ANN ARBOR, MI, United States
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Maxwell S.K.,Biomedware | Sylvester K.M.,University of Michigan
Remote Sensing of Environment | Year: 2012

A time series of 230 intra- and inter-annual Landsat Thematic Mapper images was used to identify land that was ever cropped during the years 1984 through 2010 for a five county region in southwestern Kansas. Annual maximum Normalized Difference Vegetation Index (NDVI) image composites (NDVI ann-max) were used to evaluate the inter-annual dynamics of cropped and non-cropped land. Three feature images were derived from the 27-year NDVI ann-max image time series and used in the classification: 1) maximum NDVI value that occurred over the entire 27year time span (NDVI max), 2) standard deviation of the annual maximum NDVI values for all years (NDVI sd), and 3) standard deviation of the annual maximum NDVI values for years 1984-1986 (NDVI sd84-86) to improve Conservation Reserve Program land discrimination.Results of the classification were compared to three reference data sets: County-level USDA Census records (1982-2007) and two digital land cover maps (Kansas 2005 and USGS Trends Program maps (1986-2000)). Area of ever-cropped land for the five counties was on average 11.8% higher than the area estimated from Census records. Overall agreement between the ever-cropped land map and the 2005 Kansas map was 91.9% and 97.2% for the Trends maps. Converting the intra-annual Landsat data set to a single annual maximum NDVI image composite considerably reduced the data set size, eliminated clouds and cloud-shadow affects, yet maintained information important for discriminating cropped land. Our results suggest that Landsat annual maximum NDVI image composites will be useful for characterizing land use and land cover change for many applications. © 2012 Elsevier Inc.


A common issue in spatial interpolation is the combination of data measured over different spatial supports. For example, information available for mapping disease risk typically includes point data (e.g. patients' and controls' residence) and aggregated data (e.g. socio-demographic and economic attributes recorded at the census track level). Similarly, soil measurements at discrete locations in the field are often supplemented with choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system. The procedure is illustrated using two data sets: (1) geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura, and (2) incidence rates of late-stage breast cancer diagnosis per census tract and location of patient residences for three counties in Michigan. In the second case, the kriging system includes an error variance term derived according to the binomial distribution to account for varying degree of reliability of incidence rates depending on the total number of cases recorded in those tracts. Except under the binomial kriging framework, area-and-point (AAP) kriging ensures the coherence of the prediction so that the average of interpolated values within each mapping unit is equal to the original areal datum. The relationships between binomial kriging, Poisson kriging, and indicator kriging are discussed under different scenarios for the population size and spatial support. Sensitivity analysis demonstrates the smaller smoothing and greater prediction accuracy of the new procedure over ordinary and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit. © 2010 International Association for Mathematical Geosciences.


Information available for mapping continuous soil attributes often includes point field data and choropleth maps such as soil or geology maps that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents two approaches to incorporate both point and areal data in the spatial interpolation of continuous soil attributes. In the first instance, area-to-point kriging is used to map the variability within soil units while ensuring the coherence of the prediction so that the average of disaggregated estimates is equal to the original areal datum. The resulting estimates are then used as local means in residual kriging. The second approach proceeds in one step and capitalizes on (i) a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point and point-to-point covariances in the kriging system, (ii) the availability of Geographical Information Systems (GIS) to discretize polygons of irregular shape and size and (iii) knowledge of the point-support variogram model that can be inferred directly from point measurements, thereby eliminating the need for deconvolution procedures. The two approaches are illustrated using the geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura. Sensitivity analysis indicates that the new procedures improve prediction over ordinary kriging and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit. © 2011 The Author. Journal compilation © 2011 British Society of Soil Science.


This paper describes the combination of three-way contingency tables and geostatistics to visualize the non-linear impact of two putative covariates on individual-level health outcomes and test the significance of this impact, accounting for the pattern of spatial correlation and correcting for multiple testing. The methodology is used to explore the influence of distance to mammography clinics and census-tract poverty level on the rate of late-stage breast cancer diagnosis in three Michigan counties. Incidence rates are significantly lower than the area-wide mean (18.04%) mainly in affluent neighbourhoods [0-5% poverty], while higher incidences are mainly controlled by distance to clinics. The new simulation-based multiple testing correction is very flexible and less conservative than the traditional false discovery rate approach that results in a majority of tests becoming non-significant. Classes with significantly higher frequency of late-stage diagnosis often translate into geographic clusters that are not detected by the spatial scan statistic. © 2009 Elsevier Ltd. All rights reserved.


Goovaerts P.,Biomedware
International Journal of Applied Earth Observation and Geoinformation | Year: 2013

Analyzing temporal trends in health outcomes can provide a more comprehensive picture of the burden of a disease like cancer and generate new insights about the impact of various interventions. In the United States such an analysis is increasingly conducted using joinpoint regression outside a spatial framework, which overlooks the existence of significant variation among U.S. counties and states with regard to the incidence of cancer. This paper presents several innovative ways to account for space in joinpoint regression: (1) prior filtering of noise in the data by binomial kriging and use of the kriging variance as measure of reliability in weighted least-square regression, (2) detection of significant boundaries between adjacent counties based on tests of parallelism of time trends and confidence intervals of annual percent change of rates, and (3) creation of spatially compact groups of counties with similar temporal trends through the application of hierarchical cluster analysis to the results of boundary analysis. The approach is illustrated using time series of proportions of prostate cancer late-stage cases diagnosed yearly in every county of Florida since 1980s. The annual percent change (APC) in late-stage diagnosis and the onset years for significant declines vary greatly across Florida. Most counties with non-significant average APC are located in the north-western part of Florida, known as the Panhandle, which is more rural than other parts of Florida. The number of significant boundaries peaked in the early 1990s when prostate-specific antigen (PSA) test became widely available, a temporal trend that suggests the existence of geographical disparities in the implementation and/or impact of the new screening procedure, in particular as it began available. © 2012 Elsevier B.V.


Jacquez G.M.,Biomedware
Spatial and Spatio-temporal Epidemiology | Year: 2012

Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis.This paper proposes a research agenda to address this need. It summarizes the basics of the geocoding process, its assumptions, and empirical evidence describing the magnitude of geocoding positional error. An overview of the impacts of positional error in health analysis, including accessibility, disease clustering, exposure reconstruction, and spatial weights estimation is presented. The proposed research agenda addresses five key needs: (1) a lack of standardized, open-access geocoding resources for use in health research; (2) a lack of geocoding validation datasets that will allow the evaluation of alternative geocoding engines and procedures; (3) a lack of spatially explicit geocoding positional error models; (4) a lack of resources for assessing the sensitivity of spatial analysis results to geocoding positional error; (5) a lack of demonstration studies that illustrate the sensitivity of health policy decisions to geocoding positional error. © 2012 Elsevier Ltd.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 183.48K | Year: 2013

DESCRIPTION (provided by applicant): A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availabilityof accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presenceof low level of contaminants. In this situation pollutant levels are rarely available for every location and time interval visited by the subjects; therefore data gaps need to be filled-in through space-time (ST) interpolation. Surprisingly, there is currently no commercial software for the geostatistical treatment of space-time data, including the interpolation at unmonitored times and locations. This SBIR project is developing the first commercial software to offer tools for geostatistical ST interpolation and modeling of uncertainty. The research product will be a stand-alone module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This software package will provide a comprehensive suite for: 1) the computation andadvisor-guided modeling of space-time covariance functions, 2) the ST interpolation and stochastic modeling of exposure data at the same scale as health outcomes (i.e. individual-level or aggregated) and using any secondary information available (e.g. remote sensing, land-use regression model, air dispersion model), and 3) the quantification and Monte-Carlo based propagation of uncertainty attached to estimates through exposure reconstruction. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, nuclear environmental engineering or climate change, broadening significantly the commercial market for the end product. This project will accomplish three aims: Compare the performance (i.e. prediction accuracy,impact on exposure-response assessment) and user- friendliness (i.e. ease of inference, potential for automatic implementation in software) of two classes of ST covariance models that encompass the main hypotheses of stationarity, full symmetry, separability and supported compactness. Develop and test a prototype module that will guide non-expert users through the selection and optimal fitting of space-time covariance models, followed by the interpolation of space-time data based on BioMedware's space-time visualization and analysis technology. Conduct a usability study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will revolutionize our ability to model geostatistically space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiological studies. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presence of low level of contaminants, increasing the likelihood of data gaps that need to be filled-in through space-time (ST) interpolation. Thus, many public health issues would greatly benefit from improved tools for estimation of environmental exposure data for every location and time interval visited by the patients under study.


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase I | Award Amount: 204.57K | Year: 2016

DESCRIPTION provided by applicant Analyzing temporal trends in cancer incidence and mortality rates can provide a more comprehensive picture of the burden of the disease and generate new insights about the impact of various interventions Join point regression developed by NCI Surveillance Research Program is increasingly used to identify the timing and extent of changes in time series of health outcomes and to project future cancer burden through the prediction of the future number of new cancer cases or deaths The analysis of temporal trends outside a spatial framework is however unsatisfactory since it has long been recognized that there is significant variation among U S counties and states with regard to the incidence of cancer It is thus critical to implement join point regression within Geographical Information Systems GIS and develop interfaces offering user friendly tools for pre processing modeling visualizing and summarizing large ensembles of time series of health outcomes This SBIR project is developing the first commercial software to offer tools for the geostatistical modeling and join point regression analysis of time series of health outcomes The research product will be a stand alone module into the desktop space time visualization core developed by BioMedware an Esri partner This software package will provide a comprehensive suite for the computation and geostatistical noise filtering kriging of time series of health outcomes at various spatial scales e g ZIP codes counties the visualization of how the parameters of the regression model e g join point years Average Annual Percent Change change in space and across spatial scales and the analysis of similarities among time series and their aggregation through multi dimensional scaling and clustering analysis These tools will be suited for the analysis of data outside health sciences such as in crime mapping fish stock assessment or climate change broadening significantly the commercial market for the end product This project will accomplish three aims Conduct simulation based studies to assess the benefits of the application of join point regression to smoothed time series kriging based and Bayesian filters for identifying temporal trends from unstable rates recorded in small geographical units multi dimensional scaling to visualize differences among ensemble of time series and clustering analysis to group geographical units with similar temporal trend Develop and test a prototype module that will guide users through the creation join point regression modeling visualization and multi dimensional analysis of time series of health outcomes based on BioMedwareandapos s space time visualization and analysis technology Conduct a usability study and identify additional methods and tools to consider in Phase II These technologic scientific and commercial innovations will revolutionize our ability to detect changes in cancer incidence and mortality across space and through time bringing important information and knowledge that will benefit substantially cancer epidemiology control and surveillance and help reducing these disparities PUBLIC HEALTH RELEVANCE The economic burden of cancer in the US is substantial and expected to increase significantly in the future due to expected growth and aging of the population and improvements in survival as well as trends in treatment patterns and costs of care following cancer diagnosis Many public health officials and policy makers require quantitative prediction about the future cancer burden in order to assist in the planning and prioritization of prevention activities allocation of health services and evaluation of cancer control interventions or treatments The analysis of temporal trends outside a spatial framework is however unsatisfactory since it has long been recognized that there is significant variation among U S counties and states with regard to the incidence of cancer Thus many public health issues would greatly benefit from improved tools for detecting and visualizing changes in cancer incidence and mortality across space and through time


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 143.42K | Year: 2010

DESCRIPTION (provided by applicant): This project is developing the first software to offer 3D visualization of health outcomes in a combined time and geography space, allowing the display of health data at multiple spatial and temporal scales within the same scene and thus taking full advantage of human visual perception that is fundamentally three dimensional. This visualization analytics environment will also provide an interface where the user can: 1) interact dynamically with the system (e.g. using data query, feature highlights, 3D scene rotation) and 2) contextualize the results, such as cancer burden, through the use of background maps that incorporate cues to the local context (e.g. orthophoto with names of major cities and highways to enhance the sense of place). This visualization module will be integrated into TerraSeer Space-Time Intelligence System (STIS ), providing a comprehensive suite of tools for quantifying nested scales of spatial variation corresponding to individual -gt neighborhood -gt region, mapping disease incidence using both area-based and individual-level data, statistical analysis (e.g. cluster detection, regression) of health disparities, and detection of their changes in both space and time. A web- based mapping and data visualization system will also be developed to facilitate the use of this new technology by public health departments and increase the impact of the research. This project will accomplish four aims: 1. Conduct a requirements analysis to identify methods and functionality to incorporate into the software. 2. Explore the use of three-dimensional display and visual analytics for the representation and exploratory data analysis of health outcomes and their relationship to putative factors in both space and time. 3. Build and test a complete set of functionalities based on the research results, and incorporate them into Biomedware's space-time visualization and analysis technology (desktop and web-based applications) that will allow easy import and export of data layers with Google Earth Products. 4. Apply the software and methods to demonstrate the approach and its unique benefits for the investigation of geographic and temporal variations in cancer stage at diagnosis and survival data, and the exploration of relationships between health outcomes and potential factors, such as socio-economic conditions and proximity to screening facilities. These technologic, scientific and commercial innovations will revolutionize our ability to visualize and interpret variation in cancer incidence at multiple spatial scales and across time, which will help generating hypotheses for in depth individual studies of risk factors that are causal, or impact survival or morbidity, and establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. The creation of disease maps that accurately represent and contextualize the cancer burden will greatly facilitate their interpretation by local communities and engage their participation in addressing health disparities. PUBLIC HEALTH RELEVANCE: The substantial benefit of this research is its utility in accessing and linking diverse individual-level and population-based data, followed by the three-dimensional visualization, interactive analysis and contextual mapping of variation in cancer incidence and mortality at multiple spatial scales and across time. The methods developed in this project will help generating hypotheses for in depth individual studies of risk factors that are causal, or impact survival or morbidity, and establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. The incorporation of these innovative visualization and mapping tools into TerraSeer Space-Time Intelligence System (STIS ), along with the development of a Web-based application, will help public health departments communicate more effectively the results of their analysis to local communities and develop participatory strategies to facilitate the translation of research results into interventions to alleviate the problems identified.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 198.02K | Year: 2014

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