EAI Endorsed Transactions on Complex Systems 13(2): e1

Research Article

Self organized hotspots and social tomography

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  • @ARTICLE{10.4108/trans.cs.1.2.e1,
        author={Jianbo Gao and Qian Han and Xiaoliang Lu and Lei Yang and Jing Hu},
        title={Self organized hotspots and social tomography},
        journal={EAI Endorsed Transactions on Complex Systems},
        volume={13},
        number={2},
        publisher={ICST},
        journal_a={COMSYS},
        year={2013},
        month={5},
        keywords={social tomography, crime hot spots, sex offender clusters, power-law distribution},
        doi={10.4108/trans.cs.1.2.e1}
    }
    
  • Jianbo Gao
    Qian Han
    Xiaoliang Lu
    Lei Yang
    Jing Hu
    Year: 2013
    Self organized hotspots and social tomography
    COMSYS
    ICST
    DOI: 10.4108/trans.cs.1.2.e1
Jianbo Gao1,2,*, Qian Han3, Xiaoliang Lu4, Lei Yang5, Jing Hu6
  • 1: PMB Intelligence LLC, PO Box 2077, West Lafayette, IN 47996, USA
  • 2: Department of Mechanical and Materials Engineering, Wright State University, Dayton, Ohio 45435, USA
  • 3: Department of Computer Science and Engineering, Wright State University, Dayton, Ohio 45435, USA
  • 4: Earth & Atmospheric Sciences, Purdue University, West Lafayette, IN 47907, USA
  • 5: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
  • 6: Affymetrix, Inc., Santa Clara, CA, USA
*Contact email: jbgao@pmbintelligence.com

Abstract

A social network often has numerous interesting attributes. When an attribute is quantified, a social tomography would arise from the underlying social network. One of the most interesting attributes is crime hotspots, whose existence has been strongly supported by observations that serious crimes ranging from residential burglary to homicide are strongly patterned in time and space, and by mathematical modeling. So far, however, the structures of hotspots, including their size distributions, have not been adequately studied. Here, we focus on a special type of hotspots, the sex offender clusters, in the United States, and show that their size distribution, where size is defined as the ratio between sex offender population and total population in a 5-digit zip code area, follows a power-law distribution. In contrast, such local total population, both general and sex offenders, do not quite follow power-laws. A heavy-tailed power-law distribution is fundamentally different from a thin-tailed distribution such as a Poisson distribution, and can be used as an objective criterion for defining sex offender clusters. More fundamentally, a power-law is a defining property of self-similarity or fractal behavior. Therefore, our finding indicates that sex offender clusters, size-wise, self-organize into a fractal, due to interplay of economic conditions of offenders, policies and public perceptions.