Mass transit is often thought to be dangerous, eliciting concerns for personal security when waiting for and traveling on mass transit. One of the first steps in preventing crime in mass transit environments is to obtain accurate figures of crime. There are two mechanisms linking mass transit to crime: extension of offenders' journey-to-crime and development of crime attractors and generators. The first mechanism has been tested by comparing crime patterns when a new light rail system was expanded into new areas; these studies reported that new transit systems do not change crime patterns. Testing the second mechanism, whether mass transit enhances crime opportunities in the neighborhoods by attracting targets and offenders alike, however, faces more challenges; testing the impact of small places on larger areas such as transit stations on crime is fraught with difficulties due to the interactions with their surrounding environments. These difficulties are even more relevant for bus stops located without clear demarcation or controlled access.;Using frameworks of routine activity, crime pattern, and rational choice theories, the present study set out to investigate the relationships between bus stops and crime using Newark, New Jersey as the study area. To delineate the impact of bus stops on their surroundings from other possible covariates, the existence of commercial activities in the areas was also examined. The present research study examined five crime types: robbery, aggravated assault, motor vehicle theft, theft from motor vehicle, and burglary.;To better understand the impacts of spatial aspects of the data, several data analyses methods were utilized. First, the study examined the magnitude and structure of spatial dependence in the data. Second, spatial process models were performed and compared with the OLS regression results to examine the impacts of spatial aspects on the regression results. Third, to address non-normality and spatial dependence of the data, the count response model was run by adding spatial lag as one of the predictors.;The data analysis results showed that both bus stops and commercial establishments were associated with increased crime in the neighborhoods. Among the business types, some of them displayed more robust relationships than others. For instance, the category of food store was almost always significant to increased crime whereas banks were not statistically significant across crime types and regression methods.;Considering the fact that fear of crime plays a strong role as actual risk of crime in making travel decisions, it is suggested that the physical and social incivilities should be analyzed by performing an environmental survey using case-control design. In addition, future research should incorporate both short-term and long-term temporal analysis.
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