Geographic Information Systems Analysis of Crime in San Luis Obispo for 2012

The research and final project that I plan to do will be composed of a few different parts. I will be taking crime report data from the city of San Luis Obispo and analyzing it with GIS software. The data will be from the most recent reported calendar year. I will be looking at the data spatially so that I can compare the areas of the city in which crime is most prevalent. I will be creating multiple maps which will be looking individually at different types of crime, such as violent crimes, burglary and theft, assault and battery, sex crimes, alcohol and drug related crimes, and others. I hope to be able to compare the maps of individual crimes to compile a complete spatial view of the city as it relates to criminal activity. It should be interesting to compare the completed maps to the proximity to schools, bars, other local businesses, and population density, to see what has the greatest impact on where the crimes are committed. The final project will consist of a number of GIS maps of the city of SLO, an analysis of each and the implications of the findings. I would also like to do a simple analysis with all the crimes committed during one year, and another with all the crimes committed five years later so that I can examine the changes over that period. Hopefully this will illuminate some trends that influence the crime in the city and may provide some solutions for the problems. In conclusion, my final project should be a comprehensive report of the crime report data in SLO, examined spatially, and analyzed in comparison to previous years, hopefully providing insight into current trends in crime in our city

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Geospatial Analysis Of Violent Crime And Premature Mortality From Chd

Background: Cardiovascular disease (CVD) is the leading cause of death in the United States and many of these deaths are preventable. Studies have shown that neighborhood-level characteristics may contribute to health outcomes, but no study has yet examined whether neighborhood crime contributes to early mortality from CVD. Objective: We examined geographic trends in the association between neighborhood crime rates and premature mortality from coronary heart disease (CHD) using New Haven, CT USA as a model city. Methods: Neighborhoods in New Haven were established by existing census tracts. CHD deaths were identified from the Connecticut Master Death Files and violent crime rates were calculated from the FBI Uniform Crime Reports. We conducted a global ordinary least squares (OLS) analysis and a geographically weighted regression (GWR) analysis to model average years of potential life lost (YPLL) by census tract. Results: Out of 687 CHD deaths in the city of New Haven from 2005-2010, 319, or 46.4%, are considered premature. The OLS model accounted for 30.8% and the GWR model accounted for 48.6% of the variability in premature deaths from CHD. An increase of 10 violent crimes per 1,000 residents was associated with an average of 2.3 additional years of life lost (p=0.043), while holding other neighborhood factors constant. Moreover, the GWR model predicted a 7-fold disparity in premature CHD mortality across census tracts, ranging from 1.73 YPLL to 12.38 YPLL. Conclusion: Our findings suggest that neighborhood violent crime rates may contribute to premature death from CHD. Modeling based on geographic variation is a powerful tool to enhance resolution of previously unidentified environmental factors contributing to preventable death from cardiovascular disease

Improving obesogenic environmental assessments with advanced geospatial methods

This thesis explores the intricate connections between the envir- onment and obesity. It develops and applies advanced geospatial methods to enhance the assessment of obesogenic environments and obesity risks. Its primary objective is to evaluate obesogenic environments and explore potential associations between environ- mental factors and obesity, crucial for effective obesity prevention. The thesis is structured around four key objectives. The first sub-objective involves an investigation into the current literature on the measurement of the built environment. Street View Imagery (SVI) and advanced urban visual intelligence technologies have transformed Built Environment Auditing (BEA) substantially, enabling large-scale auditing at a detailed geographical level. A me- ticulous review of 96 articles published before September 15, 2023, reveals key areas for improvement in SVI-based BEA. Recommend- ations include standardized datasets for more accurate audits, the integration of multi-source SVI for comprehensive assessments, and the design of auditing tools tailored to developing countries. Ad- dressing these areas enhances the potential of SVI in environmental auditing, as they contribute to a better understanding of the built environment’s health impact and facilitate informed decision-making in urban planning and public health initiatives. The second sub-objective focuses on analyzing exposure to in- creasing PM2.5 pollution, associated with rising morbidity and mor- tality. An ensemble machine learning model, integrating multi-source geospatial data, is presented to map hourly street-level PM2.5 concen- trations in the city of Nanjing, China, at a 100 m spatial resolution. The study concludes that mapping these concentrations reveals spati- otemporal trends, supporting the establishment of exposome studies. The third sub-objective addresses the development of a framework to evaluate Physical Activity (PA) opportunities (bikeability) in urban environments, aiming to enhance sustainable urban transportation planning. A framework is proposed that comprises safety, comfort, accessibility, and vitality sub-indices. It uses open-source data, ad- vanced deep neural networks, and GIS spatial analysis, to eliminate subjective evaluations and enhance efficiency. Experimental results in the city of Xiamen, China, demonstrate the framework’s effectiveness in identifying areas for improvement and enhancing cycling mobility. The fourth sub-objective investigates the associations between PA opportunities, specifically walkability, and obesity. Using a cross- sectional cohort from Nanjing, China. A Logistic regression model with a double robust estimator estimates the effects of walkability on obesity risks. A newly developed walkability index shows a sig- nificant negative association with obesity, particularly when using a data-based-buffer derived from web-mapping navigation that better represents individual activity spaces. These findings provide evidence for developing explicit strategies for obesity prevention. In summary, this thesis contributes to addressing the knowledge gap in health geography between obesogenic environments and obesity risks, employing advanced geospatial methods. The integration of multisource geospatial data, machine learning methods like deep learning in a GIS environment, and spatial statistics presents a major step forward

Analyzing the Relationship Between Perception of Safety and Reported Crime in n Urban Neighborhood Using GIS and Sketch Maps

This study analyzes the perception of safety among residents of Main South neighborhood in Worcester, MA, USA and compares it to reported crimes. This neighborhood is the focus of a community-based crime reduction project funded by the Bureau of Justice Assistance, the policy development arm of the U.S. Department of Justice. We collected social disorder and violent crime data from the Worcester Police Department and conducted 129 household surveys to understand residents’ perception of safety in the neighborhood and trust in community institutions. The surveys included a map on which residents indicated where they felt unsafe. The goal of this research was twofold: (1) to use geographic information systems (GIS) to analyze the differences in perception of neighborhood safety by gender and length of residency in the neighborhood and (2) to explore the relationship between reported crime and perception of safety in the community. Findings indicate that the strength of the correlation between perceived safety and reported crime varies and that gender and length of residency are significant factors that shape perceptions of safety. Implications of this research suggest the need for comprehensive community-based development initiatives to offer differentiated strategies that address a broad range of safety perceptions and crime experiences among a diverse group of residents

BIG DATA APPLICATIONS AND CHALLENGES IN GISCIENCE (CASE STUDIES: NATURAL DISASTER AND PUBLIC HEALTH CRISIS MANAGEMENT)

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials. The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic\u27s incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic\u27s severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread. By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks

Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

Geospatial analysis and living urban geometry

This essay outlines how to incorporate morphological rules within the exigencies of our technological age. We propose using the current evolution of GIS (Geographical Information Systems) technologies beyond their original representational domain, towards predictive and dynamic spatial models that help in constructing the new discipline of "urban seeding". We condemn the high-rise tower block as an unsuitable typology for a living city, and propose to re-establish human-scale urban fabric that resembles the traditional city. Pedestrian presence, density, and movement all reveal that open space between modernist buildings is not urban at all, but neither is the open space found in today's sprawling suburbs. True urban space contains and encourages pedestrian interactions, and has to be designed and built according to specific rules. The opposition between traditional self-organized versus modernist planned cities challenges the very core of the urban planning discipline. Planning has to be re-framed from being a tool creating a fixed future to become a visionary adaptive tool of dynamic states in evolution

Crime in Context: Utilizing Risk Terrain Modeling and Conjunctive Analysis of Case Configurations to Explore the Dynamics of Criminogenic Behavior Settings.

Risk terrain modeling (RTM) is a geospatial crime analysis tool designed to diagnose environmental risk factors for crime and identify the places where their spatial influence is collocated to produce vulnerability for illegal behavior. However, the collocation of certain risk factors’ spatial influences may result in more crimes than the collocation of a different set of risk factors’ spatial influences. Absent from existing RTM outputs and methods is a straightforward method to compare these relative interactions and their effects on crime. However, as a multivariate method for the analysis of discrete categorical data, conjunctive analysis of case configurations (CACC) can enable exploration of the interrelationships between risk factors’ spatial influences and their varying effects on crime occurrence. In this study, we incorporate RTM outputs into a CACC to explore the dynamics among certain risk factors’ spatial influences and how they create unique environmental contexts, or behavior settings, for crime at microlevel places. We find that most crime takes place within a few unique behavior settings that cover a small geographic area and, further, that some behavior settings were more influential on crime than others. Moreover, we identified particular environmental risk factors that aggravate the influence of other risk factors. We suggest that by focusing on these microlevel environmental crime contexts, police can more efficiently target their resources and further enhance place-based approaches to policing that fundamentally address environmental features that produce ideal opportunities for crime

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining