Increases in alcohol availability have caused crime rates to escalate across multiple regions around the world. As individuals consume alcohol they experience impaired judgment and a dose-response escalation in aggression that, for some, leads to criminal behaviour. By limiting alcohol availability it is possible to reduce crime; however, the literature remains mixed on the best practices for alcohol access restrictions. Variances in data quality and statistical methods have created an inconsistency in the reported effects of price, hour of sales, and alcohol outlet restrictions on crime. Most notably, the research findings are influenced by the different effects of alcohol establishments on crime. The objective of this PhD research was to develop novel quantitative approaches to establish the extent alcohol access (outlets) influences the frequency of crime (liquor, disorder, violent) at a fine level of spatial detail (x,y locations and block groups). Analyses were focused on British Columbia’s largest cities where policies are changing to allow greater alcohol access, but little is known about the crime-alcohol access relationship. Two reviews were conducted to summarize and contrast the effects of alcohol access restrictions (price, hours of sales, alcohol outlet density) on crime, and evaluate the state-of-the-art in statistical methods used to associate crime with alcohol availability. Results highlight key methodological limitations and fragmentation in alcohol policy effects on crime across multiple disciplines. Using a spatial data science approach, recommendations were made to increase spatial detail in modelling to limit the scale effects on crime-alcohol association. Providing guidelines for alcohol-associated crime reduction, kernel density space-time change detection methods were also applied to provide the first evaluation of active policing on alcohol-associated crime in the Granville St. entertainment district of Vancouver, British Columbia. Foot patrols were able to reduce the spatial density of crime, but hot spots of liquor and violent assaults remained within 60m proximity to bars (nightclubs). To estimate the association between alcohol establishment size, and type on disorder and violent crime reports in block groups across Victoria, British Columbia a Poisson Generalized Linear Model with spatial lag effects was applied. Estimates provided the factor increase (1.0009) expected in crime for every additional patron seat added to an establishment capacity, and indicated that establishments should be spaced greater than 300m a part to significantly reduce alcohol-associated crime. These results offer the first evaluation of seating capacity and establishment spacing on alcohol-associated crime for alcohol license decision making, and are pertinent at a time when alcohol policy reform is being prioritized by the British Columbia government. In summary, this dissertation contributes 1) cross-disciplinary policy and methodological reviews, 2) expands the application of spatial statistics to alcohol-attributable crime research, 3) advances knowledge on local scale of effects of different alcohol establishment types on crime, 4) and develops transferable models to estimate the effects of alcohol establishment seating capacity and proximity between establishments on the frequency of crime. / Graduate / 2018-02-27

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This paper analyzes if neighborhood income inequality has an effect on informal regulation of environmental quality, using census tract-level data on industrial air pollution exposure from EPA´s Risk Screening Environmental Indicators and income and demographic variables from the American Community Survey and EPA´s Smart Location Database. Estimating a spatial lag model and controlling for formal regulation at the states level, we find evidence that overall neighborhood inequality - as measured by the ratio between the fourth and the second income quintile or the neighborhood Gini coefficient - increases local air pollution exposure, whereas a concentration of top incomes reduces local exposure. The positive coefficient of the general inequality measure is driven by urban neighborhoods, whereas the negative coefficient of top incomes is stronger in rural areas. We explain these findings by two contradicting effects of inequality: On the one hand, overall inequality reduces collective action and thus the organizing capacities for environmental improvements. On the other hand, a concentration of income at the top enhances the ability of rich residents to negotiate with regulators or polluting plants in their vicinity. (authors' abstract) / Series: Department of Economics Working Paper Series

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / O atual nível das mudanças uso do solo causa impactos nas mudanças ambientais globais. Os processos de mudanças do uso e cobertura do solo são processos complexos e não acontecem ao acaso sobre uma região. Geralmente estas mudanças são determinadas localmente, regionalmente ou globalmente por fatores geográficos, ambientais, sociais, econômicos e políticos interagindo em diversas escalas temporais e espaciais. Parte desta complexidade é capturada por modelos de simulação de mudanças do uso e cobertura do solo. Uma etapa do processo de simulação do modelo CLUE-S é a quantificação da influência local dos impulsores de mudança sobre a probabilidade de ocorrência de uma classe de uso do solo. Esta influência local é obtida ajustando um modelo de regressão logística. Um modelo de regressão espacial é proposto como alternativa para selecionar os impulsores de mudanças. Este modelo incorpora a informação da vizinhança espacial existente nos dados que não é considerada na regressão logística. Baseado em um cenário de tendência linear para a demanda agregada do uso do solo, simulações da mudança do uso do solo para a microbacia do Coxim, Mato Grosso do Sul, foram geradas, comparadas e analisadas usando o modelo CLUE-S sob os enfoques da regressão logística e espacial para o período de 2001 a 2011. Ambos os enfoques apresentaram simulações com muito boa concordância, medidas de acurácia global e Kappa altos, com o uso do solo para o ano de referência de 2004. A diferença entre os enfoques foi observada na distribuição espacial da simulação do uso do solo para o ano 2011, sendo o enfoque da regressão espacial que teve a simulação com menor discrepância com a demanda do uso do solo para esse ano. / Present state of land use changes impacts global environmental changes. Land use and cover changes are complex processes and do not occur at random pattern in an area. In general, they are determined locally, regionally and globally by geographic, environmental, social, economic and political factors interacting at diverse temporal and spatial scales. Part of this complexity can be modeled by land use and cover change simulation models. An important step of simulation process in CLUE-S model is local influence of driving forces over the occurrence of a land use type. This influence is obtained by logistic regression model. A spatial lag regression model is proposed to select driving forces. This model incorporates spatial neighborhood information which is ignored by logistic regression. Based on a lineal trend scenario of land use demand, simulations of land use changes for Coxim microbasin, Mato Grosso do Sul, were generated, analyzed and compared using CLUE-S model under logistic and spatial regression approaches. The period of simulations was 2001-2011. Both approaches revealed elevated concordance, high global accuracy and Kappa index, to land use for 2004 reference year. Differences were observed for spatial distribution for land use simulations for 2011. Spatial lag regression simulation for 2011 reached less discrepancy to land use demand for that year.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / O atual nível das mudanças uso do solo causa impactos nas mudanças ambientais globais. Os processos de mudanças do uso e cobertura do solo são processos complexos e não acontecem ao acaso sobre uma região. Geralmente estas mudanças são determinadas localmente, regionalmente ou globalmente por fatores geográficos, ambientais, sociais, econômicos e políticos interagindo em diversas escalas temporais e espaciais. Parte desta complexidade é capturada por modelos de simulação de mudanças do uso e cobertura do solo. Uma etapa do processo de simulação do modelo CLUE-S é a quantificação da influência local dos impulsores de mudança sobre a probabilidade de ocorrência de uma classe de uso do solo. Esta influência local é obtida ajustando um modelo de regressão logística. Um modelo de regressão espacial é proposto como alternativa para selecionar os impulsores de mudanças. Este modelo incorpora a informação da vizinhança espacial existente nos dados que não é considerada na regressão logística. Baseado em um cenário de tendência linear para a demanda agregada do uso do solo, simulações da mudança do uso do solo para a microbacia do Coxim, Mato Grosso do Sul, foram geradas, comparadas e analisadas usando o modelo CLUE-S sob os enfoques da regressão logística e espacial para o período de 2001 a 2011. Ambos os enfoques apresentaram simulações com muito boa concordância, medidas de acurácia global e Kappa altos, com o uso do solo para o ano de referência de 2004. A diferença entre os enfoques foi observada na distribuição espacial da simulação do uso do solo para o ano 2011, sendo o enfoque da regressão espacial que teve a simulação com menor discrepância com a demanda do uso do solo para esse ano. / Present state of land use changes impacts global environmental changes. Land use and cover changes are complex processes and do not occur at random pattern in an area. In general, they are determined locally, regionally and globally by geographic, environmental, social, economic and political factors interacting at diverse temporal and spatial scales. Part of this complexity can be modeled by land use and cover change simulation models. An important step of simulation process in CLUE-S model is local influence of driving forces over the occurrence of a land use type. This influence is obtained by logistic regression model. A spatial lag regression model is proposed to select driving forces. This model incorporates spatial neighborhood information which is ignored by logistic regression. Based on a lineal trend scenario of land use demand, simulations of land use changes for Coxim microbasin, Mato Grosso do Sul, were generated, analyzed and compared using CLUE-S model under logistic and spatial regression approaches. The period of simulations was 2001-2011. Both approaches revealed elevated concordance, high global accuracy and Kappa index, to land use for 2004 reference year. Differences were observed for spatial distribution for land use simulations for 2011. Spatial lag regression simulation for 2011 reached less discrepancy to land use demand for that year.

This research presented in this study demonstrates that county level population growth, economic growth, and localized social structure are interrelated. An analysis of the spatial distribution of these factors in the Southeast Region of the United States during the period of 1995-2000 also indicates the importance of differences in rural versus urban and coastal versus non-coastal areas as well as the importance of such factors as highways, large cities, and universities in economic, population, and social structure interrelationships. An extensive dataset is used in the analysis as a number of analysis tools including statistical analysis, econometric models, spatial econometric models, structural equation models, and GIS mapping.