This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods” a priori and then studying how resident attributes change over time, this approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both aspects of a neighborhood transform from one period to the next. The approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. The authors also develop indicators of spatial change at both the macro (city) level and the local (neighborhood) scale. The authors illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the United States for the period 1990-2000.

The uneven income distribution in Mexico has frequently being subject to both scrutiny and concern. This phenomenon seems to have a spatial counterpart much less explored and increasingly complex. This paper introduces a novel approach to analyse space-time integration of income distribution dynamics in Mexico. We draw on recent extensions in Exploratory Space-Time Data Analysis (ESTDA) and directional statistics, to investigate spatial dynamics and income movement patterns between Mexican states over 70 years. We use spatial randomization to test directional co-movements in spatially contiguous units as evidence of spatial dependence in income distribution dynamics. Strong evidence of spatial integration of income distribution dynamics is found along the study period. Our results suggest that, in many instances, states do not act independently and we identify a range of integrated movements. The lack of independence itself has important implications for income distributional outcomes as higher mobility will not necessarily imply mixing in the distribution, which is more likely to occur among neighbouring states within a similar income strata. This can be illustrated by the findings of two spatial, yet, contrasting effects: spatial integration in (low) top-down mobility, on the one hand, and negative spatial dependence on the other. While the latter is mostly explained by losses in states and distributional gains of the neighbours with income mixing (rank shifts among neighbouring states) within a group of states, the former phenomena obey a different spatial process where a state and neighbours move in the same direction in the distributional space. Altogether, our findings suggest the presence of slow spatial change in Mexico, mainly due to the presence of heterogeneous, spatially integrated dynamics influencing net change in income distributional outcomes.

Un aspecto central en el estudio de las disparidades regionales en México es la potencial nivelación entre las desigualdades económicas y las espaciales. Tal preocupación original se encuentra asociada con la acentuación de los antagonismos regionales y la generación de focos de tensión política y social. Al respecto, existe un especial interés por entender la heterogeneidad espacial, es decir, cómo ciertos procesos difieren entre regiones y en qué medida las unidades espaciales —estados, municipios, ciudades, regiones, etcétera— pudieran influirse mutuamente en la adopción de ciertas prácticas sociales y económicas, condicionando con ello su respuesta dinámica dentro del sistema regional. El análisis de estos temas se ha diluido en la literatura bajo el actual sesgo metodológico y teórico. A pesar de que varios estudios en México reflejan la preocupación por el cambio espacial, hay grandes rezagos debido a la falta de la consideración de métodos espacialmente explícitos de análisis de datos.

a b s t r a c tIn this paper we examine the trajectory of regional income inequality dynamics for two neighboring national systems. Using data on 3038 US counties and 2418 Mexico municipios, from 2000, 2005, and 2010, we employ recent extensions of spatial Markov chains and space-time mobility measures, to consider the following questions: Are regional inequality dynamics fundamentally distinct between Mexico and the United States? Does the role of spatial context influence the distributional dynamics of the two systems? Finally we examine if there is a distinct international border region that displays inequality dynamics different from those of the internal regions of the two national systems. Strong evidence of spatial heterogeneity in regional income mobility is found between the two national systems, with Mexico having higher mobility relative to the US. The international border region is found to have distinct mobility dynamics from either national system, experiencing the strongest mobility. Extensive evidence of spatial contextual effects are found throughout the US-Mexican pooled data set indicating that a region's transitional dynamics are influenced by incomes of neighboring regions.

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