Comparison of Spatial Weight Matrices in Spatial Autoregressive Model: Case Study of Intangible Cultural Heritage in Indonesia

Muhamad Sobari; Armalia Desiyanti; Devi Yanti; Putri Monika; Atje Setiawan Abdullah; Budi Nurani Ruchjana

doi:10.31764/jtam.v7i1.10757

Comparison of Spatial Weight Matrices in Spatial Autoregressive Model: Case Study of Intangible Cultural Heritage in Indonesia

Muhamad Sobari, Armalia Desiyanti, Devi Yanti, Putri Monika, Atje Setiawan Abdullah, Budi Nurani Ruchjana

Abstract

Intangible Cultural Heritage (ICH) can effectively contribute to Sustainable Development Goals (SDGs) in all economic, social, and environmental dimensions, along with peace and security. Studying ICH in Indonesia cannot be separated from the spatial aspect of how an area's attributes are related to other areas located close to each other. Spatial regression modeling needs to be done by considering the selection of spatial weight matrix. Using the wrong spatial weight matrix will increase the standard error in parameter estimation. Therefore, this study aims to determine: the best spatial weight matrix to accommodate the spatial autocorrelation in analyzing the description of the spread of ICH in Indonesia; and the variables that are thought to influence the number of ICH determination in Indonesia. The spatial regression modeling used in this study is the Spatial Autoregressive (SAR) model and the spatial weight matrices compared in this study are queen contiguity and inverse distance. The best model is the SAR model used the queen contiguity spatial weight matrix because it has minimum values of AIC, BIC, RMSE and MAPE which are 310.397, 319.555, 18.857 and 57.169 respectively. Simultaneously, involved in performing arts, wearing traditional dress, knowing Indonesian folklore and the spatial lag contribute significantly to number of ICH determination in Indonesia. Partially, only knowing Indonesian folklore have a significant effect on number of ICH determination in Indonesia at significance level α=5%. Each additional 1% of population that knowing Indonesian folklore in an area increases number of ICH determination in that area by 0.6719 units .

Keywords

Intangible Cultural Heritage; Spatial Autoregressive; Queen Contiguity; Inverse Distance

Full Text:

DOWNLOAD [PDF]

References

Anselin, L. (1988). Lagrange Multiplier Test Diagnostics for Spatial Dependence and Spatial Heterogeneity. Geographical Analysis, 20(1), 1–17. https://doi.org/10.1111/j.1538-4632.1988.tb00159.x

Anselin, L. (1990). Spatial Econometrics: Methods and Models. Journal of the American Statistical Association, 85(411). https://doi.org/10.2307/2290042

Anselin, L. (2001a). A Companion to Theoretical Econometrics: Spatial Econometrics (Badi H. Baltagi, Ed.). Blackwell Publishing Ltd.

Anselin, L. (2001b). Spatial Econometrics. In B. H. Baltagi (Ed.), A Companion to Theoretical Econometrics (pp. 310–330). Blackwell Publishing Ltd.

Anselin, L., & Rey, S. J. (2014). Modern spatial econometrics in practice: A guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC.

Arlinghaus, S. L., Griffith, D. A., Arlinghaus, W. C., Drake, W. D., & Nystuen, J. D. (1996). Practical handbook of spatial statistics. CRC Press.

BPS. (2019). Statistik Sosial Budaya 2018. In Statistik Sosial Budaya 2018. Badan Pusat Statistik.

Cressie, N. A. C. (1993). Statistics For Spatial Data (W. A. Snewhart, S. S. Wllks, V. Barnett, R. A. Bradley, N. I. Fisher, J. S. Hunter, J. B. Kadane, D. G. Kendall, A. F. M. Smith, S. M. Stigler, G. S. Watson, & J. L. Teugels, Eds.). John Wiley & Sons, Inc.

Dubin, R. A. (2009). The SAGE Handbook of Spatial Analysis (F. A.S & P. A. Rogerson, Eds.; First). Sage Publications Inc.

Fischer, M. M., & Wang, J. (2011). Spatial Data Analysis. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-21720-3

Graham, B. J., & Howard, P. (2008). The Ashgate research companion to heritage and identity. Ashgate Publishing, Ltd..

Grasa, A. A. (1989). Econometric Model Selection: A New Approach (Vol. 16). Springer Netherlands. https://doi.org/10.1007/978-94-017-1358-0

Gujarati, D. N. (2003). Basic econometrics. McGraw Hill.

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (Sixth). Pearson Education, Inc.

Johnston, R. B. (2016). Arsenic and the 2030 Agenda for sustainable development. Arsenic Research and Global Sustainability - Proceedings of the 6th International Congress on Arsenic in the Environment, AS 2016, 12–14. https://doi.org/10.1201/b20466-7

Lee, J., & Wong, D. W. S. (2001). Statistical Analysis with Arcview Gis. John Wiley & Sons, Inc.

Lembo, A. J. (2006). Spatial Autocorrelation.

Lesage, J. P. (1999). The Theory and Practice of Spatial Econometrics. http://www.econ.utoledo.edu

Lesage, J. P. (2004). Lecture 1: Maximum likelihood estimation of spatial regression models.

medcom.id. (2021). 2021, Jumlah Warisan Budaya Tak Benda Indonesia Bertambah 289. https://www.medcom.id/pendidikan/news-pendidikan/nbwXAB6k-2021-jumlah-warisan-budaya-tak-benda-indonesia-bertambah-289

Meng, L., Zhu, C., Pu, J., Wen, B., & Si, W. (2022). Study on the Influence Mechanism of Intangible Cultural Heritage Distribution from Man–Land Relationship Perspective: A Case Study in Shandong Province. Land, 11(8). https://doi.org/10.3390/land11081225

Patru-Stupariu, I., Pascu, M., & Bürgi, M. (2019). Exploring tangible and intangible heritage and its resilience as a basis to understand the cultural landscapes of saxon communities in southern Transylvania (Romania). Sustainability (Switzerland), 11(11). https://doi.org/10.3390/su11113102

Shapiro, S. S., & Wilk, ; M B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3–4), 591–611. http://links.jstor.org/sici?sici=0006-3444%28196512%2952%3A3%2F4%3C591%3AAAOVTF%3E2.0.CO%3B2-B

Sharma, Suresh, & Sharma, V. (2012). Comparative Analysis of Machine Learning Techniques in Sale Forecasting. In International Journal of Computer Applications (Vol. 53, Issue 6, pp. 975–8887).

Stoica, P., Selén, Y., & Li, J. (2004). Multi-model approach to model selection. Digital Signal Processing: A Review Journal, 14(5), 399–412. https://doi.org/10.1016/j.dsp.2004.03.002

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). https://doi.org/10.1088/1757-899X/335/1/012052

Trisilia, M. (2014). Perbandingan Matriks Pembobot Spasial Dalam Model Spatial Autoregressive (SAR) (Studi Kasus Daya Dukung Lahan Pertanian Tanaman Pangan di Kota Batu) [Pascasarjana]. Universitas Brawijaya.

Tutchener, D. (2013). Collective Identity and Heritage: Ebb and Flow at National and Global Levels. In Journal of Globalization Studies (Vol. 4, Issue 2). https://doi.org/10.1.1.1057.3583

United Nations. (2011). Intangible Cultural Heritage and Sustainable Development.

Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists Second Edition (Second). John Wiley & Sons Inc.

Wei, W. W. S. (2006). Time series analysis: univariate and multivariate methods. Pearson Addison Wesley.

Yamagata, T., Halunga, A., & Orme, C. D. (2011). A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data Models A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data Models.

Zebua, H. I., & Jaya, I. G. N. M. (2022). Spatial Autoregressive Model of Tuberculosis Cases in Central Java Province 2019. Cauchy, 7(2), 240–248. https://doi.org/10.18860/ca.v7i2.13451

DOI: https://doi.org/10.31764/jtam.v7i1.10757