Abstract
Public health researchers are increasingly shifting their focus from models of disease epidemiology that focus exclusively on individual risk factors to models that also consider the complex and important effects of the socio-physical environment (Geanuracos et al. 2007). The application of spatial analysis in the context of epidemiological surveillance and research has increased exponentially (Pfeiffer et al. 2009). Geographic information systems (GIS), global positioning systems (GPS) and remote sensing (RS) have been increasingly used in public health research since the 1990s (Kaiser et al. 2003). At the same time, geographers have started to extend their collaborations with public health researchers leading to the still young discipline of health geography that uses geographical concepts and techniques to investigate health-related topics (Meade and Earickson 2005; Gatrell and Elliott 2009).
Keywords
- Global Position System
- Geographic Information System
- Spatial Autocorrelation
- Public Health Research
- Housing Quality
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
- 2.
A data model is an abstract model describing how data is structured. Data models are used to integrate different kinds of information, putting them into a thematic, semantic or – in the case of spatial data – in a geometric-topological structure.
- 3.
A relation R is in the first normal form (1NF) if all underlying domains contain atomic values only. A relation R is in the second normal form (2NF) if it is in 1NF and every non-key attribute is fully dependent on the primary key. A relation R is in the third normal form (3NF) if it is in 2NF and every non-key attribute is non-transitively dependent on the primary key. However, two more NF exist but are rarely implemented as the data structure then often ends up in overly flat tables.
- 4.
Heterogeneity, the violation of homogeneity, happens if the spread of the data is not the same at each X value, and this can be checked by comparing the spread of the residuals for the different X values (Zuur 2009).
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Acknowledgements
We would like to thank the German Research Foundation (DFG) for funding the project Dhaka INNOVATE under the priority programme 1233 “Megacities-Megachallenges”. We further thank Tobia Lakes, Sven Lautenbach and Daniel Müller for thoughtful comments on the manuscript.
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Gruebner, O., Khan, M.M.H., Hostert, P. (2011). Spatial Epidemiological Applications in Public Health Research: Examples from the Megacity of Dhaka. In: Krämer, A., Khan, M., Kraas, F. (eds) Health in Megacities and Urban Areas. Contributions to Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2733-0_16
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