These variables are included in the present model to test the explanatory power of social disorganization theory, which associates higher crime rates with “exogenous sources of social disorganization” , including socioeconomic disadvantage, residential instability, and family disruption. Additional control variables presented in Table 4.2 under “Other Tract Characteristics” include population size, population density, percent of land commercially zoned, and percent of the population that is male. For the vast majority of census tracts the value of the primary independent variable, MCD density, is zero. The 26 dispensaries operating in San Francisco in 2010 were largely clustered downtown. MCDs are not uniformly distributed throughout San Francisco. But neither is crime. Most crime also occurs downtown, with a large amount being reported in a cluster of densely populated census tracts. The fact that some of these high-crime tracts also contain the majority of San Francisco’s dispensaries makes it especially difficult, in the present analysis, to rule out a spurious correlation. This presents a limitation to the present analysis, albeit one that is intimately tied to the nature of the research question. Table 4.3 presents the results of four different linear regression analyses. The independent variables are the same in each,dry racks for weed and so are the cases . What differs is the dependent variable: total property crimes, property crimes per 1,000 residents, total violent crimes, and violent crimes per 1,000 residents.
The primary independent variables are MCD density and five indicators of social disorganization: poverty, unemployment, family stability, vacancy, and percent of the tract population between the ages of 18 and 29. Additional control variables include population size, percent of the population that is male, and percent of land that is commercially zoned. As Table 4.3 indicates, the four models tested by this study are fairly consistent in their findings with respect to which variables predict what, and with what power. One interesting difference is that MCD density is a stronger predictor of violent crime than property crime— although the property crime models have slightly better explanatory power over all . The relationship between MCD density and crime is weak but statistically significant, according to this model. These findings cast doubt on the claim that MCDs are magnets for criminal activity. Although the relationship between MCDs and crime is not completely insignificant, it is consistently weak across all four regression analyses conducted in this study. This is particularly striking given the clustering of MCDs in downtown census tracts . Of the other independent variables under review, the strongest predictors of crime—much more so than MCD density—are poverty and family stability. Poverty provides the strongest explanation for differences in violent crime rates across tracts, while family disruption provides the strongest explanation for property crime rates.
As with MCD density, poverty is found to be a better predictor of violent crime than property crime. The opposite is true for family stability. As family stability increases, crime rates decrease. This holds across all four models. But the strength of that correlation for violent crime is nearly twice what it is for property crime . These findings are consistent with past research on social disorganization theory . Taken together, these findings cast doubt on the claim that MCDs are magnets for crime. These findings should be considered in light of the limitations faced by the present study, which are several. Foremost among these is the narrow conception of the question. The link between MCDs and crime is an intriguing puzzle academically, and one that deserves greater attention. But for policymakers it is just one piece of a much larger puzzle. There are a multitude of factors to consider when deciding whether and how to regulate MCDs. Public safety is, of course, a necessary consideration. But in California so is the provision of state law, passed by a majority of voters in 1996, that qualified patients should have legal access to medical cannabis. And so are a number of other factors, ranging from potential effects on children and teenagers to the budgetary impact of MCD regulation and taxation by local governments. Thus, this study should be considered in light of its narrow academic approach to a broad real world problem. To the extent that this study says anything about the relationship between MCDs and crime, it must also be noted how little it has to say about these other factors. In addition, there are certain technical limitations to this study that merit discussion.
This study lacks breadth in both the quantity and quality of its case selection, which means that one should be cautious in generalizing from these findings. Moreover, the research design is purely observational . This makes causal identification impossible—observed associations could run in either direction, or they could be spurious. This study does not account for longer term trends in crime frequency or neighborhood characteristics, a problem that could be corrected by future research that incorporates longitudinal data. If, for example, the crime data obtained for this study could also be obtained for 1990 and 2000—years in which MCDs were nonexistent and virtually non-existent, respectively—then a “before and after” analysis could be conducted for San Francisco and other cities. The neighborhood data examined in this study also have their own limitations. While census tracts are convenient units of analysis, they are not perfect. They are imagined boundaries. The demographic data provided by the ACS provide a reasonably accurate picture of the people who live in a census tract, but do not account for the fact that people—and criminals in particular—tend to move from tract to tract. Nor do they account for tourists, transients, or anyone else unlikely to respond to census surveys. The MCD data are also limited. It is possible that some of the MCDs included in this analysis were not open for the entire calendar year in 2010, or that there were some additional MCDs operating in 2010 without an official permit . There is also the question of other cannabis distributors not classified as MCDs. These include illicit dealers, medical cannabis delivery services, and small patient collectives without storefront locations. Whatever effect these groups may have on crime is not captured by the present study. The crime data suffer from limitations as well. This study inherits all of the imperfections that accrue in reporting, recording, and transmitting data within and from police departments. Furthermore, because they are reported by approximate location , these crime data suffer from a lack of precision. Furthermore, because this study limits its analysis to a subset of crimes classified as Part I offenses by the FBI, it is not a very broad measure of crime. Policymakers might also be interested in less serious categories of crime when making decisions related to MCD regulation. In this paper I have sought to contribute towards a more informed understanding of the spatial relationship between MCDs and crime. Preliminary findings suggest that there may be a relationship between the density of MCDs within a census tract and the rate of reported crime , but this link is confounded by the fact that many MCDs are clustered in downtown San Francisco. Do MCDs cause crime, or do they simply tend to locate in higher crime areas? Results from a more sophisticated regression analysis—which considers important factors related to crime including socioeconomic disadvantage, family stability,vertical farming pros and cons and residential turnover—paint a more nuanced picture of urban crime. Findings cast doubt on the claim that MCDs are magnets for crime by suggesting that certain neighborhood characteristics are better predictors of crime than MCD density. The best predictors of high property crime rates are, in descending order of strength and significance: family instability, poverty, percent of housing units that are vacant, and percent of individuals between the ages of 18 and 29. The best predictors of violent crime rates are poverty and family instability. In many cases the relationship between one or more of these factors and crime is stronger than the relationship between MCDs and crime.
Thus it may be the case that certain neighborhoods would have even higher rates of crime if not for the presence of MCDs—ongoing longitudinal research should test whether this is the case. And generally speaking, future research should address the uncertainty in these findings by collecting larger bodies of data across longer periods of time. As more thorough and better controlled analyses are conducted across the many jurisdictions currently grappling with MCD regulation, a clearer picture will emerge of the relationship between MCDs and crime. Eventually, policymakers may be able to break free of the “smoke and mirrors” that plague the current debate surrounding medical cannabis in California and other states.In the past decade overall, there have been increases in adolescent nicotine and cannabinoid use. Yet the long-term implications of this drug exposure, in particular the coexposure of both of these drugs, on cognition, reward-related behaviors, later drug intake, and relapse-related behaviors is largely understudied. This dissertation explores the novel studies conducted to assess the long-term implications of adolescent nicotine and cannabinoid exposure. Using various behavioral paradigms and intravenous nicotine self administration in a mouse model, we have shown that adolescent exposure to a cannabinoid or co-exposure to both nicotine and a cannabinoid alters anxiety-related behaviors, cognitive flexibility, natural reward consumption, and nicotine intake in a sex-dependent manner . Moreover, we have shown that adolescent drug exposure can alter the responsivity to cue-induced drug seeking later in life . The final chapter of this dissertation branches off into a more global perspective focusing on the importance of proper mentorship and support for people from historically marginalized backgrounds in the field of neuroscience .Substance use disorder is typically characterized by excessive and compulsive drug seeking behaviors, in which the individual continues to use the drug despite harmful consequences. Worldwide, nicotine is the leading cause of preventable death and cannabis is the most abused illicit substance. Following initial experimentation, continued use may lead to future patterns of abuse. Thus, tobacco use disorder and cannabis use disorder represent the consequences of dependence on either nicotine or an exogenous cannabinoid, respectively. Over 1.3 billion people around the world report using tobacco products, which includes cigarettes, cigars, e-cigarettes, and smokeless tobacco. Nicotine is the main psychoactive component derived from the leaves of the tobacco plant, Nicotiana tabacum. Nicotine use in humans can lead to multiple positive effects, including mild euphoria, decreased appetite, reduced stress/anxiety, and improvements in memory and concentration. In addition to nicotine, tobacco smoke contains many other toxic chemicals, such as ammonia, arsenic, formaldehyde, acetaldehyde, and tar. Thus, it is perhaps not surprising that tobacco smoking has been causally linked to multiple types of cancer, stroke, coronary heart disease, lung disease, chronic obstructive pulmonary disease and periodontitis. In the US alone, the combined direct cost of healthcare to treat smoking related disease and the human capital losses amass to more than $300 billion each year.In the past decade, there has been the emergence of a potentially safer alternative to tobacco cigarettes, electronic nicotine delivery systems . ENDS heat liquid to produce an aerosol that users inhale, and like tobacco smoke, allows for nicotine absorption through the lungs. In recent years overall, tobacco cigarette use has been declining, but the use of ENDS conversely increased. In 2011, it was estimated that there were seven million ENDS users worldwide, but by 2018, the number dramatically increased to 41 million. ENDS have been beneficial as a therapeutic approach to assist individuals in reducing the number of tobacco cigarettes smoked. For instance, in one study, over 80% of former tobacco smokers reported that ENDS helped them quit smoking tobacco cigarettes. However, ENDS use by individuals who have never smoked tobacco cigarettes remains a major concern, due to the increased potential of developing nicotine dependence and later tobacco cigarette use. Indeed, of the estimated 5.6 million US adults who currently used ENDS, 1.3 million of them were never smokers. Moreover, of those that were dual users of both tobacco cigarettes and e-cigarettes, 70% of them reported trying to use ENDS to quit smoking, which then led to dual use . Although ENDS are considered to be safer than tobacco cigarettes, ENDS can omit harmful constituents, including carcinogens, nickel, and lead. Further, to appeal to youth, additives in the ENDS solutions may flavor the vapor as candy or fruit, but these flavoring chemicals may lead to adverse health consequences when inhaled.