Population surveys and species recognition for roosting bats are either based on capture, sight or optical-mechanical count methods. However, these methods are intrusive, are tedious and, at best, provide only statistical estimations. Here, we demonstrated the successful use of a terrestrial Light Detection and Ranging (LIDAR) laser scanner for remotely identifying and determining the exact population of roosting bats in caves. LIDAR accurately captured the 3D features of the roosting bats and their spatial distribution patterns in minimal light. The high-resolution model of the cave enabled an exact count of the visibly differentiated Hipposideros larvatus and their roosting pattern within the 3D topology of the cave. We anticipate that the development of LIDAR will open up new research possibilities by allowing researchers to study roosting behaviour within the topographical context of a cave's internal surface, thus facilitating rigorous quantitative characterisations of cave roosting behaviour.

Developing a model allow a better understanding of the nature of a complicated phenomenon. With advancement of tools and technology, model development has been applied widely to mimic the phenomena of interest, spatial or non-spatial wise, allowing a guided decision making to be made. In this paper, the phenomena of burglary vulnerability and susceptibility are modelled based on expert opinion input to create a model that imitates the expert profiling of burglary occurrences, which is dependent on individual expert wisdom and experience in handling the burglary investigation. Due to seriousness of burglary crime offences in Malaysia, especially the urban areas, a prediction model is needed to correlates the factor of crime and further estimates the spatial susceptibility to work hand in hand with other government initiatives in reducing crime. Eighteen (18) indicators and 63 sub-indicators has been identified to be significant in defining the susceptibility of burglary. Apart from input of rating and ranking of indicators and sub-indicators obtained from questionnaire distribution to expert in handling burglary, the geospatial based data were also incorporated into the model to add the element of spatial accuracy in susceptibility prediction. The geospatial data includes the distribution of burglary incidence from 2010 – 2016, the census data, the building footprint data and the demarcation area. For the collected questionnaire feedback, the procedure of Analytical Hierarchy Process (AHP) were adapted to determine the weight value considering the rating input of expert from the distributed questionnaire. The input of weight and scoring were applied to the corresponding spatial features and combined with the operation of weighted sum to yield the total burglary susceptibility of a place. The results of the model were validated with the real reported burglary frequency based on True Positive Rate correlation matrix. The model validation finds that the model have a sensitivity of 82% in classifying the burglary susceptibility of the building polygon inside the study area. However this model still requires some improvement as it is still lacking to perform the classification of incidence intensity correctly.

Geospatial technology advancement has boost the ability of crime assessment in terms of the accuracy of crime location and prediction. Aforetime, the crime assessment tend to focus on the development of sanction and law, as well as behaviour studies of why certain people are prone to be a victim of crime and why certain people are prone in committing crime, but none of them incorporating the idea of place of crime until 1971 (Jeffery, 1971). With technology advancement, the crime assessment of place has evolved from pin map to large scale digital mapping, effective inventory method, and adept crime analysis as well as crime prediction. The residential area of Damansara-Penchala, Kuala Lumpur and its vicinity are chosen as study area for its urban location and vastness of socioeconomic status. According to the data in Safe City Monitoring System (Sistem Pemantauan Bandar Selamat, SPBS), the monetary loss due to burglary crime activities in the study area for 2016 are sum up to RM 5,640,087 (RM 5.6 million) within 172 burglary incidence, with the mean loss of RM 32,791.00 with every offend of burglary. Apart from monetary loss, burglary also affecting the social values of the society and in terms of the perception of safe living. Instead of providing an analysis of area with high density of burglary, this paper embarks on finding the correlated social and environmental factor that leaning towards being the target of burglary crime. Utilizing the method of information value modelling, a bi-variate statistical method in the layout of raster data analysis, the vulnerability of each premise are calculated based on its association with the identified burglary indicators. The results finds that 17 significant indicators out of 18 indicators are identified as index contributing to burglary susceptibility. The burglary susceptibility mapping are acquired to contribute in predicting the premise’s potential risk for the sake of future crime prevention.