The San Francisco Medical Cannabis Act sets up a permitting system for MCDs and places certain restrictions on their location and operation. Speaking directly to the issue of crime, Section 3308 of the Act states that dispensaries must “provide and maintain adequate security on the premises, including lighting and alarms reasonably designed to ensure the safety of persons and protect the premises from theft.” The Act further requires that all MCDs comply with California state law as well as guidelines written in 2008 by then-Attorney General Jerry Brown entitled “Guidelines For The Security And Non-Diversion Of Marijuana Grown For Medical Use”. Brown’s guidelines state that “a properly organized and operated collective or cooperative that dispenses medical marijuana through a storefront may be lawful under California law” if they meet certain requirements, including: operating on a not-for-profit basis, obtaining all of the relevant permits and licenses, taking steps to verify that their members are qualified patients or caregivers under state law, acquiring and distributing only marijuana that has been cultivated legally, prohibiting sales to non-members, and providing adequate security . Regarding security, Brown states that MCDs must “provide adequate security to ensure that patients are safe and that the surrounding homes or businesses are not negatively impacted by nuisance activity such as loitering or crime.” Expanding on this theme, the San Francisco Medical Cannabis Act prohibits “any breach of peace… or any disturbance of public order or decorum by any tumultuous,cannabis grow room riotous or disorderly conduct” within permitted MCDs.MCDs are required to submit security plans as part of their permit application.This study examines whether such security protocols amount to capable guardianship, which is an effective deterrent against crime according to routine activities theory .
In cities that do not regulate MCDs, it is difficult to determine when and where dispensaries operate, and for how long. It is also much more difficult, in the case of unregulated dispensaries, to infer whether MCDs implement security protocols amounting to capable guardianship against crime, from a routine activities perspective . Simply put, San Francisco is the largest California city to have enacted meaningful legislation with respect to MCDs. It has done so in a way that reasonably controls for crime, at least in theoretical terms. Thus it provides an excellent case study for analyzing the spatial relationship between crime and locally regulated MCDs. All data analyzed in this study are for the year 2010, a year in which there were 26 permitted MCDs in San Francisco. Although it is too early to say, it is possible, due to the current federal crackdown on MCDs in California and MCDs, that 2010 will end up being the last full year for which this type of analysis can be conducted. Since research for this project began in the summer of 2011, at least ten Bay Area MCDs have closed, including five in San Francisco, under pressure from Melinda Haag, the US Attorney for the Northern District of California.Not surprisingly, Haag’s justification for the crackdown is that MCD’s attract crime and endanger communities. Crime data were collected from the San Francisco Police Department in late 2011. The Crime Analysis Unit provided lists of serious crimes reported in 2010 along with the date and approximate location of each crime. Here, “serious crimes” refer to those classified as Part I offenses by the Federal Bureau of Investigation in its Uniform Crime Reports. The crime variables used in this analysis include measures of “violent crime” as well as “property crime” ; both as total counts and as rates per 1,000 residents. The lists of crimes and addresses were geocoded and aggregated into census tracts using ArcGIS software.Geocoding refers to the process by which tabular data are attributed spatial components by a geodatabase.
Geocoding resulted in a successful match for more than 98% of all crimes, which were the aggregated into census tracts using a “spatial join” analysis. The remaining 1-2% of crimes were discarded from analysis. In addition, some reported crimes were removed from analysis because their geocoding confidence ratings were below 95%. In the end 43,688 reported crimes were analyzed out of the original 44,422 for which the San Francisco Police Department provided 2010 data. For the purpose of analysis, these crime measures were aggregated together by census tract. This measure was then transformed by natural logarithm to address a right-skewed distribution . The primary independent variable under examination is the density of MCDs. Lists of MCD names and addresses were compiled using information provided by the San Francisco Department of Public Health. These MCDs were located across 16 census tracts primarily in the downtown area, as illustrated by Maps 4.1 and 4.2. The MCD addresses were geocoded to 100%. As with crime frequency, data for MCDs are presented in two forms. Descriptive statistics presented in Table 4.1 include MCD density as the number of dispensaries per square mile in a given census tract. For the regression analyses presented by Table 4.2, this variable is transformed by natural logarithm to address a right-skewed distribution.Crime rates by census tract are presented by Maps 4.1 and 4.2 . All data presented are for the year 2010. MCD locations are marked by green crosses. These addresses were obtained from the San Francisco Department of Public Health. Crime rates were calculated using data obtained from the San Francisco Police Department. Census tracts are assigned to one of five classes based on their crime rates. Because crime rates were transformed by natural logarithm to address a right-skewed distribution, units are not given. As Maps 4.1 and 4.2 illustrate, MCDs are largely concentrated in downtown San Francisco. This could confound the relationship between MCD density and crime. Downtown areas are densely populated and highly trafficked. In terms of routine activities theory , they contain larger numbers of likely offenders and suitable targets.
The high rate and volume of human activity also poses a challenge in terms of guardianship. Thus it is likely that these areas will have high rates of crime, independent of any other factor . This potentially confounding factor highlights the need to consider other variables in the forthcoming analysis, namely, the “exogenous sources of social disorganization” identified by Sampson and Groves . The story told by Maps 4.1 and 4.2 is too simple to be of use to policymakers wishing to understand the relationship between MCDs and crime. Alongside MCD density, it is also important to analyze socioeconomic disadvantage, family stability, and residential turnover. I discuss these factors in the following section.In addition to the primary variables already discussed, data were also collected for several neighborhood characteristics that could potentially confound the relationship between MCDs and crime. These neighborhood characteristics are drawn from social disorganization theory, which associates higher rates of crime with socioeconomic disadvantage, family disruption, residential instability, and population heterogeneity . From these, the present study examines the criminogenic effect of poverty, unemployment, percent of single-parent households, percent of housing units that are vacant, and percent of the population between the ages of 18 and 24. Demographic data are collected from the American Community Survey database of the United States Census Bureau via the American FactFinder website, as well as the Demographic Research Unit of the California Department of Finance. With regard to the census data, variables are constructed from the ACS 5-year estimates for the year 2010.Criminological research has found that indicators of socioeconomic disadvantage—including poverty and unemployment—have been associated with higher crime rates . In the present study economic data are collected from the ACS. The U.S. Census Bureau calculates the poverty status of individuals based on whether their total income in the past 12 months falls below the applicable poverty threshold,grow trays which is determined by age, family size, and family composition . The 2010 poverty thresholds range from $11,139 for a single individual living alone to $42,156 for a family of eight or more people living in the same household.For the present analysis, “poverty” means the number of individuals with incomes under their applicable poverty threshold in the past twelve months, divided by the total number of people for whom poverty status is calculated within a given tract.According to the United States Census Bureau, an individual is considered unemployed if he or she did not have a job and has been actively looking for work during the last four weeks and was available to start a job at the time of the survey . “Unemployment”, in the present analysis, means the unemployment rate in each tract as estimated by the ACS.
Research examining crime rates in the United States during the 1990’s suggests that the job market can provide powerful explanations for criminal behavior . Poverty and unemployment are important measures in the model currently being tested, as they control for varying levels of socioeconomic disadvantage across city neighborhoods, which according to social disorganization theory affect crime rates in significant ways .In this study I use “family stability” as an inverse measure of family disruption. I calculate family stability by taking the number of individuals living in married couple family housing and dividing it by the number of people living in single-parent family households. Scholars of both routine activities theory and social disorganization theory predict that higher concentrations of married-couple families are associated with lower crime rates in urban areas, because more parents can provide more supervision and therefore more social control. From a routine activities perspective, both “family stability” and “residential stability” correspond with the notion of capable guardianship. According to social disorganization theory, residential turnover weakens a community’s social cohesion and therefore its ability to deter and prevent crime within its territory. In this study I use vacancy rates as measure of residential instability. I calculate “vacancy” by dividing the number of vacant housing units within a census tract by the total number of units within that tract. Data for this measure comes from the Census 2010 Redistricting Plan.Another indicator of residential turnover discussed in the criminological literature is the percent of the sample population that is young . The idea is that neighborhoods with a high concentration of young adults will have correspondingly fewer older adults and children, which results in a lack of social cohesion and crime-preventive capacity much in the same way as the other precursors of “social disorganization” already discussed. The variable “percent young” was constructed using ACS population estimates by dividing the total number of individuals between the ages of 18 and 29 by the total tract population.Although they are not of particular theoretical interest, the following measures are included as demographic control variables: population size, percent of the population that is male, and percent of land that is commercially zoned. Population size and gender composition are adapted from the 2010 Census. “Percent of land commercially zoned” was calculated in ArcGIS using zoning shape files provided by the San Francisco Planning Department. Tables 4.1 and 4.2 present descriptive statistics for the measures analyzed in this study. Here the crime data are provided as total counts by category, but in the regression analysis that follows the crime variables are transformed by natural logarithm to address a right-skewed distribution . All other variables are presented as described in the previous section. A total of 189 census tracts within San Francisco are analyzed using data for the year 2010. Five census tracts were removed from analysis because only partial data were available; these were low population tracks with no MCDs and therefore their loss is not analytically significant. Of the tracts analyzed, the average population size is 4,234. The average crime rate is 197.38 property crimes per year . The average violent crime rate is much lower: only 28.65 reported instances per year . According to the results of regression analyses presented in Table 4.3, the current model is better at explaining property crime than it is violent crime. The simplest explanation for this is that substantially more property crimes are committed on a yearly basis than violent crimes, as illustrated by Table 4.1 below on the following page.Descriptive statistics for the independent variables analyzed by this study are also presented in Table 4.2 These include “% Unemployed” and “% Under Poverty” as measures of socioeconomic disadvantage; “% of Housing Units Vacant” and “% of Population Ages 18-29” and measures of residential instability; and “% In Married-Couple Families” as a measure of family stability .