An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data

The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations.     

A Nested Logit Approach to Household Mobility

This study analyzes personal and site characteristics in a model of intraMSA and interMSA mobility. Households are assumed to choose a single type of move, intraMSA or interMSA, while simultaneously choosing a central city or suburban destination. We demonstrate that a nested logit model is appropriate on both theoretical and empirical grounds. The sample consists of intrametropolitan and intermetropolitan movers drawn from the 1990 U.S. Census PUMS. Personal characteristics drawn from the PUMS are matched to numerous site characteristics (climatic measures, other amenities, state and local fiscal characteristics, and other urban quality measures) drawn from a variety of sources. Nested logit direct and cross elasticities are presented for a number of site attributes.     

The Fall of the Empire State

Book reviewed in this article:
B. J. C. McKercher, Transition of Power: Britain's Loss of Global Pre-eminence to the United States, 1930–1945