Modeling the residential distribution of enrolled students to assess boundary-induced disparities in public school access

Autoři: Louie John M. Rubio aff001;  Damian N. Dailisan aff001;  Maria Jeriesa P. Osorio aff002;  Clarissa C. David aff002;  May T. Lim aff001
Působiště autorů: National Institute of Physics, University of the Philippines Diliman, Quezon City, Philippines aff001;  College of Mass Communication, University of the Philippines Diliman, Quezon City, Philippines aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


Given school enrollments but in the absence of a student residence census, we present a gravity-like model to infer the residential distribution of enrolled students across various administrative units. Multi-scale analysis of the effects of aggregation across different administrative levels allows for the identification of administrative units with sub-optimally located schools and highlights the challenges in allocating resources. Using this method, we verify that the current scheme of free cross-enrollment across administrative boundaries is needed in achieving universal education in the Philippines.

Klíčová slova:

Data visualization – England – Children – Public administration – Schools – Transportation – United Kingdom – Philippines


1. United Nations Division for Sustainable Development. Goal 4: Sustainable Development Knowledge Platform; 2015.

2. Philippine Statistics Authority. Sustainable Development Goals Watch; 2018.

3. United Nations Statistics Division. Learning archives; 2016.

4. Maligalig D, Cuevas S. Is the net enrollment rate estimate of the Philippines accurate? Asian Development Bank; July 2010. ISSN 2218-2675, No. 2.

5. Albert JRG, David CC, Monterola SC, Lazo LS. Addressing late school entry and other demand-side barriers to primary schooling. Philippine Institute for Development Studies; 2012. ISSN 1656-5266, No. 2012-08.

6. Read L, Atinc TM. Investigations into Using Data to Improve Learning—Philippines Case Study. Washington D.C.: Brookings Institution; 2017.

7. CheckMySchool. SDG indicators; 2018.

8. Department of Education—Support. Bulletin (LIS K-12); 2018.

9. Anderson JE. The Gravity Model. Annual Review of Economics. 2011;3(1):133–160. doi: 10.1146/annurev-economics-111809-125114

10. Davidson KB. Accessibility in transport/land-use modelling and assessment. Environment and Planning A. 1977;9(12):1401–1416. doi: 10.1068/a091401

11. David CC, Monterola SLC, Paguirigan A, Legara EFT, Tarun AB, Batac RC, et al. School hazard vulnerability and student learning. International Journal of Educational Research. 2018;

12. Figueroa LL, Lim S, Lee J. Spatial analysis to identify disparities in Philippine public school facilities. Regional Studies, Regional Science. 2016;3(1):1–27. doi: 10.1080/21681376.2015.1099465

13. DepEd. DO 19, s. 1994—Guidelines on the Acquisition, Construction and Maintenance of Public Elementary and Secondary School Sites, Buildings and Grounds; 1994. Available from:

14. Senate of the Philippines. Chiz seeks titling of all public school sites; 2015.

15. Glaeser EL, Kahn ME, Rappaport J. Why do the poor live in cities? The role of public transportation. Journal of Urban Economics. 2008. doi: 10.1016/j.jue.2006.12.004

16. Figlio DN, Lucas ME. What’s in a Grade? School Report Cards and the Housing Market. American Economic Review. 2004;94(3):591–604. doi: 10.1257/0002828041464489

17. Albacea ZVI, Gironella AN. Where can we find the poor in the Philippines? The Philippine Statistician. 2000;49(1-4):30–37.

18. Boquet Y. The Growth of Greater Manila. In: The Philippine Archipelago. Springer; 2017. p. 521–566.

19. Graves J. The academic impact of multi-track year-round school calendars: A response to school overcrowding. Journal of Urban Economics. 2010;67(3):378–391.

20. DepEd. DO 62, s. 2004—Adoption of double shift policy in public school to address classroom shortage; 2004. Available from:

21. Cacho RM, Cacho LC, Raňeses MM. Decoding double shift effects on pupils, parents and teachers’ lived experiences: Alternative inputs for policy improvement. International Journal of Research. 2019;8(1):77–88.

22. Maligalig DS, Caoli-Rodriguez RB, Martinez A, Cuevas S. Education outcomes in the Philippines; 2010.

23. UNESCO Institute for Statistics. Pupil-teacher ratio by level of education (headcount basis); 2013. Available from:

24. DepEd. DO 7, s. 2015—Hiring guidelines for Teacher I positions for School Year (SY) 2015-2016; 2015. Available from:

25. Philippine Statistics Authority. Data Kit of Official Philippine Statistics (DATOS); 2002.

26. Philippine Statistics Authority. Philippine Standard Geographic Code Publication (September 2017 Release); 2017.

27. Department for Education. School capacity: academic year 2016 to 2017; 2018.

28. Department for Education. Locations of all UK primary and secondary schools - a Freedom of Information request to Department for Education; 2016.

29. Office for National Statistics. Local Authority Districts (December 2015) Full Extent Boundaries in Great Britain; 2017.

30. Boquet Y. Spatial Structures of the Philippines: Urbanization and Regional Inequalities. In: The Philippine Archipelago. Springer; 2017. p. 419–464.

31. Adair LS, Popkin BM, Akin JS, Guilkey DK, Gultiano S, Borja J, et al. Cohort Profile: The Cebu Longitudinal Health and Nutrition Survey. International Journal of Epidemiology. 2010;40(3):619–625. doi: 10.1093/ije/dyq085 20507864

32. Narboneta C, Teknomo K. A Study of Metro Manila’s Public Transportation Sector: Implementing a Multimodal Public Transportation Route Planner. Asian Transport Studies. 2016; p. 460–477.

33. Lall SV, Selod H, Shalizi Z. Rural-Urban Migration in Developing Countries: A Survey of Theoretical Predictions and Empirical Findings. Policy Research Working Paper; No 3915 World Bank. 2006.

Článek vyšel v časopise


2019 Číslo 10
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