Socio-economic, environmental, and mobility conditions associated with crime: A study of multiple cities

In this project we study the relationship between crime and places, people and their mobility. Presentation at ICCS 2018

Transcript

1. Marco De Nadai, Yanyan Xu, Emmanuel Letouzé,
   Marta C. González, Bruno Lepri
   Socio-economic, environmental, and
   mobility conditions associated with crime:
   A study of multiple cities
   ICCS 2018
   

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2. 2
   Urban population is exploding
   Gomez-Lievano et al. "Explaining the prevalence, scaling and
   variance of urban phenomena." Nature Human Behaviour (2017)
   

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3. 3
   How can we describe crime?
   Socio-economic
   characteristics
   Built environment Mobility
   

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4. Socio-economic
   characteristics
   

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5. The social-disorganization theory
   Crime is a result of lack of cooperation and trust
   • Economic deprivation
   • Ethnic heterogeneity
   • Residential instability
   5
   Sampson, R. J., et al. (1989). Community structure and crime: Testing social-
   disorganization theory. American journal of sociology
   SOCIAL
   DISORGANIZATION
   

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6. Bayesian Poisson model
   6
   Leroux et al. "Estimation of disease rates in small areas: a new mixed model for
   spatial dependence." Statistical models in epidemiology, the environment, and
   clinical trials.
   log &
   = ( *
   *
   ,
   *-.
   + CAR process
   Auto-correlation matrix
   Features
   (e.g. disadvantage)
   Crime
   in a district
   (ground truth)
   SOCIAL
   DISORGANIZATION
   

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7. Social disorganization - Results
   7
   SOCIAL
   DISADVANTAGE
   ETHNIC
   MIX
   RESIDENTIAL
   STABILITY
   THEORY
   THEORY
   THEORY
   

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8. Social disorganization - Results
   8
   SOCIAL
   DISADVANTAGE
   ETHNIC
   MIX
   RESIDENTIAL
   STABILITY
   THEORY
   THEORY
   THEORY
   

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9. Social disorganization - Results
   9
   SOCIAL
   DISADVANTAGE
   ETHNIC
   MIX
   RESIDENTIAL
   STABILITY
   THEORY
   THEORY
   THEORY
   

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10. Social disorganization - Results
    10
    SOCIAL
    DISADVANTAGE
    ETHNIC
    MIX
    RESIDENTIAL
    STABILITY
    THEORY
    THEORY
    THEORY
    

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11. Social disorganization - Results
    11
    THE SOCIAL DISORGANIZATION
    IS NOT ENOUGH
    

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12. The built environment
    

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13. The built environment
    Jane Jacobs has been one of the most influential
    people in urban planning
    13
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    BUILT
    ENVIRONMENT
    

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14. The built environment
    Jane Jacobs has been one of the most influential
    people in urban planning
    14
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    Informal surveillance: Guardianship by
    ordinary citizen, and police
    BUILT
    ENVIRONMENT
    

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15. The built environment
    Four essential conditions:
    1. Land use mix
    15
    Land use mix
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    BUILT
    ENVIRONMENT
    

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16. The built environment
    Four essential conditions:
    1. Land use mix
    2. Small blocks
    16
    Small blocks
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    BUILT
    ENVIRONMENT
    

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17. The built environment
    Four essential conditions:
    1. Land use mix
    2. Small blocks
    3. Aged buildings
    17
    Aged buildings
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    BUILT
    ENVIRONMENT
    

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18. The built environment
    Four essential conditions:
    1. Land use mix
    2. Small blocks
    3. Aged buildings
    4. Density
    18
    Density
    Jacobs, Jane. The death and life of great American cities. Vintage, 1961
    BUILT
    ENVIRONMENT
    

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19. The built environment
    Four essential conditions:
    1. Land use mix
    2. Small blocks
    3. Aged buildings
    4. Density
    19
    De Nadai, Marco, et al. "The death and life of great Italian cities: a mobile phone
    data perspective." WWW, 2016.
    OpenStreetMap + census!
    BUILT
    ENVIRONMENT
    

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20. Bayesian Poisson model
    20
    log &
    = ( *
    *
    ,
    *-.
    +
    CAR process
    Spatial matrix
    Features
    (e.g. land use mix)
    Crime
    in a district
    (ground truth)
    De Nadai, Marco, et al. "The death and life of great Italian cities: a mobile phone
    data perspective." WWW, 2016.
    

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21. 21
    Built environment – Results (errors)
    Model Bogota Boston Los Angeles
    Social disorganization 163 374 356
    Built environment 109 352 317
    

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22. 22
    Built environment – Results (errors)
    Model Bogota Boston Los Angeles
    Social disorganization 163 374 356
    Built environment
    (+ Social disorganization)
    109
    (104)
    352
    (205)
    317
    (333)
    

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23. 23
    Built environment – Results (errors)
    Model Bogota Boston Los Angeles
    Social disorganization 163 374 356
    Built environment
    (+ Social disorganization)
    109
    (104)
    352
    (205)
    317
    (333)
    CRIME ESTIMATION
    FROM URBAN DATA
    

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24. 24
    Built environment - Discrepancies
    Small blocks
    Just an example…
    

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25. 25
    Built environment - Discrepancies
    THE PLACE IS NOT ENOUGH
    Just an example…
    

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26. Mobility
    

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27. Mobility
    27
    Graif, Corina et al. "Neighborhood isolation in Chicago: Violent crime effects on
    structural isolation and homophily in inter-neighborhood commuting networks."
    Social networks 51 (2017)
    • People are everyday exposed to different
    conditions and places
    • Poorly connected neighborhoods weaken
    cooperation
    • Mobility affects crime
    MOBILITY
    

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28. Mobility
    28
    Digitalization of our life => mobile phone data
    Jiang, Shan, et al. "The TimeGeo modeling framework for urban mobility without
    travel surveys." Proceedings of the National Academy of Sciences 113.37 (2016)
    MOBILITY
    

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29. Bayesian Poisson model
    29
    O/D
    NETWORK
    C B
    A
    34
    21
    90
    MOBILITY
    

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30. Bayesian Poisson model
    30
    LOW
    HIGH
    POPULATION
    O/D
    NETWORK
    C B
    A
    34
    21
    90
    MOBILITY
    

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31. Bayesian Poisson model
    31
    LOW
    HIGH
    POPULATION
    O/D
    NETWORK
    log &
    = ( *
    *
    ,
    *-.
    +
    Crime
    in a district
    (ground truth)
    C B
    A
    34
    21
    90
    MOBILITY
    

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32. Results (errors)
    32
    Model Bogota Boston Los Angeles
    Social disorganization 163 374 356
    Built environment 109 352 317
    Mobility 141 310 280
    

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33. Results (errors)
    33
    Model Bogota Boston Los Angeles
    Social disorganization 163 374 356
    Built environment 109 352 317
    Mobility 141 310 280
    Full model 82 267 240
    PLACE + PEOPLE + DYNAMISM
    

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34. 34
    How can we describe crime?
    Socio-economic
    characteristics
    Built environment Mobility
    

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35. 35
    How can we describe crime?
    Socio-economic
    characteristics
    Built environment Mobility
    ✓ Precise data ✓ Availability
    ✓ Unbiased
    (population)
    ✗ Rarely updated
    ✗ Availability
    ✗ Bias over volunteers ✗ Availability
    

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36. 36
    How can we describe crime?
    Socio-economic
    characteristics
    Built environment Mobility
    ALL BLOCKS TOGETHER
    BETTER DESCRIBE CRIME
    THEORY HAS DISCREPANCIES
    OVER DIFFERENT CITIES
    

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37. 37
    How can we describe crime?
    New theory
    

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38. Feedback time!
    @denadai2
    

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