Towards a Deep Learning Approach for Urban Crime Forecasting

Piraján, Freddy; Fajardo, Andrey; Melgarejo, Miguel

doi:10.1007/978-3-030-31019-6_16

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Third, condition (C) emphasizes how we can decide whether a specific event prediction method is successful in predicting future events. Furthermore, the condition (C) captures the fact that not all prediction methods predict occurrence times, locations and semantics of future events simultaneously, as many focus on a single domain that is sufficient for the prediction goal, e.g., [78] focuses on the time domain by predicting the occurrence of abnormal deformations for a single machine tool of a production facility, [237] focuses on the location domain by predicting the area that a particular crime will occur and [200] focus on the semantic domain by predicting the future activity of a business process instance [337].…”

Section: Definition 22 (Event Prediction Methodsmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters.The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive classification, integration, and assessment of event prediction methods, we combine taxonomies and take a systems perspective to integrate event prediction methods into a single system, elicit requirements and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.

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Section: Definition 22 (Event Prediction Methodsmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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“…Existing methods in this category typically formulate a spatial map as input to predict another spatial map that denotes future event hotspots [139]. Such a formulation is analogous to the "image translation" problem popular in recent years in the computer vision domain [36].…”

Section: Raster-based Location Predictionmentioning

confidence: 99%

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

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“…Existing methods [19,150,164,209] in this category typically formulate a spatial map as input to predict another spatial map that denotes future event hotspots. Such a formulation is analogous to the "image translation" problem popular in recent years in the computer vision domain [46].…”

Section: Raster-based Location Predictionmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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Towards a Deep Learning Approach for Urban Crime Forecasting

Cited by 4 publications

References 23 publications

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Event Prediction in the Big Data Era

Event Prediction in Big Data Era: A Systematic Survey

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