The widespread of geotagged data combined with modern map services allows for the accurate attachment of data to spatial networks. Applying statistical analysis, such as hotspot detection, over spatial networks is very important for precise quantification and patterns analysis, which empowers effective decision-making in various important applications. Existing hotspot detection algorithms on spatial networks either lack statistical evidence on detected hotspots, such as clustering, or they provide statistical evidence at a prohibitive computational overhead. In this paper, we propose efficient algorithms for detecting hotspots based on the network local K-function for predefined and unknown hotspot radii. The network local K-function is a widely adopted statistical approach for network pattern analysis that enables the understanding of the density and distribution of activities and events in the spatial
network. However, its practical application has been limited due to the inefficiency of existing algorithms, particularly for large-sized networks. Extensive experimental evaluation using real and synthetic datasets shows that our algorithms are up to 28 times faster than the state-of-the-art algorithms in computing hotspots with a
predefined radius and up to more than four orders of magnitude faster in identifying hotspots without a predefined radius.
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Linear Hotspot Discovery on All Simple Paths: A Summary of Results
Spatial hotspot discovery aims at discovering regions with statistically significant concentration of activities. It has shown great value in many important societal applications such as transportation engineering, public health, and public safety. This paper formulates the problem of Linear Hotspot Detection on All Simple Paths (LHDA) which identifies hotspots from the complete set of simple paths enumerated from a given spatial network. LHDA overcomes the limitations of existing methods which miss hotspots that naturally occur along linear simple paths on a road network. To address the computational challenges, we propose a novel algorithm named bidirectional fragment-multi-graph traversal (ASP_FMGT) and two path reduction approaches ASP_NR and ASP_HD. Experimental analyses show that ASP_FMGT has substantially improved performance over state-of-the-art approach (ASP_Base) while keeping the solution complete and correct. Moreover, a case study on real-world datasets showed that ASP_FMGT outperforms existing approaches.
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- NSF-PAR ID:
- 10170278
- Date Published:
- Journal Name:
- SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Page Range / eLocation ID:
- 476 to 479
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The spatial distribution of the geochemical domains hosting recycled crust and primordial (high‐3He/4He) reservoirs, and how they are linked to mantle convection, are poorly understood. Two continent‐sized seismic anomalies located near the core‐mantle boundary—called the Large Low Shear Wave Velocity Provinces (LLSVPs)—are potential geochemical reservoir hosts. It has been suggested that high‐3He/4He hotspots are spatially confined to the LLSVPs, hotspots sampling recycled continental crust are associated with only one of the LLSVPs, and recycled continental crust shows no relationship with latitude. We reevaluate the links between LLSVPs and isotopic signatures of hotspot lavas using improved mantle flow models including plume conduit advection. While most hotspots with the highest‐3He/4He can indeed be traced to the LLSVP interiors, at least one high‐3He/4He hotspot, Yellowstone, is located outside of the LLSVPs. This suggests high‐3He/4He is not geographically confined to the LLSVPs. Instead, a positive correlation between hotspot buoyancy flux and maximum hotspot3He/4He suggests that it is plume dynamics (i.e., buoyancy), not geography, which determines whether a dense, deep, and possibly widespread high‐3He/4He reservoir is entrained. We also show that plume‐fed EM hotspots (enriched mantle, with low‐143Nd/144Nd), signaling recycled continental crust, are spatially linked to both LLSVPs, and located primarily in the southern hemisphere. Lastly, we confirm that hotspots sampling HIMU (“high‐μ,” or high238U/204Pb) domains are not spatially limited to the LLSVPs. These findings clarify and advance our understanding of deep mantle reservoir distributions, and we discuss how continental and oceanic crust subduction is consistent with the spatial decoupling of EM and HIMU.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3347146.3359100
