Skip to main content
SpringerLink
Log in

Exploratory Spatial Data Analysis

  • Living reference work entry
  • First Online:
Handbook of Regional Science

  • 255 Accesses

Abstract

In this chapter, we discuss key concepts for exploratory spatial data analysis (ESDA). We start with its close relationship to exploratory data analysis (EDA) and introduce different types of spatial data. Then, we discuss how to explore spatial data via different types of maps and via linking and brushing. A key technique for ESDA is local indicators of spatial association (LISA). ESDA needs to be supported by software. We discuss two main lines of software developments: GIS-based solutions and stand-alone solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Andrienko N, Andrienko G, Savinov A, Voss H, Wettschereck D (2001) Exploratory analysis of spatial data using interactive maps and data mining. Cartogr Geogr Inform Sci 28(3):151–165

    Article  Google Scholar 

  • Anscombe FJ (1973) Graphs in statistical analysis. Am Statistician 27(1):17–21

    Google Scholar 

  • Anselin L (1994) Exploratory spatial data analysis and geographic information systems. In: Painho M (ed) New tools for spatial analysis. Eurostat, Luxembourg, pp 45–54

    Google Scholar 

  • Anselin L (1995) Local indicators of spatial association – LISA. Geogr Anal 27(2):93–115

    Article  Google Scholar 

  • Anselin L, Dodson RF, Hudak S (1993) Linking GIS and spatial data analysis in practice. Geogr Sys 1(1):3–23

    Google Scholar 

  • Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38(1):5–22

    Article  Google Scholar 

  • Bivand RS (2010) Exploratory spatial data analysis. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis: software tools, methods and applications. Springer, Berlin/Heidelberg, pp 219–254

    Chapter  Google Scholar 

  • Brunsdon C, Comber L (2019) An introduction to R for spatial analysis & mapping, 2nd edn. Sage, London

    Google Scholar 

  • Carr DB, Pickle LW (2010) Visualizing data patterns with micromaps. Chapman & Hall/CRC, Boca Raton

    Book  Google Scholar 

  • Carr DB, Wallin JF, Carr DA (2000) Two new templates for epidemiology applications: linked micromap plots and conditioned choropleth maps. Stat Med 19(17–18):2521–2538

    Article  Google Scholar 

  • Cressie NAC (1993) Statistics for spatial data, revised edn. Wiley, New York

    Google Scholar 

  • Fischer MM, Wang J (2011) Spatial data analysis: models, methods and techniques. Springer, Berlin/Heidelberg/New York

    Book  Google Scholar 

  • Fotheringham AS (1992) Exploratory spatial data analysis and GIS. Environ Plann A 24(2):1675–1678

    Google Scholar 

  • Harrower MA, Brewer CA (2003) ColorBrewer.org: an online tool for selecting color schemes for maps. Cartogr J 40(1):27–37

    Article  Google Scholar 

  • Haslett J, Wills G, Unwin A (1990) SPIDER – an interactive statistical tool for the analysis of spatially distributed data. Int J Geogr Inform Syst 4(3):285–296

    Google Scholar 

  • Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5(3):299–314

    Google Scholar 

  • Liu L, Marble D (1997) Brushing spatial flow data sets. In: 1997 proceedings of the Section on Statistical Graphics. American Statistical Association, Alexandria, pp 67–72

    Google Scholar 

  • Monmonier M (1989) Geographic brushing: enhancing exploratory analysis of the scatterplot matrix. Geogr Anal 21(1):81–84

    Article  Google Scholar 

  • Monmonier M (1996) How to lie with maps, 2nd edn. University of Chicago Press, Chicago

    Book  Google Scholar 

  • Openshaw S (1984) The modifiable areal unit problem. In: Concepts and techniques in modern geography no. 38. Geo Books, Norwich

    Google Scholar 

  • Ord JK, Getis A (2012) Local spatial heteroscedasticity (LOSH). Ann Reg Sci 48(2):529–539

    Article  Google Scholar 

  • Osiecki KM, Kim S, Chukwudozie IB, Calhoun EA (2013) Utilizing exploratory spatial data analysis to examine health and environmental disparities in disadvantaged neighborhoods. Environ Justice 6(3):81–87

    Article  Google Scholar 

  • R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/

    Google Scholar 

  • Ripley BD (1981) Spatial statistics. Wiley, New York

    Book  Google Scholar 

  • Rosling H, Johansson C (2009) Gapminder: liberating the x–axis from the burden of time. Stat Comput Stat Graph Newslett 20(1):4–7

    Google Scholar 

  • Snow J (1936) Snow on cholera: being a reprint of two papers by John Snow, M.D. together with a biographical memoir by B. W. Richardson, M.D. and an introduction by Wade Hampton Frost, M.D. The Commonwealth Fund/Oxford University Press, New York/London

    Google Scholar 

  • Symanzik J (2012) Interactive and dynamic graphics. In: Gentle JE, Härdle WK, Mori Y (eds) Handbook of computational statistics – concepts and methods, 2nd edn. Springer, Berlin/Heidelberg, pp 335–373

    Chapter  Google Scholar 

  • Symanzik J, Carr DB (2008) Interactive linked micromap plots for the display of geographically referenced statistical data. In: Chen C, Härdle W, Unwin A (eds) Handbook of data visualization. Springer, Berlin/Heidelberg, pp 267–294. & 2 color plates

    Chapter  Google Scholar 

  • Symanzik J, Cook D, Lewin-Koh N, Majure JJ, Megretskaia I (2000) Linking ArcView and XGobi: insight behind the front end. J Comput Graph Stat 9(3):470–490

    Google Scholar 

  • Tukey JW (1977) Exploratory data analysis. Addison Wesley, Reading

    Google Scholar 

  • Unwin A, Wills G, Haslett J (1990) REGARD – graphical analysis of regional data. In: 1990 proceedings of the Section on Statistical Graphics. American Statistical Association, Alexandria, pp 36–41

    Google Scholar 

  • Wang X, Chen JX, Carr DB, Bell BS, Pickle LW (2002) Geographic statistics visualization: web–based linked micromap plots. Comput Sci Eng 4(3):90–94

    Article  Google Scholar 

  • Wilson B, Greenlee AJ (2016) The geography of opportunity: an exploratory spatial data analysis of U.S. counties. GeoJournal 81(4):625–640

    Article  Google Scholar 

Further Reading

  • Bao S, Anselin L (1997) Linking spatial statistics with GIS: operational issues in the SpaceStat–ArcView link and the S + Grassland link. In: 1997 proceedings of the Section on Statistical Graphics. American Statistical Association, Alexandria, pp 61–66

    Google Scholar 

  • Carr DB, Chen J, Bell BS, Pickle LW, Zhang Y (2002) Interactive linked micromap plots and dynamically conditioned choropleth maps. In: Proceedings of the second national conference on digital government research, Digital Government Research Center (DGRC), pp 61–67. http://www.dgrc.org/conferences/2002_proceedings.jsp

  • Cook D, Majure JJ, Symanzik J, Cressie N (1996) Dynamic graphics in a GIS: exploring and analyzing multivariate spatial data using linked software. Comput Stat 11(4):467–480. Special issue on computeraided analysis of spatial data

    Google Scholar 

  • Kahle D, Wickham H (2013) ggmap: spatial visualization with ggplot2. R Journal 5(1):144–161

    Article  Google Scholar 

  • Payton QC, McManus MG, Weber MH, Olsen AR, Kincaid TM (2015) micromap: a package for linked micromaps. J Stat Softw 63(2). http://www.jstatsoft.org/v63/i02/

  • Pickle LW, Pearson JB Jr, Carr DB (2015) micromapST: exploring and communicating geospatial patterns in U.S. state data. J Stat Softw 63(3). http://www.jstatsoft.org/v63/i03/

  • Tennekes M (2018) tmap: thematic maps in R. J Stat Softw 84(6). http://www.jstatsoft.org/v84/i06/

  • Unwin A (1994) REGARDing geographic data. In: Dirschedl P, Ostermann R (eds) Computational statistics. Physica–Verlag, Heidelberg, pp 315–326

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Utah State University, Logan, UT, USA

    Jürgen Symanzik

Authors
  1. Jürgen Symanzik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jürgen Symanzik .

Editor information

Editors and Affiliations

  1. Institute for Economic Geography and GIScience, Vienna University of Economics and Business, Vienna, Austria

    Manfred M. Fischer

  2. and Business Administration Department of Spatial Economics, Free University Faculty of Economics, Amsterdam, The Netherlands

    Peter Nijkamp

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Symanzik, J. (2019). Exploratory Spatial Data Analysis. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36203-3_76-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36203-3_76-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36203-3

  • Online ISBN: 978-3-642-36203-3

  • eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation