20 Apr 2020

PUBLICATION NOTIFICATION: Local Spatial Dispersion for Multiscale Modeling of Geospatial Data: Exploring Dispersion Measures to Determine Optimal Raster Data Sample Sizes

ABSTRACT: Scale, or spatial resolution, plays a key role in interpreting the spatial structure of remote sensing imagery or other geospatially dependent data. These data are provided at various spatial scales. Determination of an optimal sample or pixel size can benefit geospatial models and environmental algorithms for information extraction that require multiple datasets at different resolutions. To address this, an analysis was conducted of multiple scale factors of spatial resolution to determine an optimal sample size for a geospatial dataset. Under the NET-CMO project at ERDC-GRL, a new approach was developed and implemented for determining optimal pixel sizes for images with disparate and heterogeneous spatial structure. The application of local spatial dispersion was investigated as a three-dimensional function to be optimized in a resampled image space. Images were resampled to progressively coarser spatial resolutions and stacked to create an image space within which pixel-level maxima of dispersion was mapped. A weighted mean of dispersion and sample sizes associated with the set of local maxima was calculated to determine a single optimal sample size for an image or dataset. This size best represents the spatial structure present in the data and is optimal for further geospatial modeling.

20 Apr 2020

PUBLICATION NOTICE: New and Enhanced Tools for Civil Military Operations (NET-CMO)

Abstract: Civil Military Operations (CMO) associated geospatial modeling is intended to enable increased knowledge of regional stability, assist in Foreign Humanitarian Assistance (FHA), and provide support to Force Health Protection (FHP) operational planning tasks. However, current geoenabled methodologies and technologies are lacking in their overall capacity to support complex mission analysis efforts focused on understanding these important stability factors and mitigating threats to Army soldiers and civilian populations. CMO analysts, planners, and decision-makers do not have a robust capability to both spatially and quantitatively identify Regions of Interest (ROI), which may experience a proliferation in health risks such as vector-borne diseases in areas of future conflict. Additionally, due to this general absence of geoenabled health assessment models and derived end-products, CMO stakeholders are adversely impacted in their Military Decision Making Process (MDMP) capabilities to develop comprehensive area studies and plans such as Course of Action (COA). The NET-CMO project is focused on fostering emerging geoenabling capabilities and technologies to improve military situational awareness for assessment and planning of potential health threat-risk vulnerabilities.

14 Apr 2020

PUBLICATION NOTICE: Spatial Downscaling Disease Risk Using Random Forests Machine Learning

Link: http://dx.doi.org/10.21079/11681/35618 Report Number: ERDC/GRL TN-20-1 Title: Spatial Downscaling Disease Risk Using Random Forests Machine Learning By Sean P. Griffin Approved for Public Release; Distribution is Unlimited February 2020 Purpose: Mosquito-borne illnesses are a significant public health concern, both to the Department of Defense (DoD) and the broader national and international public health community. A thorough grasp of the spatial distribution, patterns, and determinants of these diseases is needed to truly understand the threats they impose on public health (Pages et al. 2010). This information, when available, is often only at a sub-national to regional scale. Such data fails to meet tactical-level applications when diseases exhibit high local variation (Rytkonen 2004; Linard and Tatem 2012). Additionally, finer spatial resolution is also required to target disease burden successfully within the population and reduce exposure. This technical note (TN) describes a methodology aimed at improving coarse epidemiological information to much finer resolutions than achieved in previous studies by combining machine-learning with open-source, high-performance cloud computing. The result is a 1,000 meter (m) gridded raster product that provides a pixel-wise magnitude of risk that can be used directly for tactical mapping applications or serve as an input dataset for additional modeling applications. 11 pages / 835.7 Kb