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Encuentro Mundial

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Project Presentation, April 2018

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Encuentro Mundial

  1. 1. Big Data to Understand and Prevent Urban Crime
  2. 2. El instrumento más poderoso para combatir crimen y violencia es la información. Crime Radar, Instituto Igarapé Rio de Janeiro
  3. 3. Information is a powerful instrument to combat crime and violence.
  4. 4. 1. Crime and information gaps 2. Big Data and crime 3.
  5. 5. Context and Relevance Crime in Latin America Information Gaps 1
  6. 6. “When you measure by the homicide rate, 14 of the 20 most dangerous countries in the world are in Latin America and the Caribbean.” Instituto Igarapé, Homicide Monitor Homicide Monitor, Instituto Igarapé Crime in LAC information Gaps1
  7. 7. Crime Statistics in LAC... Crime in LAC Information Gaps1 ...in Colombia
  8. 8. “ ” A recent proliferation of violence observatories seen in Latin America suggests that governments are paying more attention to the need for a greater focus based on evidence for security policies. However, even now only two thirds of the 60 observatories track when and where murders occur, and only one half try to determine the motives, according to IDB. The Economist, Briefing Murder in Latin America April 7th-13th 2018 Crime in LAC Information Gaps1
  9. 9. Crime: A Multifaceted Problem • Exposure to stress • Lack of control • Low self-esteem • Low education • History of homelessness • Stigma / Discrimination • History of abuse • Family • Conflict • Rupture • Disfunction • Education/ age • History of crime • Abuse • Alcohol abuse • Community • Peer pressure • Gang participation Employment • Poverty • Low socioeconomic level • Income inequality • Education inequality • Access to services • Women leaders • Armed groups • Community resources Geography Lack of public spaces • Transportation • Urbanization • Vacancies • Stability, • Application of the law • Bars / Restaurants • Population heterogeneity• Immigrations • Education spending • Strength of the education system • Social networks • Social norms • Social inclusion Social and Political Community Relationship PersonalRisks 1 Crime in LAC Information Gaps
  10. 10. The big questions about criminality Socioeconomic variables (education, salary, unemployment) Human-centric perspective Crime “Hotspots” Location-centric perspective 2 Why does crime concentrate where it does? Why do some individuals stray towards delinquency and others do not? Big Data Contributions of Big Data
  11. 11. “ In some countries, for example, fewer than 10% of municipalities make up almost half of all homicides (as in the case of Central America) (Granguillhome, 2017). At a more disaggregated level, crime concentrates in microspaces commonly known by street segments. Interamerican Development Bank, Citizen Security and Justice Sector Framework Document. 2017. Crime Hotspots 1 Crime in LAC Information Gaps
  12. 12. Crime Hotspots
  13. 13. Why does crime concentrate where it does? Why do some people turn to crime and others do not? 1 Crime in LAC Information Gaps
  14. 14. We need advanced diagnostic tools that produce a more disaggregated analysis.
  15. 15. Big Data and Crime Big Data Contributions of Big Data 2
  16. 16. Big Data Contributions of Big Data2 Types of Big Data
  17. 17. Capture of human behavior Big Data Contributions of Big Data2 What is the added value of Big Data? D igital crumbs of data emitted and collected passively by digital devices that constitute large sets and data flows that provide a unique view of their behaviors and beliefs;; Level of space-time granularity
  18. 18. 2 • Approximate Location of calls / sms / data • Approximate time of calls / sms / data • Does not store content • Call Detail Records Information Learned: Big Data Contributions of Big Data
  19. 19. Metadata of posts Online open content
  20. 20. Testing the hypotheses of Jane Jacobs with Big Data: “eyes on the street” Big Data Contributions of Big Data2 What are the physical characteristics of the city that promote a sustained low crime level? “ ” A busy urban street has a propensity to be safe and a deserted urban street has a propensity to be unsafe.
  21. 21. Life conditions 1 32 Urban environment Mobility The aggregated value of Big Data to understand crime in cities 2 Big Data Contributions of Big Data
  22. 22. Life conditions 1 Model of social conditions ➢ Economic disadvantages ➢ Ethnic diversity ➢ Housing stability CENSUS 2 Big Data Contributions of Big Data
  23. 23. 2 Entorno urbano “Urban fabric” - Jane Jacobs ➢ Barrios multifuncionales ➢ Manzanas pequeñas ➢ Diversidad ➢ Alta concentración de personas DATOS ESPACIALES (i.e. Open Street Map, Foursquare etc.) 2 Big Data Contributions of Big Data
  24. 24. 3 Mobility Daily routines and mobility ➢ Matrix of origin - destination ➢ Floating population BEHAVIORAL DATA: Call Detail Records 2 Big Data Contributions of Big Data
  25. 25. Conclusions of the Study 2 Big Data Contributions of Big Data
  26. 26. Objective and Scope Methodology Lines of Work 3
  27. 27. Analysis based on data (Crime predictors of urban locations, personal risk factors, perception of safety...) through Options of public policies and plans of action. towards... Objective and Scope Lines of workMethodology Understand and approach crime dynamics in 6 cities 3
  28. 28. Coordination and technical expertise Academic and technical expertise Strategic support Technical expertise in crime and violence and strategic support 3 Objective and Scope Lines of workMethodology
  29. 29. Crime = violent and property crimes : a. Aggravated assault, rape, robbery, murder. b. Theft-robbery, theft from motor vehicles, theft and arson. What crimes do we focus on? 3 Objective and Scope Lines of workMethodology
  30. 30. ➢ Diversity of contexts ➢ Manifestations of crime and violence Six Colombian Cities Bogotá Valledupar Medellín Montería Barranquilla Tumaco City Selection 3 Objective and Scope Lines of workMethodology
  31. 31. Who do we work with? National Institutions 3 Objective and Scope Lines of workMethodology
  32. 32. Taking advantage of traditional and new data sources for the construction of a flexible comprehensive model with a unique perspective Granular spatio-temporal diagnostic tool for crime risk factors for each city BIG DATA SURVEYS + FOCUS GROUPS STATISTICS DEMOG. & CRIME 3 Objective and Scope Lines of workMethodology
  33. 33. Pre-identification of risk factors Data Inventory Big Data Analysis Validation with Surveys and Focus Groups Identification of Risks Inputs for Public Policies Lines of Work 3 January 2018 Dec. 2018 Objective and Scope Lines of workMethodology
  34. 34. Work Products Final Results Data Inventory Evaluation of data systems and data collection on crime and data related to risk factors (ie, statistics and demographics, etc.) Survey Representative municipal level survey to understand the perception of security / feeling of insecurity. Modeling the risks associated with crime Complete model that describes the crime through all i information on mobility, physical characteristics of the city and social disorganization. Policy Action Plan Public policy options and recommendations for the control and prevention of crime in each city based on the findings. Big Data Analysis Analysis that provides very granular and frequent information, complemented with mobility information extracted from mobile phone metadata. Focus Groups Understand the drivers of criminality at the individual level. Capacity Evaluation Human and institutional resources of public actors involved in the criminal information system. Training Capacity building workshops in 2 cities + + 3 Objective and Scope Lines of workMethodology
  35. 35. Results Promote value generation of data Data Systems Public policy recommendations along two lines: Policies Support the generation of options for more focused and specific public policies. Human Systems 3 Objective and Scope Lines of workMethodology
  36. 36. Julie Ricard, Project Manager Ciudata Segura, Data-Pop Alliance | jricard@datapopalliance.org Emmanuel Letouzé, Founder and Director, Data-Pop Alliance | eletouze@datapopalliance.org Nathalie Alvarado, Citizen Security Principal Specialist and Head of the Citizen Security Team, IDB | nathaliea@iadb.org Contact

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