312
Index
Indicators (cont.)
world happiness Index, vii, xv, 205, 206,
208–211, 216
Indicator values, 169
Inequality, 235–237, 239
Inequality of outcomes (IoO), 206, 208
Information noise, 23, 26
International Atomic Energy Agency (IAEA),
181
International Organization for Standardization
(ISO), 272
Interval order, weights, 48, 54, 57
J
Joint Research Centre-European Commission,
228
Judgment post-stratified sampling (JPS),
136–138
Jupyter Notebooks, 297, 305
L
Latent variables, 21–22, 245, 246, 248
Lattice theoretical method, 71, 73, 75, 76, 91
Leti index, 237
Lichens
analysis of biomonitoring
average height, 68–69
construction, Hasse diagram, 67
data set, 64–65
estimation method, 73–76
Hav calculation, 69–71
HDT, 67
“naturality/alteration scales”, 77
partial order, 65–67
weak order, 67–68
description, 64
metals/metalloids, 64
MIS, 63
thallus surface, 64
Life expectancy (LEX), 206, 208
Linear extensions (LEs), 68–69, 78, 144–148,
150, 156, 191–192, 207, 222, 223,
232–237
Local partial order models (LPOM), 183, 192
L-Tuple RSS (LTR), 139
M
Mazziotta-Pareto Index (MPI), 253, 260–261
Metals
biosorption, 182
chemical elements, 65–67
Hasse diagram, 74
heavy metals, 165, 175
in lichens, 65
and metalloids, 64–65
ranking positions, 76
in seawater, 126
trapping metal ions, 198
Microorganisms, 184–190
Miscanthus straw
AC, 165–167, 175, 177
Hasse diagram, 170
methods
agricultural waste, 167
chemical and physical activation, 167
data, 167–168
partial ordering, 168–170
more elaborate analyses
average ranks, 170–171, 173
indicator conflicts, 171–172, 175
isolated element, 175
minimal element, 173
probabilities, 174
production, 173
PyHasse software, 173, 174
relative indicator importance, 174
Rkav values, 173
sensitivity analysis, 170, 171
software, 172
tripartite graphs, 171–172, 175
soil cleaning, 165
Modeling
agent-based, vii
complex indicators, 11–12
decision support systems, 47–52
hierarchical model, 13
and simulation, 10–12
system modelling, 5–6
test and validation, 14–15
Modules, 294
Multi-criteria decision aid (MCDA) systems,
291
Multi-criteria decision analysis (MCDA), 109,
154, 163, 182–183, 291, 292
Multi-criteria decision making (MCDM), 267
Multidimensional ordinal traits, 229
Multidimensional poverty, 19, 20, 228
Multi-indicator synthesis, 248–249
Multi-indicator systems (MIS), 154, 220
elements, interconnections and functions,
20–21
German Sustainability Strategy, 115
indicators role, 47
lichen biomonitoring, 63
mathematical problem, vii–viii
Index
Indicators (cont.)
world happiness Index, vii, xv, 205, 206,
208–211, 216
Indicator values, 169
Inequality, 235–237, 239
Inequality of outcomes (IoO), 206, 208
Information noise, 23, 26
International Atomic Energy Agency (IAEA),
181
International Organization for Standardization
(ISO), 272
Interval order, weights, 48, 54, 57
J
Joint Research Centre-European Commission,
228
Judgment post-stratified sampling (JPS),
136–138
Jupyter Notebooks, 297, 305
L
Latent variables, 21–22, 245, 246, 248
Lattice theoretical method, 71, 73, 75, 76, 91
Leti index, 237
Lichens
analysis of biomonitoring
average height, 68–69
construction, Hasse diagram, 67
data set, 64–65
estimation method, 73–76
Hav calculation, 69–71
HDT, 67
“naturality/alteration scales”, 77
partial order, 65–67
weak order, 67–68
description, 64
metals/metalloids, 64
MIS, 63
thallus surface, 64
Life expectancy (LEX), 206, 208
Linear extensions (LEs), 68–69, 78, 144–148,
150, 156, 191–192, 207, 222, 223,
232–237
Local partial order models (LPOM), 183, 192
L-Tuple RSS (LTR), 139
M
Mazziotta-Pareto Index (MPI), 253, 260–261
Metals
biosorption, 182
chemical elements, 65–67
Hasse diagram, 74
heavy metals, 165, 175
in lichens, 65
and metalloids, 64–65
ranking positions, 76
in seawater, 126
trapping metal ions, 198
Microorganisms, 184–190
Miscanthus straw
AC, 165–167, 175, 177
Hasse diagram, 170
methods
agricultural waste, 167
chemical and physical activation, 167
data, 167–168
partial ordering, 168–170
more elaborate analyses
average ranks, 170–171, 173
indicator conflicts, 171–172, 175
isolated element, 175
minimal element, 173
probabilities, 174
production, 173
PyHasse software, 173, 174
relative indicator importance, 174
Rkav values, 173
sensitivity analysis, 170, 171
software, 172
tripartite graphs, 171–172, 175
soil cleaning, 165
Modeling
agent-based, vii
complex indicators, 11–12
decision support systems, 47–52
hierarchical model, 13
and simulation, 10–12
system modelling, 5–6
test and validation, 14–15
Modules, 294
Multi-criteria decision aid (MCDA) systems,
291
Multi-criteria decision analysis (MCDA), 109,
154, 163, 182–183, 291, 292
Multi-criteria decision making (MCDM), 267
Multidimensional ordinal traits, 229
Multidimensional poverty, 19, 20, 228
Multi-indicator synthesis, 248–249
Multi-indicator systems (MIS), 154, 220
elements, interconnections and functions,
20–21
German Sustainability Strategy, 115
indicators role, 47
lichen biomonitoring, 63
mathematical problem, vii–viii