Hostname: page-component-5c6d5d7d68-thh2z Total loading time: 0 Render date: 2024-08-18T00:59:42.131Z Has data issue: false hasContentIssue false

Residential Location and Electoral Cohesion: The Pattern of Urban Political Conflict*

Published online by Cambridge University Press:  01 August 2014

Timothy A. Almy*
Affiliation:
University of Georgia

Abstract

This study examines the assertions of urban scholars that the spatial arrangement of urban populations is important in determining the amount of conflict displayed within American cities. The article analyzes the spatial distribution of class groups within 18 cities and the degree of voting solidarity and conflict displayed within segregated and integrated sections of each community. Data were gathered from precinct voting returns for several local referenda in each city to test the following hypotheses: (1) The residential distribution of social-class groups will significantly influence the degree of electoral cohesion these groups display; (2) The spatial distribution of class groups will significantly influence the amount of electoral disagreement between class groups. The study found that communities that displayed segregated class groups had a high degree of class electoral solidarity. Within cities that manifested spatially integrated class groups, however, the electoral cohesion of each class was low. A social-class group located in an area of a city possessing wide class dissimilarity was not likely to vote in agreement with other groups of the same class located elsewhere in the city. The findings of this article suggest that location may be one of the sources of urban political conflicts.

Type
Articles
Copyright
Copyright © American Political Science Association 1973

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This article follows from the author's doctoral dissertation, “Ecological and Structural Determinants of Urban Electoral Conflict” (University of California, Riverside, 1971). Acknowledgments are due Charles R. Adrian and Ronald O. Loveridge for comments on the dissertation. I owe special thanks to Harlan Hahn who made these data available for analysis and who read and evaluated both the dissertation and manuscript.

References

1 For one discussion of some ecological factors and intercity cooperation, read Williams, Oliver P., Herman, Harold, Liebman, Charles S., and Dye, Thomas R., Suburban Differences and Metropolitan Policies, A Philadelphia Story (Philadelphia: University of Pennsylvania Press, 1965)Google Scholar.

2 Duncan, Otis Dudley and Duncan, Beverly, “Residential Distribution and Occupational Stratification,” American Journal of Sociology, 60 (March, 1965), 493503 CrossRefGoogle Scholar; Keller, Suzanne I., The Urban Neighborhood (New York: Random House, 1968), pp. 9092 Google Scholar.

3 Whyte, William H. Jr., The Organization Man (Garden City: Doubleday and Company, Inc., 1957), pp. 310344 Google Scholar.

4 Williams, Oliver P., Metropolitan Political Analysis, A Social Access Approach (New York: The Free Press, 1971), p. 28 Google Scholar.

5 Among the major works in this area are Beshers, James, Urban Social Structure (New York: The Free Press, 1962)Google Scholar; Michelson, William H., Man and His Urban Environment (Reading, Mass.: Addison-Wesley Publishing Company, 1970)Google Scholar; Sirjamaki, John, The Sociology of Cities (New York: Random House, 1964)Google Scholar.

6 Coleman, James S., Community Conflict (New York: The Free Press of Glencoe, 1957)Google Scholar; Coser, Lewis A, The Functions of Social Conflict (New York: The Free Press of Glencoe, 1956)Google Scholar.

7 Peoria: 125, 736; St. Joseph: 71, 996; Medford: 29, 750; Rockford: 144, 707; Greensboro: 140, 672; Dayton: 239, 591; Fremont: 100, 739; Yakima: 45, 060; Flint: 193, 371; Lima: 53, 373; Berkeley: 113, 165; Allentown: 108, 926; Knoxville: 169, 766; Trenton: 102, 211; Lincoln: 148, 092; Eugene: 77, 284; Beaumont: 115, 716; Sioux Falls: 72, 557.

8 A good discussion on the use of these specific data is provided by Harlan Hahn, “Ecological Data and Structural Characteristics: Some Notes on the Homogeneity of Precincts and Census Tracts (paper presented at the annual meeting of the Western Political Science Association, Albuquerque, N.M., April 8–10, 1971).

9 Social-class groups, computed on the basis of mean house values, were the following: Rank 1: mean values less than $7,320; Rank 2: mean values from $7,321 to $11,880; Rank 3: mean values from $11,881 to $18,120; Rank 4: mean values from $18,121 to $22,680; Rank 5: mean values above $22,681.

10 Each city was ranked according to its mean socialclass variance in voting for each referendum. The ranks were split at the median into two groups. Two-way analysis of variance statistics also indicated that splitting the ranks at the median resulted in the best homogeneous groupings possible. For fluoridation referenda the cutting-point was 73.6; for education, 70.6; for parks, 70.4; for civic improvements, 76.8; for public works, 82.2.

11 Essentially the same technique was used to determine high and low disagreement among classes. For fluoridation referenda, the dichotomy was 136.8; for education, 49.2; for parks, 44.2; for civic improvements, 68.8; for public works, 121.4.

12 Social class dissimilarity is computed in this way:

where D is the amount of dissimilarity between precincts in the ith case, and all surrounding precincts, Xi , is the social class of the ith precinct, and Xi , is the rank of the ith precinct which surrounds the ith precinct. Ni is the number of precincts with data that are spatially proximate to the ith precinct. M equals maximum (Ni Xi , Xi — 1), where Ni is the number of class groups (Nc = 5).

The numerator of the above ratio sums the rank dissimilarities between a precinct and its nearest surrounding precincts. This sum is weighted by the inverse of the square of the geographical distances (dil 2). (My thanks to W. Bruce Shepard, Oregon State University, for his assistance in developing this formula.) Socialclass dissimilarity scores ranged from 5.497 to 22.730. For example, cities above 8.918 were categorized as high in dissimilarity, i.e., residentially integrated.

13 Siegel, Sidney, Nonparametric Statistics (New York: McGraw-Hill Book Company, 1956), p. 96104 Google Scholar.

14 Beshers, , Urban Social Structure, p. 122 Google Scholar.

15 Tryon, Robert C., The Identification of Social Area By Cluster Analysis (Berkeley: University of California Press, 1955), p. 8 Google Scholar.

16 Beshers, p. 111.

17 Timms, D. W. G., The Urban Mosaic (Cambridge: Cambridge University Press, 1971), p. 2 CrossRefGoogle Scholar.

18 Michelson, , Man and His Urban Environment, pp. 119125 Google Scholar.

19 Sirjamaki, , Sociology of Cities, p. 270 Google Scholar.

20 Siegel, , Nonparametric Statistics, pp. 213223 Google Scholar.

21 Selltiz, Claire, Jahoda, Marie, Deutsch, Morton, and Cook, Stuart W., Research Methods in Social Relations (New York: Holt, Rinehart and Winston, 1959), p. 429 Google Scholar.

22 For a thorough discussion of these concepts, see Park, Robert, Burgess, Ernest, and McKenzie, Roderick, The City (Chicago: The University of Chicago Press, 1925)Google ScholarPubMed.

Submit a response

Comments

No Comments have been published for this article.