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PySAL: A Python Library of Spatial Analytical Methods

Author

Listed:
  • Rey, Sergio J.

    (San Diego State U)

  • Anselin, Luc

    (U IL)

Abstract

PySAL is an open source library for spatial analysis written in the object-oriented language Python. It is built upon shared functionality in two exploratory spatial data analysis packages--GeoDA and STARS--and is intended to leverage the shared development of these components. This paper presents an overview of the motivation behind and the design of PySAL, as well as suggestions for how the library can be used with other software projects. Empirical illustrations of several key components in a variety of spatial analytical problems are given, and plans for future development of PySAL are discussed.

Suggested Citation

  • Rey, Sergio J. & Anselin, Luc, 2007. "PySAL: A Python Library of Spatial Analytical Methods," The Review of Regional Studies, Southern Regional Science Association, vol. 37(1), pages 5-27.
  • Handle: RePEc:rre:publsh:v:37:y:2007:i:1:p:5-27
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    File URL: http://journal.srsa.org/ojs/index.php/RRS/article/view/134/85
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    References listed on IDEAS

    as
    1. Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, vol. 34(1), pages 5-34, February.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. Luc Anselin & Yong Wook Kim & Ibnu Syabri, 2004. "Web-based analytical tools for the exploration of spatial data," Journal of Geographical Systems, Springer, vol. 6(2), pages 197-218, June.
    5. Luc Anselin & Harry H. Kelejian, 1997. "Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors," International Regional Science Review, , vol. 20(1-2), pages 153-182, April.
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    Citations

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    Cited by:

    1. repec:kap:jgeosy:v:20:y:2018:i:1:d:10.1007_s10109-017-0255-0 is not listed on IDEAS
    2. Rey, Sergio, 2016. "Space-time patterns of rank concordance: Local indicators of mobility association with application to spatial income inequality dynamics," MPRA Paper 69480, University Library of Munich, Germany.
    3. Wenze Yue & Yuntang Zhang & Xinyue Ye & Yeqing Cheng & Mark R. Leipnik, 2014. "Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang, China," Sustainability, MDPI, Open Access Journal, vol. 6(9), pages 1-22, August.
    4. Sergio Rey & Alan Murray & Luc Anselin, 2011. "Visualizing regional income distribution dynamics," Letters in Spatial and Resource Sciences, Springer, vol. 4(1), pages 81-90, March.
    5. Sergio Rey, 2009. "Show me the code: spatial analysis and open source," Journal of Geographical Systems, Springer, vol. 11(2), pages 191-207, June.
    6. Pedro V. Amaral & Luc Anselin, 2014. "Finite sample properties of Moran's I test for spatial autocorrelation in tobit models," Papers in Regional Science, Wiley Blackwell, vol. 93(4), pages 773-781, November.
    7. Pedro Amaral & Luc Anselin & Daniel Arribas-Bel, 2013. "Testing for spatial error dependence in probit models," Letters in Spatial and Resource Sciences, Springer, vol. 6(2), pages 91-101, July.
    8. Mack, Elizabeth A. & Rey, Sergio J., 2014. "An econometric approach for evaluating the linkages between broadband and knowledge intensive firms," Telecommunications Policy, Elsevier, vol. 38(1), pages 105-118.
    9. David C. Folch & Daniel Arribas-Bel & Julia Koschinsky & Seth E. Spielman, 2016. "Spatial Variation in the Quality of American Community Survey Estimates," Demography, Springer;Population Association of America (PAA), vol. 53(5), pages 1535-1554, October.
    10. Alan T. Murray, 2010. "Quantitative Geography," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 143-163.

    More about this item

    Keywords

    Open Source; Software; Spatial;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • L17 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Open Source Products and Markets
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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