Violence is the use of physical, verbal, and/or emotional force to injure, harm, abuse, damage, or destroy. Interpersonal violence includes child abuse and neglect, youth violence, intimate partner violence, sexual violence, and elder abuse. Violence can involve injury to self, including self-harm, suicide attempts, and death by suicide. Violence can also take the form of power against a group or community.

Violence is a public health issue because it is a cause of morbidity and mortality, leading to physical and emotional harm, an increase in negative health outcomes, premature death, psychological trauma and persistent fear to individuals and communities.

Violence is also an equity issue because it disproportionately affects groups that have been oppressed due to their socioeconomic status, race/ethnicity, sexual orientation, gender identity, geographic location, or some combination of these factors. Data from multiple sources illustrate tremendous disparities in the distribution and burden of violence throughout NYC. For instance, rates of firearm-related violence are at least two times higher in high poverty neighborhoods when compared to lower poverty neighborhoods. And, violence persists as a leading cause of premature death among NYC’s men of color.

Factors that Contribute to Violence

Restricting the explanation of disparities in violence to differences in individuals and group characteristics most affected by the violence only initiates bias and constrains problem-solving*.* To understand what drives violence, it is important to look at the context and systems within which it occurs and consider the negative impact that historic practices (e.g., redlining) and ongoing social structures (e.g., institutional racism) have on particular groups and communities.

For example, racialized systems have created and upheld stereotypes that link Black men and criminality, while ignoring the imbalanced distribution of power and resources experienced by Black men and Black communities. A myriad of  social, political, economic, historical, and environmental factors contribute to violence. These factors convey messages about which populations and communities are deprived of society’s resources and which are privileged by them. Some of these factors include:

• Poverty and neighborhood disinvestment
• Housing and historic redlining practices
• Education and employment opportunities
• Policing and incarceration practices
• Chronic stress and intergenerational trauma Community and social norms
• Access to health and health care
• Built environment
• Racism and other forms of oppression

Examining factors like these that disproportionately marginalize certain populations illuminate opportunities for prevention.

About the Data and Indicators

Violence is frequently described through homicide statistics. While it is a leading cause of death and captures the most severe form of violence, non-fatal violence-related injuries are much more common and help paint the larger picture on the burden of violence and its impact on the health and future of individuals, families, and communities.

Interpreting the Data and Informing Solutions

Violence-related data must be interpreted in ways where we move beyond demonizing those causing the harm and instead look at the context and systems that gives rise to violence. Interpreting data this way illuminates ways to mitigate the social, political, economic, historical, and environmental factors that contribute to violence.

With any public health epidemic, be it influenza, obesity, opioid addiction or gun violence, prevention is key.  Prevention is achievable in ways that are similar to multi-pronged approaches that have been used to address the risk and resilient factors for health outcomes like asthma, motor vehicle crashes, and opioid overdoses. Indeed, multi-faceted approaches to health promotion that empower individuals, families, communities, and systems equitably are where sustainable solutions for violence reduction and prevention reside.

There are many schemes of neighborhood boundaries (or geographies) available for different datasets. These include:

• Citywide
• Borough
• United Hospital Fund 42 (UHF42) and United Hospital Fund 34 (UHF34) neighborhoods
• Community Districts (CD), and, related, Community District Tabulation Areas (CDTA)
• Public Use Microdata Areas (PUMA)
• Neighborhood Tabulation Areas (NTA)

We try to make data available at as fine a geographic resolution as we can. Read more about different geographies at this data story.

It would be convenient if each dataset could be made available at the same geography, but this is not always possible. The neighborhood boundaries available depend on the specifics of the underlying dataset.

When we have multiple years of data, we show the data as trend charts.

Trends for individual neighborhoods tend to be very unstable (or "bouncy"), due to small counts for outcomes in individual neighborhoods. To show clearer trends, we usually aggregate up and show trend data by Borough.

Options on some indicators let you compare this indicator to other similar indicators.

We try to make data available as soon as we can.

Many datasets take a long time to reach us — a "lag" of several years, during which data are gathered from different entities to try to create a complete dataset; data are processed (cleaned, deduplicated, etc), and analyzed. It can take several years to prepare a dataset — so a dataset that is several years old is sometimes the most up-to-date information available. Still, this data continues to show us valuable trends and insights.

We also try to display datasets that have been available for many years. Some datasets (like those that are asked on surveys) are not asked every year of the survey, which means that we cannot provide annual data.

For some datasets, we have to aggregate time periods in order to break down data by borough or neighborhood.

Measures are the numeric ways that a dataset (or indicator) are expressed. Examples of different measures might be: number, percent or rate.

A number tells us how much of an outcome is in a certain area. But a rate (usually expressed as, for example, 40 per 100,000) tells us how much of an outcome there is, relative to the population of the area in question. This lets us compare neighborhoods of different sizes.

On this site, we try to show how environmental factors might be connected to health outcomes. One way we do this is with Correlates. We choose potentially-related datasets and display them on a scatterplot so you can see how these two datasets are correlated.

The Disparities view displays neighborhoods by poverty category to show underlying disparities in the data.

Read more about correlation here.