Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

While environmental justice (EJ) research in the U.S. has traditionally focused on inequities in the distribution of technological hazards, the disproportionate impacts of Hurricane Katrina on racial minorities and socioeconomically disadvantaged households have prompted researchers to investigate the EJ implications of natural hazards such as flooding. Recent EJ research has also emphasized the need to examine social inequities in access to environmental amenities. Unlike technological hazards such as air pollution and toxic waste sites, areas exposed to natural hazards such as hurricanes and floods have indivisible amenities associated with them. Coastal property owners are exposed to flood hazards, but also enjoy water views and unhampered access to oceans and the unique recreational opportunities that beaches offer. Conversely, dense urban development and associated impervious surfaces increase likelihood of floods in inland areas which may lack the amenities of proximity to open water. This dissertation contributes to the emerging literature on EJ and social vulnerability to natural hazards by analyzing racial, ethnic, and socioeconomic inequities in the distribution of flood risk exposure in the Miami Metropolitan Statistical Area (MSA), Florida--one of the most hurricane-prone areas in the world and one of the most ethnically diverse MSAs in the U.S. The case study evaluates the EJ implications of residential exposure to coastal flood risk, inland flood risk, and no flood risk, in conjunction with coastal water related amenities, using geographic information science (GIS)-based techniques and logistic regression modeling to estimate flood risk exposure. Geospatial data from the Federal Emergency Management Agency (FEMA) are utilized to delineate coastal and inland 100-year flood hazard zones. Socio-demographic variables previously utilized in EJ research are obtained from tract level data published in the 2010 census and 2007-2011 American Community Survey five-year estimates. Principal components analysis is employed to condense several socio-demographic attributes into two neighborhood deprivation indices that represent economic insecurity and instability, respectively. Indivisible coastal water related amenities are represented by control variables of percent seasonal homes and proximity to public beach access sites. Results indicate that racial/ethnic minorities and those with greater social vulnerability based on the neighborhood deprivation indices are more likely to reside in inland flood zones and areas outside 100-year flood zones, while residents in coastal flood zones are disproportionately non-Hispanic White. Moreover, residents exposed to coastal flood risk tend to live in areas with ample coastal water related amenities, while racial/ethnic minorities and individuals with higher neighborhood deprivation who are exposed to inland flood risk or no flood risk reside in areas without coastal water related amenities. This dissertation elucidates the importance of EJ research on privilege and access to environmental amenities in conjunction with environmental hazards because areas exposed to natural hazards are likely to offer indivisible benefits. Estimating people and places exposed to hazards for EJ research becomes difficult when the boundaries of census areal units containing socio-demographic data do not match the boundaries of hazard exposure areas. This challenge is addressed with an application of dasymetric spatial interpolation using GIS-based techniques to disaggregate census tracts to inhabited parcels. Several spatial interpolation methods are assessed for relative accuracy in estimating population densities for the Miami MSA, and the output units from the most accurate method are employed in EJ regression analyses. The dasymetric mapping efforts utilized herein contribute to research on the modifiable areal unit problem (MAUP) and its effects on statistical analyses. Since the dasymetric mapping technique used for EJ analyses disaggregates census tracts to the inhabited parcel level, the results of the associated analyses for flood hazards exposure and access to coastal water related amenities should be more reliable than those based on tracts. The enhanced accuracy associated with inhabited parcels is a result of using a more precise geospatial depiction of residential populations, which leads to a more accurate portrayal of disproportionate exposure to flood hazards. Consequently, this dissertation contributes methodologically to GIS-based techniques of dasymetric spatial interpolation and empirically to EJ analysis of flood hazards with indivisible coastal water related amenities.

Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

Many health geography studies, including the Ottawa Neighbourhood Study (ONS), have faced significant challenges uncovering local variation in patterns of community health in rural areas. This is due to the fact that sparsely populated rural areas make it difficult to define neighbourhoods that are representative of the social and resource utilization patterns of the individuals therein. Moreover, rural areas yield small samples from population-based regional health surveys and this leads to insufficient sample sizes for reliable estimation of health determinants and outcomes. In response to this issue this thesis combines geographical and statistical techniques which allow for the simulation of health variables within small areas and populations within rural Ottawa. This methodological approach combines the techniques of dasymetric mapping and statistical micro-simulation in an innovative way, which will allow health geography researchers to explore health determinants and health outcomes at small spatial scales in rural areas. Dasymetric mapping is used to generate a statistical population surface over Ottawa and then estimate socio-economic (SES) variables within small neighbourhood units within rural Ottawa. The estimated SES variables are then used as correlate variables to simulate health determinant and health outcome variables form the Canadian Community Health Survey (CCHS) using statistical micro-simulation. Through this methodology, simulations of specific health determinants and outcome can be investigated at small spatial scales within rural areas. Dasymetric mapping provided neighbourhood-level population estimates that were used to re-weight as set of SES variables that were correlates with those in the Canadian Community Health Survey (CCHS). These neighbourhood-level correlates allowed microsimulation and consequent spatial exploration of prevalence for smoking, binge drinking, obesity, self-rated mental health, and the presence of two or more chronic conditions. The methodology outlined in this paper, provides and innovative way of exploring health determinants and health outcomes in neighbourhoods for which population and health statistics are not traditionally collected at levels that would allow traditional statistical analyses of prevalence.

The spatial distribution of future population and employment has become a focus of recent academic enquiry and planning policy concerns. This is largely driven by the rapid urban expansion in major Australian cities and the need to plan ahead for new housing growth and demand for urban infrastructure and services. At a national level forecasts for population and employment are produced by the government and research institutions; however there is a further need to break these forecasts down to a disaggregate geographic scale for growth management within regions. Appropriate planning for the urban growth needs forecasts for fine-grained spatial units. This thesis has developed methodologies to predict the future settlement of the population, employment and urban form by applying a spatial disaggregation approach. The methodology uses the existing regional forecasts reported at regional geographic units and applies a novel spatially-based technique to step-down the regional forecasts to smaller geographical units. South East Queensland (SEQ) is the experimental context for the methodologies developed in the thesis, being one of the fastest-growing metropolitan regions in Australia. The research examines whether spatial disaggregation methodologies that can be used to enhance the forecasts for urban planning purposes and to derive a deeper understanding of the urban spatial structure under growth conditions. The first part of this thesis develops a method by which the SEQ population forecasts can be spatially disaggregated. This is related to a classical problem in geographical analysis called to modifiable area unit problem, where spatial data disaggregation may give inaccurate results due to spatial heterogeneity in the explanatory variables. Several statistical regression and dasymetric techniques are evaluated to spatially disaggregate population forecasts over the study area and to assess their relative accuracies. An important contribution arising from this research is that: i) it extends the dasymetric method beyond its current simple form to techniques that incorporate more complex density assumptions to disaggregate the data and, ii) it selects a method based on balancing the costs and errors of the disaggregation for a study area. The outputs of the method are spatially disaggregated population forecasts across the smaller areas that can be directly used for urban form analysis and are also directly available for subsequent employment disaggregation. The second part in this thesis develops a method to spatially disaggregate the employment forecasts and examine their impact on the urban form. A new method for spatially disaggregating the employment data is evaluated; it analyses the trend and spatial pattern of historic regional employment patterns based on employment determinants (for example, the local population and the proximity of an area to a shopping centre). The method we apply, namely geographically weighted regression (GWR), accounts for spatial effects of data autocorrelation and heterogeneity. Autocorrelation is where certain variables for employment determinants are related in space, and hence violate traditional statistical independence assumptions, and heterogeneity is where the associations between variables change across space. The method uses a locally-fitted relationship to estimate employment in the smaller geography whilst being constrained by the regional forecast. Results show that, by accounting for spatial heterogeneity in the local dependency of employment, the GWR method generates superior estimates over a global regression model. The spatially disaggregate projections developed in this thesis can be used to better understand questions on urban form. From a planning perspective, the results of spatial disaggregation indicate that the future growth of the population for SEQ is likely to maintain a spatially-dispersed growth pattern, whilst the employment is likely to follow a more polycentric distribution focused around the new activity centres. Overall, the thesis demonstrates that the spatial disaggregation method can be applied to supplement the regional forecasts to seek a deeper understanding of the future urban growth patterns. The development, application and validation of the spatial disaggregation methods will enhance the planner’s toolbox whilst responding to the data issues to inform urban planning and future development in a region.

Humanitarian help organisations today may benefit on improving their location analysis when placing field hospitals in countries hit by a disasters or catastrophe. The main objective of this thesis is to develop and evaluate a spatial decision support method for strategic placing of field hospitals for two time perspectives, long term (months) and short term (weeks). Specifically, the possibility of combining existing infrastructure and satellite data is examined to derive a suitability map for placing field hospitals. Haut-Katanga in Congo is used as test area where exists a large variety of ground features and has been visited by aid organisations in the past due to epidemics and warzones. The method consists of several steps including remote sensing for estimation of population density, a Multi Criteria Evaluation (MCE) for analysis of suitability, and visualization in a webmap. The Population density is used as a parameter for an MCE operation to create a decision support map for locating field hospitals. Other related information such as road network, water source and landuse is also taken into consideration in MCE. The method can generate a thematic map that highlights the suitability value of different areas for field hospitals. By using webmap related technologies, these suitability maps are also dynamic and accessible through the Internet. This new approach using the technology of dasymetric mapping for population deprival together with an MCE process, yielded a method with the result being both a standalone population distribution and a suitability map for placing field hospitals with the population distribution taken into consideration. The use of dasymetric mapping accounted for higher resolution and the ability to derive new population distributions on demand due to changing conditions rather than using pre-existing methods with coarser resolution and a more seldom update rate. How this method can be used in other areas is also analysed. The result of the study shows that the created maps are reasonable and can be used to support the locating of field hospitals by narrowing down the available areas to be considered. The results from MCE are compared to a real field hospital scenario, and it is shown that the proposed method narrows down the localisation options and shortens the time required for planning an operation. The method is meant to be used together with other decision methods which involves non spatial factors that are beyond the scope of this thesis.