Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

by Nicolas Gakrelidz
November 28, 2018
in Artificial Intelligence, BI & Analytics, Big Data, Case Studies, Data Natives
Home Topics Data Science Artificial Intelligence
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

Last year, we set up a prediction model on crime in London. We had established the model already, grounded in open data, but updated it to make predictions about 2017. We took the data provided by the police in the greater London area, and by enriching this data with Points Of Interest from Ordnance Survey and UK Census data, we created multiple predictive models with Dataiku in order to give these predictions for 2017 at the local LSOA level.

The model was reasonably accurate, considering we only had access to open data (taking into account the level of control we have on open data models). But let’s break down how we established our model’s performance.

Table of Contents

  • Building A Data Preparation Flow for Aggregating on Monthly Observations vs Predictions
  • Graphically Establishing Our Model Performance
  • Let’s Automate Everything!

Building A Data Preparation Flow for Aggregating on Monthly Observations vs Predictions

The first step was to collect the 2017 police data. I just downloaded the data (fairly) manually onto my computer. The partitioning system of Dataiku adjusts to fit the collected files’ structure, which is how Dataiku controls inserting and updating dataset rows into a meaningful organized structure. Partitioning also helps automate the recurring tasks implied by big data usage.
I partitioned the data based on the month, which helps automate our workflow. Here’s what the updated project flow looked like:How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

After preparing our data and joining the predictions and the LSOA boundaries, we could compare our predictions to the real observed data. To do that, I computed the residuals, numerical differences between the values as prediction – observed_crimes:


Join the Partisia Blockchain Hackathon, design the future, gain new skills, and win!


How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

Now we could compute different indicators and reshape our data in order to analyse the predictions:

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

Graphically Establishing Our Model Performance

It was then time to generate some insights into the process, which are useful for analyzing the data. On a monthly basis, our R^2metric—an essential reading on model accuracy—is 0.88, which is fair especially when using limited datasets. When we look at the full year, the R^2 metric for LSOA is 0.95, which is better than expected, with a global prediction error of 8.7%.

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

The areas (LSOA) where the residuals are the highest are blue (when the prediction was lower than the actual) and red (when the prediction was higher) on the map. As expected, the predictive model tends to underestimate the number of crimes, which is particularly apparent in the London city centre.

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

To break down the model performance for 2017:

  • Median Average Error (MedAE): 19 crimes
  • Global fit: 95%
  • Difference vs reality 8.7%

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

On a monthly basis, extreme residuals were observed in June, September, and December. This highlighted some limitations of the model. One way to improve it would be to add some features related to public events or weather conditions.

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

Let’s Automate Everything!

I created a scenario that will calculate the accuracy statistics for the previous month. Here are the steps:

1. Build the joined observed data and predictions for the previous month (or a specific month as a partition passed by the DSS API if the data are delivered later, this can be easily automated thanks to the DSS Public API
2. Refresh a Jupyter notebook containing some charts and metrics.
3. Build others datasets and refresh the charts cache for sharing the updated insights in a dashboard.

How Effective Is AI Crime Prediction? Evaluating Our London Crime Prediction Model

Developing the monthly automation only took a couple of hours at an airport, since Dataiku makes it really easy to push predictive projects into production. This way you don’t have to keep updating them manually, always have up-to-date projects, and most importantly, you retain control of your predictive models. Learn more about pushing analytics into production with our free white paper.

Dataiku will be presenting at Data Natives– the data-driven conference of the future, hosted in Dataconomy’s hometown of Berlin. On the 22nd & 23rd November, 110 speakers and 1,600 attendees will come together to explore the tech of tomorrow, including AI, big data, blockchain, and more. As well as two days of inspiring talks, Data Natives will also bring informative workshops, satellite events, art installations and food to our data-driven community, promising an immersive experience in the tech of tomorrow.

Tags: AIAutomationBig Datacrime preventionDataikulondon

Related Posts

What is Google Bard AI? Learn how to use Google Bard AI. Google Bard AI vs ChatGPT: Which one is better? Keep reading and find out.

Google unveils its experimental conversational AI service Bard

February 7, 2023
AI Asmongold video: In the Athene AI Show, a Twitch streamer's funny deepfake revealed and people love it. So how did this happen? Keep reading and find out.

AI Asmongold may have been one of the very first examples of AI streamers

February 6, 2023
Google starts testing its ChatGPT rival AI chatbot called Apprentice Bard

Google starts testing its ChatGPT rival AI chatbot called Apprentice Bard

February 7, 2023
Artificial intelligence in education: Examples

How AI improves education with personalized learning at scale and other new capabilities

February 3, 2023
What is ChatGPT Plus, and how to get it? Learn its features, price, and how to join ChatGPT Plus waitlist. Is it worth it? Keep reading and find out

ChatGPT Plus: How does the paid version work?

February 2, 2023
AI Text Classifier: OpenAI's ChatGPT detector can distinguishes AI-generated text

AI Text Classifier: OpenAI’s ChatGPT detector indicates AI-generated text

February 2, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

LATEST ARTICLES

Google unveils its experimental conversational AI service Bard

Achieving data resilience with StaaS

AI Asmongold may have been one of the very first examples of AI streamers

Mastering the art of efficiency through business process transformation

Google starts testing its ChatGPT rival AI chatbot called Apprentice Bard

How AI improves education with personalized learning at scale and other new capabilities

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.