DOI: https://doi.org/10.14710/medstat.16.1.100-111

COMPARISON OF SPATIAL WEIGHTED MATRIX BETWEEN POWER AND QUEEN ON THE SPATIAL EMPIRICAL BEST LINEAR UNBIASED PREDICTION MODEL (Study on Per Capita Expenditure in East Java Province in 2019)

*Luthfatul Amaliana  -  Department of Statistics, Brawijaya University, Indonesia
Andi Prasetya  -  Department of Statistics, Brawijaya University, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
This study aims to make a comparison related to the spatial weighted matrix of power and queen in the SEBLUP model to estimate per capita expenditure in East Java in 2019. The data used is secondary data then the data were analyzed by the Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). The results of this study indicate that the best spatial weighted matrix for estimating per capita expenditure in East Java using the SEBLUP model is the spatial weighted matrix of Queen, because it produces the smallest MSE value. In this study, the factors that significantly affect East Java's per capita expenditure are population density (X1), number of health facilities (X2), number of public elementary schools (X3), and the percentage of residents who have BPJS as the Fund Assistance Recipients (X5). The novelty of this study are combining multiple determinant factors that have demonstrated their substantial/significant effect on the average per capita expenditure and focusing on the regions characters in intermediate size (16<n<64).
Fulltext View|Download
Keywords: Spatial Analysis; Indirect Estimation; Queen; Power; Expenditure Per Capita; Small Area Estimation

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH KAPLAN-MEIER AND NELSON-AALEN ESTIMATORS FOR CREDIT SCORING SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA More recent articles
Most cited articles
Front-Matter MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP ANALISIS PENGARUH KARAKTERISTIK WILAYAH (KELURAHAN) TERHADAP BANYAKNYA KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA SEMARANG ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS Front-Matter More cited articles
  1. Asfar (2016). Studi Penentuan Matriks Pembobot Spasial Optimum dalam Pendugaan Area Kecil. Dissertation. Bogor Agricultral University (IPB)
  2. Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., & MacMillan, R. A. (2018). Spatial Modelling with Euclidean Distance Fields and Machine Learning. European Journal of Soil Science, 69(5), 757-770
  3. BPS (2016). Indonesia - Survei Sosial Ekonomi Nasional 2016 Maret (KOR). https://mikrodata.bps.go.id/mikrodata/index.php/catalog/769 accessed in September 1st, 2020
  4. Cressie, N.A. (1989). Spatial Data Analysis of Regional Counts. Biometrical Journal, 31(6), 699-719
  5. Cressie, N.A. (1993), Statistics for Spatial Data. New York: John Wiley & Sons, Inc
  6. Darsyah, M.Y. (2013). Small Area Estimation terhadap Pengeluaran per Kapita di Kabupaten Sumenep dengan Pendekatan Nonparametrik. Jurnal Statistika Universitas Muhammadiyah Semarang, 1(2), 28-36
  7. Dewi, M.L.S. (2020). Metode Prediksi Tak Bias Linier Terbaik Empiris Pada Area Kecil Untuk Pengeluaran Per Kapita Per Kecamatan Di Provinsi Bali. Universitas Brawijaya, Malang
  8. Fitriani, R. & Efendi, A. (2019). Ekonometrika Spasial Terapan dengan R. Malang: Universitas Brawijaya Press
  9. Getis, A. & Aldstadt, J. (2004). Constructing the Spatial Weights Matrix using a Local Statistic. Geographical Analysis, 36(2), 90-104
  10. Jajang, J., Pratikno, B., & Mashuri, M. (2017). Kajian Matriks W-Amoeba dan W-Contiguity dalam Spatial Lag Model dengan Metode Estimasi Maximum Likelihood. Prosiding Seminar Nasional LPPM Unsoed, 7(1)
  11. Ma, X., Zhang, J., Ding, C., & Wang, Y. (2018). A Geographically and Temporally Weighted Regression Model to Explore The Spatiotemporal Influence of Built Environment on Transit Ridership. Computers, Environment and Urban Systems, 70, 113-124
  12. Molina, I., Salvati, N., & Pratesi M. (2007). Bootstrap for Estimating the MSE of the Spatial EBLUP. Working Paper 07-34, Statistic and Econometric Series 08
  13. Mutualage, D. (2012). Metode Prediksi Tak Bias Linear Terbaik Empiris Spasial Pada Area Kecil Untuk Pendugaan Pengeluaran Per Kapita. Bogor: Sekolah Pasca Sarjana, IPB
  14. Ningtyas, R., Rahmawati, R., Wilandari, Y. (2015). Penerapan Metode Empirical Best Linear Unbiased Prediction (EBLUP) pada Model Penduga Area Kecil dalam Pendugaan Pengeluaran Per Kapita Di Kabupaten Brebes. Jurnal Gaussian, 4(4), 977-986
  15. Nusrang, M., Annas, S., Asfar, A., Hastuty, H., & Jajang, J. (2017). Spatial EBLUP dalam Pendugaan Area Kecil. Sainsmat, 6(1), 59-66
  16. Rao, J. N. K. (2003). Small Area Estimation. New Jersey (US): John Wiley and Sons
  17. Saei, A. & Chambers, R. (2003). Small Area Estimation: A Review of Methods Based on The Application of Mixed Models. S3RI Methodology Working Paper, M03/16
  18. Salvati, N. (2004). Small Area Estimation by Spatial Models: The Spatial Empirical Best Linear Unbiased Prediction (Spatial EBLUP), Dipartimento di Statistica "Giuseppe Parenti" viale Morgagni, Working Paper 2004/03, University of Florence, Italy
  19. Smith T. E. (2014). Areal Data Analysis (Part III)-Spatial Weights Matrices. Notebook for Spatial Data Analysis
  20. https://annas-archive.org/md5/c478e6cebea1d10930ad0bf4e4238cf3 Accessed 13rd June 2020
  21. Suryowati, K., Bekti, R. D., & Faradila, A. (2018). A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation. IOP Conference Series: Materials Science and Engineering, 335(1), p. 012052

Last update:

No citation recorded.

Last update: 2024-11-21 01:31:57

No citation recorded.