Complete description
Statistical Geoinformatics for Human Environment Interface presents two paradigms for studying both space and interface with regard to human/environment: localization and multiple indicators. The first approach localizes thematic targets by treating space as a pattern of vicinities, with the pattern being a square grid and the placement of vicinities centrically referenced. The second approach explores human/environment interface as an abstraction through indicators, neutralizing the common conundrum of how to reconcile disparate spatial structures such as points, lines, and polygons. These paired paradigms enable: The capacity to cope with complexity Systematic surveillance Visualization and communication Preliminary prioritization Coupling of GIS and statistical software Avenues for automation Illustrating the interdisciplinary nature of geoinformatics, this book offers a novel approach to the spatial analysis of human influences and environmental resources. It includes practical strategies for statistical and spatial analysis.
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General info
Publisher & Imprint:
Chapman & Hall/CRC
City:
Philadelphia, PA
Pages:
223
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Age recommended:
Professional and scholarly
Subject Indexing & Classification
Dewey:(DC22) 304.23015195
Departments:
Probability & statistics;
Record updated at:
08 February, 2013
time:
04:35
Summary
Statistical Geoinformatics for Human Environment Interface
Statistical Geoinformatics of Human Linkage with Environment Introduction Human Environment Informational Interface and Its Indicators The "-matics" of Geoinformatics Spatial Synthesis of Disparate Data by Localization as Vicinity Variates Spatial Posting of Tabulations (SPOTing) Exemplifying County Context Posting Points and Provisional Proximity Perimeters for Lackawanna County Surveillance with Software Sentinels Backdrop: Distributed Data Depots and Digital Delivery Localizing Fixed-Form Features Introduction Locality Layer as Poly-Place Purview Localizing Layer of Proximity Perimeters Localizing Linears by Determining Densities Transfer from Perimeters to Points Apportioning Attributes of Partial Polygons Backdrop: GIS Generics Precedence and Patterns of Propensity Introduction Prescribing Precedence Product-Order Precedence Protocol Precedence Plot Propensities as Progression of Precedence Progression Plot Reversing Ranks Inconsistency Indicator Backdrop: Statistical Software Raster-Referenced Cellular Codings and Map Modeling Introduction Fixed-Frame Micromapping with Conceptual Cells Cover Classes and Localizing Logic Raster Regions and Associated Attributes Map Modeling Layer Logic Similar Settings as Clustered Components Introduction CLAN Clusters CLUMP Clusters CLAN Cluster Centroids Salient Centroids Graded Groups by Representative Ranks Rank Rods Salient Sequences by Representative Ranks Intensity Images and Map Multimodels Introduction Intensity as Frequency of Occurrence Hillshades and Slopes Interposed Distance Indicators Backdrop: Pictures as Pixels and Remote Sensing High Spots, Hot Spots, and Scan Statistics Introduction SaTScan(t) Concentration of CIT Core Development Complexion of CIT Developments Particular Proximity Upper Level Set (ULS) Scanning Backdrop: Python Programming Shape, Support, and Partial Polygons Introduction Inscribed Octagons Matching Margins and Adjusting Areas Shape and Support for Local Roads Precedence Plot for Shapes and Supports Supports Spanning Several Partial Polygons Semisynchronous Signals and Variant Vicinities Introduction Distal Data Median Models Pairing/Placement Patterns of Signal Strengths Auto-Association: Local Likeness and Distance Decline Introduction Cluster Companions Kindred Clusters Local Averages LISA: Local Indicator of Spatial Association Picking Pairs at Lagged Locations Empirical (Semi-)Variogram Moran's I and Similar Spatial Statistics Regression Relations for Spatial Stations Introduction Trend Surfaces Regression Relations among Vicinity Variates Restricted Regression Spatial Stations as Surface Samples Introduction Interpolating Intensity Indicators as Smooth Surfaces Spline Smoothing Kriging Shifting Spatial Structure Introduction Space-Time Hotspots Salient Shifts Paired Plots Primary Partition Plots Backdrop: Spectral Detection of Change with Remote Sensing Synthesis and Synopsis with Allegheny Application Introduction Localization Logic Locality Layer Localizing Layer Poly-Place Purviews Significant Spatial Sectors with Scan Statistics Scale Sensitivity and Partial Precedence Cluster Components and Cluster Companions Trend Surfaces Surveillance Systems: Sentinel Stations and Signaling Scripted Sentinels Smart-Sentinel Socialization Index References appear at the end of each chapter.
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