Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

Automatic bat counting and identification of bat species using terrestrial laser scanning

The current practice in roosting bat population survey and species identification is either based on net capture, visual observation or optical-mechanical count methods. However, these methods are intrusive, tedious, time consuming and at best, only reports an estimation of the roosting population of bats. Here, the present study showed the use of Light Detection and Ranging (LIDAR) concept using terrestrial laser scanner was successful in remotely identifying and determining the exact population of roosting bats in caves. The laser scans accurately captured the three dimensional (3D) features of the roosting bats and their spatial distribution pattern in total darkness. Using LIDAR, the determination number of bats can be conducted, spatially analyze the 3D distribution of bat populations as well as generate a 3D topological structure of the roosting cave. This resulted in a high resolution model of the cave, enabling exact count of visibly differentiated individual bats. This successfully leads to the species identification of the Hipposideros larvatus and Hipposideros armiger reported in this study. This studies anticipate that the development of the LIDAR into a non-intrusive technique will open up new possibilities in bat roosting studies. This novel method would possibly allow future works accomplishment of researchers to study roosting behavior such as maternity roosting patterns, roost sharing and roost-switching patterns within the topographical context of the speleological (caves, subterranean spaces and caverns) internal surface, thus making rigorous quantitative characterizations of cave roosting behavior possible. The final results of this study would be an automated procedure for bat population count and the function of point cloud data in assisting the species identification

Pemetaan hubungan jarak perjalanan pesalah juvana dan lokasi kejadian jenayah menggunakan sistem maklumat geografi

Peningkatan kes jenayah dalam kalangan juvana amat ketara dari tahun ke tahun telah merisaukan banyak pihak terutamanya ibu bapa dan masyarakat. Juvana merupakan golongan yang mudah terdedah kepada pelbagai ancaman yang boleh wujud dalam berbagai bentuk. Pemahaman taburan kejadian jenayah juvana yang merangkumi sudut geografi, spatial dan sosial adalah penting bagi pembentukan intervensi pencegahan jenayah juvana yang tepat. Atas kesedaran ini, kajian ini telah dijalankan dengan memfokuskan golongan kanak-kanak (di bawah 18 tahun) dan pesalah muda (18 hingga 21 tahun) yang meliputi Semenanjung Malaysia menggunakan teknologi Sistem Maklumat Geografi (GIS). GIS membenarkan data diurus secara geografi yang mana lokasi sesuatu objek di mukabumi dan hubungannya dengan persekitarannya diambil kira dalam menganalisis sesuatu fenomena. GIS adalah alat yang dapat membantu dalam menentukan lokasi kejadian jenayah dan menentukan faktor-faktor geografi seperti infrastruktur, faktor persekitaran dan hubung kait antara alamat suspek dan lokasi kejadian jenayah. Hasil pemetaan dan analisis ini membenarkan kita mencari jawapan untuk persoalan apa, di mana, mengapa dan bila bagi sesuatu fenomena. Data jenayah indeks juvana telah dibekalkan oleh PDRM yang merangkumi statistik dari tahun 2010 hingga 2014 di Semenanjung Malaysia. Analisis kluster alamat suspek dan lokasi kejadian jenayah telah dijalankan. Dapatan utama kajian ini mendapati 83% daripada pesalah juvana melakukan jenayah dalam linkungan 25 kilometer atau kurang dari alamat tempat tinggal mereka yang mempunyai ciri-ciri infrastruktur dan fizikal yang sama