SUICIDE & MEDIAN INCOME ANALYSIS

 

Eric Herbert

GEOG 4050 Final Project




I. Introduction

II. Methodology

III. Results

IV. Conclusion

 

 

Introduction:

Suicides represent one of the major social problems in the United States, both presently and throughout the past. To portray the magnitude of this problem in out culture, here are some statistics:

GENDER - Men are much more likely to kill themselves than women

AGE - Suicide rates increase with age.

ETHNICALLY - Whites have the biggest suicide rates among ethnic groups.

MENTAL DISORDERS AND SUBSTANCE ABUSE - Major risk factors.

ADOLESCENTS - suicide is the third leading cause of death, and climbing.

SUICIDE METHODS - Most suicides are committed by firearms.

JAILS AND PRISONS - High suicide rates.

Depression represents the major cause of suicides. To understand the leading causes of depression is essential for suicide prevention. Some of the factors which influence depression are distinct to particular regions. To analyze these spatial patterns, GIS can be utilized. GIS makes it possible to compare different variables to assess how these variables are related spatially. The purpose of this study is to find how suicide rates compare to regional economic conditions. In theory, suicide rates should be inversely dependent upon income levels. In other words, higher suicide rates would be located in lower income areas.*

 

*Since these two variables are inherently independent of each other, a regression analysis should be run to find exactly how related these variables are to each other. For the purpose of this study, however, a regression was not run, the two variables are simply assumed to be inversely related.

 

Methodology:

Acquisition: Data for the suicide rates and median income for the year 1989 was gathered from the Vital Health Statistics of the United States, Volume II-Mortality, and the US Bureau of the Census respectively. To fairly accurately represent the entire country, 170 MSA's (Metropolitan Statistical Areas), were selected, with approximately 3 MSA's from each state. For each MSA, the suicide rate and the median income were calculated. The suicide rate was calculated as the percent of the total number of non felonious deaths for each MSA:

Suicide Rate = Number of Suicides / (Total Number of Deaths - Number of Homicides)

The number of homicides in each MSA is not included because this variable is highly dependent upon the size of the MSA, where larger MSA's have a much greater proportion of deaths resulting from homicides.

Median income was chosen as the second variable to analyze. Median income is the best economic indicator for usage when comparing different regions throughout the country because it is not as skewed as raw income data.

Procedures: After gathering the suicide rates and median income for the 168 MSA's, the x and y coordinates for the cities were collected. These coordinates and the variables were put into a list as a text file and submitted to Surface III. Since the MSA's are only point data, this program was used to interpolate the values for all regions between the cities of the entire country. The x and y coordinates are used as the base points, and one z value, suicide rates, was analyzed. Interpolation values are based upon the statistics of the nearest MSA locations, and therefore may not accurately represent non urban areas. After the interpolation, a tabular grid is formed containing the resulting values. This matrix was ready to be submitted to further analysis. To obtain the second matrix, the process was repeated using the median income statistics as the z value.

The matrix file containing the results of the interpolation was imported into MapFactory in order to perform basic algebraic functions upon the data and for an overlay. To view the suicide data in a more understandable fashion, the suicide rates were multiplied by 100, thus creating the rate of suicides per 10,000 non felonious deaths. Using the addition procedure in MapFactory, these results were then overlaid onto a base map of the United States, showing the spatial distribution of suicide rates throughout the country:

 

Next, the median income matrix was imported, and these values were divided by 100 to create values incrementing by one thousand. These values were then placed upon the base map to show the distribution of income values throughout the country:

 

To show how the two variables are distributed together across the country, the division procedure in MapFactory was used. The suicide rate matrix was divided by the median income matrix, thus resulting in the suicide rate per median income matrix. This matrix represents the number of suicides per 10,000 deaths / median income, in thousands of dollars. Finally, these results were overlaid upon a base map creating the final map:

 

 

Procedure Steps:

  1. Gather suicide data from the 1989 Vital Health Statistics of the United States, Volume II-Mortality
  2. Obtain a list of median income statistics from the 1989 US Census
  3. Compute the suicide rate for the 170 selected MSA's
  4. Create a proper base map in MapFactory
  5. Gather the list of x, y coordinates for the locations of the MSA's
  6. Import the table with MSA coordinates, suicide rates, & median income values into SurfaceIII
  7. Use the grid function in SurfaceIII to interpolate values which lie between MSA's (for suicide rates & median income)
  8. Export the created z-matrix files from SurafaceIII & import them into MapFactory
  9. Perform arithmetic procedures upon the coverages to obtain data in preferred measurement rates
  10. Divide suicide rate coverage by the median income coverage
  11. Add the resulting coverage to the original base map
  12. Cleanup of the coverage
  13. Create the map layout & print the final products to the PDF Writer

 

Results:

The suicide rate coverage map shows areas with very high suicide rates in the northern half of the Rocky Mountain front range and in the areas surrounding Reno, Nevada. Noteworthy, the entire Rocky Mountain front range is characterized by high suicide rates, with the one exception around Fort Collins, Colorado. The Northeast section of the country has low suicide rates, as well as the western half of Arkansas. The peak and valley in Texas is also interesting. Dallas has a low suicide rate when compared to the Abilene area.

Median income remains the highest in the megalopolis region, extending from northern Virginia to Massachusettes. The eastern Midwest also has a fairly high income level, as well as southern California and the Seattle area. Once again, the side by side peak and valley is located in Texas. Dallas and Houston have much higher median incomes than the cities which lie between them, such as Waco. The low income levels in Waco create an exaggerated peak, especially when compared to Dallas, during the interpolation process. Areas with low median income include the southern tip of Texas, and northeastern Louisiana/southeastern Arkansas.

After analyzing the two maps, five areas appear to have relatively higher suicide rates compared to the median income. Jacksonville, Florida, and Columbia, Missouri, are two small centers where this feature occurs. Much more intensive regions include the Reno, Nevada, area, and the Rocky Mountain front range, excluding a small area from Denver to Fort Collins. Finally, Texas yet again displays the two extremes when comparing the Dallas area to the regions to the west/southwest regions. The low values in the megalopolis area and New England run west throughout most of the Midwest. In Nebraska, Omaha has a lower level due to the lower suicide rate and slightly higher median income.

Once again, the interpolated values which lay between the MSA's may not give an accurate representation of the suicide rates nor the median income for those areas.

 

Conclusions:

The use of GIS to analyze demographic phenomena will allow for a better understanding of spatial trends. Not only will this understanding help monitor and predict the future trends, it will also allow for the creation of a plan to provide more concentrated assistance to regions in need. Demographic information is essential to urban planning, and GIS is essential to gain a better understanding demographic phenomena.

This type of study nearly scratches the surface as far as the possibilities of using GIS, and also understanding the suicide problems. More in depth research should take place to truly understand the factors which influence suicide rates in order to stop its drastic rise throughout the country.

 


References:

1989 Vital Health Statistics for the United States, Volume II-Mortality.

1989 United States Census, U.S. Census Bureau.

Suicide & Life-Threatening Behavior, Volume 25, Num. 1, Spring, 1995.

 

 

 

 

 

 

 


 

Submitted by Eric Herbert on April 26, 1998.