Abstract
Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.
Highlights
Spatial data is data with territorial reference containing two information that are observation information and region information
Sub-district mean per capita income distribution is skewed [15] it was transformed in log value
Based on [37], it is known that relationship between the auxiliary variables and 139 sub-district log mean per capita income in Bangka Belitung province are not sufficiently linier and conceive of spatial correlations
Summary
Spatial data is data with territorial reference containing two information that are observation information and region information. For example: the number of people with certain diseases in some areas of the country [1]. The regional information can present spatial patterns for data presented in geographical units such as municipalities, districts or sub-districts. Geographic information is able to provide the pattern of poverty [2, 3, 4, 5, 6, 7 and 8]. Policy makers and researchers take into consideration the information of poverty such as the well-being or the living condition of people in a certain area, since poverty is the first Sustainable development goals that carried by the United Nations. The poverty estimation only allows for larger regions or larger population subgroups since the limitations in its data collection methodologies. Less of sample adequacy to deliver direct estimation is one of those limitation, so the Small Area Estimation (SAE) developed by sample survey statisticians. The existing auxiliary information from the neighborhood areas are useful in small area estimation techniques [9]
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