Arc_Mat, a Toolbox for Using ArcView Shape Files for Spatial Econometrics and Statistics

  • James P. LeSage
  • R. Kelley Pace
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3234)


The ability to use statistical functionality for spatial modeling and analysis in conjunction with a mapping interface in the same environment has received a great deal of attention in the spatial analysis literature. We demonstrate the feasibility of extracting map polygon and database information from ESRI’s ArcView shape files for use in statistical software environments. Specifically, we show that information containing map polygons can be used in these environments to produce high quality mapping functionality. Improvements in recent computer graphics hardware and software allow basic plotting functionality that is part of statistical software environments to produce mapping functionality based on the high quality ArcView map polygons.


Graphical User Interface Application Program Interface Spatial Weight Matrix Geographically Weight Regression Model Spatial Econometric 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Anselin, L., Syabri, I., Smirnov, O.: Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows. In: Anselin, L., Rey, S. (eds.) Proc. CSISS Workshop on New Tools for Spatial Data Analysis, Santa Barbara, CA, Center for Spatially Integrated Social Science (2002) CD-ROM (pdf file, 20 pp.)Google Scholar
  2. Barry, R., Pace, R.K.: A Monte Carlo Estimator of the Log Determinant of Large Sparse Matrices. Linear Algebra and its Applications 289, 41–54 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  3. Bivand, R.S.: Spatial econometrics functions in R: Classes and methods. Journal of Geographical Systems 4, 405–421 (2002)CrossRefGoogle Scholar
  4. Brunsdon, C., Fotheringham, A.S., Charlton, M.E.: Geographically weighted regression: A method for exploring spatial non-stationarity. Geographical Analysis 28, 281–298 (1996)CrossRefGoogle Scholar
  5. Geweke, J.: Bayesian Treatment of the Independent Student t Linear Model. Journal of Applied Econometrics 8, 19–40 (1993)CrossRefGoogle Scholar
  6. Heba, I., E. Malin, and C. Thomas-Agnan: Exploratory spatial data analysis with GEOXP. European Regional Science Association conference papers Google Scholar
  7. LeSage, J.P.: Bayesian Estimation of Spatial Autoregressive Models. International Regional Science Review 20, 113–129 (1997)CrossRefGoogle Scholar
  8. LeSage, J.P.: The Theory and Practice of Spatial Econometrics (1999) pdf file, 296 pp. available at
  9. LeSage, J.P.: A Family of Geographically Weighted Regression Models. In: Anselin, L., Florax, J.G.M., Rey, S.J. (eds.) Advances in Spatial Econometrics, Springer, Heidelberg (to appear)Google Scholar
  10. Pace, R. K.: Spatial Statistics Toolbox 2.0. (2002) pdf file, 36 pp. available at
  11. Pace, R.K., Barry, R.: Quick Computation of Regressions with a Spatially Autoregressive Dependent Variable. Geographical Analysis 29, 232–247 (1997)CrossRefGoogle Scholar
  12. Pace, R.K., LeSage, J.P.: Spatial Autoregressive Local Estimation. In: Mur, J., Zoller, H., Getis, A. (eds.) Recent Advances in Spatial Econometrics, pp. 31–51. Palgrave Publishers (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • James P. LeSage
    • 1
  • R. Kelley Pace
    • 2
  1. 1.Department of EconomicsUniversity of ToledoToledoUSA
  2. 2.LREC Endowed Chair of Real Estate, Department of FinanceLousiana State UniversityBaton RougeUSA

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