Crime risk prediction is crucial for city safety and residents' life quality. However, without labeled data, it is challenging to predict crime risk in cities. Due to municipal regulations and maintenance costs, it is not trivial for many cities to collect high-quality labeled crime data. In particular, some cities have lots of labeled data while others may have few. It has been possible to develop a crime prediction model for a city without labeled crime data by learning knowledge from a city with abundant data. Nevertheless, the inconsistency of relevant context data between cities exacerbates the difficulty of this prediction task. To this end, this article proposes an effective unsupervised domain adaptation model (UDAC) for crime risk prediction across cities while addressing the contexts' inconsistency issue. More specifically, we first identify several similar source city grids for each target city grid. Based on these source city grids, we then construct auxiliary contexts for the target city, to make contexts consistent between the two cities. A dense convolutional network with unsupervised domain adaptation is designed to learn high-level representations for accurate crime risk prediction and simultaneously learn domain-invariant features for domain adaptation. The effectiveness of our model is verified through extensive experiments using three real-world datasets.

Binbin Zhou

Longbiao Chen

Sha Zhao

Shijian Li

Zengwei Zheng

Gang Pan

IEEE Transactions on Computational Social Systems

IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.

The IEEE Transactions on Computational Social Systems focuses on modeling, simulation, analysis, and understanding of social systems from quantitative and/or computational perspectives. "Systems" encompass man-man, man-machine, and machine-machine organizations, adversarial situations, as well as social media structures and their dynamics. Specifically, the journal publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. It also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, computational behavior modeling, and their applications.

《IEEE计算社会系统汇刊》专注于从定量和/或计算的角度对社会系统进行建模、仿真、分析和理解。这里的“系统”包括人际关系、人与机器、机器与机器之间的组织和对抗情境，以及社交媒体结构及其动态。具体而言，该期刊发表关于社会系统动态建模、将社会文化和行为方面纳入计算建模的方法论、社会系统行为和结构分析，以及社会系统建模和仿真的范式的文章。期刊还涵盖社交网络动态、社会智能与认知、社会系统设计与架构、社会文化建模与表现、计算行为建模及其应用等主题的文章。

Unsupervised Domain Adaptation for Crime Risk Prediction Across Cities

A dense convolutional network with unsupervised domain adaptation is designed to learn high-level representations for accurate crime risk prediction and simultaneously learn domain-invariant features for domain adaptation.

Category	Quartile
计算机科学	2
计算机科学, 计算机信息系统	2
计算机科学, 计算机控制论	3

Unsupervised Domain Adaptation for Crime Risk Prediction Across Cities

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	1