Related Experiment Video
Updated: Jun 27, 2025

14:55
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
Published on: January 20, 2023
3.3K
Exploring the association between park size and crime
Lewis H Lee1, Gibran Mancus2, Akhlaque Haque3
1School of Social Work, The University of Alabama, Tuscaloosa, AL, USA.
Summary
Larger parks are linked to lower crime rates, according to a study of Alabama parks. This research on park size and crime risk offers insights for urban planning and public safety strategies.
Area of Science:
- Environmental Criminology
- Urban Planning
- Public Health
Background:
- Crime rates are a significant concern in urban areas.
- The role of urban green spaces, such as parks, in influencing crime is an area of ongoing research.
- Understanding factors associated with crime risk is crucial for effective policy development.
Purpose of the Study:
- To investigate the relationship between the size of public parks and crime risk in Alabama.
- To identify key demographic and socioeconomic factors that may mediate this relationship.
Main Methods:
- Analysis of 564 parks across 73 cities in Alabama with populations over 10,000.
- Park dimensions measured using Google Earth Pro.
- Crime data (violent and property crimes) obtained from Applied Geographic Solutions.
- Multiple regression analysis incorporating data on population density, mental health, social vulnerability, and alcohol expenditure.
Main Results:
- A significant negative association was found between park size and crime risk, indicating larger parks correlate with lower crime rates.
- Mental health prevalence, social vulnerability, and alcohol spending were also significantly related to crime rates.
- These covariates play a role in understanding the park size-crime relationship.
Conclusions:
- Park size is a significant factor in crime risk assessment.
- Urban planning and public health initiatives should consider the impact of green space dimensions on community safety.
- Findings provide evidence-based recommendations for local governments and community organizations to reduce crime.
Related Concept Videos
Cause and Effect
10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
Correlations
32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
Scatter Plot
6.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.8K
Coefficient of Correlation
6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.1K
Correlation
11.7K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.7K
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K

