Duration Modeling of Spatial Point Patterns

This paper attempts to further the research by Odland and Ellis (1992) in applying event history methodology to the analysis of spatial point patterns (that is, event patterns). Its empirical focus is the event pattern derived from the adoption of an agricultural innovation, the Harvestore, in southern Ontario, Canada, from 1963 to 1986. Event history analysis involves the use of discrete-state, continuous-time stochastic models to investigate a temporal longitudinal record on discrete variables. Event history models are usually concerned with durations of time between events and the effects of intertemporal time dependencies on future event occurrences. As such, they are often referred to as duration models. Many of the methods used in event history analysis allow the use of other nonnegative interval measurements in place of standard temporal intervals to investigate a series of events. In particular, spatial intervals (or durations) of distances between events may also be accommodated by event history models. Our analysis extends the previous research of Odland and Ellis (1992) by using a wider range of parametric models to explore duration dependence, investigating the role of spatial censoring, and using a more extensive set of explanatory variables. In addition, simulation experiments and graphical tests are used to evaluate the empirical event pattern against one generated from Complete Spatial Randomness. Results indicate that the event pattern formed by the Harvestore adopter farms is clustered (that is, is described by positive duration dependency), the sales agent is a significant factor in the distribution of adopters, and that contrasting results are obtained from the analysis using censored data versus uncensored data.     

Local Indicators of Network-Constrained Clusters in Spatial Point Patterns

The detection of clustering in a spatial phenomenon of interest is an important issue in spatial pattern analysis. While traditional methods mostly rely on the planar space assumption, many spatial phenomena defy the logic of this assumption. For instance, certain spatial phenomena related to human activities are inherently constrained by a transportation network because of our strong dependence on the transportation system. This article thus introduces an exploratory spatial data analysis method named l ocal i ndicators of n etwork-constrained c luster s (LINCS), for detecting local-scale clustering in a spatial phenomenon that is constrained by a network space. The LINCS method presented here applies to a set of point events distributed over the network space. It is based on the network K -function, which is designed to determine whether an event distribution has a significant clustering tendency with respect to the network space. First, an incremental K -function is developed so as to identify cluster size more explicitly than the original K -function does. Second, to enable identification of cluster locations, a local K -function is derived by decomposing and modifying the original network K -function. The local K -function LINCS, which is referred to as KLINCS, is tested on the distribution of 1997 highway vehicle crashes in the Buffalo, NY area. Also discussed is an adjustment of the KLINCS method for the nonuniformity of the population at risk over the network. As traffic volume can be seen as a surrogate of the population exposed to a risk of vehicle crashes, the spatial distribution of vehicle crashes is examined in relation to that of traffic volumes on the network. The results of the KLINCS analysis are validated through a comparison with priority investigation locations (PILs) designated by the New York State Department of Transportation.     

A Comparison of Three Methods for the Extraction of Phytoliths from Sediments

This paper compares three methods commonly used to extract fossil phytoliths from sediments. A basic procedure using heavy liquid flotation and oxidation is compared with two other procedures across a range of sediment types commonly encountered in archaeological studies. The three procedures are: (1) a heavy liquid flotation method (HLF); (2) a burning method (POW); and (3) another heavy liquid flotation method (HLFPol) similar to HLF, but adapted to allow the extraction of pollen and spores as well as phytoliths, within a single process. Comparisons of the resulting output using these three techniques for phytolith extraction show that different methods can produce different results, and therefore basic techniques should be modified according to the characteristics of the sediments for which they are used. While all the techniques showed similarities in assemblage results, there were problems associated with disaggregation and effective separation of light and heavy fractions, in particular with the POW procedure. The evidence suggests that morphotype selection occurred both within the physical sorting process and in the process of inverting slides to shed excess residue; in both cases it is difficult to suggest a solution to the problem. The results show clearly that the advantages gained by using the POW procedure are largely outweighed by the problems encountered with its use, and because of possible size/shape selection, it is not recommended for general extraction procedures. The heavy liquid flotation procedures, on the other hand, are shown to produce more concentrated residues with higher levels of clarity and less potential than the POW procedure for sample bias. The use of a non-toxic heavy liquid, sodium polytungstate, now allows the process to be used in relative safety. It is recommended that analysts use heavy liquid flotation procedures with chemical treatments specific to sediment requirements.     

A Scale‐Sensitive Test of Attraction and Repulsion Between Spatial Point Patterns

There exist a variety of tests for attraction and repulsion effects between spatial point populations, most notably those involving either nearest‐neighbor or cell‐count statistics. Diggle and Cox (1981) showed that for the nearest‐neighbor approach, a powerful test could be constructed using Kendall's rank correlation coefficient. In the present paper, this approach is extended to cell‐count statistics in a manner paralleling the K‐function approach of Lotwick and Silverman (1982). The advantage of the present test is that, unlike nearest‐neighbors, one can identify the spatial scales at which repulsion or attraction are most significant. In addition, it avoids the torus‐wrapping restrictions implicit in the Monte Carlo testing procedure of Lotwick and Silverman. Examples are developed to show that this testing procedure can in fact identify both attraction and repulsion between the same pair of point populations at different scales of analysis.     

New Perspectives on Spatial Patterns of Agrarian Reform: A Comparison of Two Russian Oblasts

The author, based on field work, interviews, and examination of local and regional literature and official statistical sources, compares the experience in agrarian reform in two disparate locations—KostToma Oblast, northeast of Moscow in the Noncher-nozem Region, and Rostov Oblast on Russia's Black Sea littoral in the fertile Chernozem (Black Earth) region. It examines both the reorganization of state and collective farms and the establishment of private peasant farms in the two oblasts, with particular emphasis on the latter. The sections on private farms represent an initial attempt, based on in-depth information for a limited sample population, to garner insights, at the rayon level, into factors that may be influencing regional variations in the number, size, and location of private farms across the Russian countryside. 2 maps, 6 tables, 53 references.     

Order Distance in Regular Point Patterns

This article examines the k th nearest neighbor distance for three regular point patterns: square, triangular, and hexagonal lattices. The probability density functions of the k th nearest distance and the average k th nearest distances are theoretically derived for k =1, 2, …, 7. As an application of the k th nearest distance, we consider a facility location problem with closing of facilities. The problem is to find the optimal regular pattern that minimizes the average distance to the nearest open facility. Assuming that facilities are closed independently and at random, we show that the triangular lattice is optimal if at least 68% of facilities are open by comparing the upper and lower bounds of the average distances.     

Transnational Flows of E-Waste and Spatial Patterns of Recycling in China

Two geographers residing in Beijing discuss the inflows, processing, and consumption of electronic waste—a topic largely neglected in the current literature on globalization. Based on extensive interviews with electronics producers and recyclers in China, the paper explores the global flows of e-waste and concentration of related recycling in coastal China. The authors suggest that recycling activities (authorized as well as illegal) grew in tandem with the dramatic increase in electronics production during the last decade. They note that the country's recycling sector has played a significant role in rural industrialization and local economic development, albeit in conflict with the objectives of environmental protection. Journal of Economic Literature, Classification Numbers: F20, L63, O17, O19. 6 figures, 23 references.     

Spatial Agglomeration of Manufacturing in Greece: Sectoral Patterns and Determinants

This article aims to demonstrate how the industry characteristics of manufacturing sectors affect the patterns of their spatial agglomeration. It also addresses several intricate issues concerning the measurement of localization economies and estimation of their main determinants in manufacturing industries. The original empirical analysis employs annual industrial data from the Hellenic Statistical Authority (ELSTAT) during the period 1993–2006 in Greece at the prefecture level, i.e. for 51 prefectures. The data processing reveals three important findings. The first is the temporal persistence of localization economies in the Greek manufacturing. The second refers to the high level of agglomeration associated with the high-technology industries as well as the resource- and scale-intensive industries. Lastly, there are significant effects of industry characteristics related to knowledge externalities, labour skills and productivity, scale economies and own-transport expenditure on spatial agglomeration, as resulted from the use of alternative geographic concentration indices and panel data models. Results obtained have implications for policy-makers, who can enhance the regional manufacturing activity by affecting these industry-specific factors. Amongst others, planning measures and policies which aim at promoting the local development and regional convergence should focus on reducing transport costs for firms or sectors, by improving the infrastructure capacity, interconnectivity and quality of services.     

Spatial Point Processes Applied to the Distribution of River Junctions

The notion of randomness has been extensively applied to topological (nondimensional) properties of drainage networks. The spatial (dimensional) organization of five fluvial hierarchies is examined herein through the application of quadrat analysis to random and clustered spatial probability models. The Poisson, Polya-Aeppli, and negative binomial models are compared with point-pattern distributions of river junction location for three basins in Indiana and two fossil systems on an erosion surface in semiarid Australia. The negative binomial model best fits all five networks, suggesting that the branching behavior of fluvial systems follows the mathematical precepts leading to clusteredness of junctions. The degree to which the Polya-Aeppli model fits the data suggests the imposition of a temporally limited set of environmental conditions optimally suited for network growth. Only for the two fossil systems does the Poisson model agree. It is speculated that the effect of prolonged subaerial erosion may be to shift a clustered distribution towards the random state as the surface approaches a pediplained state.     

日喀则旅游发展模式研究

本通过对日喀则地区旅游发展条件的综合分析,提出了以政府启动、交通引动、外资促动、国际联动、开放推动和全域化、生态化、网络化、差异化、小型化为核心的“日喀则旅游发展模式”,并确立了发展战略体系。     

Modeling Micro-Spatial Employment Location Patterns: A Comparison of Count and Choice Approaches

This article studies employment location patterns in the Puget Sound Region of Washington State at a micro level of geography. Traditional discrete choice modeling using multinomial logit (MNL) models may be problematic at a micro level of geography due to the high dimensionality of the set of alternative locations and the likely violations of the independence from irrelevant alternatives (IIA) assumption. Count models are free from the IIA assumption and, unlike logit models, actually benefit from large numbers of alternatives by adding degrees of freedom. This study identifies the best-fitting count model as the zero-inflated negative binomial (ZINB) model, because this model more effectively addresses the large number of cells with no jobs and reflects a dual process that facilitates the identification of threshold clustering effects such as those found in specialized employment centers. The estimation and prediction results of ZINB are compared with those of MNL with a random sampling of alternatives estimated on an equivalent data set. The ZINB and MNL models largely agree on major trends, with the ZINB model providing more insightful details, but with less capacity to predict large count situations.     

民航业与旅游业市场营销方式比较

民航业与旅游业存在很强的行业相关性和市场定位方式的相似性。本通过两行业在市场营销方式上的比较,对正在实施企业化经营、市场化运作改革的民航企业提供很好的借鉴。     

Combining Geostatistical Methods and Hierarchical Scene Models for Analysis of Multiscale Variation in Spatial Data

Concepts from Hierarchical Analysis of Variance (ANOVA) can be combined with ideas from geostatistics to describe the multiscale structure of spatial data. Hierarchical ANOVA involves modeling spatial data as the sum of effects associated with processes acting at different spatial scales. These effects can be modeled as stationary regionalized variables, whose spatial structure can be described using the variogram. According to this model, the variogram of the spatial data is the sum of variograms and cross‐variograms of the effects. Whereas hierarchical ANOVA reveals the relationship between scale and variability, the hierarchical decomposition of the variogram relates scale with spatial structure. This analysis method can reveal otherwise undetected features of spatial data, and can guide further analysis.