Controlling the False Discovery Rate: A New Application to Account for Multiple and Dependent Tests in Local Statistics of Spatial Association

Assessing the significance of multiple and dependent comparisons is an important, and often ignored, issue that becomes more critical as the size of data sets increases. If not accounted for, false-positive differences are very likely to be identified. The need to address this issue has led to the development of a myriad of procedures to account for multiple testing. The simplest and most widely used technique is the Bonferroni method, which controls the probability that a true null hypothesis is incorrectly rejected. However, it is a very conservative procedure. As a result, the larger the data set the greater the chances that truly significant differences will be missed. In 1995, a new criterion, the false discovery rate (FDR), was proposed to control the proportion of false declarations of significance among those individual deviations from null hypotheses considered to be significant. It is more powerful than all previously proposed methods. Multiple and dependent comparisons are also fundamental in spatial analysis. As the number of locations increases, assessing the significance of local statistics of spatial association becomes a complex matter. In this article we use empirical and simulated data to evaluate the use of the FDR approach in appraising the occurrence of clusters detected by local indicators of spatial association. Results show a significant gain in identification of meaningful clusters when controlling the FDR, in comparison to more conservative approaches. When no control is adopted, false clusters are likely to be identified. If a conservative approach is used, clusters are only partially identified and true clusters are largely missed. In contrast, when the FDR approach is adopted, clusters are fully identified. Incorporating a correction for spatial dependence to conservative methods improves the results, but not enough to match those obtained by the FDR approach.     

Refining the definition of cultural levels at Karabi Tamchin: a quantitative approach to vertical intra-site spatial analysis

The visual identification of archaeological levels can be difficult when stratigraphy is complex. This study emphasizes the importance of three-dimensional intra-site spatial analysis as a means of testing the integrity of archaeological levels, including the identification of palimpsest deposits. A geographical information system (GIS) is applied to a three-dimensional spatial analysis of lithic and bone distributions from Karabi Tamchin, a Middle Palaeolithic site from the Crimea, Ukraine. K-means statistical clustering is combined with a series of data transformations to identify and interpret the vertical and horizontal spatial organization of the site. The results indicate that K-means cluster analysis, used in conjunction with GIS, provides an exceptional method of identifying discrete clusters of archaeological materials in three dimensions. Through an analysis of cluster contents within levels, it is possible to reconstruct and compare patterns of spatial organization at Karabi Tamchin, contributing to current debates regarding the cognitive complexity of Neanderthal populations.     

INTERPRETING THE SOCIAL MEANING OF DIFFERENT SHAPES OF HOUSE STRUCTURES BY EXAMINING THE FLOW OF RESOURCES: A CASE STUDY OF SAQACENGALJ AT THE SOUTHERN TIP OF TAIWAN

Social differentiation is characterized by differences in social status and wealth, which are established and maintained by controlling and constraining the labour and flow of resources. This would affect the development of various aspects of a settlement in establishing hierarchical relationships. This study focuses on identifying the variations in the distribution of certain resources, such as imported goods, that help interpret the social meaning, particularly with regard to social status and wealth, of house structures having different shapes in an ethnic settlement in Taiwan. A petrographic study and residue analysis of the pottery along with spatial examination of the material remains were conducted to achieve the goal.     

The Spatial Patterns of Networks,Hierarchies and Subsidiaries

This article reviews evidence and arguments linking the networking behaviour of firms with geographic distance, before examining the spatial relationships of electronics firms in the three major electronics centres in Spain. The focus is on the spatial pattern and extent of different types of inter-firm relations. Based on the analysis of 184 surveyed establishments, displaying different ownership and organization characteristics, the results show that while regional linkages are important, significant extra-regional linkages are also maintained by firms in regional clusters. The spatial extent of linkages depends on the mode of relations; arm's length, network and hierarchy relations show different spatial patterns, as do different types of cooperation. The importance of extra-regional linkages also varies with firm- and plant-specific characteristics. Extra-regional linkages are more common among larger and more R&D-intensive firms, firms with greater presence in the rest of the country and firms with more experience of cooperation and more stable relationships.     

An Analysis of Arrow and String Bag Craft Variability in the Upper Sepik and Central New Guinea

ABSTRACT This paper describes an analysis of relationships between language, geography and material culture in the upper Sepik region of New Guinea. We used Mantel tests and principal coordinate analysis to assess and compare the associations between arrow and string bag crafts and language and geographical distance. The Mantel tests resulted in a significant association between each class’ craft variability and both geographical distance and language, however, after statistical control was applied to either of the independent variables only a significant association with geographical distance remained. We argue that these results indicate craft techniques were readily disseminated and that craft distributions are unlikely to reflect any deeper historical relationships between groups. The spatial autocorrelation of arrow crafts was particularly great. A comparison of the principal coordinate analysis plots indicates a greater degree of diffusion and synthesis had taken place between lowland and highland arrow craft traditions. We conclude that the more intensive learning regimes needed to master string bag craft techniques, as well as a greater degree of interdependence between string bag components, had ensured more abrupt differences between bag types resulting in a more moderate degree of spatial autocorrelation.     

Comparison of XRF and PXRF for analysis of archaeological obsidian from southern Perú

Chemical sourcing is becoming an increasingly important component of archaeological investigation. Instruments used for elemental analysis generally must be operated in a controlled laboratory environment. Further, many methods require destruction of a small portion of the objects under investigation. These facts inhibit the application of chemical sourcing studies in a number of research contexts. Use of portable non-destructive instruments would resolve these issues. Sixty-eight obsidian artifacts from the site of Jiskairumoko, in southern Perú, were examined by X-ray fluorescence spectrometry (XRF) and portable X-ray fluorescence spectrometry (PXRF). Results were compared for consistency in terms of source determination and individual element concentrations. Both instruments determined that the same sixty-six artifacts derived from the Chivay obsidian source and both identified the same two artifacts that could not be assigned to source. Individual element comparisons showed significant differences, but these can be resolved through instrument cross calibration, and differences had no bearing on source identification. PXRF was found suitable for determining obsidian sources in southern Perú and for identifying specimens that require more sensitive analytical methods such as, instrumental neutron activation analysis (INAA). Regular use of Chivay at Jiskairumoko suggests consistent trade relationships developed during the Archaic.     

Single and Multiscale Models of Process Spatial Heterogeneity

Recent work in local spatial modeling has affirmed and broadened interest in multivariate local spatial analysis. Two broad approaches have emerged: Geographically Weighted Regression (GWR) which follows a frequentist perspective and Bayesian Spatially Varying Coefficients models. Although several comparisons between the two approaches exist, recent developments, particularly in GWR, mean that these are incomplete and missing some important axes of comparison. Consequently, there is a need for a more thorough comparison of the two families of local estimators, including recent developments in multiscale variants and their relative performance under controlled conditions. We find that while both types of local models generally perform similarly on a series of criteria, some interesting and important differences exist.     

Public and Private Policy Change: Pension Reform in Four Countries

This article offers a comparative, qualitative analysis of the changing nature of—and relationship between—public and private old age pensions in the United States, Canada, Britain, and Japan. Stressing the impact of institutional legacies on policy change, the article explains why these countries have taken contrasting paths toward the restructuring of public and private pension policies. The study finds that the four countries fall into two distinct clusters. On the one hand are Canada and the United States, which have essentially witnessed policy drift toward a greater reliance on private savings. On the other hand are Britain and Japan, which have reshaped their pension systems largely through legislative revision. The last section explains the differences between and within these two country clusters. The article concludes that institutional forces explain the distinctive policy patterns between the two country clusters but that it is necessary to bring in other factors (i.e., demographic aging, union density, and the role of ideas) to account for differences within each of these clusters.     

A Programming Approach to Minimizing and Maximizing Spatial Autocorrelation Statistics

A programming approach is presented for identifying the form of the weights matrix W which either minimizes or maximizes the value of Moran's spatial autocorrelation statistic for a specified vector of data values. Both nonlinear and linear programming solutions are presented. The former are necessary when the sum of the links in W is unspecified while the latter can be used if this sum is fixed. The approach is illustrated using data examined in previous studies for two variables measured for the counties of Eire. While programming solutions involving different sets of constraints are derived, all yield solutions in which the number of nonzero elements in W is considerably smaller than that in W defined using the contiguity relationships between the counties. In graph theory terms, all of the Ws derived define multicomponent graphs. Other characteristics of the derived Ws are also presented.     

地貌与村落扩展:1753-1982年河北南部村落研究

本文采用历史文献、地名考证、GIS空间定位等方法,复原了河北南部四十余县清前期村落分布状况,并结合现代资料,探讨了200多年来该地区村落扩展的空间特征。研究发现河北南部地区的村落,在过去二百多年间,不论从数量上还是分布上都有很大的变化。而且这种变化存在明显的区域差异。作者认为地貌形态是造成这种区域差异的重要原因,是控制长时间大尺度村落发展非常重要的影响因素。     

Integrating pattern and context in spatial archaeology

Traditional approaches to intrasite spatial analysis in archaeology have concentrated on identifying associations among classes of artifacts over a site surface. This focus has tended to ignore the possible effects of contextual constraints on inter-class relationships, for example the “gravity effects” of hearth features on object deposition. Consequently, sets of co-occurring artifact classes defined in space are usually ascribed behavioural significance as “tool kits”, even though they may represent unrelated objects simply discarded into the same spatial location. This paper attempts to illustrate this problem through contextual integration of artifact and feature distributions. Quantitative methods are employed to carry out contextual spatial analysis of artifact distributions from the Upper Palaeolithic rockshelter site Le Flageolet I (Dordogne, France). Results indicate that associations among artifact classes in space might be constrained by feature locations at Le Flageolet I and that contextual interpretations are warranted for the observed inter-class relations.     

Spatial Cluster Detection in Spatial Flow Data

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.     

Exploring Goodness-of-Fit and Spatial Correlation Using Components of Tango's Index of Spatial Clustering

The ability to detect anomalies such as spatial clustering in data sets plays an important role in spatial data analysis, leading to interest in test statistics identifying patterns exhibiting significant levels of clustering. Toward this end, Tango (1995) proposed a statistic (and its associated distribution under a null hypothesis of no clustering) assessing overall patterns of spatial clustering in a set of observed regional counts. Rogerson (1999) observed that Tango's index may be decomposed into the summation of two distinct statistics, the first mirroring standard tests of goodness-of-fit (GOF), and the second an index of spatial association (SA) similar to Moran's I . In this article, we investigate the effectiveness of Rogerson's expression of Tango's statistic in separating GOF from SA in data sets containing clusters. We simulate data under the null hypothesis of no clustering as well as two alternative hypotheses. The first alternative hypothesis induces a poor fit from the null hypothesis while maintaining independent observations and the second alternative hypothesis induces spatial dependence while maintaining fit. Using Rogerson's decomposition and leukemia incidence data from upstate New York, we show graphically that one is unable to statistically distinguish poor fit from autocorrelation.     

flowAMOEBA: Identifying Regions of Anomalous Spatial Interactions

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set.     

Adjustment or renewal in regional clusters? A study of diversification amongst SMEs in the Aberdeen oil complex

A renewed interest in the spatial clustering or agglomeration of economic activity is evident within economic geography and related fields. This paper considers processes of change within 'mature' regional clusters, developing a conceptual framework which links firm-level strategies to development scenarios at the cluster level through a set of intervening mechanisms. Whange, enabling the cluster to sustain its prosperity, and adjustment which is associated with stagnation and gradual decline. This conceptual framework is applied to an analysis of one key mechanism of change, diversification, as pursued by small and medium-sized enterprises (SMEs) in the Aberdeen oil cluster. Our analysis indicates that some diversification is occurring, with the implications for the cluster lying midway between adjustment and renewal. In conclusion, we emphasize the need to assess and 'unpack' the clusters concept and suggest that research should focus on the relationships between proximity, learning and evolution.     

ACCOUNTING FOR NEIGHBORING EFFECTS IN MEASURES OF SPATIAL CONCENTRATION*

ABSTRACT A common problem with spatial economic concentration measures based on areal data (e.g., Gini, Herfindhal, entropy, and Ellison‐Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions or to neighboring effects. Clearly, since spillovers do not recognize areal units, economic clusters may cross regional boundaries. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far‐flung, nonadjacent areas. This paper shows how popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects. With a U.S. application, we also demonstrate that the new instruments we propose are easy to implement and can be valuable in regional analysis.     

Exploring Spatial Process Dynamics Using Irregular Cellular Automaton Models

Cellular automaton models have enjoyed popularity in recent years as easily constructed models of many complex spatial processes, particularly in the natural sciences, and more recently in geography also. Most such models adopt a regular lattice (often a grid) as the basis for the spatial relations of adjacency that govern evolution of the model. A number of variations on the cellular automaton formalism have been introduced in geography but the impact of such variations on the likely behavior of the models has not been explored. This paper proposes a method for beginning to explore these issues and suggests that this is a new approach to the investigation of the relationships between spatial structure and dynamics of spatial processes. A framework for this exploration is suggested, and details of the required methods and measures are provided. In particular, a measure of spatial pattern—spatial information—based on entropy concepts is introduced. Initial results from investigation along the proposed lines are reported, which suggest that a distinction can he made between spatially robust and fragile processes. Some implications of this result and the methodology presented are briefly discussed.     

Exploring a Relationship Between Aggregate and Individual Levels Spatial Data Through Semivariogram Models

Analysis of social data is frequently done using aggregate-level data. There may not be a direct interest in spatial relationships in the data, but the presence of spatial interdependence may still need to be taken into account. This article explores the aggregation effect from a spatial perspective by assuming nonzero covariance for individual data from two different groups. We investigate the bias associated with aggregate-level data for semivariogram analysis. We show that the bias mainly arises from the average of the semivariogram within the groups. It is also shown how aggregated-level data may be used to estimate parameters of an individual-level semivariogram model. A nonlinear regression method is proposed to carry out this estimation procedure and a simulation is done to clarify the results.     

基于DBSCAN算法的民宿集群识别、分布格局及影响因素——以南京市为例

以南京市Airbnb网站民宿商家为例,综合利用DBSCAN聚类算法、空间变异函数、地理加权回归模型刻画南京市民宿的空间特征及影响因素.结果表明:①南京市民宿已经形成"25124"的5等级14集群格局,呈现"两横两纵"的"井"字形空间布局特征;②民宿密度分布存在空间异质性,各个街道之间存在较强的空间自相关,民宿密度分布受...