) software to disseminate spatial data on the internet. In: International ESRI User Conference. Palm Springs
Nyerges TL (1993) Understanding the scope of GIS: its relationship to environmental modeling. In: Goodchild MF, Parks B, Steyaert L (eds) Environmental modeling with GIS. Oxford University Press, New York, pp 75–93
Openshaw S, Charlton ME, Wymer C, Craft A (1987) A Mark I geographical analysis machine for the automated analysis of point data sets. Int J Geogr Inf Syst 1:335–358
Openshaw S, Cross A, Charlton ME (1990) Building a prototype geographical correlates exploration machine. Int J Geogr Inf Sci 4:297–311
Pickles J (1995) Ground truth: the social implications of geographic information systems. Guilford, New York
Skupin A, Hagelman R (2005) Visualizing demographic trajectories with self-organizing maps. GeoInformatica 9:159–179
Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240
Ungerer MJ, Goodchild MF (2002) Integrating spatial data analysis and gis: a new implementation using the component object model (com). Int J Geogr Inf Sci 16:41–53
Zeiler M (1999) Modeling our world: the ESRI guide to geodatabase design. ESRI, Redlands, CA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Goodchild, M.F. (2010). Whose Hand on the Tiller? Revisiting “Spatial Statistical Analysis and GIS”. In: Anselin, L., Rey, S. (eds) Perspectives on Spatial Data Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01976-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-01976-0_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01975-3
Online ISBN: 978-3-642-01976-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)