Abstract: The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions. In particular, it is proved that appropriate modeling of all area-to-area and area-to-point covariances required by the geostatistical framework yields coherent (mass-preserving or pycnophylactic) predictions. In other words, the areal average (or areal total) of point predictions within any arbitrary area informed by an areal-average (or areal-total) datum is equal to that particular datum. In addition, the proposed geostatistical framework offers the unique advantage of providing a measure of the reliability (standard error) of each point prediction. It is also demonstrated that several existing approaches for area-to-point interpolation can be viewed within this geostatistical framework. More precisely, it is shown that (i) the choropleth map case corresponds to the geostatistical solution under the assumption of spatial independence at the point support level; (ii) several forms of kernel smoothing can be regarded as alternative (albeit sometimes incoherent) implementations of the geostatistical approach; and (iii) Tobler’s smooth pycnophylactic interpolation, on a quasi-infinite domain without non-negativity constraints, corresponds to the geostatistical solution when the semivariogram model adopted at the point support level is identified to the free-space Green’s functions (linear in 1-D or logarithmic in 2-D) of Poisson’s partial differential equation. In lieu of a formal case study, several 1-D examples are given to illustrate pertinent concepts.

Abstract: Contemporary theoretical debate in ecology and biogeography is often focused on equilibrium vs. nonequilibrium behavior in ecosystems and on the nature and source of ecosystem dynamics. It is suggested that these debates be recast in terms of the way ecosystems develop and respond to disturbances, rather than in terms of concepts often imported from mathematics, physics, and other fields. Using nonlinear dynamical systems theory, it is shown that key theoretical implications can be cast in terms of geoecologically significant phenomenologies such as divergent evolution, sensitivity to initial conditions and small disturbances, historical contingency, and path dependence. Examples show these phenomena are widely observed in ecosystems. Ecological and biogeographical theory can be problematized from within geography and ecology rather than fuzzy, abstract concepts such as equilibrium, self-organization, Obalance of nature,O or chaos. Complexity, sensitivity, variability, nonsteady states, and other concepts often associated with nonequilibrium or complexity-theory frameworks have manifestations that are evident in observable ecological phenomena, in addition to theory and models.

Abstract: In the study of economic-geographic structures, the shifting focus from the national state to the city and its region has highlighted the lack of reliable interurban data sets. The abundance of usable data sets on international structures and ?ows has no counterpart when studying interurban relations, which makes it hard to draw any extensive conclusions regarding the structure of world city networks. Instead of relying on available data sets, Peter Taylor has developed a method for generating data sets that, it is argued, can be used in research on the structure of the world city network. In this approach, actors are defined as cities with internal attribute service values, values reflecting the presence of different transnational service- producing corporations in each city. The structural values between each pair of cities are then established by a mathematical formula based on the service value of each firm in each pair of cities. This procedure can be criticized on two accounts. First, although internal attributes on exceptional occasions can be used as a proxy and as a rough estimate for structural values, such studies must have a firm theoretical underpinning in order to be valid from a network-analytical perspective. If not, such generated structural values become nothing more than a function of internal attributes, thus losing the whole basic idea of social network analysis. Second, the Taylor function used for generating structural values can be questioned. Why should a large presence of TNC offices in a pair of cities imply a larger city interlock link than would be the case between a high-ranked city and a low-ranked city, as the city with the larger service value probably serves cities with a lower service value with economic command, control, and support functions? (Less)

Abstract: Fractality in cities implies that a city possesses a similar structure at several different scales. Its existence is of great significance because it suggests the presence of some hidden process that operates at different urban scales and generates similarity. Recently, we studied the Tel Aviv metropolis at the levels of the entire metropolis and of one of its constituent towns. We concluded that the apparent growth mechanism (called leapfrogging) operates at these two levels. We hypothesized that since it appears at several levels, growth by leapfrogging may be one the fundamental processes that generates urban fractality. In this paper we present results of simulations used to verify this assumption in the case of randomly generated structures.

Abstract: Most standard methods of statistical analysis used in the social and environmental sciences are built upon the basic assumptions of independence, homogeneity, and isotropy. A notable exception to this rule is the collection of methods used in geographical analysis, which have been designed to take into account serial dependence often observed in spatial data. In addition, recent developments, in particular the method of geographically weighted regression, have provided the tools to model nonstationary processes, and thus evidence that challenges the assumption of homogeneity. The assumption of isotropy, however, although suspect, has received considerably less attention, and there is thus a need for tools to study anisotropy in a more systematic fashion. In this paper we expand the method of geographically weighted regression in a simple yet effective way to explore the topic of anisotropy in spatial processes. We discuss two different estimation situations and exemplify the proposed technical development by means of a case study. The results suggest that anisotropy issues might be a fairly common occurrence in spatial processes and/or in the statistical modeling of spatial processes.

Abstract: It is often desirable to sample in those locations where uncertainty associated with a variable is highest However, the importance of knowing the variable's value may vary across space We are interested in the spatial distribution of Received Signal Strength Indicator (RSSI), a measure of the signal strength from a cell tower received at a particular location It is crucial to estimate RSSI values accurately in order to evaluate the effectiveness of mayday systems designed for rapid emergency notification following vehicle crashes RSSI estimation is less important for locations where the probability of a crash is low and where the likelihood of call completion is either close to zero or one We develop a method for augmenting an initial spatial sample of RSSI values to achieve a high-precision estimate of the probability of call completion following a crash We illustrate the approach using data on RSSI and vehicle crashes in Erie County, NY

Abstract: This paper applies shift-share analysis, a tool often used in economic geography and regional science, to regional fertility change in Italy, 1952-1991. During this post-World War II period, Italian fertility declined by over 33 percent, but the decline varied widely from region to region. Moreover, the demographic originations of the decline in births are not fully understood. Using birth data for nineteen Italian regions, this analysis is able to break regional change in births into three main components: a national effect, a cohort effect, and a regional differential effect, which in turn provide insight into the roots of fertility change at the regional level. These three components of change are then further disaggregated to account for the differences between changes due to population change and those related to actual changes in birth rates (the number of children produced by each woman). Strong regional differences between the north and south of Italy are demonstrated.

Abstract: ����� ��� This article considers models for multivariate mortality outcomes (e.g., bivariate, trivariate, or higher dimensional) observed over a set of areas and through time. The model outlined here allows for spatially structured and white noise errors and for their intercorrelation. It also includes possible temporal continuity in such types of error via structured temporal effects. An extension to spatially varying regression effects is considered, as well as the option of nonparametric specification of priors for spatial residuals and regression effects. Allowing for spatially correlated intercepts or regression effects may alter inferences regarding the changing impact on mortality of socioeconomic or environmental predictors. The modeling framework is illustrated by an application to male and female suicide mortality in London, focusing on the impact on suicide of deprivation and social fragmentation (“anomie”) in the 33 London boroughs during three periods: 1979‐83, 1984‐88 and 1989‐93. Suicide trends by age group are also considered and show considerable differences in the trends in impacts of deprivation and social fragmentation.

Abstract: When adequate data on migration are unavailable, demographers infer such data indirectly, usually by turning to residual methods of estimating net migration. This paper sets out and illustrates an inferential method that uses population totals in the first age group of birthplace-specific counts of residents in each region of a multiregional system to indirectly infer the entire age schedule of directional age-specific migration flows. Specifically, it uses an estimate of infant migration that is afforded by a count of infants enumerated in a region other than their region of birth to infer all other age-specific migration flows. Since infants migrate with their parents, the migration propensities of both are correlated, and the general stability of the age profiles of migration schedules then allows the association to be extended to all other age groups.

Abstract: 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 KendallOs 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 toruswrapping 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.

Abstract: Facility location problems often involve movement between facilities to be located and customers/demand points, with distances between the two being important. For problems with many customers, demand point aggregation may be needed to obtain a computationally tractable model. Aggregation causes error, which should be kept small. We consider a class of minimax location models for which the aggregation may be viewed as a second-order location problem, and use error bounds as aggregation error measures. We provide easily computed approximate Osquare rootO formulas to assist in the aggregation process. The formulas establish that the law of diminishing returns applies when doing aggregation. Our approach can also facilitate aggregation decomposition for location problems involving multiple OseparateO communities.

Abstract: The November 2002 North American Meetings of the Regional Science Association International in San Juan, Puerto Rico included five sessions with roughly 20 papers devoted to spatial statistics and econometrics. These paper presentations reflected recent areas of interest by those engaged in methodological as well as applied spatial statistical research. This special issue includes five papers that are representative of current methodological developments as well as innovative application and extension of existing methods. Many traditional spatial statistical estimation methods rely on a weight matrix to model connectivity relations between the units of observation. Not surprisingly, much research centers on determining an appropriate specification of this weight matrix. Two papers in this special issue represent work in this arena, one by Arthur Getis and Jared Aldstadt entitled “Using Local Statistics in the Specification of the Spatial Weights Matrix,” and another by Donald Lacombe, “Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques.” The Getis and Aldstadt paper proposes partitioning the spatial structure into two parts, one that reflects pairwise spatial relations among the observations and the other that models the individual contribution of unconnected observations. In contrast to the classical approaches in spatial statistics and econometrics, which specify global spatial relations on hypothetical grounds or by “practical convenience” as either distance-based functions or neighborhood relations, the Getis and Aldstadt approach estimates the spatial relations of each observation from the data. They apply a local statistical concept, the Gi statistic, to determine the range beyond which no more spatial dependence for each observation can be expected. A series of simulation experiments compares the proposed weight matrix against other specifications of weight.

Abstract: New York, Los Angeles, and Miami are primate immigrant centers within the U.S. metropolitan system, attracting new immigrant arrivals as well as serving as focal points for internal migrants. Using the segmented assimilation framework as a foundation, this paper emphasizes the role of geography and migration within the assimilation process. Focusing upon selected origin groups, migrant selectivity and the determinants of migration are evaluated and compared, highlighting the differential role of primate centers. While the New York and Miami metropolitan centers clearly dominate Dominican and Cuban migration systems respectively, the role of primate centers is less clear among other national origin groups.

Abstract: Previous research has shown that households are sensitive to commuting distance. In particular, households beyond a threshold distance move closer to the job when they change residence. The questions that motivate this paper are: how does race affect the probability of moving closer to the job when households change residence, and is there a trade off between commuting distance and neighborhood composition? Using a specialized data set the research shows that the commuting behaviors of minority and white households are consistent with the overall hypothesis that households minimize their commuting distance whenever possible. The research also shows that there is a tendency for both white and black households to choose slightly more integrated settings after changing residences. Yet, black households have to juggle the trade-off between neighborhoods with high socioeconomic status and commute distance and those who choose higher socioeconomic status neighborhoods have longer commutes.

Abstract: The diffusion of new product or technical innovation over space is here modeled as an event-based process in which the likelihood of the next adopter being in region r is influenced by two factors: (i) the potential interactions of individuals in r with current adopters in neighboring regions, and (ii) all other attributes of individuals in r that may influence their adoption propensity. The first factor is characterized by a logit model reflecting the likelihood of adoption due to spatial contacts with previous adopters, and the second by a logit model reflecting the likelihood of adoption due to other intrinsic effects. The resulting spatial diffusion process is then assumed to be driven by a probabilistic mixture of the two. A number of formal properties of this model are analyzed, including its asymptotic behavior. But the main analytical focus is on statistical estimation of parameters. Here it is shown that standard maximumlikelihood estimates require large sample sizes to achieve reasonable results. Two estimation approaches are developed which yield more sensible results for small sample sizes. These results are applied to a small data set involving the adoption of a new Internet grocery-shopping service by consumers in the Philadelphia metropolitan area.

Abstract: This paper develops a method for analyzing surfaces with a focus on their qualitative similarity. The method describes the qualitative similarity between surfaces defined in the same region in both quantitative and qualitative ways. Given a location and a direction in the region, mathematical functions evaluate the similarity between surfaces. Integrals of the functions with respect to location and direction give quantitative measures of the total similarity between surfaces. A qualitative method uses spatial characteristics shared by surfaces: a-peak regions, a-pit regions, and p monotonic lines. a-peak and a-pit regions indicate approximate locations where many surfaces have peaks and pits, respectively. p-monotonic lines are line segments on which most surfaces change monotonically in the same direction. Those spatial objects reveal the spatial structure shared by surfaces. The method is applied to the analysis of the daily market structure of a supermarket in Japan as an empirical study.

Abstract: I do not treat cities as agents. I do not claim to be an alchemist. I do not consider my original illustrative data set to be an adequate basis for discussing actual intercity relations. Theoretical elaboration was not part of the remit of my original paper.

Abstract: The aim of this paper is to analyze the intraurban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut-offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline-exponential, and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity, and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments, and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.

Abstract: This paper reports the fitting of a number of Bayesian logistic models with spatially structured or/and unstructured random effects to binary data with the purpose of explaining the distribution of high-intensity crime areas (HIAs) in the city of Sheffield, England. Bayesian approaches to spatial modeling are attracting considerable interest at the present time. This is because of the availability of rigorously tested software for fitting a certain class of spatial models. This paper considers issues associated with the specification, estimation, and validation, including sensitivity analysis, of spatial models using the WinBUGS software. It pays particular attention to the visualization of results. We discuss a map decomposition strategy and an approach that examines properties of the full posterior distribution. The Bayesian spatial model reported provides some interesting insights into the different factors underlying the existence of the three police-defined HIAs in Sheffield. High-intensity crime areas, or HIAs, are areas identified by urban police forces in England that experience high levels of violent, often drug-related, crime. Violence involves the use of knives and/or firearms. There may be further problems when bringing charges because of high levels of witness intimidation. The reason for this is that individuals or families resident in the neighborhood often perpetrate the crimes. HIAs therefore are more than simply areas with high levels of particular types of offenses (“hot spots”); they are areas with a particularly dangerous cocktail of violent crime perpetrated by offenders who are also resident in the area. They present particularly difficult policing problems. Craglia, Haining, and Signoretta (2001) reported the results of work into the spatial distribution of police-defined HIAs for a sample of English cities. The boundaries of HIAs were defined by senior police officers familiar with their cities. They first identified which of their basic command units (BCUs) had HIAs within them and

Abstract: A popular approach to examining the effects of public policy has been to rely on a spatial data sample of border counties as in Holmes (1998)Nborder counties from a sample of states that are used in conjunction with least-squares estimation techniques in an attempt to isolate the policy impact while controlling for spatial dependence that often arises from latent or unobserved variables. This technique is in the spirit of control-group methodologies from the laboratory sciences. This paper contrasts border-county estimation results from Holmes' (1998) approach and those from a related methodology set forth in Holcombe and Lacombe (2003), with estimates from a spatial autoregressive model explicitly accounting for within-state and between-state public policy effects. As an illustration, the paper examines the effects of Aid to Families with Dependent Children (AFDC) and Food Stamp payments on female-headed households and female labor force participation using the three different methods.

Abstract: Studies of potential neighborhood effects have been constrained in most situations by the absence of small-area data generated to characterize the local contexts within which individuals operate. Using small-area data from the U.K. Census, this paper illustrates the creation of bespoke neighborhoods—local areas defined separately for each individual in a sample survey—at a variety of scales, and their characterization using factor analysis techniques. Theories of neighborhood effects are uncertain as to the spatial scale at which the relevant processes operate, hence the value of exploring patterns consistent with those processes at a range of spatial scales. One problem with such comparative study is the incommensurability of regression coefficients derived from analyses using factor scores as the independent variables. The work reported here adapts a procedure introduced for reconstituting partial regression coefficients to circumvent that problem, and illustrates that patterns of voting at a recent British general election showed neighborhood-effect-like patterns at two separate scales simultaneously—with individual voter characteristics held constant.