CrimeVec—Exploring Spatial-Temporal Based Vector Representations of Urban Crime Types and Crime-Related Urban Regions
نویسندگان
چکیده
The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats as mutually independent when they do not branch the same higher-level category, therefore lacking inter-category semantic relations. issue then extends over distribution analysis urban regions, often reporting statistics based type counts, but neglecting implicit relations between different categories. Our study aims fill this information gap, providing more complete understanding in both qualitative and quantitative terms. Specifically, we propose vector-based representation, constructed via unsupervised machine learning temporal geographic factors. general idea is define “related” if occur area at time span, regardless any initial categorization. opens new metric comparison that goes beyond pre-defined structures, revealing hidden relationships by generating vector space completely data-driven manner. Crime are represented points space, their relative distances disclose stronger or weaker A direct application stands out form visualization tools for intuitive data investigations convenient measures composite vectors regions. Meaningful insights distributions better characteristics determine valuable asset management development.
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ژورنال
عنوان ژورنال: ISPRS international journal of geo-information
سال: 2021
ISSN: ['2220-9964']
DOI: https://doi.org/10.3390/ijgi10040210