Foreword |
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xi | |
Preface |
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xiii | |
Digital Assets |
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xxi | |
Introduction |
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xxiii | |
Part 1 Introductory Section |
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3 | (22) |
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3 | (1) |
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1.2 Inferential versus descriptive statistics and data mining |
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4 | (1) |
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1.3 Population versus samples |
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4 | (3) |
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7 | (1) |
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8 | (9) |
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1.6 Overfitting the model |
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17 | (1) |
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1.7 Generalizability versus accuracy |
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18 | (2) |
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20 | (5) |
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Chapter 2 Domain expertise |
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25 | (6) |
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25 | (1) |
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2.2 Domain expertise for analysts |
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26 | (2) |
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28 | (3) |
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Chapter 3 Data mining and predictive analytics |
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31 | (20) |
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3.1 Discovery and prediction |
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33 | (1) |
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3.2 Confirmation and discovery |
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34 | (1) |
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35 | (1) |
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36 | (1) |
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37 | (1) |
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3.6 Exploratory graphics and data exploration |
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38 | (5) |
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43 | (2) |
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3.8 Non-obvious relationship analysis (NORA) |
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45 | (1) |
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45 | (1) |
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46 | (5) |
Part 2 Methods |
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Chapter 4 Process models for data mining and predictive analysis |
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51 | (24) |
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4.1 CIA intelligence process |
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53 | (2) |
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4.2 Cross-industry standard process for data mining |
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55 | (3) |
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58 | (1) |
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58 | (1) |
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58 | (1) |
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58 | (1) |
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58 | (1) |
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4.8 Actionable mining and predictive analysis for public safety and security |
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59 | (16) |
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75 | (32) |
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77 | (1) |
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77 | (1) |
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77 | (4) |
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5.4 Types of data resources |
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81 | (16) |
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97 | (6) |
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5.6 How do we overcome these potential barriers? |
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103 | (4) |
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Chapter 6 Operationally relevant preprocessing |
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107 | (30) |
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6.1 Operationally relevant recoding |
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109 | (1) |
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110 | (9) |
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119 | (1) |
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120 | (1) |
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120 | (2) |
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6.6 Conference call example |
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122 | (7) |
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129 | (2) |
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6.8 Operationally relevant variable selection |
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131 | (6) |
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Chapter 7 Identification, characterization, and modeling |
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137 | (20) |
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137 | (2) |
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7.2 How to select a modeling algorithm, part I |
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139 | (3) |
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142 | (3) |
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7.4 How to select a modeling algorithm, part II |
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145 | (4) |
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7.5 General considerations and some expert options |
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149 | (8) |
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Chapter 8 Public-safety-specific evaluation |
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157 | (28) |
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160 | (9) |
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169 | (4) |
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8.3 Training and test samples |
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173 | (4) |
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177 | (4) |
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8.5 Updating or refreshing the model |
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181 | (1) |
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8.6 There are no free lunches |
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181 | (4) |
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Chapter 9 Operationally actionable output |
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185 | (26) |
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186 | (7) |
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9.2 Geospatial capabilities and tools |
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193 | (5) |
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198 | (13) |
Part 3 Applications |
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211 | (12) |
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212 | (1) |
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213 | (2) |
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10.3 "Normal" criminal behavior |
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215 | (1) |
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10.4 Get to know "norma" crime trends and patterns |
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216 | (3) |
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219 | (4) |
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Chapter 11 Behavioral analysis of violent crime |
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223 | (34) |
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227 | (5) |
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11.2 Motive determination |
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232 | (4) |
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11.3 Behavioral segmentation |
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236 | (5) |
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241 | (2) |
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243 | (7) |
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250 | (3) |
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11.7 Moving from investigation to prevention |
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253 | (4) |
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Chapter 12 Risk and threat assessment |
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257 | (28) |
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259 | (3) |
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12.2 Vulnerable locations |
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262 | (4) |
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12.3 Process model considerations |
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266 | (5) |
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271 | (8) |
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12.5 Novel approaches to risk and threat assessment |
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279 | (6) |
Part 4 Case Examples |
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285 | (28) |
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13.1 Risk-based deployment |
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286 | (1) |
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286 | (4) |
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290 | (5) |
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13.4 Risk-based deployment case studies |
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295 | (18) |
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Chapter 14 Surveillance detection |
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313 | (36) |
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14.1 Surveillance detection and other suspicious situations |
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314 | (1) |
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315 | (2) |
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317 | (11) |
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14.4 Surveillance detection case studies |
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328 | (15) |
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343 | (6) |
Part 5 Advanced Concepts and Future Trends |
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Chapter 15 Advanced topics |
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349 | (18) |
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15.1 Additional "expert options" |
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349 | (1) |
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350 | (1) |
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15.3 Geospatial capabilities and tools |
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351 | (2) |
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353 | (2) |
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15.5 Social network analysis |
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355 | (1) |
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356 | (2) |
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358 | (2) |
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15.8 Application to other/adjacent functional domains |
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360 | (3) |
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363 | (4) |
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367 | (18) |
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367 | (2) |
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369 | (2) |
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371 | (1) |
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16.4 Technology and tools |
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372 | (1) |
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16.5 Potential challenges and constraints |
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373 | (8) |
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381 | (4) |
Index |
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385 | |