PETROGRAPHIC AND STABLE ISOTOPE ANALYSES OF LATE CLASSIC ULÚA MARBLE VASES AND POTENTIAL SOURCES*

Ulúa marble vases from the Ulúa Valley of northwestern Honduras are a hallmark luxury good from Late Classic (ad 600–900) Mesoamerica. Archaeological and stylistic data point to centralized production at one site, Travesía. This paper analyses stable isotope and petrographic data from the vases and three potential procurement areas. The results indicate that the vases were produced from one primary source with one, potentially two, secondary sources. Procurement patterns most probably corresponded to contemporary communication routes. The results clearly indicate that a multi‐method approach is necessary for sourcing marble from Honduras.     

Interpreting Intra-site Spatial Patterns in Seasonal Contexts: an Ethnoarchaeological Case Study from the Western Alps

This paper deals with the ethnoarchaeological analysis of the spatial pattern of artefacts and ecofacts within two traditional pastoral huts (a dwelling and a seasonal dairy) in the uplands of Val Maudagna (Cuneo province, Italian western Alps). The composition of the ethnoarchaeological assemblages of the two huts was studied and compared; point pattern analysis was applied to identify spatial processes mirrored in the interactions between objects; Moran’s I correlogram and empirical variogram were used to investigate the effects of trampling on the displacement of objects on the floor. The results were compared with information provided by the herder who still used the huts. The quantitative and ethnographical data enabled inferences to be made that can help in the interpretation of archaeological seasonal sites. The function of a seasonal site can be recognized, as can the impact of delayed curation on the composition of the assemblage and the importance of the intensity of occupation compared with the frequency of occupation. The spatial organization of activities is reflected in the spatial patterns of objects, with clearer identification of activity areas in intensively occupied sites, and there is evidence for the behaviour behind the spatial segregation of activities. Trampling is a crucial post-depositional factor in the displacement of artefacts and ecofacts, especially in non-intensively exploited sites. From a methodological point of view, this research is another example that highlights the importance of integrating quantitative methods (especially spatial analysis and geostatistical methods) and ethnoarchaeological data in order to improve the interpretation of archaeological sites and assemblages.     

旅游流空间模式基本理论:问题分析及其展望

旅游流是旅游地理学的基本问题,而旅游流空间模式的研究,自20世纪50年代以来则是旅游流研究的热点问题。通过文献分析法,本文将旅游流空间模式基本理论梳理为圈层结构理论、核心边缘理论、空间扩散理论。在此基础上,揭示其存在的若干问题,包括研究中存在的"区域视角"和"旅游者个体视角"混淆的认知问题、旅游客源地和旅游目的地在区域视角下如何界定的问题、空间模式研究中广泛存在的"二元陷阱"问题、区域间旅游职能分化及其分化程度尚无科学分析的问题等。文末对未来研究进行了展望,以期从理论意义上深化旅游流基本问题的建构。需指出的是,旅游流作为"非工作流"的一种,其"流现象"、"流空间"和"流效应"同样可适用于其它形式的"流研究"。     

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.     

Some Remarks About the Future of Geographical Analysis: The Journal and the Sub‐Discipline

This paper expands upon a GA 50^th Anniversary panel presentation made at the 2018 New Orleans American Association of Geographers meeting. It notes that retrospectives for this topic already exist, both in terms of the journal and the sub‐discipline. This paper presents discussion about the following selected prominent transitional themes bridging the past and the future of geographical analysis: space–time data; geographic uncertainty and error; and, dimensional reduction spatial statistical modeling. It also presents discussion about the following three themes that are expected to receive considerable future attention: negative spatial autocorrelation; synergies between spatial statistics and spatial optimization; and, big spatial data. The first two of these topics essentially have been ignored in the literature to date.     

Divided Post‐Soviet Small Cities? Residential Segregation and Urban Form in Leninogorsk and Zyryanovsk,Kazakhstan

This paper maps and analyses ethnic and socio‐economic residential segregation in two small post‐Soviet mining and enrichment cities in Eastern Kazakhstan, Leninogorsk and Zyryanovsk. The study is based on data collected by the author in collaboration with the Eastern Kazakhstan oblast' statistical authority in an extensive questionnaire survey carried out during January 2001. The paper investigates the linkages between the physical spatial structure of small post‐Soviet cities and the socio‐spatial landscape that has unfolded in their context, and attempts to identify the principal factors that underlie the observed segregation patterns.     

Geographic information system‐based edge effect correction for Ripley's K‐function under irregular boundaries

Ripley's K‐function is a test to detect geographically distributed patterns occurring across spatial scales. Initially, it assumed infinitely continuous planar space, but in reality, any geographic distribution occurs in a bounded region. Hence, the edge problem must be solved in the application of Ripley's K‐function. Traditionally, three basic edge correction methods were designed for regular study plots because of simplified geometric computation: the Ripley circumference, buffer zone, and toroidal methods. For an irregular‐shaped study region, a geographic information system (GIS) is needed to support geometric calculation of complex shapes. The Ripley circumference method was originally implemented by Haase and has been modified into a Python program in a GIS environment via Monte Carlo simulation (hereafter, the Ripley–Haase and Ripley–GIS methods). The results show that in terms of the statistical powers of clustering detection for irregular boundaries, the Ripley–GIS method is the most stable, followed by the buffer zone, toroidal, and Ripley–Haase methods. After edge effects of irregular boundaries have been eliminated, Ripley's K‐function is used to estimate the degree of spatial clustering of cities in a given territory, and in this paper, we demonstrate that by reference to the relationship between urban spatial structure and economic growth in China.     

Sewn‐Plank Reconstructions of Oman: construction and documentation

This paper discusses three medieval sewn‐plank reconstructions undertaken in the Sultanate of Oman in the past 40 years: the Sohar, Jewel of Muscat, and al‐Hariri Boat. It describes the specific methods of sewn‐plank construction for each vessel and examines the different methods of documentation applied during the three projects. It concludes with a comparison of the data derived from single‐wadding (al‐Hariri Boat) and double‐wadding (Jewel of Muscat) sewing techniques to highlight the differences between the two methods and emphasize the importance of documenting such reconstructions.     

Power of the Rank Adjacency Statistic to Detect Spatial Clustering in a Small Number of Regions

The rank adjacency statistic D is a statistical method for assessing spatial autocorrelation or clustering of geographical data. It was originally proposed for summarizing the geographical patterns of cancer data in Scotland (IARC 1985). In this paper, we investigate the power of the rank adjacency statistic to detect spatial clustering when a small number of regions is involved. The investigations were carried out using Monte Carlo simulations, which involved generating patterned/clustered values and computing the power with which the D statistic would detect it. To investigate the effects of region shapes, structure of the regions, and definition of weights, simulations were carried out using two different region shapes, binary and nonhinary weights, and three different lattice structures. The results indicate that in the typical example of considering Canadian total mortality at the electoral district level, the D statistic had adequate power to detect general spatial autocorrelation in twenty‐five or more regions. There was an inverse relationship between power and the level of connectedness of the regions, which depends on the weighting function, shape, and arrangement of the regions. The power of the D statistic was also found to compare favorably with that of Moran's I statistic.     

The Spatial Distribution of French Agricultural Cooperatives: An Exploratory Spatial Data Analysis

Abstract

This paper studies the spatial dynamics of French agricultural cooperatives using the recently developed exploratory spatial data analysis tool. Analysis at the level of French districts in 1995 and 2005 shows strong evidence for global and local spatial autocorrelations in the geographical distribution of agricultural cooperatives. The presence of spatial disparities between French districts is confirmed by the detection of such specific spatial patterns as district clusters, a group of neighbouring districts with the same high or low level of agricultural cooperative activities. A typology of all the different Regions is developed to examine the specific spatial patterns of the agricultural cooperative activities. The results indicate that major organizational changes in cooperatives do not significantly modify the initial dynamics concerning the location of activities.     

The Comings and Goings of Sheep and Pottery in the Coastal Desert of Namaqualand,South Africa

This paper evaluates chronological trends in the presence and absence of domestic animal bone (sheep, goat, and cattle) and pottery in Namaqualand, the proposed gateway to the rest of South Africa for early herders or hunter-gatherers with sheep and ceramics. We update date calibrations with local ΔR corrections and mixtures of recent calibration curves and include five previously unpublished dates. We use histograms of calibrated medians, sorted in 100-year bins, to assess sustained regional patterns with dates associated with domestic animal bone and pottery (n = 73). While too small to be useful as a population proxy, the current set of dates does reveal three pulses of occupation separated by two clear gaps, which we evaluate with a Bayesian model of three sequential phases. The model's boundaries are used as estimates of the dates of Early (AD 80–210), Middle (AD 490–790), and Late (AD 1180–1690) occupational phases separated by two substantial lapses of 280 and 380 years, respectively. The alternating phases of presence and absence are suggestively correlated with climate shifts, leading to a discussion of the idea that effective moisture was a crucial factor in choosing whether to occupy Namaqualand. The set of archaeological dates has greater temporal and spatial resolution than many regional climate data, so we suggest that these trends may more accurately reflect the variable conditions specific to Namaqualand, at least until they are refined by future climate research.     

Policy Coalitions in the U.S. Congress: A Spatial Duration Modeling Approach

This paper applies spatial duration models to the analysis of cosponsorship coalitions in the U.S. House of Representatives. This approach provides a unique and simultaneous statistical analysis of ideological space (specifically, coalition formation) and geographical space. Typically, duration models are associated with temporal longitudinal data, but recently have been adapted to the spatial domain (Pellegrini and Reader 1996). In this paper, spatial duration models are further adapted to examine ideological space including a consideration of unobserved sources of spatial variation (or omitted variable bias). We examine two features of cosponsorship coalitions, breadth and clustering. Breadth is defined as the ideological distance between the two most extreme members of the coalition which is an important “signal” to the rest of Congress regarding the scope and broad appeal of the proposed legislation. In contrast, clustering refers to the distance between individual members of a coalition and reveals the tendency, or not, of ideologically similar members of Congress to support various bills. To examine breadth and clustering, we employ spatial duration models of cosponsorship that permit a multivariate analysis incorporating both the characteristics of members of Congress and the geographical regions they represent. Results indicate that cosponsorship coalition patterns are primarily determined by the content of the legislation, not the actions of the coalition leadership. While the leadership characteristics of sponsors have a limited effect on cosponsorship breadth, the size of the coalition is the primary determinent. Leadership characteristics also have little effect on cosponsorship clustering. Rather, clustering is due to members' policy preferences, as measured by distance to the coalition leader. In addition, the duration analysis results suggest that geographical proximity between members of Congress “overcomes” ideological distance. Finally, the spatial duration approach is noted as a fruitful methodology for examining explicitly spatial patterns in both ideological or geographical space.     

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.     

The Modifiable Areal Unit Problem,in a Regression Model Whose Independent Variable Is a Distance from a Predetermined Point

This paper deals with the modifiable areal unit problem in the context of a regression model where a dependent variable is an attribute value (say, income) of an atomic data unit (say, a household) and an independent variable is a distance from a predetermined point (say, a central business district) to the atomic data unit (a disaggregated model). We apply this disaggregated model to spatially aggregated data in which the dependent variable is the average income over a spatial unit and the independent variable is the average distance from each household in a spatial unit to the predetermined point (an aggregated model). First, estimating the slope coefficient by the least squares method, we prove that the variance of the estimator for the slope coefficient in the aggregated model is larger than that in the disaggregated model. Second, focusing on variations in the variance of the estimator for the slope coefficient in the aggregated model with respect to the number of zones, we obtain the number of zones in which the variance is close to that in the disaggregated model. Third, we obtain the zoning system that has the minimum variance for a fixed number of zones. We also calculate the maximum variance in order to examine the range of the variance.     

Comparison of Moran's I and Geary's c in Multivariate Spatial Pattern Analysis

This article compares multivariate spatial analysis methods that include not only multivariate covariance, but also spatial dependence of the data explicitly and simultaneously in model design by extending two univariate autocorrelation measures, namely Moran's I and Geary's c. The results derived from the simulation datasets indicate that the standard Moran component analysis is preferable to Geary component analysis as a tool for summarizing multivariate spatial structures. However, the generalized Geary principal component analysis developed in this study by adding variance into the optimization criterion and solved as a trace ratio optimization problem performs as well as, if not better than its counterpart the Moran principal component analysis does. With respect to the sensitivity in detecting subtle spatial structures, the choice of the appropriate tool is dependent on the correlation and variance of the spatial multivariate data. Finally, the four techniques are applied to the Social Determinants of Health dataset to analyze its multivariate spatial pattern. The two generalized methods detect more urban areas and higher autocorrelation structures than the other two standard methods, and provide more obvious contrast between urban and rural areas due to the large variance of the spatial component.     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.     

An Empirical Comparison of Edge Effect Correction Methods Applied to K‐function Analysis

This paper explores various edge correction methods for K‐function analysis via Monte Carlo simulation. The correction methods discussed here are Ripley's circumference correction, a toroidal correction, and a guard area correction. First, simulation envelopes for a random point pattern are constructed for each edge correction method. Then statistical powers of these envelopes are analyzed in terms of the probability of detecting clustering and regularity in simulated clustering/regularity patterns. In addition to the K‐function, K(h), determined for individual distances, h, an overall statistic k is also examined. A major finding of this paper is that the K‐function method adjusted by either the Ripley or toroidal edge correction method is more powerful than what is not adjusted or adjusted by the guard area method. Another is that the overall statistic k outperforms the individual K(h) across almost the entire range of potential distances h.     

Local Spatial Autocorrelation in a Biological Model

We review the recently developed local spatial autocorrelation statistics I_i, c_i, G_i, and G_i*. We discuss two alternative randomization assumptions, total and conditional, and then newly derive expectations and variances under conditional randomization for I_i and c_i, as well as under total randomization for c_i. The four statistics are tested by a biological simulation model from population genetics in which a population lives on a 21 × 21 lattice of stepping stones (sixty-four individuals per stone) and reproduces and disperses over a number of generations. Some designs model global spatial autocorrelation, others spatially random surfaces. We find that spatially random designs give reliable test results by permutational methods of testing significance. Globally autocorrelated designs do not fit expectations by any of the three tests we employed. Asymptotic methods of testing significance failed consistently, regardless of design. Because most biological data sets are autocorrelated, significance testing for local spatial autocorrelation is problematic. However, the statistics are informative when employed in an exploratory manner. We found that hotspots (positive local autocorrelation) and coldspots (negative local autocorrelation) are successfully distinguished in spatially autocorrelated, biologically plausible data sets.     

An interactive point pattern analysis web application and teaching exercise

ABSTRACT

This paper introduces a web-based, interactive point pattern analysis “game” that allows users to generate quickly and repeatedly a point pattern on screen and immediately learn whether the pattern is statistically different from random. It uses two point pattern analysis methods: quadrat analysis (QA) and nearest neighbor analysis (NNA). Studies have shown that gaming approaches, digital tools, and interactive instruction for teaching spatial thinking complement and enhance students’ geospatial skills. This game challenges students to achieve randomness, demonstrates the differences between QA and NNA, and easily permits in-class use by running in any web browser.     

The K‐Function Method on a Network and Its Computational Implementation

This paper proposes two statistical methods, called the network K‐function method and the network cross K‐function method, for analyzing the distribution of points on a network. First, by extending the ordinary K‐function method defined on a homogeneous infinite plane with the Euclidean distance, the paper formulates the K‐function method and the cross K‐function method on a finite irregular network with the shortest‐path distance. Second, the paper shows advantages of the network K‐function methods, such as that the network K‐function methods can deal with spatial point processes on a street network in a small district, and that they can exactly take the boundary effect into account. Third, the paper develops the computational implementation of the network K‐functions, and shows that the computational order of the K‐function method is O(n²_Q log n_Q) and that of the network cross K‐function is O(n_Q log U3Q), where n_Q is the number of nodes of a network.