INTERPRETING SPATIAL ECONOMETRIC ORIGIN‐DESTINATION FLOW MODELS

Spatial interaction or gravity models have been used to model flows that take many forms, for example population migration, commodity flows, traffic flows, all of which reflect movements between origin and destination regions. We focus on how to interpret estimates from spatial autoregressive extensions to the conventional regression‐based gravity models that relax the assumption of independence between flows. These models proposed by LeSage and Pace ( 2008 , 2009 ) define spatial dependence involving flows between regions. We show how to calculate partial derivative expressions for these models that can be used to quantify these various types of effect that arise from changes in the characteristics/explanatory variables of the model.     

A Natural-Resource Regionalization of the USSR

A regionalization of natural resources is treated as a particular case of economic regionalization, in the sense that it makes an economic interpretation of the physical base of an area from the point of view of its capacity to generate an economic region of appropriate rank. The resource regionalization is based on two particular regionalizations of bioclimatic and mineral resources, yielding a set of 86 regions that combine both types of resources. A typology of resource regions distinguishes seven basic types of regions in which bioclimatic resources are dominant and four basic types in which mineral resources are dominant. Combinations of bioclimatic and mineral resources yield 24 groups of integral resource regions.     

A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record

The assessment of spatial patterns in archaeology is hampered by a number of constraints, one of the most serious of which is the intrinsic temporal uncertainty associated with most of the archaeological record. Different types of chronological definition or different degrees of temporal knowledge will suggest different kinds of spatial pattern, ultimately obscuring and restricting our interpretation of the background process, especially in cases where we are seeking a diachronic perspective. This paper addresses these problems by adopting both a probabilistic approach and a more standardised framework for diachronic analysis. First, we define the notion of temporal uncertainty and explore its analytical consequences. Second, we consider two methods by which it might be formally quantified, emphasising (a) the advantages of probability-weighted spatial analysis and (b) the comparison of alternative spatio-temporal patterns via Monte-Carlo simulation. Finally, we apply these methods to a case study that considers the distribution of Middle to Late Jomon (ca.5000–3000 BP) pithouses recorded in the Chiba New Town area of Japan.     

Development of Probabilistic Framework for Performance-Based Seismic Assessment of Structures Considering Residual Deformations

Recently, the importance of considering residual (permanent) deformations in the performance assessment of structures has been recognized. Advanced structural systems with re-centering properties as those based on unbonded post-tensioning tendons are capable of controlling or completely eliminating residual deformations. However, for more traditional systems, which count for the vast majority of buildings, residual deformations are currently considered an unavoidable result of structural inelastic response under severe seismic shaking.

In this article, a probabilistic framework for a performance-based seismic assessment of structures considering residual deformations is proposed. The development of a probabilistic formulation of a combined three-dimensional performance matrix, where maximum and residual deformations are combined to define the performance level corresponding to various damage states for a given seismic intensity levels, is first presented. Combined fragility curves expressing the probability of exceedence of performance levels defined by pairs of maximum-residual deformations are then derived using bivariate probability distributions. The significance of evaluating and accounting for residual deformations within a Performance-based Earthquake Engineering (PBEE) approach is further confirmed via numerical examples on the response of Single Degree of Freedom (SDOF) systems, with different hysteretic behavior, under a selected suite of earthquake records. Joined fragility curves corresponding to various performance levels, defined as a combination of maximum and residual response parameters, are derived while investigating the effects of hysteretic systems and strength ratios. It is observed that stiffness degrading Takeda systems result in lower residual deformations than elasto-plastic systems and show lower probability of exceeding a jointed maximum-residual performance level. For a chosen performance level, Takeda systems with higher strength ratios show better performance, particularly with lower intensity of excitations.     

Probabilistic Regional Population Forecasts: The Example of Queensland, Australia

The variability of demographic trends at the subnational scale, particularly internal and international migration, renders subnational population forecasting more difficult than at the national scale. Illustrating the uncertainty of the demographic future for subnational regions is therefore a crucial element of any set of subnational population forecasts. However, subnational forecasts are currently prepared using deterministic models, which fail to properly address the issue of demographic uncertainty. The traditional high, medium, and low variants approach employed by many national statistical offices poses a number of problems. Probabilistic population forecasting models have the potential to overcome many of these problems, but these models have so far been limited to national-level forecasts. This article reports a first attempt to implement a probabilistic approach to subnational population forecasting using a biregional projection framework. The article sets out the forecasting framework, outlines the approach adopted to formulate each of the assumptions, and presents probabilistic forecasts for 2002–2051 for Queensland and the rest of Australia. The forecasts show a two-thirds probability that Queensland's population in 2051 will be between 5.4 and 7.7 million while the same range for the rest of the country is 18.6 and 22.7 million. The forecasts quantify to what extent greater uncertainty exists about the demographic future at the subnational compared with the national scale.     

An information-theoretic approach to the analysis of location and colocation patterns

The study of location and colocation of economic activities lies at the heart of economic geography and related disciplines, but the indices used to quantify these patterns are often defined ad hoc and lack a clear statistical foundation. We propose a statistical framework to quantify location and colocation associations of economic activities using information-theoretic measures. We relate the resulting measures to existing measures of revealed comparative advantage, localization, specialization, and coagglomeration and show how different measures derive from the same general framework. To support the use of these measures in hypothesis testing and statistical inference, we develop a Bayesian estimation approach to provide measures of uncertainty and statistical significance of the estimated quantities. We illustrate this framework in an application to an analysis of location and colocation patterns of occupations in US cities.     

p‐Functional Clusters Location Problem for Detecting Spatial Clusters with Covering Approach

Regionalization or districting problems commonly require each individual spatial unit to participate exclusively in a single region or district. Although this assumption is appropriate for some regionalization problems, it is less realistic for delineating functional clusters, such as metropolitan areas and trade areas where a region does not necessarily have exclusive coverage with other regions. This paper develops a spatial optimization model for detecting functional spatial clusters, named the p‐functional clusters location problem (p‐FCLP), which has been developed based on the Covering Location Problem. By relaxing the complete and exhaustive assignment requirement, a functional cluster is delineated with the selective spatial units that have substantial spatial interaction. This model is demonstrated with applications for a functional regionalization problem using three journey‐to‐work flow datasets: (1) among the 46 counties in South Carolina, (2) the counties in the East North Central division of the US Census, and (3) all counties in the US. The computational efficiency of p‐FCLP is compared with other regionalization problems. The computational results show that detecting functional spatial clusters with contiguity constraints effectively solves problems with optimality in a mixed integer programming (MIP) approach, suggesting the ability to solve large instance applications of regionalization problems.     

Cartographic Techniques of Agricultural Regionalization

The use of a series of correlated maps is proposed as an approach to agricultural regionalization. Because of the dynamic character of farm production, two stages in the regionalization process are distinguished: a delimitation of existing agricultural regions and a delimitation of future regions. The basic principle underlying the compilation of agricultural regionalization maps IS to combine the resource base of agriculture with the actual types of farming. Maps of existing regions would also show the volume and structure of farm production, population and farm employment. In the delimitation of long-term future agricultural regions, the emphasis is on determining the potential biological productivity (climate and soils) of regions and the expected volume of production, based on food needs, industrial processing of farm products and the prospects of interregional trade in farm products.     

SOIL-CLIMATIC BELTS–DO THEY ACTUALLY EXIST?

The use of combined soil-climatic belts (or soil-bioclimatic belts) has become widespread in the Soviet literature to designate the highest taxonomic entities of a world soil-geographic regionalization. These belts, which include such designations as polar (cold), boreal (cold-temperate), subboreal (temperate), etc., have been defined on the basis of mean annual temperature and the sum of temperatures during the growing season. The author finds that the entire concept of soil-climatic belts lacks a sound theoretical basis and is of little practical significance, with no attempt made to define such belts in terms of soils. A new approach to defining the highest taxonomic units in a world soil-geographic regionalization is urged.     

Resolution of the Fifth Congress of the Geographical Society USSR

A 1978 collection of articles devoted to geocybernetics, a subfield of geography concerned with spatial aspects of management, focuses on the problem of administration of the Soviet economy through a system of socio-economic regions at three levels–macro, meso and micro levels. The reviewer examines critically some of the basic ideas of regionalization underlying the proposed new discipline. He questions, in particular, what he considers an exaggerated research emphasis on problems of middle–level and lower–level regionalization without adequate attention to the issue of the Soviet Union's macroregions.     

Mapping the Geography of R&D: What Can We Learn for Regional Innovation Policy in the Czech Republic and Beyond?

Administrative regions do not necessarily correspond to areas that are homogenous in terms of innovation intensity. Although this has been recognized in the literature, quantitative evidence that explicitly considers this problem is rare. Using spatial exploratory analysis on detailed regional data derived from a census of R&D performers in the Czech Republic, we identify local spatial clusters of R&D activities and assess the extent of their (mis)match with administrative borders. Overall, the results support the arguments for regionalization of innovation policy. However, the existing policy units do not appear well suited for this purpose. On one hand, there is a need for policy coordination between multiple administrative regions. On the other hand, however, there are diverse patterns within them. Similar problems are likely to haunt the regionalization process in many other countries, which is alarming, as the regional accent of innovation policies becomes ever more vehement over time.     

Bayesian Analysis of Knowledge Spillovers in European Regions*

Abstract. This paper estimates the effects of knowledge spillovers on patent growth rates across 335 European regions over the 1989–1999 period. We propose a dynamic model based on an innovation production function. A Bayesian approach is used to take into account area‐specific innovation and spatial spillovers. The estimation of the model proceeds via Markov Chain Monte Carlo simulation. The results show significant positive and negative spatial effects on innovative activity. The model allows for a rich spatial specification, which we illustrate by incorporating transport proximity measured by transportation time between regions to augment the typical spatial proximity measure of connectivity between regions. Doing this produces more pronounced spatial spillovers that exhibit a more polarized spatial pattern than a model relying on spatial proximity alone.     

Policy Strategies for New Regionalism: Different Spatial Logics for Cultural and Business Policies in Norwegian City Regions

This article asks about differences and similarities in the way cultural policy and business policy deal with regions in Norwegian city regions. The article discusses New Regionalism as a particular spatial practice, and stresses the difference between regionalism as a bottom-up process driven by local stakeholders and regionalization as a top-down process driven by state bodies. The role and significance of New Regionalism in city-regional policy-making is investigated. Empirical findings shows that cultural policy at the city-regional level is still under strong influence from a top-down state regionalization, while business policy at the city-regional level is, to a large extent, an example of bottom-up regionalism. The spatial logic of these two policy-fields differs from each other. Business policy rests on an interpretation of region/place as a container of established networks, relations and interactions that should be coordinated in order to strengthen the region in its competition with other regions. Cultural policy rests on another interpretation that is not territorial in the same degree, but rather on a logic that place/region is created as relations between persons, groups and institutions within a geographical scope that is not predefined and fixed with borders and boundaries.     

Potential Use of Radiometric Data for Wildlife Habitat Modelling

The potential use of existing radiometric data sets, previously collected for prospecting purposes, has very rarely been used as a variable predictor in wildlife habitat modelling. The utility of radiometric data for predicting vegetation community patterns and wildlife habitat was investigated in the Australian arid zone using the Burt Plain bioregion as a case study. Using spatial datasets and a Species Distribution Modelling Toolkit, arid zone vertebrate species were modelled with Generalised Linear Modelling (GLM) regression modelling techniques. These models were used to predict the probability of occurrence of a species at any given location, defined in terms of its environmental attributes. A statistical correlation between the radioactive elements uranium, thorium and potassium, and terrain aspect was found. No statistical correlations were established between the radioactive elements and vegetation patterns; although we suspect these exist at finer scales of mapping. Radiometric data were identified as explanatory variables in the habitat models of all of the 32 vertebrate species examined, and used as illustration in the development of probabilistic spatial predictions of three species (Red Kangaroo, Macropus rufus; Lesser Hairy‐footed Dunnart, Sminthopsis youngsoni; and Rabbit, Oryctolagus cuniculus) in the bioregion. Our analyses suggest that radiometric data sets involving the radioactive elements: (uranium, thorium, and potassium), and vegetation could be used as predictors of biodiversity patterns at the bioregional and landscape level. This is an important finding given the challenges posed in undertaking broad‐scale biological surveys in the arid zone of Australia.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

Fluvial spatial taphonomy: a new method for the study of post-depositional processes

Traditional taphonomic analyses do not consider the emergent properties resulting from the relationship of variables. Several of these emergent properties are expressed spatially. Here, we present a spatial taphonomic approach to the understanding of post-depositional processes affecting experimental assemblages modified by fluvial currents. Spatial statistical methods are a great tool to understand how different taphonomic variables relate at a spatial scale. The results of the application of this method to bone assemblages affected by low- and high-energy flows show the loss or modification of most of the original spatial taphonomic relationships. It also shows the degree of bone loss according to element, anatomical section and bone structure, and shape types. Anisotropy is identified in both experimental contexts. These results add new criteria to assess the impact of water flows in bone assemblages and contribute to define the properties of lag assemblages.     

Problems in the Economic Regionalization of India

The authors propose a framework of a system of economic regions based on economic production principles. This principle is designed, on the one hand, to promote a regional economic specialization, and, on the other hand, to ensure integrated development of the regional economy. The Soviet geographers find that some major economic regions are already evident, for example, Western India (including West Bengal, Bihar and possibly Orissa), but that large parts of the country, especially in the north, lack sufficiently clear characteristics to make possible an economic regionalization without further detailed study.     

Probabilistic models of seasonal <Emphasis Type="Italic">Bison</Emphasis> exploitation based on fetal prey osteometry and reproductive phenology

Seasonality studies are a staple of Great Plains archaeology, but obtaining evidence for seasonal food procurement is challenging. This study explores seasonality through the lens of fetal bison remains. A method is developed that produces intra-annual probability distributions for the date of fetal death. These distributions are informed by osteometric data from fetal bison in archaeological contexts, fetal skeletal growth curves, and data-driven models of bison conception dates. Probabilities can be assigned to seasonality hypotheses by examining date intervals across these intra-annual distributions. This method is implemented through an R program and applied to seasonality hypotheses at three archaeological sites containing fetal bison remains: Big Goose Creek (Wyoming), Baker Cave III (Idaho), and the Upper Tucker Site (Texas). Unlike previous efforts to infer seasonality from fetal bison remains, this method provides explicit probabilities showing the level of agreement between the osteometric data and seasonality hypotheses. This probabilistic approach could be extended to seasonality studies involving other animal taxa both within and outside of North America.     

Information Entropy of Non‐Probabilistic Processes

We derive an expression for the entropy of non‐probabilistic distributions encountered in spatial and mathematical mappings. The entropy of non‐probabilistic distributions can be formulated using probabilistic notions of the hypothetical random redistribution of finite information. We show that the discrete approximation to the information content of spatial maps can be based on the discrete hypergeometric distribution. The resultant “associative” entropy is distinct from the Shannon entropy for probability distributions and addresses several shortcomings of the current entropy paradigm as applied to spatial analysis. The associative entropy statistic is distributed approximately as a chi‐squared random variable under limitations of variation. We formulate a univariate logical equivalent of the associative entropy statistic, freeing the paradigm from the degrees of freedom constraint to which it has been traditionally shackled. This entropy has application in spatial analysis and fuzzy set theory. The associative entropy is based on the concept of proportional information and is related to the Getis G‐statistics of spatial association and the Chi‐squared statistics of sample means. We explore the utility of the theory when applied to spatial distribution of vegetation in New Brunswick, Canada. The limitations and implications of the entropy expression are discussed and suggestions are made for future applications of the theory. This work is part of the development of an information theory framework for the analysis of landscape patterns of animal habitat.