Spatio-Temporal-Network Point Processes for Modeling Crime Events with Landmarks Article Swipe
Zheng Dong
,
Jorge Mateu
,
Yao Xie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.10882
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.10882
Self-exciting point processes are widely used to model the contagious effects of crime events living within continuous geographic space, using their occurrence time and locations. However, in urban environments, most events are naturally constrained within the city's street network structure, and the contagious effects of crime are governed by such a network geography. Meanwhile, the complex distribution of urban infrastructures also plays an important role in shaping crime patterns across space. We introduce a novel spatio-temporal-network point process framework for crime modeling that integrates these urban environmental characteristics by incorporating self-attention graph neural networks. Our framework incorporates the street network structure as the underlying event space, where crime events can occur at random locations on the network edges. To realistically capture criminal movement patterns, distances between events are measured using street network distances. We then propose a new mark for a crime event by concatenating the event's crime category with the type of its nearby landmark, aiming to capture how the urban design influences the mixing structures of various crime types. A graph attention network architecture is adopted to learn the existence of mark-to-mark interactions. Extensive experiments on crime data from Valencia, Spain, demonstrate the effectiveness of our framework in understanding the crime landscape and forecasting crime risks across regions.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.10882
- https://arxiv.org/pdf/2409.10882
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403705594
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https://openalex.org/W4403705594Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.10882Digital Object Identifier
- Title
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Spatio-Temporal-Network Point Processes for Modeling Crime Events with LandmarksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-17Full publication date if available
- Authors
-
Zheng Dong, Jorge Mateu, Yao XieList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.10882Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2409.10882Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2409.10882Direct OA link when available
- Concepts
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Point (geometry), Point process, Computer science, Cartography, Artificial intelligence, Geography, Mathematics, Geometry, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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