PL EN


Journal
2015 | 3 (49) | 45-54
Article title

A spatial regression model of retail chains development in Poland

Content
Title variants
PL
Model regresji przestrzennej rozwoju sieci handlowych w Polsce
Languages of publication
EN
Abstracts
EN
The key research goal is to identify predictors of retail chain space development in Poland, as well as to define if there is any spatial correlation between increasing retail area and the spatial proximity of the other malls. In order to do so, the study covers an analysis of the retail area in Poland in the framework of the socio-economic development of cities. The study covers a macroeconomic overview of the retail map of Poland. The unit of the analysis are cities with a population of 40,000-400,000 inhabitants. The method of analysis is based on two models: non-spatial OLS and the spatial regression model. The model’s goal is to predict the retail development of a city based on the most effective predictors. The independent variables included a range of socio-economic indicators such as: city population, city unemployment rate and average salary in the private sector.
Journal
Year
Issue
Pages
45-54
Physical description
Contributors
References
  • Anselin, L., 1995, Local Indicators of Spatial Association — LISA, Geographical Analysis, 27(2), pp. 93-115, available at: www.drs.wisc.edu/people/faculty/curtis/documents/RS977/Anselin1995.pdf\n www.spatialanalysisonline.com/output/html/LocalindicatorsofspatialassociationLISA.html.
  • Beck N., Gleditsch K., Beardsley K., 2006, Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy, International Studies Quarterly, 50, pp. 27-44.
  • Bivand R.S., Edzer J.P., Gómez-Rubio V., 2008, Applied Spatial Data with R, Springer, New York; London.
  • CSISS, 2015, Center for Spatially Integrated Social Science, available at: http://www.csiss.org/GISPopSci/research/tools/spatial.php (accessed May 5, 2015).
  • Franzese R.J., Hays J.C., 2007, Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data, Political Analysis, 15(2), pp. 140-164.
  • Franzese R.J., Hays J.C., Kachi A., 2012, Modeling History Dependence in Network-Behavior Coevolution, Political Analysis, 20(2), pp. 175-190.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-aab0abce-0667-4b4b-b5d3-24271a1f3c0e
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