Safeguard Against Bias in Big Data

The growing use of ‘big data’ and analytics by police departments is often equated with over-policing or increased law enforcement at crime hot spots. In contrast, problem-solving with risk terrain modeling (RTM) can be data-informed, transparent and civilly just. RTM has proven valuable for positive public engagements and better public safety. It helps safeguard against known and potential biases.

Unlike other products that rely on large amounts of data over many years to produce crime patterns that are outdated and not relevant to present times, RTM uses small and current data sets so opportunities for biases to accumulate are limited. Start by agreeing on how to report and collect acceptable and usable data about new crime incidents, then start risk terrain modeling.

RTM analyzes crime incident locations, not arrests. It focuses on physical features of the landscape, not characteristics of people. RTM diagnoses crime patterns to reveal external correlates and connections with the environment. RTM reveals both low-risk places within high-crime towns and high-risk places within low-crime neighborhoods. Vulnerable places for crime are relative, and RTM is sensitive to this.

RTM stands out from the crowd. Other products are sold only to police departments for analyses using old crime data to predict future events. In contrast, RTM focuses on environmental conditions that aggravate crime risks. It is diagnostic — not predictive, AI or machine learning. RTM informs decision-making and values local experience. It helps you prevent crime by mitigating crime risks, which enhances public safety and improves community wellness.

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