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相关概念视频

Cause and Effect01:53

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?
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Correlations02:20

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 Plot01:15

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 Correlation01:12

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...
6.1K
Correlation01:09

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:
11.7K
Residuals and Least-Squares Property01:11

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...
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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探索公园规模与犯罪之间的关联.

Lewis H Lee1, Gibran Mancus2, Akhlaque Haque3

  • 1School of Social Work, The University of Alabama, Tuscaloosa, AL, USA.

International journal of environmental health research
|May 2, 2024
PubMed
概括

根据对阿拉巴马州公园的研究,较大的公园与较低的犯罪率有关. 这项关于公园规模和犯罪风险的研究为城市规划和公共安全战略提供了洞察力.

关键词:
社区/社区公园犯罪风险 犯罪风险

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相关实验视频

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科学领域:

  • 环境犯罪学环境犯罪学
  • 城市规划 城市规划
  • 公共卫生 公共卫生

背景情况:

  • 城市地区的犯罪率是一个重大问题.
  • 城市绿地,如公园在影响犯罪方面的作用是正在进行的研究领域.
  • 了解与犯罪风险相关的因素对于有效的政策制定至关重要.

研究的目的:

  • 为了调查公共公园的大小与阿拉巴马州犯罪风险之间的关系.
  • 确定可能调解这种关系的关键人口和社会经济因素.

主要方法:

  • 分析了阿拉巴马州73个人口超过1万的城市的564个公园.
  • 园区的尺寸使用Google Earth Pro.进行测量.
  • 从应用地理解决方案获得的犯罪数据 (暴力和财产犯罪).
  • 多重回归分析包括人口密度,心理健康,社会脆弱性和酒精消费的数据.

主要成果:

  • 在公园规模和犯罪风险之间发现了显著的负相关性,表明较大的公园与较低的犯罪率相关.
  • 心理健康患病率,社会脆弱性和酒精消费也与犯罪率有显著的关系.
  • 这些共变量在理解公园规模与犯罪关系方面发挥着重要作用.

结论:

  • 公园的大小是犯罪风险评估的一个重要因素.
  • 城市规划和公共卫生倡议应考虑绿色空间维度对社区安全的影响.
  • 调查结果为地方政府和社区组织提供了基于证据的建议,以减少犯罪.