Street crime prediction model based on the physical characteristics of a streetscape: Analysis of streets in low-rise housing areas in South Korea (Record no. 11632)
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| fixed length control field | 02398nab a2200277 4500 |
| 005 - DATE & TIME | |
| control field | 20210413114157.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210413b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lee, Inhye |
| 245 ## - TITLE STATEMENT | |
| Title | Street crime prediction model based on the physical characteristics of a streetscape: Analysis of streets in low-rise housing areas in South Korea |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sage, |
| Date of publication, distribution, etc | 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol 46, Issue 5, 2019,(862-879 p.) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Previous crime prediction research focusing on regional characteristics is lacking in terms of the examination of physical characteristics of individual crime scenes. This study, therefore, presents a street crime prediction model by analysing streetscape features within an actual field of vision for a low-rise housing area in South Korea, which serves as a gauge for potential offenders to carry out crime. First, we performed logistic regression to analyse the correlation between street crime opportunities and the elements of streets to derive an equation for predicting street crime using selected variables. Next, we created a crime prediction map based on a geographic information system that contains attribute data on these physical characteristics and presented a street crime prediction model based on the derived prediction equation. Finally, to test the prediction model, we compared actual crime data from the selected area with the results obtained from the prediction model. The test results showed that the prediction model classified 11 out of 29 actual crime spots as crime occurrence; among the 312 non-crime spots, 257 were classified as non-crime occurrence. Based on these test results, we confirm that the occurrence of street crime is affected by the physical characteristics within the actual field of vision and discuss the improvement of the prediction model. |
| 650 ## - Subject | |
| Subject | Crime prevention through environmental design theory, |
| 650 ## - Subject | |
| Subject | crime prediction, |
| 650 ## - Subject | |
| Subject | built environment, |
| 650 ## - Subject | |
| Subject | residential street, |
| 650 ## - Subject | |
| Subject | street crime |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Jung, Sungwon |
| 700 ## - Added Entry Personal Name | |
| Relationship | Lee, Jaewook |
| 700 ## - Added Entry Personal Name | |
| Relationship | Macdonald, Elizabeth |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 11590 |
| Host Itemnumber | 15512 |
| Place, publisher, and date of publication | Sage 2019. |
| Title | Environment and Planning B: Urban Analytics and City Science |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/2399808317735105 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Articles |
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