The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps
Само за регистроване кориснике
2019
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
When it comes to hot spot identification, spatial analysis techniques come to the fore. One of such techniques, that has gained great popularity among crime analysts, is the Kernel Density Estimation (KDE). Small variation in KDE parameters can give different outputs and hence affect predictive accuracy of hotspot map. The influence these parameters have on KDE hotspot output sparked many researches, mostly analyzing the influence of the cell size and bandwidth size. Yet, the influence of different classification methods applied to calculated cell values, including the choice of threshold value, on the KDE hotspot predictive accuracy remained neglected. The objective of this research was to assess the influence of different classification methods to KDE predictive accuracy. In each KDE computation, calculated cell values were divided into five thematic classes, using three the most common (default) classification methods provided by Environmental Systems Research Institute (ESRI) Geogr...aphical Information System (Arc GIS) (equal interval classification, quantile classification and natural breaks classification) and incremental multiples of the grid cells’ mean. Based upon calculated hit rates, predictive accuracy indices and recapture rate indices and taking into account the necessity that mapping output should satisfy some operational requirements as well as statistical rules, this research suggest that incremental mean approach with hotspot threshold of 3 and above multiples of the grid cell’s mean, should be used.
Кључне речи:
crime mapping / hot spot / kernel density / classification methodsИзвор:
The International Arab Journal of Information Technology (IAJIT), 2019, 16, 6, 1053-1062Издавач:
- Zarqa University, Jordan
Институција/група
JakovTY - JOUR AU - Milić, Nenad AU - Popović, Brankica AU - Mijalković, Saša AU - Marinković, Darko PY - 2019 UR - https://jakov.kpu.edu.rs/handle/123456789/1599 AB - When it comes to hot spot identification, spatial analysis techniques come to the fore. One of such techniques, that has gained great popularity among crime analysts, is the Kernel Density Estimation (KDE). Small variation in KDE parameters can give different outputs and hence affect predictive accuracy of hotspot map. The influence these parameters have on KDE hotspot output sparked many researches, mostly analyzing the influence of the cell size and bandwidth size. Yet, the influence of different classification methods applied to calculated cell values, including the choice of threshold value, on the KDE hotspot predictive accuracy remained neglected. The objective of this research was to assess the influence of different classification methods to KDE predictive accuracy. In each KDE computation, calculated cell values were divided into five thematic classes, using three the most common (default) classification methods provided by Environmental Systems Research Institute (ESRI) Geographical Information System (Arc GIS) (equal interval classification, quantile classification and natural breaks classification) and incremental multiples of the grid cells’ mean. Based upon calculated hit rates, predictive accuracy indices and recapture rate indices and taking into account the necessity that mapping output should satisfy some operational requirements as well as statistical rules, this research suggest that incremental mean approach with hotspot threshold of 3 and above multiples of the grid cell’s mean, should be used. PB - Zarqa University, Jordan T2 - The International Arab Journal of Information Technology (IAJIT) T1 - The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps VL - 16 IS - 6 SP - 1053 EP - 1062 UR - https://hdl.handle.net/21.15107/rcub_jakov_1599 ER -
@article{ author = "Milić, Nenad and Popović, Brankica and Mijalković, Saša and Marinković, Darko", year = "2019", abstract = "When it comes to hot spot identification, spatial analysis techniques come to the fore. One of such techniques, that has gained great popularity among crime analysts, is the Kernel Density Estimation (KDE). Small variation in KDE parameters can give different outputs and hence affect predictive accuracy of hotspot map. The influence these parameters have on KDE hotspot output sparked many researches, mostly analyzing the influence of the cell size and bandwidth size. Yet, the influence of different classification methods applied to calculated cell values, including the choice of threshold value, on the KDE hotspot predictive accuracy remained neglected. The objective of this research was to assess the influence of different classification methods to KDE predictive accuracy. In each KDE computation, calculated cell values were divided into five thematic classes, using three the most common (default) classification methods provided by Environmental Systems Research Institute (ESRI) Geographical Information System (Arc GIS) (equal interval classification, quantile classification and natural breaks classification) and incremental multiples of the grid cells’ mean. Based upon calculated hit rates, predictive accuracy indices and recapture rate indices and taking into account the necessity that mapping output should satisfy some operational requirements as well as statistical rules, this research suggest that incremental mean approach with hotspot threshold of 3 and above multiples of the grid cell’s mean, should be used.", publisher = "Zarqa University, Jordan", journal = "The International Arab Journal of Information Technology (IAJIT)", title = "The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps", volume = "16", number = "6", pages = "1053-1062", url = "https://hdl.handle.net/21.15107/rcub_jakov_1599" }
Milić, N., Popović, B., Mijalković, S.,& Marinković, D.. (2019). The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps. in The International Arab Journal of Information Technology (IAJIT) Zarqa University, Jordan., 16(6), 1053-1062. https://hdl.handle.net/21.15107/rcub_jakov_1599
Milić N, Popović B, Mijalković S, Marinković D. The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps. in The International Arab Journal of Information Technology (IAJIT). 2019;16(6):1053-1062. https://hdl.handle.net/21.15107/rcub_jakov_1599 .
Milić, Nenad, Popović, Brankica, Mijalković, Saša, Marinković, Darko, "The Influence of Data Classification Methods on Predictive Accuracy of Kernel Density Estimation Hotspot Maps" in The International Arab Journal of Information Technology (IAJIT), 16, no. 6 (2019):1053-1062, https://hdl.handle.net/21.15107/rcub_jakov_1599 .