Abstract
Measuring spatial concentration is a key problem when studying geographical phenomena in many areas, including economic activities, atmospheric pollution, animal habitats, and so on. Two important aspects related to the measurement of spatial concentration are data variability and spatial autocorrelation. Rather than combining different indicators for each of these characteristics, this article proposes a new indicator based on the reconstruction of local spatial decompositions of the classical Gini coefficient. A Monte Carlo simulation study is conducted to evaluate the properties of the new indicator, and the results demonstrate that the indicator is highly linearly correlated with both spatial autocorrelation and data dispersion. Moreover, its elasticities to either the spatial autocorrelation or data dispersion are extremely close to 1 under various experimental circumstances. These findings indicate that the new indicator can reflect not only the absolute level of but also the change in spatial concentration effectively. Applying the indicator to the per capita gross domestic product data set for the middle reach of the Yangtze River, we also demonstrate that the new indicator and its local components are easy to implement and are useful in local spatial association analyses.
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