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The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform the spatial and temporal patterning of crime. Being able to capture citizen mobility and delineate a crime-specific population denominator is a vital prerequisite of the endeavour to both explain and address crime. This paper introduces the concept of an exposed population-at-risk, defined as the mix of residents and non-residents who may play an active role as an offender, victim or guardian in a specific crime type, present in a spatial unit at a given time. This definition is deployed to determine the exposed population-at-risk for violent crime, associated with the night-time economy, in public spaces. Through integrating census data with mobile phone data and utilising fine-grained temporal and spatial violent crime data, the paper demonstrates the value of deploying an exposed (over an ambient) population-at-risk denominator to determine violent crime in public space hotspots on Saturday nights in Greater Manchester (UK). In doing so, the paper illuminates that as violent crime in public space rises, over the course of a Saturday evening, the exposed population-at-risk falls, implying a shifting propensity of the exposed population-at-risk to perform active roles as offenders, victims and/or guardians. The paper concludes with a discussion of the theoretical and policy relevance of these findings.

This paper proposes a novel Adaptive Regionbased Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space;

Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4 % and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %.