Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District

https://doi.org/10.22146/ijg.17601

Prima Widayani(1*), Totok Gunawan(2), Projo Danoedoro(3), Sugeng Juwono Mardihusodo(4)

(1) 
(2) 
(3) 
(4) 
(*) Corresponding Author

Abstract


Abstract Geographically Weighted Regression (GWR) is regression model that developed for data modeling with continuous respond variable and considering the spatial or location aspect. Leptospirosis case happened in some regions in Indonesia, including in Bantul District, Special Region of Yogyakarta. The purpose of this study are to determine local and global variable in making vulnerable area model of Leptospirosis disease, determine the best type of weighting function and make vulnerable area map of Leptospirosis. Alos satelite imagery as primary data to get settlement and paddy fields area. The others variable are the percentage of population’s age, flood risk, and the number of health facility that obtained from secondary data. Determinant variables that affect locally are flood risk, health facility, percentage of age 25-50 years old and the percentage of settlement area. Meanwhile, independent variable that affects globally is the percentage of paddy fields area. Vulnerability map of Leptospirosis disease resulted from the best GWR model which used weighting function Fixed Bisquare. There are 3 vulnerable area of Leptospirosis disease, high vulnerability area located in the middle of Bantul District, meanwhile the medium and low vulnerability area showed clustered pattern in the side of Bantul District.

 

Abstrak Geographically Weighted Regression (GWR) adalah model regresi yang dikembangkan untuk memodelkan data dengan variabel respon yang bersifat kontinu dan mempertimbangkan aspek spasial atau lokasi.  Kejadian Leptospirosis terjadi di beberapa wilayah di Indonesia termasuk di wilayah Kabupaten Bantul Daerah Istimewa Yogyakarta. Tujuan dari penelitian ini adalah menentukan variabel lokal dan global dalam membuat model  kerentanan Leptospirosis dan menentukan jenis fungsi pembobot yang terbaik serta membuat peta kerentanan wilayah Leptospirosis menggunakan aplikasi GWR. Citra Satelit Alos digunakan untuk mendapatkan data penggunaan lahan, yang selanjutnya diturunkan menjadi prosentase luas permukiman dan sawah. Parameter lainya adalah prosentase umur penduduk, resiko banjir dan jumlah fasilitas kesehatan yang diperoleh dari data sekunder. Variabel yang berpengaruh secara lokal adalah  Risiko Banjir, Fasilitas Kesehatan Presentase Usia 25-50 tahun, Prosentase Luas Pemukiman, sedangkan variabel independen yang bepengaruh secara global adalah Presentase Luas Sawah.  Peta kerentanan Leptospirosis yang dihasilkan dari model GWR terbaik yaitu menggunakan fungsi pembobot  Fixed Bisquare. Terdapat 3 kelas kerentanan Leptospirosis yaitu kelas kerentanan tinggi berada di desa-desa di tengah Kabupaten Bantul, sedangkan kelas sedang dan rendah menunjukkan pola menggelompok di wilayah pinggiran Kabupaten Bantul


Keywords


Geographically Weighted Regression; Leptospirosis; Vulnerability

Full Text:

PDF


References

Chiara Bocci, Alessandra Petrucci and Emilia Rocco. (2000). An application of Geographically Weighted Regression to Agricultural Data for SmallArea Estimates Dipartimento di Statistica “G. Parenti”. Universit`a degli Studi di Firenze, viale Morgagni, 59 – 50134 Firenze, Italy.

Dwi Sarwani. (2005). Environmental Risk Factor of Leptospirosis in Semarang City. Tesis. Diponegoro University.

Bantul Public Health Service, (2010). Surveilans Leptospirosis Data 2010. Bantul.

Fotheringham. (2002). Geographically Weighted Regression: The Ananlysis of Spatially Varying Relationships. UK.

Ima Nurisa. (2005). Penyakit Bersumber Rodensia ( Tikus dan Mencit) di Indonesia dalam. Jurnal Ekologi Kesehatan. 4 (3), 308 – 319.

Nakaya Tomok. (2009). GWR User Manual. Department of Geography, Ritsumeikan University.

NSDA. (2009). ALOS Spesification. LAPAN.

Pavel Propastin, Martin Kappas and Stefan Erasmi. (2007). Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetation and Climate. International. Journal of Spatial Data Infrastructures Research. 3, 73-94.

Pfeiffer, Robinson, Stevenson, and Stevent. (2008). Spatial Analysis in Epidemiology. Oxford University Press.

Priyanto. (2008). Risk Factors of Leptospirosis ( Case Study in Demak District). Article Post Graduate Program. Diponegoro University.

Riyaningsih, Suharyo Hadisaputro, and Suhartono.

(2010). Enivironmental Risk Factors That Influence The Incidence of Leptospirosis in Central Java (Case Study in The City of Semarang, Demak Regency and Paty). Jurnal Kesehatan Lingkungan Indonesia. 11 (1) / April 2012.

WHO. (2003). Leptospirosis. World Health Organization. http://www.who.int/en/ (downloaded 2 December 2014)

Yoeniarti Shara, Henny Pramoedyo, and Maria Bernadetha

Mitakda. (2008). Geographically Weighted Regression Modeling Used Fixed Bisquare Kernel for Spatial Data (Case Study : Malnutrition in West Java Province).Brawijaya University.



DOI: https://doi.org/10.22146/ijg.17601

Article Metrics

Abstract views : 5623 | views : 4146

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2017 Indonesian Journal of Geography

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC