Volumen 44 Número 87 (2021)

Permanent URI for this collectionhttp://54.81.141.168/handle/123456789/186801

Tabla de Contenido
  • Presentation Herrera-Gómez, Marcos

    • Articles
  • Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation Izaguirre, Alejandro; 1-19
  • Price and Spatial Distribution of Office Rental in Madrid: A Decision Tree Analysis Camacho, Máximo; Ramallo, Salvador; Ruiz, Manuel; 20-40
  • A Simple Test of Spatial Autocorrelation for Centered Variables Mur, Jesús; 41-55
  • Empirical Identification of Intra-Urban Subcentralities: A New Methodological Approach with an Application for a Developing Country Campos, Rodger B. A; Azzoni, Carlos; 56-73
  • The Scan-LM to Test Instability in the Constant Coefficient of Spatial Autoregressive Models López Hernández, Fernando A; Mínguez Salidos, Román; 74-88
  • Where Are the Poor Located? A Spatial Heterogeneity Analysis of Monetary Poverty in Peru Palomino, Juan; Sánchez, Thyara; 89-114
  • Human Capital Constraints, Spatial Dependence, and Regionalization in Bolivia: A Spatial Clustering Approach Mendez, Carlos; Gonzales, Erick; 115-145
  • Spatial Diffusion of Civil Liberty Chasco, Coro; Lacalle-Calderon, Maricruz; Alfonso-Gil, Javier; 146-169

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      Empirical Identification of Intra-Urban Subcentralities: A New Methodological Approach with an Application for a Developing Country
      (Pontificia Universidad Católica del Perú, 2021-05-06) Campos, Rodger B. A.; Azzoni, Carlos
      We present a new empirical approach for identifying sub-centers within urban areas and apply it to the São Paulo metropolitan area (SPMA). We use geographically weighted regressions (GWR) to overcome the limitations presented by previous methods, which rely on previous knowledge of the employment distribution and use arbitrary threshold values and band sizes. We find three SBD in 2002 and only two in 2014, suggesting that SPMA is polycentric but presents only one business core that concentrates more than 90% of all employees working in an SBD. We apply the widely recognized method of McMillen and Smith (2003) to our database and compare the results. Our method is more conservative in identifying areas as sub-centers (SBD) and presents lower standard errors.