CrimeVec—Exploring Spatial-Temporal Based Vector Representations of Urban Crime Types and Crime-Related Urban Regions

The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats crime types as mutually independent when they do not branch from the same higher-level category, therefore lacking inter-category semantic relations. The issue then extends over crime distribution analysis of urban regions, often reporting statistics based on crime type counts, but neglecting implicit relations between different crime categories. Our study aims to fill this information gap, providing a more complete understanding of urban crime in both qualitative and quantitative terms. Specifically, we propose a vector-based crime type representation, constructed via unsupervised machine learning on temporal and geographic factors. The general idea is to define crime types as “related” if they often occur in the same area at the same time span, regardless of any initial hierarchical categorization. This opens to a new metric of comparison that goes beyond pre-defined structures, revealing hidden relationships between crime types by generating a vector space in a completely data-driven manner. Crime types are represented as points in this space, and their relative distances disclose stronger or weaker semantic relations. A direct application on urban crime distribution analysis stands out in the form of visualization tools for intuitive data investigations and convenient comparison measures on composite vectors of urban regions. Meaningful insights on crime type distributions and a better understanding of urban crime characteristics determine a valuable asset to urban management and development.

Abstract TP6: Current Trends in Thrombolytic Use for Acute Ischemic Stroke

Introduction: Alteplase is approved in the United States for acute ischemic stroke (AIS). Guidelines recommend IV weight-based alteplase dosing for AIS treatment with or without endovascular therapy (EVT). With increasing use of EVT, current thrombolytic use and dosing practices in AIS are poorly understood. This study assesses current and historical trends in thrombolytic use. Methods: All patients who received alteplase from 2007 to 2017 in the US Premier Hospital database were included. Hierarchical categorization identified indications by the presence of primary or secondary diagnoses including AIS > pulmonary embolism (PE) > myocardial infarction (MI) > or other. Patients undergoing EVT were subcategorized. Dosing was estimated by vial size. Demographics were analyzed descriptively. Results: Of 78,216 patients included, 33,530 (43%) had AIS, 7442 (9%) PE, 1696 (2%) MI, and 35,548 (45%) off-label indications. Patients with AIS had mean age of 68, and 2409 (7%) received alteplase + EVT. Of those with alteplase + EVT, 1428 (59%) were solely Medicare beneficiaries and 600 (25%) had solely commercial insurance vs 19,572 (63%) Medicare and 6585 (21%) commercially insured patients receiving alteplase alone. Only 37 patients (2%) with AIS receiving alteplase + EVT had care at rural hospitals, whereas 2946 rural patients with AIS (9%) received alteplase alone. Before 2011, EVT was associated with use of 50-mg vials of alteplase to treat AIS (Fig). After 2011, more patients with AIS receiving EVT had 100-mg vials of alteplase, consistent with dosing closer to the 90-mg maximum. Conclusions: AIS is the most common indication for current alteplase use. Since 2011, weight-based dosing has been widely adopted for treatment with and without EVT, which represents adherence to guidelines. Differences in payer mix and rurality among patients receiving alteplase + EVT may represent opportunities to improve access to care.

Hierarchical Categorization and Review of Recent Techniques on Image Forgery Detection

Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work.