Bayesian Areal Wombling for Geographical Boundary Analysis

In the analysis of spatially referenced data, interest often focuses not on prediction of the spatially indexed variable itself, but on boundary analysis , that is, the determination of boundaries on the map that separate areas of higher and lower values. Existing boundary analysis methods are sometimes generically referred to as wombling , after a foundational article by Womble (1951). When data are available at point level (e.g., exact latitude and longitude of disease cases), such boundaries are most naturally obtained by locating the points of steepest ascent or descent on the fitted spatial surface (Banerjee, Gelfand, and Sirmans 2003). In this article, we propose related methods for areal data (i.e., data which consist only of sums or averages over geopolitical regions). Such methods are valuable in determining boundaries for data sets that, perhaps due to confidentiality concerns, are available only in ecological (aggregated) format, or are only collected this way (e.g., delivery of health-care or cost information). After a brief review of existing algorithmic techniques (including that implemented in the commercial software BoundarySeer), we propose a fully model-based framework for areal wombling, using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo methods. We explore the suitability of various existing hierarchical and spatial software packages (notably S-plus and WinBUGS) to the task, and show the approach's superiority over existing nonstochastic alternatives, both in terms of utility and average mean square error behavior. We also illustrate our methods (as well as the solution of advanced modeling issues such as simultaneous inference) using colorectal cancer late detection data collected at the county level in the state of Minnesota.     

Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms

This article presents a geostatistical methodology that accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on semivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e., direction-dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data, the procedure allows the decomposition of the structured component into several spatial components (i.e., local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.     

Fieldwork Methodology in South American Maritime Archaeology: A Critical Review

In archaeology, data obtained from the analysis of material evidence (i.e., the archaeological record) from extensive excavations have been a significant means for the ultimate development of interpretations about human life in the past. Therefore, the methodological procedures and tools employed during fieldwork are of crucial importance due to their effect on the information likely to be recovered. In the case of maritime archaeology, the development of rigorous methods and techniques allowed for reaching outcomes as solid as those from the work performed on land. These improvements constituted one of the principal supports—if not, the most important pillar—for its acceptance as a scientific field of study. Over time, the growing diversity of sites under study (e.g., shipwrecks, ports, dockyards, and prehistoric settlements) and the underwater environments encountered made it clear that there was a need for the application of specific methodological criteria, in accordance with the particularities of the sites and of each study (e.g., the research aims and the available resources). This article presents some ideas concerning the methodologies used in South American investigations that have exhibited a strong emphasis on the analysis of historical shipwrecks (the sixteenth to twentieth centuries). Based on a state-of-the-knowledge review of these research projects, in particular where excavations were conducted, the article focuses on the details of the main strategies adopted and results achieved. The ideas proposed in this article can be useful as a starting point for future activities of surveying, recording, and excavating shipwrecks.     

Why Citizens Do and Do Not Attend Public Meetings about Local Cancer Cluster Investigations

Why do citizens choose to attend or not to attend community public meetings about possible cancer clusters? To answer this question, we examine self‐report data collected during a series of mail surveys conducted in six communities experiencing current health investigations into suspect levels of cancer or cancer clusters. We analyze the data using quantitative content analysis while also providing qualitative summaries and categorizations of survey participants' reasons for attending or not attending a specific public meeting in their community. In addition, we use survey data related to respondents' past participation and sociodemographic characteristics. The results found that rational reasons (e.g., to get information) dominated citizens' justifications for attending the public meetings, whereas socioeconomic and mobilization factors (e.g., did not hear about meeting, too busy) were most commonly cited as reasons for not attending. Less common but still present were relational considerations, such as believing the people in charge were fair. Taken together, the findings suggest that the majority of citizens who attended the public meetings could be categorized as the curious, the fearful, and the available. In comparison, the majority of citizens who did not attend could be described as the uninformed, the indifferent, the occupied, and the disaffected.     

Spatial Analysis in Physical Geography and the Challenge of Deterministic Uncertainty

The application of complex and nonlinear dynamical systems (NDS) theory in physical geography and geosciences has proceeded through several stages, and has recently entered a phase where field-testable hypotheses and historical or mechanistic explanations are being generated. However, there are some fundamental challenges. It seems clear that chaos and dynamical self-organization are present, and may be common in earth surface systems, and that these phenomena have spatial manifestations in the landscape. However, NDS theory and methods have been formulated primarily in the temporal domain and are typically ill-suited to real-world spatial data. Spatial analytical methods are not generally capable of distinguishing deterministic complexity and uncertainty from noise. Thus, the detection of the signals of complex deterministic dynamics in real landscapes and spatial data is a major challenge. Entropy-based methods of spatial analysis can be directly linked to nonlinear dynamics, and are at present the best available method to approach this problem. However, there is evidence in the spatial analysis literature suggesting that development of techniques to detect deterministic uncertainty is possible. Pending such a break-through, three general approaches are described, based on spatial analysis of chronosequences, the characteriziation of changes in spatial structure over time, and the spatial-domain testing of specific hypotheses relevant to deterministic uncertainty. Current trends generally suggest a shift in mathematical modeling and spatial analysis in physical geography away from traditional determinism toward approaches that incorporate locational, historical, and scale contingency.     

THE IMPACT OF ENVIRONMENTAL FACTORS ON THE INCIDENCE OF LUNG CANCER AMONG RURAL RESIDENTS

Lung cancer mortality in the rural areas of Moscow Oblast is found to be virtually identical with data for urban areas. In an effort to establish a set of potential causative factors in rural areas, selected factors are correlated with lung cancer mortality by Moscow Oblast rayons. The highest positive correlation is found for the use of farm pesticides from the carcinogenic dithiocarbamate group, followed by the percentage of workers employed in agriculture, the amount of smoking per capita, the amount of dust-causing plowing, the use of farm machinery (producing exhaust gases) and the use of kerosene (producing household soot). A negative correlation is found between lung cancer and the percentage of white-collar workers employed in rural areas. The selected set of factors accounts for 45 percent of the variance in lung-cancer incidence, but no single specific factor can be isolated to explain the high mortality in rural areas, which evidently stems from a combination of causes.     

Reconstructing Population Density Surfaces from Areal Data: A Comparison of Tobler's Pycnophylactic Interpolation Method and Area-to-Point Kriging

We compare Tobler's pycnophylactic interpolation method with the geostatistical approach of area-to-point kriging for distributing population data collected by areal unit in 18 census tracts in Ann Arbor for 1970 to reconstruct a population density surface. In both methods, (1) the areal data are reproduced when the predicted population density is upscaled; (2) physical boundary conditions are accounted for, if they exist; and (3) inequality constraints, such as the requirement of non-negative point predictions, are satisfied. The results show that when a certain variogram model, that is, the de Wijsian model corresponding to the free-space Green's function of Laplace's equation, is used in the geostatistical approach under the same boundary condition and constraints with Tobler's approach, the predicted population density surfaces are almost identical (up to numerical errors and discretization discrepancies). The implications of these findings are twofold: (1) multiple attribute surfaces can be constructed from areal data using the geostatistical approach, depending on the particular point variogram model adopted—that variogram model need not be the one associated with Tobler's solution and (2) it is the analyst's responsibility to justify whether the smoothness criterion employed in Tobler's approach is relevant to the particular application at hand. A notable advantage of the geostatistical approach over Tobler's is that it allows reporting the uncertainty or reliability of the interpolated values, with critical implications for uncertainty propagation in spatial analysis operations.     

A SIMPLE METHOD FOR ESTIMATING QUARTERLY LOCAL-AREA PERSONAL INCOME*

ABSTRACT A single-equation econometric approach is developed for estimating personal income on a quarterly basis for counties and county aggregates (e.g., metropolitan areas). An experiment is conducted on state data to test the accuracy of the estimates. The experiment indicates that they would be highly accurate for large local areas; for local areas comprising at least 20 percent of their state's total personal income, the estimates would fall within 2 percent of BEA-type estimates 95 percent of the time. An aggregate estimation approach is shown to be preferable to a component approach. Using data on local department store sales, the quarterly personal income estimates are shown to forecast better than the currently available annual estimates.     

An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas

Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant concern during the coronavirus disease (COVID-19) pandemic. Our study examined the stochastic distribution of spatial accessibility under the uncertainties underlying the availability of intensive care unit (ICU) beds and ease of mobility in the Greater Houston area of Texas. Based on the randomized supply and mobility from their historical changes, we employed Monte Carlo simulation to measure ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method. We then conducted hierarchical clustering to classify regions of adequate (sufficient and reliable) accessibility and inadequate (insufficient and unreliable) accessibility. Lastly, we investigated the relationship between the accessibility measures and the case fatality ratio of COVID-19. As result, locations of sufficient access also had reliable accessibility; downtown and outer counties, respectively, had adequate and inadequate accessibility. We also raised the possibility that inadequate health care accessibility may cause higher COVID-19 fatality ratios.     

Hair today,gone tomorrow: Analysing potential mercury exposure in 19th-century New Zealand gold miners using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry

Toxic metal or element exposure has the potential to cause significant negative health effects in human populations. During the goldrushes of the colonial period, mercury amalgamation was one of the most common methods of extracting gold from alluvial deposits or crushed ore, and exposure to mercury was an occupational health hazard. In this study we examine mercury exposure in mining populations from New Zealand’s first major goldrush, which began in Central Otago in 1861. We explore mercury toxicity through laser ablation inductively coupled mass spectrometric analysis of archaeological hair and sediment samples associated with the Tuapeka goldfields. Our analysis highlights ubiquitous low-level mercury exposure on the goldfields, as well as one individual with such high mercury concentrations in their hair that we suspect direct mercury intake, perhaps medicinally, rather than from environmental contamination.     

Foreclosure Risk and Community Health: Does Social Capital Have a Protective Effect?

This study draws upon the social determinants of health framework to model and test the extent to which a community’s social capital is health protective in the face of a substantial economic shock, namely the recent foreclosure crisis. U.S. county–level data are used to analyze potential moderating effects of social capital on health given a community’s foreclosure risk. We rely upon established social capital measures for U.S counties and merge them with county level foreclosure risk scores constructed by the U.S. Department of Housing and Urban Development (HUD). While theorists suggest that social capital’s effect on health and other outcomes may be durable over time there have been few empirical tests of this. We interact established social capital indicators measured at two points in time with foreclosure risk to predict overall self-reported health. Our results provide strong support suggesting that high levels of social capital are health protective. Communities with high levels of social capital that are facing high foreclosure risks report significantly better aggregate health outcomes than comparable communities facing the same level of foreclosure risk, but lower levels of social capital.     

国家级贫困县旅游资源优势度与脱贫力耦合分析——以433个脱贫县为例

依托旅游资源发展旅游业成为中国脱贫攻坚战略的重要路径。以433个已脱贫的国家级贫困县为研究对象,构建旅游资源优势度与脱贫力的耦合协调模型,分析两者耦合协调度等级和类型空间分布特征。研究表明：①脱贫县旅游资源优势度4个等级数量呈“梯形”结构,而脱贫力4个等级数量呈“钵型”结构;②旅游资源优势度区域差异明显,而脱贫力区域差异不大;③高度、中度和低度耦合协调等级分别呈“零星式”“带状式”和“簇块+带状混合式”分布特征;④耦合类型可分为高同步型、低同步型、潜力型和乏力型,其中乏力型和低同步型数量较多,类型空间分异显著。     

Identifying Areas with Elevated Disease Incidence Rates Using Empirical Bayes Estimators

Using maps of observed disease incidence rates to identify regions with potentially elevated risk may be misleading due to the instability of the observed rates in regions with small populations. We use a simulation study to examine the use of maps based on observed incidence rates in identifying such high-risk areas as compared to maps based on empirical Bayes and constrained empirical Bayes rate estimates. In addition, because the existence of clusters of areas with elevated risk violates the usual distributional assumptions underlying the empirical Bayes approach, we also examine the robustness of the estimates and the impact of incorrect assumptions on identification of high-risk regions. The simulation results indicate that the observed incidence rates were quite sensitive in terms of identifying areas with truly elevated rates. However, due to the instability of the observed rates, maps based on observed incidence incorrectly identified areas as high risk more frequently than did maps based on the estimators. The standard and constrained empirical Bayes estimates were more stable than the observed rates even when based on incorrect distributional assumptions. The standard empirical Bayes estimates, however, were oversmoothed in that too few areas were identified as having elevated risk. The constrained empirical Bayes approach provided sensitivity closer to that of the observed rates yet with the percentage of areas incorrectly identified as high risk equal to that of the standard empirical Bayes estimates. We illustrate an application of these results with an analysis of the geographic distribution of brain cancer mortality among counties in Ohio.     

Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County,Kenya

Maternal mortality is a major problem in middle‐income and low‐income countries, and the availability and accessibility of healthcare facilities offering safe delivery is important in averting maternal deaths. Siaya County, in Kenya, has one of the highest maternal mortality rates in the country—far more than the national average. This study aimed to evaluate geographic access to health facilities offering delivery services in Siaya County. A mixed‐methods approach incorporating geographic information system analysis and individual data from semi‐structured interviews was used to derive travel time maps to facilities using different travel scenarios: AccessMod5 and ArcGIS were used for these tasks. The derived maps were then linked to georeferenced household survey data in a multilevel logistic regression model in R to predict the probability of expectant women delivering in a health facility. Based on the derived travel times, 26 per cent (13,140) and 67 per cent (32,074) of the estimated 46,332 pregnant women could reach any facility within one and two hours, respectively, while walking with the percentage falling to seven per cent (3,415) and 20 per cent (8,845) when considering referral facilities. Motorised transport significantly increased coverage. The findings revealed that the predicted probability of a pregnant woman delivering in a health facility ranged between 0.14 and 0.86. Significant differences existed in access levels with transportation‐based interventions significantly increasing coverage. The derived maps can help health policy planners identify underserved areas and monitor future reductions in inequalities. This work has theoretical implications for conceptualising healthcare accessibility besides advancing the literature on mixed methodologies.     

Spillovers and the Locational Effects of Public Infrastructure

This paper examines the possibility of negative output spillovers frompublic infrastructure. A model of productive public capital shows that when input factors aremobile, public infrastructure investments in one location can draw production away from otherlocations. In a linear production-function framework, this effect would be manifested as anegative output spillover from public capital. Using data for California counties from 1969through 1988, such negative spillover effects are shown to exist in the case ofstreet-and-highway capital. The data show that changes in county output are positivelyassociated with changes in street-and-highway capital within the same county, but outputchanges are negatively associated with changes in street-and-highway capital in other counties.     

Seasonal fluctuations in diet and death during the late horizon: a stable isotopic analysis of hair and nail from the central coast of Peru

The goal of the isotopic analysis of hair and nail from Puruchuco-Huaquerones is to reconstruct short term paleodiet using the stable isotopes of carbon and nitrogen in one centimetre increments of hair and two discrete points (distal and proximal) on the nail. This sampling method allows for the reconstruction of diet on a monthly basis (hair) and also diet at two periods (nail): four months and two weeks antemortem. The analysis of these two tissues demonstrates that diet varied from month to month and that food was not stored in any significant quantity. The patterning of the isotope data appears to be closely tied to the agricultural cycle, although a vertical economy cannot be rejected as an alternative explanation. Isotope data for both short (e.g., hair and nail) and long term diet (e.g., bone) are similar; this is interpreted as evidence for the continuity of the settlement in this area. There are no significant differences in the season of death between males or females or between the winter and summer months. The slightly higher mortality rate in the summer may be due to crop failure and subsequent food shortages.     

Contagious Social Practice?

This paper explores the usefulness of spatial innovation diffusion theory for understanding the observed evolution of the social practice of attaining public support as a substitute for working. The empirical example chosen is sick leave. Several European countries have experienced a striking increase in costs for sick leave during the recent years, nowhere however as dramatic as in Sweden. Public costs for sick leave have doubled since 1997 while general health and life expectancy continually improve. No more than a small percentage of this change can be assigned to compositional changes in the population and labour market with respect to age, sex, education, location and sector of employment. Neither has economic benefit conditions changed substantially during this period. What essentially remain, as possible explanations, are changes in attitudes and practices among beneficiaries and physicians. One can observe substantial regional differences in the level of sick leave and apparent traces of spatial autocorrelation in the pattern. The spatial diffusion hypothesis was examined by help of a multivariate analysis based on socio‐economic panel data including sick leave benefits and location for each individual in the labour force 1996–2000. The analysis indicates that the neighbourhood effect is substantial on the local (290 municipalities) and the regional (21 counties) level. Largely, the regional diffusion effect remains when also controlling for more persistent average differences between counties. A semi‐public spatial attitude diffusion process, mainly operating at municipality and county levels, seems to be responsible for a substantial part of the observed increase in cost for sick leave in the period 1996–2000.     

Developing Geostatistical Space–Time Models to Predict Outpatient Treatment Burdens from Incomplete National Data

Basic health system data such as the number of patients utilizing different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS), which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterized by widespread underreporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modeling frameworks were developed and tested in which space–time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a cross-validation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space–time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision makers.     

纺织品类文物材质与染料分析方法

"丝绸之路"的起点位于中国/东亚,中国在"丝路"的发展中占有重要的地位。古代纺织品文物种类繁多,常用的纺织品科学研究方法有色谱、光谱、质谱等。本研究基于目前纺织品文物的分析现状,从纤维、染料两个重要角度,系统地阐述目前已有的扫描电子显微镜、傅里叶红外光谱、光纤光谱等纺织纤维分析技术,以及薄层色谱、高效液相色谱、拉曼光谱、光纤光谱等纺织品染料分析方法。对于纺织纤维鉴别,扫描电镜、傅里叶红外等方法可靠性较高;对于纺织品染料分析,高效液相色谱的分析精度更受认可;而拉曼光谱、光纤光谱技术具有原位、无损的优势,在纺织品文物研究中也日益被重视。对这些方法的原理、特点及其在古代纺织品分析中的研究进展与发展潜力的归纳与综述,将为纺织品文物保护相关研究提供参考与科学支持。     

内蒙古自治区农村人口多维贫困特征测算与分析

针对国家精准扶贫需求,构建基于A-F双临界值法的多维贫困度量模型,进行内蒙古自治区农村人口多维贫困特征的总体测算与分析,并分别分析多维贫困的空间集聚效应及不同分类体系下的贫困分异特征。结果显示:①研究区西部存在大面积高度贫困县单元,中部存在呈东西条带状分布的中度贫困县单元,东部地区贫困程度呈现"南高北低"状态;②研究区主要致贫因素为:人均纯收入、家庭健康、平均教育年限,一般致贫因素为燃料类型、资产、房屋结构,次要致贫因素为:饮水情况、通电情况、儿童入学率;且大部分贫困指标呈显著空间集聚效应;③研究区西部县际贫困特征差异较小,东部存在显著的南北分布差异;④不同类型县各贫困指标差异不同。