An official website of the United States government
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.
more »
« less
This content will become publicly available on September 11, 2026
Progressive and Scalable Hotspot Detection through Local K-Function in Spatial Networks
The widespread availability 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 sufficient 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 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 happening within the spatial network. However, its practical application has been limited due to the inefficiency of state-of-the-art 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. Additionally, to address dynamic changes in the spatial network, we propose an incremental hotspot detection approach that efficiently updates hotspot computations by leveraging prior results as new events are added.
more »
« less
- Award ID(s):
- 2118329
- PAR ID:
- 10637196
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Spatial Algorithms and Systems
- ISSN:
- 2374-0353
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual segmentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filtering in the cascade architecture, while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method is extended to UniPoseLSTM for multi-frame processing and achieves state-of-theart results for temporal pose estimation in Video. Our results on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-ofthe-art results in single person pose detection for both single images and videosmore » « less
-
Given N geo-located point instances (e.g., crime or disease cases) in a spatial domain, we aim to detect sub-regions (i.e., hotspots) that have a higher probability density of generating such instances than the others. Hotspot detection has been widely used in a variety of important urban applications, including public safety, public health, urban planning, equity, etc. The problem is challenging because its societal applications often have low-tolerance for false positives, and require significance testing which is computationally intensive. In related work, the spatial scan statistic introduced a likelihood ratio based framework for hotspot evaluation and significance testing. However, it fails to consider the effect of spatial nondeterminism, causing many missing detections. Our previous work introduced a nondeterministic normalization based scan statistic to mitigate this issue. However, its robustness against false positives is not stably controlled. To address these limitations, we propose a unified framework which can improve the completeness of results without incurring more false positives. We also propose a reduction algorithm to improve the computational efficiency. Experiment results confirm that the unified framework can greatly improve the recall of hotspot detection without increasing the number of false positives, and the reduction algorithm can greatly reduce execution time.more » « less
-
An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.more » « less
