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STARS: Space-Time Analysis of Regional Systems

Author

Listed:
  • Sergio J. Rey

    (San Diego State University)

  • Mark V. Janikas

    (San Diego State University)

Abstract

Space-Time Analysis of Regional Systems (STARS) is an open source package designed for dynamic exploratory analysis of data measured for areal units at multiple points in time. STARS consists of four core analytical modules: [1] ESDA: exploratory spatial data analysis; [2] Inequality measures; [3] Mobility metrics; [4] Spatial Markov. Developed using the Python object oriented scripting language, STARS lends itself to three main modes of use. Within the context of a command line interface (CLI), STARS can be treated as a package which can be called from within customized scripts for batch oriented analyses and simulation. Alternatively, a graphical user interface (GUI) integrates most of the analytical modules with a series of dynamic graphical views containing brushing and linking functionality to support interactive exploration of the spatial, temporal and distributional dimensions of socioeconomic and physical processes. Finally, the GUI and CLI modes can be combined for use from the Python shell to facilitate interactive programming and access to the many libraries contained within Python. This paper provides an overview of the design of STARS, its implementation, functionality and future plans. A selection of its analytical capabilities are also illustrated that highlight the power and flexibility of the package.

Suggested Citation

  • Sergio J. Rey & Mark V. Janikas, 2004. "STARS: Space-Time Analysis of Regional Systems," Urban/Regional 0406001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpur:0406001
    Note: Type of Document - pdf; pages: 20
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/urb/papers/0406/0406001.pdf
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    References listed on IDEAS

    as
    1. Sergio J. Rey & Boris Dev, 2004. "Sigma-convergence in the presence of spatial effects," Urban/Regional 0404008, University Library of Munich, Germany, revised 22 Apr 2004.
    2. Sergio J. Rey & Mark V. Janikas, 2003. "Convergence and space," Urban/Regional 0311002, University Library of Munich, Germany, revised 16 Nov 2003.
    3. Sergio Rey & Brett Montouri, 1999. "US Regional Income Convergence: A Spatial Econometric Perspective," Regional Studies, Taylor & Francis Journals, vol. 33(2), pages 143-156.
    4. Carlino, Gerald A. & Mills, Leonard O., 1993. "Are U.S. regional incomes converging? : A time series analysis," Journal of Monetary Economics, Elsevier, vol. 32(2), pages 335-346, November.
    5. Symanzik, J. & Kötter, T. & Schmelzer, S. & Klinke, S., 1997. "Spatial Data Analysis in the Dynamically Linked ArcView/XGobi/XploRe Environment," SFB 373 Discussion Papers 1997,49, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Getis, Arthur, 2007. "Reflections on spatial autocorrelation," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 491-496, July.
    2. repec:asg:wpaper:1007 is not listed on IDEAS
    3. repec:asg:wpaper:1009 is not listed on IDEAS
    4. Sergio Rey & Richard Smith, 2013. "A spatial decomposition of the Gini coefficient," Letters in Spatial and Resource Sciences, Springer, vol. 6(2), pages 55-70, July.
    5. Mark V. JANIKAS & Sergio J. REY, 2005. "Spatial Clustering, Inequality And Income Convergence," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 21, pages 45-64.
    6. Daniel ARRIBAS-BEL & Fernando SANZ GRACIA & Domingo P. XIMENEZ-DE-EMBUN, 2012. "Kangaroos, Cities And Space: A First Approach To The Australian Urban System," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 36, pages 165-187.
    7. Mark V. Janikas & Sergio J. Rey, 2005. "Spatial Clustering, Inequality and Income Convergence," Urban/Regional 0501002, University Library of Munich, Germany.
    8. repec:asg:wpaper:1008 is not listed on IDEAS
    9. Alan T. Murray, 2010. "Quantitative Geography," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 143-163, February.
    10. Ye, Xinyue & Yue, Wenze, 2014. "Comparative analysis of regional development: Exploratory space-time data analysis and open source implementation," Economics Discussion Papers 2014-20, Kiel Institute for the World Economy (IfW Kiel).
    11. Marcia Castro, 2007. "Spatial Demography: An Opportunity to Improve Policy Making at Diverse Decision Levels," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 477-509, December.
    12. Agovino, Massimiliano & Crociata, Alessandro & Sacco, Pier Luigi, 2019. "Proximity effects in obesity rates in the US: A Spatial Markov Chains approach," Social Science & Medicine, Elsevier, vol. 220(C), pages 301-311.
    13. Julian Ramajo & Miguel A. Marquez & Geoffrey J.D. Hewings, 2013. "Spatio-temporal Analysis of Regional Systems: A Multiregional Spatial Vector Autoregressive Model for Spain," ERSA conference papers ersa13p159, European Regional Science Association.
    14. Mack, Elizabeth A. & Maciejewski, Ross, 2015. "A profile of visual analytical toolkits for understanding the spatio-temporal evolution of broadband provision," Telecommunications Policy, Elsevier, vol. 39(3), pages 320-332.
    15. repec:asg:wpaper:1047 is not listed on IDEAS
    16. Luc Anselin, 2012. "From SpaceStat to CyberGIS," International Regional Science Review, , vol. 35(2), pages 131-157, April.

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    More about this item

    Keywords

    Space-time; geovisualization; geocomputation; distributional dynamics; inequality.;
    All these keywords.

    JEL classification:

    • R - Urban, Rural, Regional, Real Estate, and Transportation Economics

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