A Local Model for Multivariate Analysis: Extending Wartenberg’s Multivariate Spatial Correlation

Local analysis can provide specific information about individual observations that is often useful in understanding nonstationary interactions among variables. This paper extends the application of Wartenberg’s Multivariate Spatial Correlation (MSC) method to a local setting. The original MSC can be considered as an adaptation of Principal Component Analysis for spatial effects with respect to spatial autocorrelation. The extended MSC method described in this paper, however, further incorporates another spatial effect, spatial heterogeneity, by the addition of geographic weights in standardizing the data and in calculating the spatial association weight matrix. The extension allows more local analysis and facilitates additional visualization of the results. The geographically weighted MSC is illustrated and justified using the classic dataset collected by André-Michel Guerry on moral statistics in 1830s France.     

Interethnic Tensions in Kyrgyzstan: A Political Geographic Perspective

Two U.S. geographers review an array of intertwining political geographic issues that provide context and set the stage for deadly armed conflict between groups of ethnic Uzbeks and Kyrgyz in the southern Kyrgyzstan city of Osh in June 2010. Applying a disaggregated and localized approach to understanding the ambiguous and complex factors underlying the current instability in Kyrgyzstan, they focus on: the role of north-south political competition; the country's uneasy economic relationship with its western neighbor, Uzbekistan; widespread official corruption and the penetration of organized crime into government structures; as well as broader geopolitical issues. The latter include Tashkent's policy toward the Uzbek diaspora, perceived threats from international terrorism/Islamist fundamentalism, the potential for the export of a "color revolution" to Uzbekistan, the presence of U.S. and Russian military forces in Central Asia, and the relative ineffectiveness of regional security structures such as the Collective Security Treaty Organization.     

A New Spatial Optimization Perspective on the Delineation of Metropolitan and Micropolitan Statistical Areas

The current method for delineating U.S. metropolitan and micropolitan statistical areas (collectively referred to as core‐based statistical areas—CBSAs) is to cluster counties based on the strength of commuting interactions between outlying areas and urban cores. The social and economic integration of a metropolitan or a micropolitan statistical area is operationalized using what we believe is an outdated, monocentric perspective on functional spatial structure: one that fixates solely on inward and reverse commuting. We propose a new spatial optimization model for delineating CBSAs that can better account for polycentric urban structure by considering all intercounty commuting linkages. Our model seeks to find the boundaries that maximize the containment of entire webs of intra‐CBSA intercounty commuting. We apply the proposed method to delineate alternative sets of metropolitan and micropolitan statistical areas for comparison with the federal government's currently defined (2010 Office of Management and Budget [OMB] standards) official CBSAs. El método actual para delimitar áreas estadísticas micropolitanas y metropolitana en los Estados Unidos (denomidado genéricamente áreas estadísticos nucleares o core‐statistical areas‐ CBSA) consiste en agrupar condados en base a la fuerza de las interacciones de los desplazamientos residencia‐trabajo entre las zonas periféricas y los núcleos urbanos. La integración social y económica de un área estadística metro y micro politana se pone en operación usando un enfoque funcional que los autores consideran obsoleto que se basa en una estructura espacial urbana monocéntrica. Dicho enfoque se limita únicamente a los desplazamientos hacia el interior y de vuelta. Los autores proponen un nuevo modelo de optimización espacial para delinear CBSAs que respresenta mejor una estructura urbana policéntrica que considera todos los desplazamientos entre condados. El modelo busca encontrar los límites que maximizan la inclusión de redes enteras de desplazamientos intra CBSA y entre condados. El método propuesto es aplicado para delinear conjuntos alternativos de áreas estadísticas metropolitanas y micropolitanas y es comparado con los límites oficiales actuales de CBSA proporcionados por gobierno federal (de acuerdo a los lineamientos de la Oficina de Administración y Presupuesto u Office of Management Budget‐OMB) en 2010). 目前用于描述美国大都市区与居住区统计区域（共同地被称为基于核心的统计区域—CBSAs）的方法,主要是基于边远地区和城市核心区间的相互作用强度对郡县进行聚类。大都市区或居住区统计区域社会与经济一体化的运作方式,采用的是一种功能空间结构上落伍的单中心视角,即仅关注内部交换或反向交换。本文提出了一种新的空间优化模型用于描述CBSAs,将所有内部通勤联系考虑在内,能更好地解释多中心城市结构。该模型力图发现能最大化包含内部CBSA通勤联系网的边界。最后,将该模型应用于大都市区和居住区的统计区域描述,并与联邦政府目前定义的官方CBSAs（管理与预算 [OMB]标准2010办公室）进行了对比研究。     

A Multivariate Model for Spatio‐temporal Health Outcomes with an Application to Suicide Mortality

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.     

A Local Indicator of Multivariate Spatial Association: Extending Geary's c

This paper extends the application of the Local Geary c statistic to a multivariate context. The statistic is conceptualized as a weighted distance in multivariate attribute space between an observation and its geographical neighbors. Inference is based on a conditional permutation approach. The interpretation of significant univariate Local Geary statistics is clarified and the differences with a multivariate case outlined. An empirical illustration uses Guerry's classic data on moral statistics in 1830s France.     

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

In crime analyses, maps showing the degree of risk help police departments to make decisions on operational matters, such as where to patrol or how to deploy police officers. This study statistically models spatial crime data for multiple crime types in order to produce joint crime risk maps. To effectively model and map the spatial crime data, we consider two important characteristics of crime occurrences: the spatial dependence between sites, and the dependence between multiple crime types. We reflect both characteristics in the model simultaneously using a generalized multivariate conditional autoregressive model. As a real‐data application, we examine the number of incidents of vehicle theft, larceny, and burglary in 83 census tracts of San Francisco in 2010. Then, we employ a Bayesian approach using a Markov chain Monte Carlo method to estimate the model parameters. Based on the results, we detect the crime hotspots, thus demonstrating the advantage of using a multivariate spatial analysis for crime data.     

Commuting Distances and Gender: A Spatial Urban Model

In any urban center the commuting distances are a function of the spatial structure of the center and of the characteristics of the commuters. In this paper theoretical relationships between commuting distances and distances of residences to city centers are derived for monocentric and polycentric cities. These relationships are then linked to the sociological determinants of commuting distances. An econometric model encompassing both spatial structure variables and social variables is constructed and estimated using data for sixteen urban centers. Gender differences are focused upon. The expansion method is used.     

华人网络的再认识：一个空间的视角

现有关于华人跨国网络的研究,为我们描绘了华人网络的三张面孔,即作为“文化载体”、“社会纽带”和“经济圈”的华人网络。本文从空间的角度,对以上研究进行了补充。它以香港及其邻近的东南亚地区的华人移民经验为依据,在检讨了滨下武志、王赓武和和斯金纳等人有关中国社会经济地理、文化中心和市场结构理论的基础上,提出了一个东南亚华人网络空间结构的理论模型。该模型以国际大都会为中心,形成了“去中心”、“多层次”和“多维度”的钻石型网络结构。在该模型中,华人网络的经济、社会和文化的面向获得了互动和统一。若干类似的子网络相互联结,共同构成了全球华人网络的宏观地域系统。     

A Spatial Logit Association Model for Cluster Detection

In this paper, I propose to set out a logit spatial association model for binary spatial events and develop a scan algorithm to search for spatial associations. I extend the traditional logit model with a spatial autocorrelated component so that the model includes not only known risk factors, but also spatially autocorrelated regions as control or explanatory factors. The case study of West Virginia lung cancer shows that the model effectively captures cool and hot spots in lung cancer mortality.     

Leading Indicators and Spatial Interactions: A Crime-Forecasting Model for Proactive Police Deployment

We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1-month-ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96-month period ending in December 1998. The study uses the rolling-horizon forecast experimental design with forecasts made over the 36-month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41–53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.     

A Model of Contiguity for Spatial Unit Allocation

We consider a problem of allocating spatial units (SUs) to particular uses to form "regions" according to specified criteria, which is here called "spatial unit allocation." Contiguity—the quality of a single region being connected—is one of the most frequently required criteria for this problem. This is also one that is difficult to model in algebraic terms for algorithmic solution. The purpose of this article is to propose a new exact formulation of contiguity that can be incorporated into any mixed integer programming model for SU allocation. The resulting model guarantees to enforce contiguity regardless of other included criteria such as compactness. Computational results suggest that problems involving a single region and fewer than about 200 SUs are optimally solved in fairly reasonable time, but that larger problems must rely on heuristics for approximate solutions. It is also found that a problem of any size can be formulated in a more tractable form when a fixed number of SUs are to be selected or when a certain SU is selected in advance.     

A Socio‐Geographic Perspective on Human Activities in Social Media

Location‐based social media make it possible to understand social and geographic aspects of human activities. However, previous studies have mostly examined these two aspects separately without looking at how they are linked. The study aims to connect two aspects by investigating whether there is any correlation between social connections and users' check‐in locations from a socio‐geographic perspective. We constructed three types of networks: a people–people network, a location–location network, and a city–city network from former location‐based social media Brightkite and Gowalla in the U.S., based on users' check‐in locations and their friendships. We adopted some complexity science methods such as power‐law detection and head/tail breaks classification method for analysis and visualization. Head/tail breaks recursively partitions data into a few large things in the head and many small things in the tail. By analyzing check‐in locations, we found that users' check‐in patterns are heterogeneous at both the individual and collective levels. We also discovered that users' first or most frequent check‐in locations can be the representatives of users' spatial information. The constructed networks based on these locations are very heterogeneous, as indicated by the high ht‐index. Most importantly, the node degree of the networks correlates highly with the population at locations (mostly with R² being 0.7) or cities (above 0.9). This correlation indicates that the geographic distributions of the social media users relate highly to their online social connections.     

A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective

Spatial autocorrelation is a concept that helps to define the field of spatial analysis. It is central to studies using spatial statistics and spatial econometrics. In this paper, we trace the early development of the concept and explain the academic links that brought the concept to the fore in the late 1960s. In geography, the importance of the work of Michael F. Dacey, Andrew D. Cliff, and J. Keith Ord is emphasized. Later, with the publication of a volume on spatial econometrics by Luc Anselin, spatial research and the use of the concept of spatial autocorrelation received a considerable boost. These developments are outlined together with comments about recent and possible future trends in spatial autocorrelation-based research.     

Organisational Needs and Priorities of Heritage Areas in the USA

Academics and practitioners alike know relatively little about the core competencies that are transferable from one heritage-area initiative to another. Often those new to the field have had to look to consultants or peers for advice regarding the essential core competencies needed to make their emerging organisations a success.This research surveyed the executive directors of the known universe of 154 heritage areas in the USA for the purposes of identifying the essential core skills they feel are needed to create and sustain a multi-jurisdictional heritage-tourism initiative. In addition, the importance-performance methodology employed in the survey instrument made it possible to assess their critical needs and priorities for training. Results of this study contributed to the design of the Heritage Development Institute, recently created to serve the executive staff of the nation's heritage areas at the College of Charleston on behalf of the Alliance of National Heritage Areas (www.cofc.edu/~heritage).     

基于交易成本节约视角的旅游景区治理模式选择研究

选择适当的景区治理模式在本质上是为了节约交易成本。本文根据我国的现实条件,提出了4种旅游景区治理模式,并从交易成本节约的视角对这4种模式进行了比较分析,得出当前我国一般旅游景区的最佳治理模式是由某一社会资本所有者对旅游景区进行长期垄断经营,同时根据这一结论提出了一些具体的政策建议。     

历史上梅州山区的开发：地名学的视野

地名是人们在社会生活中给地理实体、行政区域或地点起的名称,地名往往带有强烈的延续性和稳定性,因而能够比较完整地保留命名时所反映的文化内涵。本文将粤东北山区梅州乡村地名为解读对象,探讨地名与生态环境,山区开发,地名与族群关系,地名与历史人物、历史事件,地名与军事设施及经济活动等的关系,以期再现客家聚落名称所蕴涵的丰富人文因素。