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
2. 2 Urban population is exploding Gomez-Lievano et al. "Explaining the prevalence, scaling and variance of urban phenomena." Nature Human Behaviour (2017)
3. 3 How can we describe crime? Socio-economic characteristics Built environment Mobility
4. Socio-economic characteristics
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
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
7. Social disorganization - Results 7 SOCIAL DISADVANTAGE ETHNIC MIX RESIDENTIAL STABILITY THEORY THEORY THEORY
8. Social disorganization - Results 8 SOCIAL DISADVANTAGE ETHNIC MIX RESIDENTIAL STABILITY THEORY THEORY THEORY
9. Social disorganization - Results 9 SOCIAL DISADVANTAGE ETHNIC MIX RESIDENTIAL STABILITY THEORY THEORY THEORY
10. Social disorganization - Results 10 SOCIAL DISADVANTAGE ETHNIC MIX RESIDENTIAL STABILITY THEORY THEORY THEORY
11. Social disorganization - Results 11 THE SOCIAL DISORGANIZATION IS NOT ENOUGH
12. The built environment
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
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
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
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
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
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
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
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.
21. 21 Built environment – Results (errors) Model Bogota Boston Los Angeles Social disorganization 163 374 356 Built environment 109 352 317
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)
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
24. 24 Built environment - Discrepancies Small blocks Just an example…
25. 25 Built environment - Discrepancies THE PLACE IS NOT ENOUGH Just an example…
26. Mobility
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
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
29. Bayesian Poisson model 29 O/D NETWORK C B A 34 21 90 MOBILITY
30. Bayesian Poisson model 30 LOW HIGH POPULATION O/D NETWORK C B A 34 21 90 MOBILITY
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
32. Results (errors) 32 Model Bogota Boston Los Angeles Social disorganization 163 374 356 Built environment 109 352 317 Mobility 141 310 280
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
34. 34 How can we describe crime? Socio-economic characteristics Built environment Mobility
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
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
37. 37 How can we describe crime? New theory
38. Feedback time! @denadai2