A dynamic model of regional population growth and decline

"This paper presents a two-sector dynamic model of regional population change. The model contains a migration equation, an earnings equation, and an amenity equation for each region. In the context of this model, migration is seen as a disequilibrium process through which the regional populations move towards equilibrium positions. Migration occurs when one region is perceived to be superior to the other on the basis of its earnings and amenity levels. Equilibrium is achieved when population movements between the regions eliminate any differences in their relative attractiveness. A comparative-static analysis of the model is presented to analyze the effects of policy changes and exogenous shocks on the distribution of population among the regions." The geographical focus is on the United States.     

Variations in the Spatial Pattern of Settlement Locations: An Analysis Based on Proportional Hazards Models

Models based on hazard functions are used to analyze spatial trends in the distance intervals separating point locations. The proportional hazards model, which has been widely applied to analyze intervals of time, is used to investigate variation in the spacing of settlements in Nebraska. This model allows spatial trends in the intervals between settlements to be investigated under very general conditions regarding the interdependence of settlement locations and permits the coordinate locations of the intervals to be treated as spatially varying covariates. An empirical analysis reveals an East-West trend in the spacing of settlements.     

The Analysis of Spatial Association by Use of Distance Statistics

Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran's I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G_i and G_i* statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.