Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China
01 Jan 2010-International Journal of Geographical Information Science (Taylor & Francis)-Vol. 24, Iss: 1, pp 107-127
TL;DR: In a real‐world study, the primary physical environment was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China) and basic nutrition was finding to be more important than man‐made pollution in the control of the spatial NTD pattern.
Abstract: Physical environment, man-made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real-world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man-made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.
Citations
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TL;DR: This handbook is a very useful handbook for engineers, especially those working in signal processing, and provides real data bootstrap applications to illustrate the theory covered in the earlier chapters.
Abstract: tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.
1,292 citations
TL;DR: In this paper, a q-statistic method is proposed to measure the degree of spatial stratified heterogeneity and to test its significance, and the q value is within [0, 1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratifying of heterogeneity).
879 citations
TL;DR: Geographical detector is software based on spatial variation analysis of the geographical strata of variables to assess the environmental risks to human health.
Abstract: Human health is affected by many environmental factors. Geographical detector is software based on spatial variation analysis of the geographical strata of variables to assess the environmental risks to human health: the risk detector indicates where the risk areas are; the factor detector identifies which factors are responsible for the risk; the ecological detector discloses the relative importance of the factors; and the interaction detector reveals whether the risk factors interact or lead independently to disease.
283 citations
Cites background or methods from "Geographical Detectors-Based Health..."
...For both of these groups and others (Christakos, 1992; Getis and Ord, 1992; Haining, 2003; Kulldorff, 1997; Rushton, 1992; Chen et al., 2011), we developed geographical detector to assist in exploring risks (Wang et al., 2010a; Hu et al., 2011)....
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...We assume that a disease would exhibit a spatial distribution similar to that of an environmental factor if the environmental attribute leads to the disease (Wang et al., 2010a), such as people living in areas with heavier polluted soil may have higher prevalence of a disease....
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...The study area is stratified into L stratums, denoted by i ¼ 1, ., L (Wang et al., 2010b), according to spatial heterogeneity (which is defined as an attribute whose statistical properties, e.g., mean and standard deviation, change in space) of a suspected determinant or its proxy of the disease....
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...The GeogDetector is grounded on the PD, i.e. Power of Determinant, which generates four detectors (Wang et al., 2010a)....
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TL;DR: In this paper, the authors analyzed the distribution characteristics of rural settlements and their impact has profound implications for rural reconstruction, such as clustered, random, and uniform discrete distribution, and found that rural settlements were denser in the southeastern regions compared to the northwestern regions.
200 citations
Cites methods from "Geographical Detectors-Based Health..."
...The model used is as follows (Wang et al., 2010; Yang et al., 2013a, 2013b): PD;U ¼ 1 1 ns2U Xm i¼1 nD;is 2 UD;i ; (5) where PD,U is the power determinant of each influencing factor in a rural settlements distribution, nD,i is the number of sub-regional samples, n is the number of samples in the…...
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TL;DR: In this article, the spatial stratified heterogeneity analysis investigates the heterogeneity among different types of spatial issues, i.e., spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial problems.
Abstract: Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among vari...
190 citations
Cites background or methods from "Geographical Detectors-Based Health..."
...The significance of the different influence of explanatory variables is tested with the F-statistic (Wang et al. 2010; Wang, Zhang, and Fu 2016): F ¼ Nu Nv 1ð Þ PMu j¼1 Nu;jσ2u;j Nv Nu 1ð Þ PMv j¼1 Nv;jσ2v;j (6) where Nu and Nv are numbers of observations, Mu and Mv are numbers of sub-regions, and PMu j¼1 Nu;jσ2u;j and PMv j¼1 Nv;jσ2v;j are sums of variance within sub-regions of variables u and v respectively....
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...The spatial stratified heterogeneity can be quantified by the geographical detector model (Wang et al. 2010; Luo et al. 2016)....
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...The difference between mean values of subregions η and κ is tested with the t-test (Wang et al. 2010; Wang, Zhang, and Fu 2016):...
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...The annual variation of research using the geographical detector model is compared with the variation of papers citing the publication first proposing the model (Wang et al. 2010), which accumulate to 213 based on the database of the Web of Science....
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...The interaction detector explores five interactions, including nonlinear-weaken, uni-variable weaken, bi-variable enhance, independent, and nonlinear-enhance (Wang et al. 2010; Wang, Zhang, and Fu 2016) (Table 1)....
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References
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01 Jan 1994
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Abstract: Note: Includes bibliographical references, 3 appendixes and 2 indexes.- Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08
19,881 citations
TL;DR: In this paper, a new general class of local indicators of spatial association (LISA) is proposed, which allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation.
Abstract: The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
8,933 citations
"Geographical Detectors-Based Health..." refers background in this paper
...Among them, hotspots statistics techniques address question (1) above by testing the statistical significance of high in situ disease incidence ratios compared to the surrounding areas (Anselin 1995, Kulldorff 1997)....
[...]
TL;DR: In this article, a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, where the area of the scanning window is allowed to vary, and the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function.
Abstract: The scan statistic is commonly used to test if a one dimensional point process is purely random, or if any clusters can be detected. Here it is simultaneously extended in three directions:(i) a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, (ii) the area of the scanning window is allowed to vary, and (iii) the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function. The main interest is in detecting clusters not explained by the baseline process. These methods are illustrated on an epidemiological data set, but there are other potential areas of application as well.
3,314 citations
Book•
01 Jan 1982
TL;DR: In this article, the authors present a survey of the history and varieties of probability for the laws of chance and their application in the context of Markov chains convergence of random variables.
Abstract: Events and their probabilities random variables and their distributions discrete random variables continuous random variables generating functions and their applications Markov chains convergence of random variables random processes stationary processes renewals queues Martingales diffusion processes. Appendices: Foundations and notations history and varieties of probability John Arburthnot's preface to "Of the Laws of Chance" (1692).
2,819 citations
"Geographical Detectors-Based Health..." refers background in this paper
...According to the central limit theorem (Grimmett and Stirzaker 1992 ), the mean disease occurrence tends to be normally distributed....
[...]
TL;DR: This handbook is a very useful handbook for engineers, especially those working in signal processing, and provides real data bootstrap applications to illustrate the theory covered in the earlier chapters.
Abstract: tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.
1,292 citations