Analyzing the Relationship Between Perception of Safety and Reported Crime in n Urban Neighborhood Using GIS and Sketch Maps

This study analyzes the perception of safety among residents of Main South neighborhood in Worcester, MA, USA and compares it to reported crimes. This neighborhood is the focus of a community-based crime reduction project funded by the Bureau of Justice Assistance, the policy development arm of the U.S. Department of Justice. We collected social disorder and violent crime data from the Worcester Police Department and conducted 129 household surveys to understand residents’ perception of safety in the neighborhood and trust in community institutions. The surveys included a map on which residents indicated where they felt unsafe. The goal of this research was twofold: (1) to use geographic information systems (GIS) to analyze the differences in perception of neighborhood safety by gender and length of residency in the neighborhood and (2) to explore the relationship between reported crime and perception of safety in the community. Findings indicate that the strength of the correlation between perceived safety and reported crime varies and that gender and length of residency are significant factors that shape perceptions of safety. Implications of this research suggest the need for comprehensive community-based development initiatives to offer differentiated strategies that address a broad range of safety perceptions and crime experiences among a diverse group of residents

Generating approximate region boundaries from heterogeneous spatial information: an evolutionary approach

Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach

A Novel Scheme for Accelerating Support Vector Clustering

Limited by two time-consuming steps, solving the optimization problem and labeling the data points with cluster labels, the support vector clustering (SVC) based algorithms, perform ineffectively in processing large datasets. This paper presents a novel scheme aimed at solving these two problems and accelerating the SVC. Firstly, an innovative definition of noise data points is proposed which can be applied in the design of noise elimination to reduce the size of a data set as well as to improve its separability without destroying the profile. Secondly, in the cluster labeling, a double centroids (DBC) labeling method, representing each cell of a cluster by the centroids of shape and density, is presented. This method is implemented towards accelerating this procedure and addressing the problem of labeling the original data set with irregular or imbalanced distribution. Compared with the state-of-the-art algorithms, the experimental results show that the proposed method significantly reduces the computational resources and improves the accuracy. Further analysis and experiments of semi-supervised cluster labeling confirm that the proposed DBC model is suitable for representing cells in clustering

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care