Study on Bhutan Census Grid Square Data Creation
Keywords:GIS; GGRF; grid square statistics; standardization; Bhutan census
AbstractIt is highly demanded to urgently maintain accurate GIS statistical data as the importance of planning a sustainable development plan due to widening disparities and inequalities; addressing new social challenges arising from rapid development and urbanization. As a former study of this paper proved the significance of creating the grid square statistics and the effectiveness of adopting the World Grid Square system rather than developing a dedicated system for Bhutan, in the context of the United Nations General Assembly adopted the UN resolution on Global Geodetic Reference Frame for Sustainable Development that was led by the Republic of Fiji in 2015.Currently in Bhutan, important GIS statistical data (e.g. population) is available only in large administrative levels, namely Gewog/Thromde (city-level), however GIS statistical data in smaller levels are required to conduct a detailed study on the new social challenges. This Grid Square system can be quickly adopted from open sources and provide granular information in smaller areas than cities and villages with a precise and globally adopted geodetic reference frame.There are two major objectives of this paper; (1) Creating practical GIS statistic data in the grid square format. (2) Clarifying the advantages, disadvantages and difficulties, in order to evaluate and improve the grid square format. Within the limitation of data availability in Bhutan, this study has assumed the population of each grid square covering the whole census area of Thimphu Thromde by an estimation model using sampled data in Enumeration Areas (EA) of ‘2017 Population & Housing Census of Bhutan’. Further, this paper discusses advantages and disadvantages between EA-units and grid squares on the presumption of taking suppression measurements and resampling from EA-units to grid squares. Consequently, we conclude that using the grid square format in practice could improve the efficiency of GIS analysis and data availability although there is one drawback in accuracy. Moreover, the population of each EA-unit has been predicted by the model comprising GIS-based variables (e.g. distance from facilities). The result has been validated by the whole population in Thimphu thromde. Accessibility to health and educational facilities is the most significant factor for the number of people living in the area.
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