This article proposes a new method for examining dynamic changes in the spatial distribution of a phenomenon. Recently introduced exploratory spatial data analysis (ESDA) techniques provide social scientists with a ne...
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This article proposes a new method for examining dynamic changes in the spatial distribution of a phenomenon. Recently introduced exploratory spatial data analysis (ESDA) techniques provide social scientists with a new set of tools for distinguishing between random and nonrandom spatial patterns of events (Anselin, 1998). Existing ESDA measures, however, are static and do not permit comparisons of distributions of events in the same space but across different time periods. One ESDA method-the Moran scatterplot-has special heuristic value because it visually displays local spatial relationships between each spatial unit and its neighbors. We extend this static cross-sectional view of the spatial distribution of events to consider dynamic features of changes over time in spatial dependencies. The method distinguishes between contagious diffusion between adjoining units and hierarchical diffusion that spreads broadly through commonly shared influences. We apply the method to homicide data,:looking for evidence of spatial diffusion of youth-gang homicides across neighborhoods in a city. Contagious diffusion between neighboring census tracts is evident only during the year of peak growth in total homicides, when high local rates of youth-gang homicides are followed by significant increases in neighboring youth-nongang rates. This pattern is consistent with a spread of homicides from gang youth to nongang youth. Otherwise, the increases in both youth-gang and youth-nongang homicides generally occur simultaneously in nonneighboring tracts.
This study reconsiders the question of US regional economic income convergence from a spatial econometric perspective. Recently developed methods of exploratory spatial data analysis provide new insights on the geogra...
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This study reconsiders the question of US regional economic income convergence from a spatial econometric perspective. Recently developed methods of exploratory spatial data analysis provide new insights on the geographical dynamics of US regional income growth patterns over the 1929-94 period. Strong patterns of both global and local spatial autocorrelation are found throughout the study period, and the magnitude of global spatial autocorrelation is also found to exhibit strong temporal co-movement with regional income dispersion. A spatial econometric analysis of the familiar Baumol specification reveals strong evidence of misspecification due to ignored spatial error dependence. Because of this dependence, shocks originating in one state can spillover into surrounding states, potentially complicating the transitional dynamics of the convergence process.
exploratory spatial data analysis has a series of aims including determining spatial structure in the data, describing and visualizing geographical distributions, exploring spatial dependencies, measuring heterogeneit...
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exploratory spatial data analysis has a series of aims including determining spatial structure in the data, describing and visualizing geographical distributions, exploring spatial dependencies, measuring heterogeneity and identifying outliers, To quantify these phenomena a rich variety of statistics has been proposed. Standard methods use all the data for the entire area under study, yet this area has usually been arbitrarily bounded and may include quite distinctive geographic features. Local statistics are relatively independent of the global boundaries and they attempt to quantify how close a given datum is to the values in its neighbourhood. Since each local statistic focuses on slightly different aspects of the data the use of more than one is suggested. Interactive graphics methods help to link the information from different local statistics and dynamic tools can be used to visualize the effects of changing the neighbourhood definition.
Interactive graphical analysis has proven to be a seminal method in exploring quantitative spatialdata. Using data from a dialect survey we demonstrate that it can also reveal structure in qualitative spatialdata. T...
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Interactive graphical analysis has proven to be a seminal method in exploring quantitative spatialdata. Using data from a dialect survey we demonstrate that it can also reveal structure in qualitative spatialdata. The flexibility offered by such exploratory tools can deal with the special problems caused by multiple responses and by local geographic effects.
Alternative software implementations for use in the exploration of local dependence are discussed in relation to six design criteria. Since exploratory spatial data analysis is largely based on the study of local depe...
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Alternative software implementations for use in the exploration of local dependence are discussed in relation to six design criteria. Since exploratory spatial data analysis is largely based on the study of local dependence, and because software tools now make it feasible to examine multiple views of spatialdata sets, it becomes important for researchers to compare the outcomes of analyses using varying implementations. Having established and discussed chosen design criteria, some implementations are compared. Special weight is given to the extensibility of the implementation, to permit the user more control over the analysis. It is concluded that such analyses should proceed with discretion, and that often prior or contextual knowledge is necessary for an adequate interpretation of results.
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