Homework #1 questions 8-10 (SECOND ATTEMPT) and Homework #2 All

1. Homework #1 questions 8-10 (SECOND ATTEMPT)and Homework #2 All SOCIOLOGY 593 – FORENSIC gis By: WOLFGANG ARTERBERRY

2. Question #8 hw#1

3. Question #9 hw#1

4. QUESTION #10 HW#1

5. QUESTION #1a hw#2 We have seen that many different variables have spatially significant relationships. However, with crime there does not seem to be any significant spatial relationship present. It is evenly dispersed throughout the City of Saint Louis, slightly favoring the South-Eastern portion.

6. Question #1b hw#2 Our homicide data presents more spatial relationship. The two primary centers of homicides found are in the North-Western and South-Eastern portions of the City of Saint Louis, with a tendency to lean more so to the North-West.

7. Question #2a hw#2 These results point to a similar conclusion as with the point data. Here we see the polygonal compactness and direction favoring the center, while the dispersion has centers of significance in the West/North-Western and South/South-Eastern portions of the City of Saint Louis.

8. Question #2b hw#2 The polygonal relationship between space and homicides is much more significant than with the crime. The entire Northern portion of the City of Saint Louis is found to be the center for homicides, with the mean center, compactness and direction all associated with the Northern half.

9. Question #3a hw#2 This startling map shows the racial dispersion/segregation of the City of Saint Louis. Nearly all clustering of white residents are found in the Southern half of the city, favoring more so to the South-West. This suggests a strong spatial relationship regarding racial dispersion.

10. Question #3b hw#2 These results were expected after viewing the dispersion of white residents and it did not disappoint. Though there are pockets in the South-Eastern portion of the city, nearly all clusters of black residents are found in the Northern half of the City of Saint Louis.

11. Question #3c hw#2 Very little changed from the results for dispersion of black residents vs. minority residents. We see some greater distribution in the South-Eastern portion of the city but the Northern half is still the center for minority clustering.

12. Question #3d hw#2 Though not as plainly obvious in its spatial dispersion, impoverished households are similar to that of minority residents, suggesting that the racial segregation of the City of Saint Louis has culminated in a greater propensity for those households to be at or below the poverty line.

13. Question #4 hw#2 Based on the P-Value of statistical significance we see that there is a strong spatial relationship regarding homicides, regardless of year, with only one year not suggesting a 100% chance of spatial relationship, though a 99.999% significance is far from false. There also is a greater spatial relationship with homicides by year than with crime in 2012. The average nearest neighbor scores back up these findings by showing that homicides in 2012 were nearly three times more clustered than with crime in the same year. These tabled results bolster the findings shown in the previous relevant maps, where crime is no where near as clustered as are homicides.

14. Question #5a.1 hw#2

15. Question #5a.2 hw#2

16. Question #5b.1 hw#2

17. Question #5b.2 hw#2

18. Question #6ahw#2 In this map we see two different things that need explanation. The first is that this map combines ten different variables into one “Risk Index” to show which areas of the city have a greater spatial relationship with risk. With green being safest and red being most at risk we see that there are definitive zones of risk in the City of Saint Louis. This map also shows one way of classifying the breaks, which is known as “Natural Breaks.” This method is most useful at showing the variance between classes without emphasizing the variance within each class.

19. Question #6bhw#2 In this map we see a continuance of the indexed risk within the City of Saint Louis. This method of classification seeks to distribute values into groups that show an equal range of values, which is why it is named “Equal Interval.”

20. Question #6chw#2 Seeking differentiation from the classifications of natural breaks and equal intervals, I manually separated the classes to illustrate the ability for statisticians to manipulate data in many different ways. There was some attempt at patternization to expand the breaks of greater significance, though little changed from that of the natural and equal interval breaks.

21. Question #6d hw#2 This map shows the same risk index as the previous three but instead is based off of a standard deviational classification. This type of classification is used to show how far the point data deviated from the mean. The lower the standard deviation score, the greater the clustering of the point data around the expected value, suggesting strong spatial relationships in the brown and dark brown areas of the City of Saint Louis.

22. Question #7ahw#2

23. Question #7bhw#2

24. Question #8hw#2 In this table we see that there is significant spatial relationship regarding some scaled variables but not all of them. Using two different means, “Zone of Indifference” and “Contiguity of Edges and Corners” the results show that just by using two different conceptualizations of spatial relationships can provide different answers to the same question. Using the .05 threshold for significance of P-Value, foreclosures, all ages of poverty, and unemployment have strong relationships while using the Zone of Indifference. Whereas, with Contiguity we see that homicides from 2005 – 2012, are also significant.

25. Question #9hw#2

26. Question #10a hw#2 In this map, we have a BiLISAmap based on the “Queen” method for conceptualization of spatial relationships between percentage of white residents and risk. Where there is strong significance regarding racial dispersion and risk in the middle of the North half and the South-Eastern portion of the City of Saint Louis.

27. Question #10b hw#2 This map is based on the same BiLISA method of mapping and the Queen method for conceptualizing spatial relationships but with percentage of black residents and risk. We see that the area where there is a strong spatial relationship between black residents and risk is larger but mostly in the same areas as that with white residents. Suggesting that black residents live a life in greater risk than that of white residents within the City of Saint Louis.

28. Question #10c hw#2 In this map a “Rook” method of conceptualization was used with the BiLISA method of mapping instead. This method expanded the areas where strong spatial relationships were found using the queen method. By using this method, researchers could show that there are greater distributions of risk for white residents when you conceptualize differently however, when results get expanded they, their areas of emphasis could be less significant than when implementing the queen method.

29. Question #10d hw#2 In this last map showing the BiLISA mapping method and the rook method of conceptualization paints a similar picture for black residents in risk as do that of the queen method for black residents and the rook method for white residents. This differentiation, or lack-there-of, suggests that this method is possibly of lesser substantiality than the queen method for identifying high risk areas for black residents.