Despite the benefits of applying methods of geographic information science (GIScience), the use of such methods in health service planning and provision remains greatly underutilized. Spread of epidemic diseases is a constant threat to mankind and the globalization of the world increases the risk for global attacks from multi-resistant bacteria or deadly virus strains. Therefore, research is needed to better understand how GIScience could be used in epidemiologic analyses and other health applications. This thesis is divided into two parts; one for epidemiologic analyses and one for neighbourhood studies. The overall objective of the epidemiologic part of this research is to understand more about the spatial spread of past pandemics and to find out if there are any common patterns. This overall objective is divided into four specific research objectives; 1) to describe the spatial spread of the Russian Influenza in Sweden, 2) to create models of propagation of the Black Death in Sweden, 3) to establish spatiotemporal characteristics common to past pandemics in Sweden and 4) to visualize the spatiotemporal occurrence of salmonella among animal herds in Sweden. This thesis also discusses some other aspects of health related to place. Are differences in neighbourhood deprivation related to the amount of presence of goods and services? Is the way cities are planned affecting the behaviour within the local population regarding spontaneous walking and physical activity? The specific research objectives for this part are to define how deprivation is related to presence of goods and services in Sweden and to create walkability indices over the city of Stockholm including a quality test of these indices. Case data reported by physicians were used for the epidemiologic studies. The pandemics discussed covered the entire world, but our data is from Sweden only and as regards the Black Death there was no case data at all. The data for the goods and services analyses are from all of Sweden, whereas the walkability indices are based on data from the city of Stockholm. Various methods have been used to clean, structure and geocode the data, including hand written reports on case data, maps of poor geometric quality, information from databases on climate, demography, diseases, goods and services, income data and more, to make this data feasible for spatial analysis, modeling and visualization. Network analysis was used to model food transports in the 14th century as well as walking in the city of Stockholm today. Proximity analysis was used to assess the spatio-temporal spread of the Russian Influenza. The impact of climatological factors on the propagation of the Asian Influenza was analyzed and geographically weighted mean (GWM) calculations were used to discover common characteristics in the spatio-temporal spread of three past pandemics. Among the results generated in the epidemiologic study the following should be noted in particular; the local peaking periods of the Asian Influenza were preceded by falling temperature, the total peaking period for the three pandemics (Russian, Asian and A(H1N1)pdm09) was approximately 10 weeks and their weekly GWM followed a path from southwest to northeast (opposite direction for the A(H1N1)pdm09). From the neighborhood studies one can note that compared to the results measured and reported by tested individuals there is a positive (small but significant) association between neighborhood walkability and physical activity outcomes. The main contribution of this work is that it gives epidemiologists and public health specialists new ideas, not only on how to formulate, model, analyze and visualize different health related research questions but also ideas on how new procedures could be implemented in their daily work. Once the data reporting is organized in a suitable manner there is a multitude of options on how to present important and critical information to officials and policy makers. / <p>QC 20140313</p>