![]() Spatial Dependence and AutocorrelationĮnvironmental properties and processes are typically related to one another in space, time, or both, making it possible to draw meaning out of environmental data. The concepts covered in this section include (1) spatial dependence and autocorrelation (2) sampling geospatial data and (3) advanced geospatial model assumptions. Practitioners must have a basic understanding of these assumptions in order to make informed decisions about which method is best for a given data set. More complex and advanced methods do make statistical assumptions about the data these assumptions are method specific and vary in their strictness. Simple geospatial methods make no assumptions and are therefore more relaxed in terms of their use and application. ![]() Each geospatial method is also unique according to its assumptions. Depending on the method employed, these relationships are either implicitly understood or explicitly quantified. ![]() In order to generate useful maps (for example, contaminant contour maps), these methods require that the data exhibit a spatial or temporal relationship. #Basic data software#Navigating this Website Overview Fact Sheets Fact Sheets Overview Fact Sheet 1: Do You Need Geospatial Analysis? Fact Sheet 2: Are Conditions Suitable for Geospatial Analysis? Fact Sheet 3: How is Geospatial Analysis Applied? Fact Sheet 4: What Software is Available to Help? PM's Tool Box PM's Tool Box Overview Review Checklist Choosing Methods Common Misapplications Optimization Questions Geospatial Analysis Support for Optimization Questions in the Project Life Cycle Data Requirements General Considerations Methods for Optimization Geospatial Methods for Optimization Questions in the Project Life Cycle Stages Release Detection Site Characterization Remediation Monitoring Closure Documenting Results Fundamental Concepts Fundamental Concepts for Geospatial Analysis Basic Data Concepts for Geospatial Analysis Interpolation Methods and Model Prediction Uncertainty in Geospatial Analyses Characteristics of Interpolation Methods Work Flow Work Flow for Conducting Geospatial Analysis Geospatial Analysis Work Flow Overview Perform Exploratory Data Analysis Select Geospatial Method Build Geospatial Model Evaluate Geospatial Method Accuracy Generate Geospatial Analysis Results Using Results Using Analysis Results for Optimization Plume Intensity and Extent Trend Maps Estimating Quantities Hot Spot Detection Sample Spacing Estimating Concentrations Based on Proxy Data Background Estimation Quantifying Uncertainty Remedial Action Optimization Monitoring Program Optimization Examples Examples Overview Example 1 Example 2 Example 3 Example 4 Methods Methods Overview Simple Geospatial Methods More Complex Geospatial Methods Advanced Methods Index of Methods Software Software Overview Software Comparison Tables Software Descriptions Workshops and Short Courses Case Studies Case Studies Overview Superfund Site Monitoring Optimization (MAROS) PAH Contamination in Sediments-Uncertainty Analysis (Isatis) Optimization of Long-Term Monitoring at Former Nebraska Ordnance Plant (GTS Summit Envirosolutions) Optimization of Lead-Contaminated Soil Remediation at a Former Lead Smelter (EVS/MVS) Extent of Radiological Contamination in Soil at Four Sites near the Fukushima Daiichi Power Plant, Japan (ArcGIS) Optimization of Groundwater Monitoring at a Research Facility in New Jersey (GWSDAT) Optimization of Sediment Sampling at a Tidally Influenced Site (ArcGIS) Stringfellow Superfund Site Monitoring Optimization (MAROS) Lead Contamination in Soil (ArcGIS) Stakeholder Perspectives Additional Information Project Life Cycle Stages History of Remedial Process Optimization Additional Resources Acronyms Glossary Index of Methods Acknowledgments Team Contacts Document Feedback ![]()
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