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.     

Heterogeneity of Attribute Sampling Error in Spatial Data Sets

This paper considers the standard error of the estimate of the mean of a spatially correlated variable in the case where data are obtained by a process of random sampling. Two distinct mean estimation problems are identified: estimating the area mean and estimating the population mean. Methods are described for obtaining standard error estimates in the two cases and, within the limits of publicly available information, the methods are implemented on average household income data at the census tract scale for Syracuse, New York. The purpose of the paper is to draw attention to issues of data precision in relation to sampled geographic information on averages and in particular to consider the problems of estimating standard errors using such data. The paper also examines the extent to which standard errors vary between census tracts.     

Sequential, Bayesian Geostatistics: A Principled Method for Large Data Sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of " basis vectors " that best represent the " true " posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.     

A Genetic Approach to the Subdivision of Tropical Soils,Regolith and their Products of Redeposition

A soil classification based on the genetic principles of Russia's Dokuchayev school is proposed for the tropical setting of Cuba. In view of the heavy degradation of the island's original forest and savanna soils, leaving fully developed soil profiles only in a few areas, such a genetic classification of Cuban soils must incorporate both the few fully developed soils proper and the various types of subsoil, consisting both of parent material and of its products of redeposition. The following types of parent material are distinguished: (a) quartz-allitic-ferritic, on acid crystalline rocks; (b) kaol-inite-allitic-ferritic, on schists; (c) zeolitic, on basic volcanics; (d) ferro-zeolitic, on ultrabasics, and (e) terra rossa, on crystalline limestones. These primary types of parent material, developed on crystalline rocks, have their secondary equivalents on sedimentary rocks.     

A Constructive Approach to Sculpture

Abstract

In the production of a sculpture, practical and technical considerations have a large influence. Some of these are here reviewed as seen through the author's eyes and as based on his personal experience. Such materials as concrete, cast iron and stainless steel are discussed, classical and contemporary fashions are compared, the importance of linking straight lines and curves in sculpture is emphasised, and the design process is outlined. The review concludes with a discussion of the heart of all artistic work, inspiration.     

A Network Approach to Commuting

In this paper we present a model for commuting in a network of towns. A basic assumption is that all individuals have a given residential location and that every node in the network has a fixed number of jobs. We then propose a general model for the commuting of labor between the nodes in the network.     

A Strategic Approach to Multistakeholder Negotiations

Environment and development practitioners increasingly are interested in identifying methods, institutional arrangements and policy environments that promote negotiations among natural resource stakeholders leading to collective action and, it is hoped, sustainable resource management. Yet the implications of negotiations for disadvantaged groups of people are seldom critically examined. We draw attention to such implications by examining different theoretical foundations for multistakeholder negotiations and linking these to practical problems for disadvantaged groups. We argue that negotiations based on an unhealthy combination of communicative rationality and liberal pluralism, which underplays or seeks to neutralize differences among stakeholders, poses considerable risks for disadvantaged groups. We suggest that negotiations influenced by radical pluralist and feminist post‐structuralist thought, which emphasize strategic behaviour and selective alliance‐building, promise better outcomes for disadvantaged groups in most cases, particularly on the scale and in the historical contexts in which negotiations over forest management usually take place.     

"文革史学"初探--以中国近代史研究为例

“文革史学”是指1966-1976年“文化大革命”期间中国“史学”的发展和演变的历史,它既是中国史学史、也是“文革”研究的重要组成部分。“文革史学”是动乱年代政治运动的特殊产物,是中国史学史上极为黑暗的一页,给中国史学蒙上了极大耻辱。它截断了中国史学的优良传统,并离群索居,与不断前进的世界史学背道而驰;中国史学成了政治史学、影射史学的代名词,失去了作为一门科学的独立性与尊严。     

A Selection Bias Approach to Destination Choice

Research on consumer search behavior commonly envisages destination choice as a two-step process: (1) delineate the search set, and (2) evaluate choices therein. However, much of the empirical work in destination choice—including logit and nested logit formulations—models only the latter, and not the set delineation itself. In the presence of correlation between error terms in set delineation and choice selection, statistical estimators are biased, a problem that Heckman and others have called selection bias. In this paper, an alternative two-stage method is proposed to estimate the parameters of models of set delineation and choice selection. Monte Carlo simulation is used to explore the properties of these two-stage estimators, and to show the magnitude of bias inherent in traditional methods of estimation.     

A Surface-Based Approach to Measuring Spatial Segregation

Quantitative indices of residential segregation have been with us for half a century, but suffer significant limitations. While useful for comparison among regions, summary indices fail to reveal spatial aspects of segregation. Such measures generally consider only the population mix within zones, not between them. Zone boundaries are treated as impenetrable barriers to interaction between population subgroups, so that measurement of segregation is constrained by the zoning system, which bears no necessary relation to interaction among population subgroups. A segregation measurement approach less constrained by the chosen zoning system, which enables visualization of segregation levels at the local scale and accounts for the spatial dimension of segregation, is required. We propose a kernel density estimation approach to model spatial aspects of segregation. This provides an explicitly geographical framework for modeling and visualizing local spatial segregation. The density estimation approach lends itself to development of an index of spatial segregation with the advantage of functional compatibility with the most widely used index of segregation (the dissimilarity index D ). We provide a short review of the literature on measuring segregation, briefly describe the kernel density estimation method, and illustrate how the method can be used for measuring segregation. Examples using a simulated landscape and two empirical cases in Washington, DC and Philadelphia, PA are presented.     

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 Changing Face of Neolithic and Bronze Age Ireland: A Big Data Approach to the Settlement and Burial Records

This paper synthesizes and discusses the spatial and temporal patterns of archaeological sites in Ireland, spanning the Neolithic period and the Bronze Age transition (4300–1900 cal BC), in order to explore the timing and implications of the main changes that occurred in the archaeological record of that period. Large amounts of new data are sourced from unpublished developer-led excavations and combined with national archives, published excavations and online databases. Bayesian radiocarbon models and context- and sample-sensitive summed radiocarbon probabilities are used to examine the dataset. The study captures the scale and timing of the initial expansion of Early Neolithic settlement and the ensuing attenuation of all such activity—an apparent boom-and-bust cycle. The Late Neolithic and Chalcolithic periods are characterised by a resurgence and diversification of activity. Contextualisation and spatial analysis of radiocarbon data reveals finer-scale patterning than is usually possible with summed-probability approaches: the boom-and-bust models of prehistoric populations may, in fact, be a misinterpretation of more subtle demographic changes occurring at the same time as cultural change and attendant differences in the archaeological record.