Bars on blocks: A cellular automata model of crime and liquor licensed establishment density

Criminologists have extensively researched the problems generated by licensed establishments. Violent offending and disorderly behavior resulting from pubs, taverns, dance clubs and bars are of particular interest to this field of study. The relative density of these liquor establishments has been found to be associated with the level of violence and disorder in surrounding areas. A complex systems approach can be used to further understand the dynamic interplay between licensed establishments, violent offending and disorder, and urban planning decisions. The model presented here utilizes cellular automata as the mathematical framework to view the varying impact of liquor licensing density on crime. This study uses a sample of liquor establishments and crime data from the City of Vancouver in British Columbia. The cellular automata model incorporates transition rules which govern the change of city blocks from low-risk blocks to high-risk blocks. The results represented by a 50 × 50 cellular grid show that high-risk blocks multiply when liquor licenses are grouped. Two scenarios are presented to contrast the impact of grouping high-risk blocks which contain more liquor establishments and dispersing such blocks. A third scenario demonstrates how increasing the positive influence in a grouped scenario stops high-risk blocks from taking over the entire grid. Future iterations of this model will incorporate census data, public transportation data, land use data and entertainment districts from other cities to further analyze the effect of licensed establishments on the distribution of crime.     

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.     

Evaluating and improving the effectiveness and efficiency of design process communication

Project information management research enables the efficient exchange of information, but does not effectively communicate process. Design process management research effectively communicates processes, but with methods too inefficient to be adopted in practice. The lack of methods for effective and efficient design process communication manifests as a struggle for Architecture, Engineering, and Construction industry professionals to: (1) collaborate within projects, (2) share processes between projects, and (3) understand processes across projects to strategically invest in improvement. These struggles motivate the paper’s first contribution: a research method for evaluating a design management methodology’s ability to effectively and efficiently communicate design processes. As a second contribution, this paper validates the Design Process Communication Methodology (DPCM). DPCM specifies elements and methods for exchanging and organizing digital information to support knowledge-intensive design processes. Cloud computing enables the operationalization of DPCM as a tool that enables interaction with a project’s information. Results from this operationalization demonstrate that designers employing DPCM accurately capture processes with little effort. When collaborating, improved process clarity and information consistency result in less rework, and positive iteration enables increased consideration of multi-disciplinary design trends. Designers share processes between project teams with fewer process mistakes. DPCM enables the understanding of processes providing more insights into the relationships between design integration and project performance; and more opportunities for strategic investment in improved processes.     

Confidential Incident Reporting on the UK Railways: The ‘CIRAS’ System

The paper describes CIRAS (Confidential Incident Reporting and Analysis System), a confidential reporting system developed by the authors in collaboration with ScotRail, the Health and Safety Executive, Railtrack and the (now defunct) British Rail Board. After a two-year pilot/developmental study with ScotRail during 1995–97, the system is now subscribed to by all but one of the major train operating companies, rail infrastructure and maintenance companies with a presence in Scotland; plus a developing profile in the rest of the UK.  CIRAS gathers data in three ways: (i) from an initial report form or telephone call, (ii) from a structured follow-up telephone questionnaire, and (iii) from an in-depth interview with a researcher (telephone or face-to-face, according to priority). The interviews bring to light details of personal motive, and of intended/unintended actions, which are not commonly found on company-run databases because of their association with disciplinary procedures. Information is processed through a human factors model and fed back to the companies involved, in disidentified form, to take corrective action.  The basic structure of the human-factors model is described; data are presented on reports received to date which have been processed through this model. The system has recently been recommended by a UK Parliamentary Committee and by Railtrack Safety and Standards Directorate for extension to the UK network as a whole.     

Unsupervised Scale-Consistent Depth Learning from Video

International Journal of Computer Vision - We propose a monocular depth estimation method SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent...     

Regressing Local to Global Shape Properties for Online Segmentation and Tracking

We propose a novel regression based framework that uses online learned shape information to reconstruct occluded object contours. Our key insight is to regress the global, coarse, properties of shape from its local properties, i.e. its details. We do this by representing shapes using their 2D discrete cosine transforms and by regressing low frequency from high frequency harmonics. We learn this regression model using Locally Weighted Projection Regression which expedites online, incremental learning. After sufficient observation of a set of unoccluded shapes, the learned model can detect occlusion and recover the full shapes from the occluded ones. We demonstrate the ideas using a level-set based tracking system that provides shape and pose, however, the framework could be embedded in any segmentation-based tracking system. Our experiments demonstrate the efficacy of the method on a variety of objects using both real data and artificial data.     

Design for emergence: experiments with a mixed reality urban playground game

In this paper we present our work in the design of ubiquitous social experiences, aiming to foster group participation and spontaneous playful behaviours in a city environment. We outline our approach of design for emergence: to provide just enough of a game context and challenge for people to be creative, to extend and enrich the experience of play through their interaction in the real world. CitiTag is our mixed reality testbed, a wireless location-based multiplayer game based on the concept of playground ‘tag’. We describe the design and implementation of CitiTag and discuss results from two user studies.