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

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

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Marco De Nadai

July 26, 2018
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  1. 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
  2. 2.

    2 Urban population is exploding Gomez-Lievano et al. "Explaining the

    prevalence, scaling and variance of urban phenomena." Nature Human Behaviour (2017)
  3. 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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    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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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.
  13. 21.

    21 Built environment – Results (errors) Model Bogota Boston Los

    Angeles Social disorganization 163 374 356 Built environment 109 352 317
  14. 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)
  15. 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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    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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    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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    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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    Results (errors) 32 Model Bogota Boston Los Angeles Social disorganization

    163 374 356 Built environment 109 352 317 Mobility 141 310 280
  21. 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
  22. 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
  23. 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