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Visualization is the process of representing data in a visual, and meaningful way, so that a user can better understand it (Wood, 2019). Geovisualization is short for geographic visualization. For Nöllenburg (2007) geographic visualizations always played an important role in the human history, especially in the earth sciences, long before computer visualizations became popular. According to the authors in (Andrienko, 2016), (Smith, 2013), (Laurini, 2017), (Caillard, 2017) and (Çöltekin, 2017, 2018), it is a scientific and technical domain concerned with the production and use of dynamic and interactive tools to display geographical data and maps that allow a user to reason about these representations.
As mentioned by MacEachern (2004) and Do (2020), rapid advances in geographic information systems (GIS) and related technologies have created a potential for dynamic geovisualization methods to be integrated with GIS in support of a range of decision-making tasks. Cartographic visualization is then considered as an extension of spatial analysis, and GIS is configured as a spatial decision support system. For Derryn (2014), Fradelos (2014), (Sandul 2015), Kirby (2016) and Do (2020) this integration can bring much to epidemiologic research and is essential for health policy planning, decision making, and ongoing surveillance efforts.
In this context, this paper presents a novel integration approach to support interactive visual exploration and analysis of epidemiological data by coupling MapInfo GIS software with the Gastner area cartogram, a particular class of map type where some aspect of the geometry of the map is modified to accommodate the studied problem. The aim of the study is to help improve public health by identifying areas of exposure and risk on tuberculosis in the city of Oran. Indeed, as shown by Sui (2008), it is obvious that the use of cartograms in public health can affect our understanding of reality, both cognitively and analytically. For Shimizu (2009), it is a highly effective method for representing statistical data visually. For Demoraes et al. (2021) cartograms representation really does provide something over and above a more conventional mode of representation such as a choropleth map, proportional symbols, etc. However, as mentioned in literature by (Bhatt et al, 2013), (Derryn 2014), (Nusrat 2016), (Soetens 2017) and (Tran 2019), cartograms are also innovative mapping techniques that allow visualization of potentially complex health relationships but are underutilized in epidemiology.