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.     

Locating Vehicle Inspection Stations to Protect a Transportation Network

We develop a new conceptual approach to locating inspection stations for hazardous vehicles, prevention, and compare it to the previous, punitive, philosophy of the flow-capturing location model. We implement this preventive protection philosophy with a new mixed integer program that maximizes hazard avoidance by locating a number of inspection stations to detect and remove hazardous vehicles as early in their trips as possible. We test the model's performance and analyze the spatial characteristics of solutions simulating several potential applications. Our computations demonstrate that a relaxed integer-linear program is overly demanding computationally and that a simple greedy heuristic lacks robustness. We suggest further approaches to developing more powerful and efficient solution methods.     

AGGREGATION ERROR EFFECTS ON THE DISCRETE-SPACE p-MEDIAN MODEL: THE CASE OF EDMONTON, CANADA

The spatial analysis literature recognizes three sources of aggregation error, termed Source A, Source B, and Source C, which affect models relying on distance measurements between populations and facilities. We consider these effects with respect to aggregating from census enumeration areas to census tracts, on a popular location model. We identify a further source of aggregation error, which we dub Source D error, arising from the representation of facility sites by discrete points. Source D effects are of the same magnitude as Source A and B combined, much greater than Source C effects. Source D error is further significant, because, unlike Source A and B error, it can be eliminated only by disaggregating .
La littérature sur Vanalyse spatiale reconnaît trois sources d'erreur d'agrégation appelées erreurs de source A, B, et C. Ces erreurs influencent les modèles qui reposent sur des mesures de distance entre des populations et des installations. On considère les effets d'agrégation des secteurs de dénombrement en secteurs de recensement sur un modèle de localisation courant. On identifie une quatrième source d'erreur d'agrégation appelée erreur de source D. Cette erreur découle de la représentation des sites des installations par des points discrets. Ses effets sont du même ordre de grandeur que ceux des sources A et B combinées et beaucoup plus grands que les effets de source G. L'erreur de source D est d'autant plus significative à cause du fait que, contrairement aux erreurs de source A et B, elle ne peut être éliminée que par la désagrégation .