LEADER 03134oam 22004935 450 001 9910708339603321 005 20180619161743.0 024 7 $a10.1596/34445 035 $a(CKB)4920000001210539 035 $a(OCoLC)1041124261 035 $a(OCoLC)953209011 035 $a(OCoLC)994920000001210539 035 $a(The World Bank)34445 035 $a(US-djbf)34445 035 $a(EXLCZ)994920000001210539 100 $a20020129d2020 uf 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital Elevation Models : $eA Guidance Note on How Digital Elevation Models are Created and Used - Includes Key Definitions, Sample Terms of Reference, and How Best to Plan a DEM-Mission /$fLouise Croneborg 205 $aThird printing (revised) 1993. 210 1$aWashington, D.C. :$cThe World Bank,$d2020. 215 $a1 online resource (iv, 53 pages) $cillustrations 225 1 $aRisk and Vulnerability Assessment 300 $a"National Mapping Program." 300 $a"US GeoData." 330 3 $aDigital elevation models (DEMs) represents the elevation of the earth's surface in the form of a digital image where each pixel contains an elevation value of the center point of the pixel. DEMs are a primary input to any modeling or process quantification involving the earth's topography and are used across several areas of development. Access to elevation, and slope maps enable responders to assess where floods will infill the landscape, create inaccessible areas, or create health risks, example, cholera. DEMs are also used prominently in infrastructure planning and mapping; road design and construction for transportation; urban environmental planning to assess construction, drainage, and green landscaping; agriculture planting and irrigation strategies; ecological modeling to assess ecosystem flora and fauna; and geological applications such as seismic and coastal monitoring. Accurate elevation information is therefore key for a wide range of development projects related to poverty reduction, urban development, water management, and other concerns. Thus, the ability to design and commission or acquire DEMs is increasing in relevance across the globe. This DEMs guidance note aims to: (a) provide sufficient information to understand the overall processes involved in the acquisition of DEMs and their uses, and (b) inform and guide the decision-making criteria; different design and implementation strategies; and options and costs that exist when acquiring DEMs. 410 0$aRisk and Vulnerability Assessment. 410 0$aWorld Bank e-Library. 517 $aDigital Elevation Models 606 $aDigital mapping$xStandards 607 $aUnited States$xAltitudes 615 0$aDigital mapping$xStandards. 700 $aCroneborg$b Louise$01426403 702 $aCaruso$b Vincent M. 712 02$aGeological Survey (U.S.), 801 0$bDJBF 801 1$bDJBF 906 $aBOOK 912 $a9910708339603321 996 $aDigital Elevation Models$93557712 997 $aUNINA LEADER 05406nam 2200685Ia 450 001 9911018903603321 005 20200520144314.0 010 $a9786613294845 010 $a9781283294843 010 $a1283294842 010 $a9781118204214 010 $a1118204212 010 $a9781118150610 010 $a1118150619 035 $a(CKB)2550000000056381 035 $a(EBL)818802 035 $a(OCoLC)757511765 035 $a(SSID)ssj0000550644 035 $a(PQKBManifestationID)11408562 035 $a(PQKBTitleCode)TC0000550644 035 $a(PQKBWorkID)10509510 035 $a(PQKB)11062412 035 $a(MiAaPQ)EBC818802 035 $a(Perlego)2776389 035 $a(EXLCZ)992550000000056381 100 $a20020522d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical group comparison /$fTim Futing Liao 210 $aNew York $cWiley-Interscience$dc2002 215 $a1 online resource (240 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780471386469 311 08$a0471386464 320 $aIncludes bibliographical references (p. 199-206) and index. 327 $aStatistical Group Comparison; Contents; Preface; 1. Introduction; 1.1 Rationale for Statistical Comparison; 1.2 Comparative Research in the Social Sciences; 1.3 Focus of the Book; 1.4 Outline of the Book; 1.4.1 Chapter 2-Statistical Foundation for Comparison; 1.4.2 Chapter 3-Comparison in Linear Regression; 1.4.3 Chapter 4-Nonparametric Comparison; 1.4.4 Chapter 5-Comparing Rates; 1.4.5 Chapter 6-Comparison in Generalized Linear Models; 1.4.6 Chapter 7-Additional Topics of Comparison in Generalized Linear Models; 1.4.7 Chapter 8-Comparison in Structural Equation Modeling 327 $a1.4.8 Chapter 9-Comparison with Categorical Latent Variables1.4.9 Chapter 10-Comparison in Multilevel Analysis; 1.4.10 Summary; 2. Statistical Foundation for Comparison; 2.1 A System for Statistical Comparison; 2.2 Test Statistics; 2.2.1 The x2 Test; 2.2.2 The t-Test; 2.2.3 The F-test; 2.2.4 The Likelihood Ratio Test; 2.2.5 The Wald Test; 2.2.6 The Lagrange Multiplier Test; 2.2.7 A Summary Comparison of LRT WT and LMT; 2.3 What to Compare?; 2.3.1 Comparing Distributions; 2.3.2 Comparing Data Structures; 2.3.3 Comparing Model Structures; 2.3.4 Comparing Model Parameters 327 $a3. Comparison in Linear Models3.1 Introduction; 3.2 An Example; 3.3 Some Preliminary Considerations; 3.4 The Linear Model; 3.5 Comparing Two Means; 3.6 ANOVA; 3.7 Multiple Comparison Methods; 3.7.1 Least Significance Difference Test; 3.7.2 Tukey's Model; 3.7.3 Scheffe?'s Method; 3.7.4 Bonferroni's Method; 3.8 ANCOVA; 3.9 Multiple Linear Regression; 3.10 Regression Decomposition; 3.10.1 Rationale; 3.10.2 Algebraic Presentation; 3.10.3 Interpretation; 3.10.4 Extension to Multiple Regression; 3.11 Which Linear Method to Use?; 4. Nonparametric Comparison; 4.1 Nonparametic Tests 327 $a4.1.1 Kolmogorov-Smirnov Two-Sample Test4.1.2 Mann-Whitney U-Test; 4.2 Resampling Methods; 4.2.1 Permutation Methods; 4.2.2 Bootstrapping Methods; 4.3 Relative Distribution Methods; 5. Comparison of Rates; 5.1 The Data; 5.2 Standardization; 5.2.1 Direct Standardization; 5.2.2 Indirect Standardization; 5.2.3 Model-Based Standardization; 5.3 Decomposition; 5.3.1 Arithmetic Decomposition; 5.3.2 Model-Based Decomposition; 6. Comparison in Generalized Linear Models; 6.1 Introduction; 6.1.1 The Exponential Family of Distributions; 6.1.2 The Link Function; 6.1.3 Maximum Likelihood Estimation 327 $a6.2 Comparing Generalized Linear Models6.2.1 The Null Hypothesis; 6.2.2 Comparisons Using Likelihood Ratio Tests; 6.2.3 The Chow Test as a Special Case; 6.3 A Logit Model Example; 6.3.1 The Data; 6.3.2 The Model Comparison; 6.4 A Hazard Rate Model Example; 6.4.1 The Model; 6.4.2 The Data; 6.4.3 The Model Comparison; 6.A Data Used in Section 6.4; 7. Additional Topics of Comparison in Generalized Linear Models; 7.1 Introduction; 7.2 GLM for Matched Case-Control Studies; 7.2.1 The 1 : 1 Matched Study; 7.2.2 The 1 : m Design; 7.2.3 The n : m Design; 7.3 Dispersion Heterogeneity; 7.3.1 The Data 327 $a7.3.2 Group Comparison with Heterogeneous Dispersion 330 $aAn incomparably useful examination of statistical methods for comparisonThe nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not 410 0$aWiley series in probability and statistics. 606 $aMathematical statistics 606 $aStatistics 615 0$aMathematical statistics. 615 0$aStatistics. 676 $a519.5 700 $aLiao$b Tim Futing$0103953 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911018903603321 996 $aStatistical group comparison$94416196 997 $aUNINA