A Spatial Analysis Of Assault Patterns In Entertainment Areas Throughout the Waikato using Geographic Information Systems

Alcohol related violence has long been a matter of social concern. Recent studies investigating the association between assaults and alcohol have found that there are certain places and locations including bars, which are more commonly associated with assaults than other places. Using different spatial analysis techniques accommodated within a Geographic Information System (GIS) including point and choropleth density, Euclidean based distance measures, clustering analysis and geographically weighted regression, this study examines the association between bars and assaults in the Waikato region. It also seeks to explain the assault patterns around bars by various theories, namely the “Social Disorganisation Theory”, “Routine Activity Theory” and the “Crime Potential Theory”. The study determined that for the two year period (2008-2009) in the Waikato Police district there was clear evidence of higher assault levels being associated with areas of higher bar densities. In Hamilton’s CBD there was a particularly strong relationship between assaults and bars where around 25% of all assaults took place within 10 metres of a bar and approximately half of all assaults took place within 50 metres of a bar. Over the study period, one meshblock in Hamilton’s CBD recorded approximately 45 assaults per square kilometre per week. Elsewhere in the Waikato, the study showed a reasonably strong relationship between assaults and bars at the coastal resorts of Whitianga, Raglan and Coromandel township. In these townships, there was a discernable, but lesser relationship to that of the Hamilton CBD, with around 15-25% of assaults taking place within 10 metres of a bar. The assault density in the centre of these coastal townships, as well as other townships throughout the Waikato was generally lower, recording 3-4 assaults per square kilometre per week. Suburban areas in Hamilton City showed similar assault densities to that recorded in the centre of townships throughout the Waikato. The study findings were found to be generally consistent with the Routine Activity and Crime Potential theories by conclusively demonstrating that place, in this instance, bars, and their location, influences the distribution of assaults. The study examined population characteristics only in respect of population density and its proxy, road density, but these variables were not found to be particularly accurate in predicting the distribution of assaults

Extreme Heat Vulnerability And Spatial Accessibility To Cooling Centers In Connecticut

Extreme heat is becoming an increasingly prevalent and prominent environmental health issue under climate change. The goal of this study is to evaluate heat vulnerability at the census tract level in the state of Connecticut and assess the spatial accessibility to cooling centers – an extreme heat intervention. A variety of environmental and sociodemographic variables related to heat and health were identified based on previous literature and used in a varimax-rotated principal component analysis to reduce dimensionality and identify key components that constitute a heat vulnerability score. In addition, cooling center locations were identified based on news media and a statewide survey of cooling centers and emergency shelters. Kernel density was calculated for cooling centers, and then population density was used to calculate the cooling center-to-population ratio. Finally, the relationship between the heat vulnerability score and cooling center-to-population ratio for each census tract was quantified in a linear regression to identify high heat vulnerable census tracts with relatively low cooling center access. A heat vulnerability score was calculated for 821 of 833 census tracts in the state of Connecticut with a range of scores from 8 to 20. High vulnerability census tracts clustered in urban and metropolitan areas. A total of 248 unique cooling center locations were geocoded, with high cooling center-to-population ratio clusters found to be located around Hartford, New Haven, and Bridgeport. Small clusters of census tracts with a high heat vulnerability score and a low cooling center-to-population ratio were identified around Manchester, Meriden, Milford, New London, Plainville, and Stratford. Urban census tracts are key units for public health interventions pertaining to heat adaptation strategies, including cooling centers. Some urban areas have a comparatively high number of cooling centers that can provide heat relief if utilized properly in conjunction with other heat response strategies. Other urban areas can improve by increasing their number of cooling centers and by using other heat adaptation strategies to help prevent heat exposure. This heat vulnerability index can be used to inform planning and provision of adequate resources to address the needs heat vulnerable populations

Spatial and Temporal Analysis of Selected Birth Defects and Risk Factors in the Baton Rouge, Louisiana Metropolitan Statistical Area

About three percent of all infants are born with a congenital defect each year ranging from minor variants to life threatening abnormalities. The investigation and treatment of these problems is both costly and emotionally trying for all involved. Finding their origins is a complex process. Birth defects create the ultimate mystery in terms of trying to tease out the various influences created by the environment of both the infant and the mother. Two genetically different individuals are simultaneously affected both by their individual makeup and by the outside world impacting the air they breathe, the food they eat, and the various stressors both big and small that are part of the world they live in. The availability of birth certificate data allows researchers to begin the process of sorting out the factors linked with birth defects. This dissertation employs data from 2005 to 2008 for live births occurring in the Baton Rouge Metropolitan Statistical Area (MSA). Geographic Information Systems (GIS) mapping, cluster analysis, spatial-temporal analysis, geographically weighted regression, and multilevel modeling were employed for the purpose of producing a baseline picture of the area in regard to the locations of mothers giving birth to infants with birth defects, the types and rates of those birth defects, and their correlates. The Baton Rouge MSA proved to be typical in terms of rates of birth defects worldwide, however there were areas which exceeded expected overall rates and some clustering of certain types of defects. Heart defects and hypospadias rates were slightly above anticipated percentages predicted by The U.S. Centers for Disease Control and Prevention. Temporal analysis revealed increases in rates of several types of birth defects in 2006 and 2007 but there were not enough years to analyze these rates statistically. Analysis of correlates did not reveal any models which could be used to impact rates in the future. However, this project provides baseline data on types and rates of birth defects and information on the best locations for services to affected families along with multiple opportunities for possible preventative efforts and future investigations of this area

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

An investigation of the spatial patterning of gambling-related harm and the total consumption theory of gambling

Gambling is an important public health issue in Australia. According to recent estimates, gambling-related harm is the third largest contributor to the burden of disability in the state of Victoria, measured in terms of disability-adjusted life years. The gambling product most associated with gambling-related harm in Australia is the electronic gaming machine (EGM), which accounts for over half of all Australian gambling expenditure. Around 30 per cent of weekly EGM gamblers experience moderate or severe adverse impacts from their gambling. This thesis consists of six studies on the spatial distribution of the impacts of electronic gaming machines (EGMs) and the relationship between EGM losses and problem gambling. All have been published or were accepted for publication in peer-reviewed academic journals at the time of submission. Jointly, these studies developed theoretical and methodological tools to advance the production of small area estimates of gambling-related harm, as well as beginning the exploration of its consequences. The six studies in this thesis can be grouped into three inter-linked themes that contribute to this aim in different ways. Two studies are concerned with developing the applied and methodological tools for investigating the spatial distribution of problem gambling. The first of these studies presents a calibrated Huff model of the spatial behaviour of gamblers. The second of these uses the Huff model to refine spatial microsimulation derived small area estimates of the prevalence of problem gambling. Together, they provide a toolkit for estimating the local impacts of EGMs. Three studies provide the theoretical underpinning of the thesis by investigating the relationship between gambling losses and problem gambling at the scales of the individual, the EGM venue and state or territory. In order to develop the methods for investigating the spatial distribution of problem gambling, a sustained engagement was required with Total Consumption Theory in the context of gambling. These studies find a consistent relationship between EGM losses and the risk of harm at all spatial scales. At the scale of the individual, there is no evidence to support a J-shaped dose-response relationship, meaning that risk of gambling problems increases monotonically with money lost. A final study estimates the spatio-temporal correlation between EGM accessibility and a single gambling-related harm, domestic violence. Whereas research in the earlier phases of this project sought to estimate the distribution of ‘problem gambling’ as an outcome measure, phase four seeks to measure the relationship between EGM accessibility and specific gambling-related harms directly. In this instance, the spatial association between EGMs and police-recorded domestic violence incidents is investigated in Victorian postcodes over a ten-year period. A significant spatio-temporal association between these two variables is found, providing evidence of a link between EGM gambling and violence. This study concludes that future research might usefully explore the spatio-temporal co-occurrence of EGM gambling and specific gambling-related harms to better understand the social and health impacts of EGM gambling. The research developed in this thesis has contributed toward bringing knowledge of the geography of the impacts of EGMs closer to that of cognate public health issues. While Total Consumption Theory was developed in the context of gambling to underpin the production of local area estimates that incorporate gambling consumption as a risk factor, the findings in this section have broader implications for gambling regulation. More broadly, the approaches developed in this thesis and the research findings have the potential to contribute to improving the regulation of EGMs and thereby reduce the incidence of gambling-related harms

Mekansal ve mekansal olmayan tekniklere dayalı konut değerleme modellerinin incelenmesi.

The aim of this thesis is to develop hedonic housing valuation models based on spatial (SAR-simultaneous spatial autoregression and GWR - geographically weighted regression) and non-spatial (OLS - ordinary least squares) techniques, to compare the performances of these models and to investigate significant factors affecting housing value. The developed housing valuation models were tested at the Çankaya and Keçiören districts of Ankara province, Turkey. The results of the analyses revealed that significant spatial non-stationarity exists between the dependent and independent variables. A semi-logarithmic hedonic model was used in order to interpret the coefficients easily and minimize the problem of heteroscedasticity. The results show that Area, Security and Distance to Shopping Center are common significant factors for both Çankaya and Keçiören districts in Ankara. Other important factors are the Type of Property and Distance to Subway for Çankaya and the Floor and Household variables for Keçiören. The SAR and the GWR spatial models gave a better approximation to the observed house values than the traditional non-spatial regression model. The SAR model showed the best performance in Çankaya and the GWR model indicated high performance in Keçiören. The GWR maps displayed the variation of the coefficients of each variable clearly.Ph.D. - Doctoral Progra

Population-level Indicators of Physical Activity, Sedentary Behaviour and Sleep in Canada based on Twitter

Social media platforms contain large amounts of freely and publicly available data that could be used to measure population characteristics across different geographical regions. Analyzing public data sources such as social media data has shown promising results for public health measures and monitoring. This thesis addresses challenges in building sys- tems that collect high-volumes of data from social media platforms. More specifically, we look at Twitter data processing, filtering, and aggregation to provide population-level in- dicators of physical activity, sedentary behavior, and sleep (PASS). In the first part of the thesis, we go over the whole machine learning pipeline built: (i) Twitter data collection from November 2017 to May 2018; (ii) data preparation through manual annotation, key- word filtering, and an active learning technique for the labelling of 10,283 tweets; and (iii) training a classifier to identify PASS related tweets. Training the model involves building an initial classifier to efficiently find relevant tweets in subsequent annotation iterations. Our classifiers include an ensemble model consisting of several shallow machine learning algorithms, along with deep learning algorithms. In the second part of the thesis, we look at the performance of different solutions. We provide benchmark results for the task of classifying PASS related tweets for the various algorithms considered. We also derive health indicators by aggregating and computing the proportion of classified tweets by province and compare our metrics with the prevalence of obesity, diabetes and mood disorders from the Canadian Community Health Survey. Our work shows how machine learning can be used to complement public health data and better inform health policy makers to improve the lives of Canadians

Predicting Violent Crime Reports from Geospatial and Temporal Attributes of US 911 Emergency Call Data

The aim of this study is to create a model to predict which 911 calls will result in crime reports of a violent nature. Such a prediction model could be used by the police to prioritise calls which are most likely to lead to violent crime reports. The model will use geospatial and temporal attributes of the call to predict whether a crime report will be generated. To create this model, a dataset of characteristics relating to the neighbourhood where the 911 call originated will be created and combined with characteristics related to the time of the 911 call. Geospatial and temporal analysis of past 911 calls and crime reports will be applied to determine which 911 calls resulted in crime reports (the dependent variable) so that supervised learning can be performed