Ring-Shaped Hotspot Detection: A Summary of Results

Eftelioglu, Emre; Shekhar, Shashi; Oliver, Dev; Zhou, Xun; Evans, Michael R.; Xie, Yiqun; Kang, James M.; Laubscher, Renee; Farah, Christopher

doi:10.1109/icdm.2014.13

Cited by 35 publications

(33 citation statements)

References 6 publications

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“…Geospatial information can also be helpful when making spatio-temporal predictions about a broad range of phenomena such as the next location of a car, the risk of forthcoming famine or cholera, a criminal's potential residence [27] or the future path of a hurricane. For example, in Fig.…”

Section: Spatio-temporal Predictionmentioning

confidence: 99%

From GPS and virtual globes to spatial computing - 2020

2015

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Spatial computing is a set of ideas, solutions, tools, technologies, and systems that transform our lives with a new prospect of understanding, navigating, visualizing and using locations. In this community whitepaper, we present a perspective on the changing world of spatial computing, research challenges and opportunities and geoprivacy issues for spatial computing. First, this paper provides an overview of the changing world of spatial computing. Next, promising technologies that resulted from the integration of spatial computing in the everyday lives of people is discussed. This integration results with promising technologies, research challenges and opportunities and geoprivacy issues that must be addressed to achieve the potential of spatial computing.

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Section: Spatio-temporal Predictionmentioning

confidence: 99%

From GPS and virtual globes to spatial computing - 2020

2015

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“…Although diffusion theory works well for disease modeling, it may not be appropriate for criminology, in which the goal is to find a potential residence of a criminal. A recent effort to find such a pattern is ring-shaped hotspot detection [18,19], which is based on routine activity theory and crime pattern theory from criminology. It models serial criminals ISPRS Int.…”

Section: Foundations Of Hotspot Detectionmentioning

confidence: 99%

Transdisciplinary Foundations of Geospatial Data Science

Xie

Eftelioglu

Ali

et al. 2017

IJGI

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Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

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“…A ring shaped hotspot can be defined by a variety of techniques; here a ring is defined as a concentric ring (c-ring) (Eftelioglu et al, 2014) in this section.…”

Section: Ring-shaped Hotspot Detection Algorithmmentioning

confidence: 99%

“…The area of the shape R is area (R). Note that, for a given inner circle circle i with (x, y, r i ), there are many outer circles circle o (each defined by a distinct activity point a outside inner circle) and many c-rings R. (Eftelioglu et al, 2014)…”

Section: Ring-shaped Hotspot Detection Algorithmmentioning

confidence: 99%

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Crime Hotspot Detection

Eftelioglu¹,

Shekhar²,

Tang³

Advances in Data Mining and Database Management

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Given a set of crime locations, a statistically significant crime hotspot is an area where the concentration of crimes inside is significantly higher than outside. The motivation of crime hotspot detection is twofold: detecting crime hotspots to focus the deployment of police enforcement and predicting the potential residence of a serial criminal. Crime hotspot detection is computationally challenging due to the difficulty of enumerating all potential hotspot areas, selecting an interest measure to compare these with the overall crime intensity, and testing for statistical significance to reduce chance patterns. This chapter focuses on statistical significant crime hotspots. First, the foundations of spatial scan statistics and its applications (i.e. SaTScan) to circular hotspot detection are reviewed. Next, ring-shaped hotspot detection is introduced. Third, linear hotspot detection is described since most crimes occur along a road network. The chapter concludes with future research directions in crime hotspot detection.

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Ring-Shaped Hotspot Detection: A Summary of Results

Cited by 35 publications

References 6 publications

From GPS and virtual globes to spatial computing - 2020

From GPS and virtual globes to spatial computing - 2020

Transdisciplinary Foundations of Geospatial Data Science

Crime Hotspot Detection

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