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| Journey to Crime Analysis× | Kernetæthedsestimering og fordelingstest (KDE)× | |
|---|---|---|
| Fagområde≠ | Criminology | Statistik |
| Familie≠ | Process / pipeline | Regression model |
| Oprindelsesår≠ | 2000 | 1956 |
| Ophavsperson≠ | D. Kim Rossmo (geographic profiling); journey-to-crime tradition | Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman |
| Type≠ | Spatial analysis of offender travel and home-location inference | Nonparametric density estimation |
| Oprindelig kilde≠ | Rossmo, D. K. (2000). Geographic Profiling. CRC Press. ISBN: 9780849381294 | Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837. DOI ↗ |
| Aliasser≠ | Journey-to-Crime Modeling, Geographic Profiling, Crime Trip Analysis, Distance-Decay Crime Analysis | kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation |
| Relaterede | 4 | 4 |
| Resumé≠ | Journey-to-crime analysis studies how far and where offenders travel from an anchor point — usually home — to commit crimes, and inverts that pattern to infer an unknown offender's likely base. The aggregate distance-decay regularity (most crimes occur near the offender's home, with frequency falling off with distance) underlies geographic profiling, formalized by D. Kim Rossmo in 2000 to prioritize the search for serial offenders. | Kernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities. |
| ScholarGateDatasæt ↗ |
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