Author: byron hon

Health Geography is the application of geographical information, perspectives and methods in the study of health, disease and health care. There are 5 core ideas within Health Geography: spatial patterning of disease and health, service provision, humanistic approaches to medical geography, structuralist and materialist approaches to medical geography, and cultural approaches to medical geography. In the past, ‘medical geography’ was prominent in the geographical study of health, disease, and health care. However, now with contemporary approaches: Humanistic, structuralist , and cultural geographies of health, it is viewed more holistically with a variety of cultural systems and biospheres taken into account as well. Humanistic approaches to medical geography typically look at understanding lay rationality. Structuralist and materialist approaches looks at the inequalities in health, through the perspective of Marxist critiques of capitalism. It assumes that the structure of social, political and economic systems are the key determinant of health and variations in health. Cultural approaches consider the field by looking and therapeutic landscapes and health promotion, assuming that it is important to reframe health in positive terms, with place being an important determinant of health.

There are also 3 main themes in health geography: disease ecology, health care delivery, and environments & health. Disease ecology deals with the interactions between viruses or bacterias versus human/non-human hosts. Health care delivery is involved with the distribution of health services with respect to human settlement as well as social inequalities in access to health services. Environment and health focuses on the environment’s contribution to people’s health and well-being.

Statistics is important as it allows for different analyses to occur within the field of GIS as it explores, summarizes and illustrates relationships that may exist within datasets. One of the forms of regressions that was touched upon was Ordinary Least Squares. Ordinary Least Squares (OLS) is a simple regression model which assumes a linear relationship between the dependent variable(s) and the independent variable(s). OLS estimates what impact unknown parameters have an attempts to minimize the differences between observed and predicted results. Ordinary Least squares regression applies the same formula to an entire study space. If the relationship between the degree of influence the independent variables influence the dependant variable, then the regression equation will not accurately estimate the relationship in all areas.

However, this gap in research tools is what required the creation of GWR analysis. The general idea behind GWR is that relationships between variables are subject to spatial non-stationarity, meaning that the relationship is not constant over space. A model exploring these relationships must alter over space to reflect the spatial variation in the structure of the data. A regression equation is formed for each point at those surrounding it, creating a subset of data for each point that weights the importance of those closest to the centre. Understanding spatial variations in relationships between dependent and independent variables within a space is critical to many different types of studies, making it a very useful and adaptable model. Many scholars utilize both OLS and GWR to compare utility of both models. When performing regression analyses in the future, I will always consider using both to investigate the spatial stationarity of the relationships I’m examining- geography always has an impact!

This week, landscape ecology and the application of GIS within the field was explored. Landscape ecology can be defined as a field that explores the importance of spatial patterning on the dynamics of interacting ecosystems. We learned with landscape ecology the landscape is the most important unit of study, where an area is spatially heterogeneous in at least one factor of interest, and is the scale at which management decisions and human effects are most commonly considered. Landscape ecology also has a fundamental assumption that the location of things can have important consequences on the surrounding environment. If spatial autocorrelation is shown by objects or events, patterns can develop from a response to an environmental factor (first-order process) or interactions between objects themselves (second-order process). Stationarity and scale are also important to landscape analysis as it measures how well patterns stay the same over space.

Within this week, the overall impact geography can have on data was touched upon. Geography is crucial because the geography of a problem must be considered to produce an effective analysis and many geographical elements of a study such as scale, boundaries and spatial dependence can impact results produced. In regards to landscape ecology, there isn’t a “natural” scale at which ecological studies should be undertaken. While most ecological phenomena have a spatial temporal domain, the definition most appropriate scale must be considered carefully. The Modifiable Areal Unit problem (MAUP) was again reviewed, which describes how scale or boundaries impact the data being collected.

MAUP must be considered in all GIS analyses to evaluate potential sources of error and uncertainty. One concept which highlights the impact geography can have on an analysis or social data collection is Gerrymandering, which refers to the practice of manipulating electoral district boundaries in order to create zones where a particular party would have a political advantage. The Simpson’s paradox was also explained as a confounding variable that is not taken into consideration in the analysis as some variables vary in correlation with another (e.g. high unemployment and other socio-economic characteristics) making it difficult to obtain a reliable estimate of true correlation between two variables.

Within our first week of GEOG 479, we went over the general structure of the course and the topics we will be covering in the future. We will be exploring three broad topics-landscape ecology, health and crime, finding geographic commonalities in their spatial analysis. Learning to see geography as a common factor in the analysis of subjects that at first seem too distant to be related seems to be the takeaway point of GIScience.

FRAGSTATS was also introduced this week. Fragstats is a software program that computes landscape metrics for categorical map designs. We will be using it in our first lab to see how patterns and interactions of a landscape changes over time. Fragstats is a useful program for the study of landscape patterns and mosaic. A program that is able to do such an analysis is critical because landscapes do not exist in isolation. They exist within context regardless of the scale defined, housed in a larger landscape within larger landscapes. While keeping patterns, processes, places, people and perspectives in mind, we will analyze landscapes and changes to them in next week’s lab using the background skills we went over this week.