Research on reporting crime to the police on the individual- and incident- levels has received much attention over the years. However, many studies examining neighborhood-level effects on reporting are limited in scope. The current study examines the relationship between neighborhood characteristics central to social disorganization theory and police notification. Data for this study were derived from Warner’s (2004) study entitled “Informal Social Control of Crime in High Drug Use Neighborhoods in Louisville and Lexington, Kentucky, 2000.” The analysis uses OLS regression models to isolate how different neighborhood characteristics impact reporting. Findings indicate that disadvantage and mobility have a positive effect on reporting but are mediated by social cohesion. Social cohesion has a negative effect on reporting while confidence in police had no significant effects. Suggestions for future research are also discussed.

Research on reporting crime to the police on the individual- and incident- levels has received much attention over the years. However, many studies examining neighborhood-level effects on reporting are limited in scope. The current study examines the relationship between neighborhood characteristics central to social disorganization theory and police notification. Data for this study were derived from Warner’s (2004) study entitled “Informal Social Control of Crime in High Drug Use Neighborhoods in Louisville and Lexington, Kentucky, 2000.” The analysis uses OLS regression models to isolate how different neighborhood characteristics impact reporting. Findings indicate that disadvantage and mobility have a positive effect on reporting but are mediated by social cohesion. Social cohesion has a negative effect on reporting while confidence in police had no significant effects. Suggestions for future research are also discussed.

The gender difference in crime is indisputable. In an attempt to explain gender differences in adolescents' involvement in crime, secondary data analysis of middle and high school students and their neighborhoods will be examined. Feminists have identified the concept of patriarchy as the root of gender differences in all behavior and particularly in criminal behavior. Hagan's Power-Control Theory incorporates the concept of patriarchy through measures within home to examine how differences in occupational authority between parents affects the gender difference in delinquency through differential controls placed on sons and daughters. However, it has been suggested that the measure of patriarchy be extended into the public sphere (Walby, 1990). Specifically, this study compares a traditional private patriarchy model to a public patriarchy model in order to determine which approach better explains the gender gap in crime. Patterns of findings were not substantively different between private and public patriarchy models; however, a number of theoretical implications point to the fact that alternate measures of patriarchy could lend support for power-control theory that it currently lacks.

Social disorganization theory proposes that neighborhood characteristics, such as residential instability, racial and ethnic heterogeneity, concentrated disadvantage, and immigrant concentration contribute to an increase in crime rates. Informal social controls act as a mediator between these neighborhood characteristics and crime and delinquency. Informal social controls are regulated by members of a community and in a disorganized community these controls are not present, therefore, crime and delinquency flourish (Sampson, 2012). Researchers have focused on these measures of social disorganization and the ability to explain a variety of crimes, specifically public crimes. Recently, researchers have focused their attention to characteristics of socially disorganized areas and the ability to predict private crimes, such as domestic violence. This study contributes to the research on social disorganization theory and domestic violence by examining domestic offenses at three different units of analysis: Chicago census tracts, Chicago neighborhoods, and Illinois counties. Demographic variables from the 2005-2009 American Community Survey were utilized to measure social disorganization within Chicago census tracts, Chicago neighborhoods, and Illinois counties. Data on domestic offenses in Chicago were from the City of Chicago Data Portal and data on domestic offenses in Illinois counties were retrieved from the Illinois Criminal Justice Information Authority (ICJIA). This study incorporated geographic information systems (GIS) mapping to examine the relationships between locations of domestic offenses and the measures of social disorganization in each unit of analysis. Results of this study indicate that different measures of social disorganization are significantly associated with domestic offenses in each unit of analysis.

Existing studies of household overcrowding in Sweden are often descriptive and examine patterns at a large scale. Levels of overcrowding have increased since the mid-1980s and the highest shares are found in the largest cities among residents with a low income, a migration background, living in rental apartments, and often with children. The aim of this thesis is to increase the understanding of the measurements of household overcrowding, its development over time, its spatial patterns and its determinants at a small-scale neighborhood level with application to the City of Stockholm. It examines how the associations between overcrowding and other neighborhood characteristics can be understood in different neighborhood settings and what the implications are of using different scales and definitions of overcrowding. Cluster, correlation and regression analyses have been conducted using administrative data aggregated to key code areas and city districts. The results demonstrate that there are two types of overcrowding within the City of Stockholm, which are spatially separated and associated differently with socio-economic, demographic and housing characteristics of neighborhoods. It is suggested that explanatory segregation theories related to preference and economic and discriminatory structures are needed to understand the uneven spatial distribution of overcrowding in the City of Stockholm.

This thesis addresses three interrelated challenges of disease mapping and contributes a new approach for improving visualization of disease burdens to enhance disease surveillance systems. First, it determines an appropriate threshold choice (smoothing parameter) for the adaptive kernel density estimation (KDE) in disease mapping. The results show that the appropriate threshold value depends on the characteristics of data, and bandwidth selector algorithms can be used to guide such decisions about mapping parameters. Similar approaches are recommended for map-makers who are faced with decisions about choosing threshold values for their own data. This can facilitate threshold selection. Second, the study evaluates the relative performance of the adaptive KDE and spatial empirical Bayes for disease mapping. The results reveal that while the estimated rates at the state level computed from both methods are identical, those at the zip code level are slightly different. These findings indicate that using either the adaptive KDE or spatial empirical Bayes method to map disease in urban areas may provide identical rate estimates, but caution is necessary when mapping diseases in non-urban (sparsely populated) areas. This study contributes insights on the relative performance in terms of accuracy of visual representation and associated limitations. Lastly, the study contributes a new approach for delimiting spatial units of disease risk using straightforward statistical and spatial methods and social determinants of health. The results show that the neighborhood risk map not only helps in geographically targeting where but also in tailoring interventions in those areas to those high risk populations. Moreover, when health data is limited, the neighborhood risk map alone is adequate for identifying where and which populations are at risk. These findings will benefit public health tasks of planning and targeting appropriate intervention even in areas with limited and poor-quality health data. This study not only fills the identified gaps of knowledge in disease mapping but also has a wide range of broader impacts. The findings of this study improve and enhance the use of the adaptive KDE method in health research, provide better awareness and understanding of disease mapping methods, and offer an alternative method to identify populations at risk in areas with limited health data. Overall, these findings will benefit public health practitioners and health researchers as well as enhance disease surveillance systems.

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