Geographic Information System (GIS) and Data Visualization

Sritart, Hiranya; Miyazaki, Hiroyuki

doi:10.1007/978-3-030-98297-3_26

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…With the proliferation of programming and scripting languages, new spatial analysis and visualisation capabilities are becoming available through the use of spatial libraries. This is because current GIS packages are effective at handling complex data thanks to their databases and languages combined with them (Murray, 2010;Sritart & Miyazaki, 2022).…”

Section: Examining the Relation Of Cpi And Per Capita Gdp And Use Of Gismentioning

confidence: 99%

Corruption Perception Index (CPI) in European countries: Monitoring with GIS

Christos,

Zacharias,

Dimitrios

et al. 2024

TEL

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This paper examines the relationship between corruption and economic growth in European countries, taking into account that corruption is considered as a political, economic, cultural and moral problem. Specifically, this paper will focus on countries of the Eurozone, the European Union, the European Economic Area and the Central and Eastern European countries, including Turkey and Kazakhstan, over the period from 2005 to 2021. Our ultimate goal is to understand how corruption affects economic development by tracking the GDP per capita and the values of measured corruption and also how changes in GDP growth rates are affected by corruption changes. In addition to graphs, the survey provides numerical results displayed in charts using Geographical Information System (GIS). Our survey shows that all European countries with high GDP per capita tend to have low corruption values, with the exception of the countries in Central and Eastern Europe, including Turkey and Kazakhstan. The survey also shows that higher GDP per capita growth rates are correlated linearly with the reduction of the corruption levels in all categories of almost all European countries, with the exception the Central and Eastern European countries, including Turkey and Kazakhstan.

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Section: Examining the Relation Of Cpi And Per Capita Gdp And Use Of Gismentioning

confidence: 99%

Corruption Perception Index (CPI) in European countries: Monitoring with GIS

Christos,

Zacharias,

Dimitrios

et al. 2024

TEL

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“…Several studies in health research have recognized the importance of GISs. This system offers a comprehensive set of tools for exploring geographic patterns of health outcomes and allows the visual representation of geographic data, as well as the recognition of spatial relationships in epidemiological data [31]. Several innovative studies have employed GISs to explore spatial clusters of obesity incidents to emphasize areas for targeted interventions [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Disparities in Childhood Obesity Prevalence and Spatial Clustering Related to Socioeconomic Factors in Isaan, Thailand

Sritart

Taertulakarn

Miyazaki

2022

IJERPH

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Globally, rapid economic growth has contributed to an overall increase in the incidence of childhood obesity. Although the prevalence of obesity has been well recognized, the disparities related to a region’s socioeconomic environment in terms of the incidence of obesity are still less understood. Therefore, the purpose of this study was to examine the spatial pattern of childhood obesity and identify the potential associations between childhood obesity and socioeconomic environment in the northeastern region of Thailand, Isaan. Using nationally collected obesity data from children aged 0–5 years in 2019, we employed a geographic information system (GIS) to perform obesity cluster analysis at the smaller regional level, investigating a total of 322 districts in study area. Global and local statistical approaches were applied to calculate spatial associations between the socioeconomic status of neighborhoods and childhood obesity. The study revealed that 12.42% of the total area showed significant clusters at the district level, with high values observed in the western and northeastern areas. The results of the spatial statistical model revealed that childhood obesity was significantly positively associated with areas exhibiting high levels of socioeconomic environment factors. Identifying the associated factors and highlighting geographic regions with significant spatial clusters is a powerful approach towards understanding the role of location and expanding the knowledge on the factors contributing to childhood obesity. Our findings, as a first step, offer valuable references that could support policy-makers and local authorities in enhancing policy development with the aim of reducing childhood obesity and improving public health.

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