《環境方法學與模型研究（英文版）》是2017年科學出版社出版的圖書，作者是耿旭、吳健生。
基本介紹
- 書名：環境方法學與模型研究（英文版）
- 作者：耿旭、吳健生
- ISBN：9787030447234
- 頁數：280
- 定價：￥136.00
- 出版社：科學出版社
- 出版時間：2017-08-01
- 裝幀：平裝
- 開本：16開
內容簡介,目錄,
內容簡介
本書主要討論了環境模型研究問題以及高效環境模型的建立。信息化時代，海量數據的衝擊對環境科學研究的組織、管理、分析產生了深遠影響。基於地理信息系統的環境模型在環境科學與環境工程領域發揮了重要的作用。本書通過豐富的實例，介紹了一系列環境模型研究方法，特別包括了套用地理信息系統的環境模型。對國內外近年來的環境模型研究進行介紹和總結。
目錄
Contents
Chapter 1 Principles of scientific research 1
1.1 Objectives 2
1.2 Hypothesis 3
1.2.1 The size of a study 6
1.2.2 The type and number of measurements 7
1.2.3 Type and level of treatments 8
1.2.4 Assignment (or arrangement) of treatments 8
1.2.5 Environmental controls 8
References 9
Chapter 2 Analysis of variances and randomized designs 10
2.1 Partitioning of the sum of squares of a completely randomized design 11
2.1.1 Experimental unit and replication 11
2.1.2 Partition of sum of squares 12
2.1.3 With subsamples 14
2.2 Blocking and stratification of factorial experiments 16
2.3 Split plot designs 17
2.4 Latin squares 20
2.4.1 Extensions of latin square designs 22
2.4.2 Repeated latin squares 23
2.5 Split block designs 24
2.6 Repeated measurement experiment 26
2.7 Random and fixed effects 27
2.7.1 Concepts of fixed and random models in one-way classification experiment 27
2.7.2 Two way classification experiments 30
References 34
Chapter 3 Tests of assumptions of analysis and variance and variance estimation of transformed variables 35
3.1 Tests of assumption of homogeneity of variances 36
3.1.1 Hartley’s test 36
3.1.2 Bartlett’s test 36
3.1.3 Miller’s jackknife test (a robust test) 36
3.1.4 Example 36
3.2 Test of nonadditivity 37
3.3 Transformation 39
3.4 Variances of combined variables 41
3.4.1 Methods 42
3.4.2 The theory 42
3.4.3 The taylor approximate 43
3.4.4 Monte carlo study 43
References 45
Chapter 4 Sample size and power analysis 46
4.1 Replication verse non-replication 46
4.2 Factors affect replications 47
4.2.1 None statistical factors 47
4.2.2 Some commonly encountered mistakes of analysis in experiments 48
4.3 Sample size for a mean and variance estimation 50
4.3.1 Stein’s two-stage procedure 50
4.3.2 Sample size to estimate a population standard deviation 51
4.3.3 Tukey’s simultaneous confidence interval procedure 52
4.3.4 Comparison of two treatment means 53
4.4 The sample size determination for randomized designs 54
4.4.1 Application to CRD experiments 55
4.4.2 Sample size for RCBD experiments 60
4.4.3 Sample size for latin square experiments 60
4.4.4 Application to split-plot experiments 64
4.4.5 Applications to 2-factor split-block experiments 67
4.4.6 Applications to repeated measurement experiments 70
4.5 Sample size for quality control 71
4.5.1 The direct method 72
4.5.2 The indirect method 73
References 75
Chapter 5Response surface and optimal designs 76
5.1 Box's response surface methodology 76
5.1.1 Response surface models design approach 76
5.2 Canonical analysis 83
5.3 Optimal input combination and isoprobs 86
5.4 Optimum designs using pretreatment data 89
5.4.1 The d-optimum designs 90
5.4.2 The h-optimum designs 93
5.5 Design evaluation method 96
5.5.1 The approach 96
5.5.2 Example 97
References 102
Chapter 6 Geographic information systems: graphic presentations and spatial analysis 103
6.1 Brief introduction to GIS 103
6.2 Spatial data preparation 105
6.2.1 Spatial data collection and acquisition 105
6.2.2 Data organization and management 108
6.2.3 Data editing 110
6.2.4 Spatial data conversion 112
6.3 Graphic presentations 115
6.3.1 Mapping for spatial difference 115
6.3.2 Distance mapping 126
6.3.3 Time series mapping 127
6.4 Spatial analysis 131
6.4.1 Areas and features 132
6.4.2 Distance and resistance 138
References 142
Chapter 7 Geographic information systems: spatial statistics and spatial data mining 144
7.1 Statistical analysis of point/line/polygon model 144
7.1.1 Point model analysis 144
7.1.2 Line model analysis 158
7.1.3 Polygon model analysis 164
7.2 Geostatistical analysis 176
7.2.1 Exploratory spatial data analysis 177
7.2.2 Spatial interpolation 189
7.3 Spatial data mining 196
7.3.1 Brief introduction to spatial data mining 196
7.3.2 Spatial regression 198
7.3.3 Classification analysis methods 202
7.3.4 Spatial association rules 208
7.3.5 Cellular automata 212
References 215
Chapter 8 Geographic information systems: spatial decision and modeling 217
8.1 Spatial overlay model 217
8.1.1 What is spatial overlay ? 217
8.1.2 Analytical hierarchy process 218
8.1.3 Application examples， landfill site construction suitability analysis 221
8.2 Revised universal soil loss equation: soil erosion assessment 226
8.2.1 Introduction 226
8.2.2 Data requirement 227
8.2.3 Indexes calculation 228
8.2.4 The pattern analysis of soil erosion 232
8.3 Minimum cumulative resistance model: the choice of animal migration path 234
8.3.1 Minimum cumulative resistance model 235
8.3.2 Optimal path analysis 235
8.3.3 Application examples: animal migration corridor extraction in the dali bai autonomous region 236
8.4 Hydrological model 240
1.1 Objectives 2
1.2 Hypothesis 3
1.2.1 The size of a study 6
1.2.2 The type and number of measurements 7
1.2.3 Type and level of treatments 8
1.2.4 Assignment (or arrangement) of treatments 8
1.2.5 Environmental controls 8
References 9
Chapter 2 Analysis of variances and randomized designs 10
2.1 Partitioning of the sum of squares of a completely randomized design 11
2.1.1 Experimental unit and replication 11
2.1.2 Partition of sum of squares 12
2.1.3 With subsamples 14
2.2 Blocking and stratification of factorial experiments 16
2.3 Split plot designs 17
2.4 Latin squares 20
2.4.1 Extensions of latin square designs 22
2.4.2 Repeated latin squares 23
2.5 Split block designs 24
2.6 Repeated measurement experiment 26
2.7 Random and fixed effects 27
2.7.1 Concepts of fixed and random models in one-way classification experiment 27
2.7.2 Two way classification experiments 30
References 34
Chapter 3 Tests of assumptions of analysis and variance and variance estimation of transformed variables 35
3.1 Tests of assumption of homogeneity of variances 36
3.1.1 Hartley’s test 36
3.1.2 Bartlett’s test 36
3.1.3 Miller’s jackknife test (a robust test) 36
3.1.4 Example 36
3.2 Test of nonadditivity 37
3.3 Transformation 39
3.4 Variances of combined variables 41
3.4.1 Methods 42
3.4.2 The theory 42
3.4.3 The taylor approximate 43
3.4.4 Monte carlo study 43
References 45
Chapter 4 Sample size and power analysis 46
4.1 Replication verse non-replication 46
4.2 Factors affect replications 47
4.2.1 None statistical factors 47
4.2.2 Some commonly encountered mistakes of analysis in experiments 48
4.3 Sample size for a mean and variance estimation 50
4.3.1 Stein’s two-stage procedure 50
4.3.2 Sample size to estimate a population standard deviation 51
4.3.3 Tukey’s simultaneous confidence interval procedure 52
4.3.4 Comparison of two treatment means 53
4.4 The sample size determination for randomized designs 54
4.4.1 Application to CRD experiments 55
4.4.2 Sample size for RCBD experiments 60
4.4.3 Sample size for latin square experiments 60
4.4.4 Application to split-plot experiments 64
4.4.5 Applications to 2-factor split-block experiments 67
4.4.6 Applications to repeated measurement experiments 70
4.5 Sample size for quality control 71
4.5.1 The direct method 72
4.5.2 The indirect method 73
References 75
Chapter 5Response surface and optimal designs 76
5.1 Box's response surface methodology 76
5.1.1 Response surface models design approach 76
5.2 Canonical analysis 83
5.3 Optimal input combination and isoprobs 86
5.4 Optimum designs using pretreatment data 89
5.4.1 The d-optimum designs 90
5.4.2 The h-optimum designs 93
5.5 Design evaluation method 96
5.5.1 The approach 96
5.5.2 Example 97
References 102
Chapter 6 Geographic information systems: graphic presentations and spatial analysis 103
6.1 Brief introduction to GIS 103
6.2 Spatial data preparation 105
6.2.1 Spatial data collection and acquisition 105
6.2.2 Data organization and management 108
6.2.3 Data editing 110
6.2.4 Spatial data conversion 112
6.3 Graphic presentations 115
6.3.1 Mapping for spatial difference 115
6.3.2 Distance mapping 126
6.3.3 Time series mapping 127
6.4 Spatial analysis 131
6.4.1 Areas and features 132
6.4.2 Distance and resistance 138
References 142
Chapter 7 Geographic information systems: spatial statistics and spatial data mining 144
7.1 Statistical analysis of point/line/polygon model 144
7.1.1 Point model analysis 144
7.1.2 Line model analysis 158
7.1.3 Polygon model analysis 164
7.2 Geostatistical analysis 176
7.2.1 Exploratory spatial data analysis 177
7.2.2 Spatial interpolation 189
7.3 Spatial data mining 196
7.3.1 Brief introduction to spatial data mining 196
7.3.2 Spatial regression 198
7.3.3 Classification analysis methods 202
7.3.4 Spatial association rules 208
7.3.5 Cellular automata 212
References 215
Chapter 8 Geographic information systems: spatial decision and modeling 217
8.1 Spatial overlay model 217
8.1.1 What is spatial overlay ? 217
8.1.2 Analytical hierarchy process 218
8.1.3 Application examples， landfill site construction suitability analysis 221
8.2 Revised universal soil loss equation: soil erosion assessment 226
8.2.1 Introduction 226
8.2.2 Data requirement 227
8.2.3 Indexes calculation 228
8.2.4 The pattern analysis of soil erosion 232
8.3 Minimum cumulative resistance model: the choice of animal migration path 234
8.3.1 Minimum cumulative resistance model 235
8.3.2 Optimal path analysis 235
8.3.3 Application examples: animal migration corridor extraction in the dali bai autonomous region 236
8.4 Hydrological model 240
