Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 1 gb of stop data from Florida. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context

On a typical day in the United States, police officers make more than 50,000 traffic stops. Our team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Our goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

Content

This dataset includes 9 Mb of stop data from Rhode Island, covering all of 2013 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration

• Do men or women speed more often?
• Does gender affect who gets searched during a stop?
• During a search, how often is the driver frisked?
• Which year had the least number of stops?
• How does drug activity change by time of day?
• Do most stops occur at night?

Those all are question waiting for you to answer them, Good Luck😃

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes 1.6 gb of stop data from North Carolina, covering all of 2010 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 1 gb of stop data from Ohio. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes 2gb of stop data from Washington state. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes stop data from MS, MT, ND, NH, NJ, NV, OR, RI, SD, TN, VA, V, WI, and WY. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 1 gb of stop data from Illinois, covering all of 2010 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 2gb of stop data from California, covering all of 2013 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes stop data from AZ, CO, CT, IA, MA, MD, MI and MO. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes 1.7 gb of stop data from South Carolina, covering all of 2010 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 2 gb of stop data from Texas, covering all of 2010 onwards. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?