By Bryan R. Roberts, Robert H. Wilson
In this quantity, the authors current reports of the rising trend of socio-economic segregation in seven significant towns of Latin the USA and, for inter-regional distinction, one within the United States.
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5 The correlation can be visualized in the Moran’s scatter-plot, which gives an idea of the relationship between the value of the variable in neighborhood i and its value in the vicinity—its spatial lag. 6 Spatial Moran’s I is the slope of the simple regression between the standardized values of these two elements. Anselin (1995) defines the local indicators of spatial association (LISAs) as “any statistic that satisfies the following two requirements: (a) the LISA for each observation gives an indication of the extent of significant spatial clustering of similar values around that observation; (b) the sum of LISAs for all observations is proportional to a global indicator of spatial association” (Anselin, 1995, p.
Washington, DC: Center on Urban and Metropolitan Policy, Brookings Institution. Smith, D. M. (1977). Human geography: A welfare approach. New York: St. Martin’s Press. Suttles, G. (1968). The social order of the slum. Chicago: University of Chicago Press. Szelényi, I. (1983). Urban inequalities under state socialism. New York: Oxford University Press. Teitz, M. B. (1968). Toward a theory of public facility location. Papers of the Regional Science Association, 21, 35–52. Telles, E. E. (2004). Race in another America: The significance of skin color in Brazil.
Either way, the first law of geography states that spatial dependence is the rule rather than the exception (Anselin and Bera, 1998); as such, accounting for the correlation of observations closely located in space is as important as dealing with other common data-related problems, such as time autocorrelation in panel data and heteroskedasticity in cross-section data. Technically, spatial dependence is a property of joint density functions and as such, it is virtually impossible to be verified in practice.