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Geographic Information System (GIS) software is constrained, to a greater or lesser extent, by a static world view that is not well-suited to the representation of time (Goodchild 2000). Space Time Intelligence System (STIS) software holds the promise of relaxing some of the technological constraints of spatial only GIS, making possible visualization approaches and analysis methods that are appropriate for temporally dynamic geospatial data. This special issue of the Journal of Geographical Systems describes some recent advances in STIS technology and methods, with an emphasis on applications in public health and spatial epidemiology.The STIS expert workshops were funded in part by grants R01CA092669 and R01CA096002 from the National Cancer Institute, and by grants R43-ES010220 and R44-ES010220 from the National Institute of Environmental Health Sciences. Gillian AvRuskin provided cheerful editorial assistance. We thank the participants at the workshops for providing invaluable expertise and critical insights. 相似文献
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Jaymie R. Meliker Geoffrey M. Jacquez 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(5):625-634
Our research group recently developed Q-statistics for evaluating space–time clustering in case–control studies with residential histories. This technique relies
on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential
histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness,
each individual’s probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend
this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant’s
age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light
on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and
for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder
cancer case–control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences
should be drawn; we analyze these data to demonstrate these novel methods. Maps of space–time clustering of bladder cancer
cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic
approach for evaluating space–time clustering has the potential to generate novel hypotheses about environmental risk factors
and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects. 相似文献
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Jacquez Geoffrey M. Essex Aleksander Curtis Andrew Kohler Betsy Sherman Recinda El Emam Khaled Shi Chen Kaufmann Andy Beale Linda Cusick Thomas Goldberg Daniel Goovaerts Pierre 《Journal of Geographical Systems》2017,19(3):197-220
Journal of Geographical Systems - As the volume, accuracy and precision of digital geographic information have increased, concerns regarding individual privacy and confidentiality have come to the... 相似文献
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Geoffrey M. Jacquez 《Journal of Geographical Systems》2000,2(1):91-97
This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology
as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease
patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of
populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify
disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology.
Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible
without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into
epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information
systems for epidemiology; the second by process-based disease models. 相似文献
6.
From fields to objects: A review of geographic boundary analysis 总被引：12，自引：0，他引：12
Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique
for defining objects – geographic boundaries – on spatial fields, and for evaluating the statistical significance of characteristics
of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected
in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis
to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes
(variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not
object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques
for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic
boundary analysis is clearly a valuable addition to the spatial statistical toolbox.? This paper presents the philosophy of,
and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their
characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques,
with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the
implementation of these methods within geographic boundary analysis software: GEM.
Received: 22 March 1999 / Accepted: 7 September 1999 相似文献
7.
This article describes the Cancer Atlas Viewer: free, downloadable software for the exploration of United States cancer mortality data. We demonstrate the software by exploring spatio-temporal patterns in colon cancer mortality rates for African-American and white females and males in the southeastern United States over the period 1970–1995. We compare the results of two cluster statistics: the local Moran and the local G*, through time. Overall, the two statistics reach similar conclusions for most locations. Where they disagree reveals functional differences in the kinds of local spatial variation to which the statistics are sensitive and identifies some interesting patterns in the data. There are only two persistent clusters of colon cancer mortality through time, and these are clusters of low values.This project was funded by grant CA92669 from the National Cancer Institute to BioMedware, Inc. The perspectives stated in this article are those of the authors and do not necessarily represent the official views of the National Cancer Institute. Constructive criticism from Heidi Durbeck of BioMedware, Peter Rogerson of SUNY-Buffalo, and three anonymous reviewers helped us improve the interpretation and presentation of these results. 相似文献
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Area-based tests for association between spatial patterns 总被引：2，自引：0，他引：2
Edge effects pervade natural systems, and the processes that determine spatial heterogeneity (e.g. physical, geochemical,
biological, ecological factors) occur on diverse spatial scales. Hence, tests for association between spatial patterns should
be unbiased by edge effects and be based on null spatial models that incorporate the spatial heterogeneity characteristic
of real-world systems. This paper develops probabilistic pattern association tests that are appropriate when edge effects
are present, polygon size is heterogeneous, and the number of polygons varies from one classification to another. The tests
are based on the amount of overlap between polygons in each of two partitions. Unweighted and area-weighted versions of the
statistics are developed and verified using scenarios representing both polygon overlap and avoidance at different spatial
scales and for different distributions of polygon sizes. These statistics were applied to Soda Butte Creek, Wyoming, to determine
whether stream microhabitats, such as riffles, pools and glides, can be identified remotely using high spatial resolution
hyperspectral imagery. These new “spatially explicit” techniques provide information and insights that cannot be obtained
from the spectral information alone.
Received 1 June 2001 / Accepted 25 October 2001 相似文献
9.
This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Morans I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morans I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Morans I was applied under the null hypothesis of constant risk.This research was funded by grants R01 CA92669 and 1R43CA105819-01 from the National Cancer Institute and R43CA92807 under the Innovation in Biomedical Information Science and Technology Initiative at the National Institute of Health. The views stated in this publication are those of the authors and do not necessarily represent the official views of the NCI. The authors also thank three anonymous reviewers for their comments that helped improve the presentation of the methodology. 相似文献
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Jaymie R. Meliker Melissa J. Slotnick Gillian A. AvRuskin Andrew Kaufmann Geoffrey M. Jacquez Jerome O. Nriagu 《Journal of Geographical Systems》2005,7(1):49-66
A thorough assessment of human exposure to environmental agents should incorporate mobility patterns and temporal changes in human behaviors and concentrations of contaminants; yet the temporal dimension is often under-emphasized in exposure assessment endeavors, due in part to insufficient tools for visualizing and examining temporal datasets. Spatio-temporal visualization tools are valuable for integrating a temporal component, thus allowing for examination of continuous exposure histories in environmental epidemiologic investigations. An application of these tools to a bladder cancer case-control study in Michigan illustrates continuous exposure life-lines and maps that display smooth, continuous changes over time. Preliminary results suggest increased risk of bladder cancer from combined exposure to arsenic in drinking water (>25 g/day) and heavy smoking (>30 cigarettes/day) in the 1970s and 1980s, and a possible cancer cluster around automotive, paint, and organic chemical industries in the early 1970s. These tools have broad application for examining spatially- and temporally-specific relationships between exposures to environmental risk factors and disease.This study was supported by grant R01 CA96002-10, Geographic-Based Research in Cancer Control and Epidemiology, from the National Cancer Institute. Development of the STISTM software was funded by grants R43 ES10220 from the National Institutes of Environmental Health Sciences and R01 CA92669 from the National Cancer Institute. Access to cancer case records was provided by Michigan Cancer Surveillance Program within the Division for Vital Records and Health Statistics, Michigan Department of Community Health. The authors thank Michigan Public Health Institute for conducting the telephone interview and Stacey Fedewa and Lisa Bailey for entering written surveys into a database. The authors thank 3 anonymous reviewers for their helpful comments. 相似文献