APPLICATION OF

LOCATION/ALLOCATION MODELS AND GIS

TO THE LOCATION OF

NATIONAL PRIMARY SCHOOLS

IN RAWANG, MALAYSIA

Nabilah Naharudin

Introduction

As education is compulsory in Malaysia, all children aged 7 to 12 are expected to attend primary schools. It is therefore a challenge for the government to provide schools which are well located to serve the children conveniently. Various criteria for siting schools may be assessed by using location allocation models (LAMs), a widely used tool for finding good locations for public facilities. The key factor in siting schools is the location of demand or number of children that will attend the schools involved, here the national primary schools of Malaysia.

This study explores various solutions to the problem of locating schools and allocating children to them in the city of Rawang, Selangor State, Malaysia. LAMs are used to examine the current locations of the schools to see how well they meet the Federal Government’s goal of all children being within 800 meters from school and to help identify any poorly served (DTRPS, 2010). Then, various possible solutions for improving accessibility to badly served areas will be assessed by using LAMs which allow the provision of one or two new schools in addition to the existing 5 schools and which also explore the consequences of closing any apparently poorly located schools.

Methodology

Key Findings

1. Figure 1 shows that the only school that located well and covers a lot of pupils is SK Tun Teja. The other four schools are either too far from pupils’ home resulting to poor catchment areas. Based on these results, 27 potential candidates for new schools are selected as illustrated on Figure 2.

Figure 1 Catchment Areas of the Existing Schools

Figure 2 Candidate Locations for Schools

2. Table 1 indicates that closing one school doesn’t affect the catchment and coverage areas of the schools as well as the accessibility. The school that was selected to be closed is always either SK Sinaran Budi or SK Rawang that are located next to each other.

Table 1 Comparison of Aggregate Travel Distance and Number of Pupils Covered with Four and Five Schools

Number of Schools	With Capacity Constraint			Without Capacity Constraint
	Aggregate Travel Distance (km)	No. of Pupils Covered		Aggregate Travel Distance (km)	No. of Pupils Covered
		800 m	1600 m		800 m	1600 m
Existing Five	22, 735.596	902	1844	22, 735.596	902	1844
Best Four	23, 012.652	902	1817	23, 336.941	902	1834
Differences	277.056	0	-27	601.345	0	-10
School Selected to be Closed	SK Rawang	SK Rawang	SK Rawang	SK Sinaran Budi	SK Rawang	SK Sinaran Budi

3. Figure 3 suggests the improvement in catchment areas if two schools are added. With two new schools, the poorly served areas previously now are well accessible. The number of unallocated pupils is also reduced.

Figure 3 Catchment Areas for the Existing Five Schools and Two Additional Schools

Discussion

In our analysis, the location of demand and the journey to school were both treated in a more detailed and realistic manner spatially than has been the case in virtually all equivalent problems found hitherto in the recent research literature, a significant contribution of the present work and a benefit of having a wide range of LAMs integrated into a GIS with network capability. At this fine scale, DTRPS’s recommendation that all children should be within 800 m of a school seems over-ambitious as only 24.6% of the estimated pupils attending national schools in Rawang enjoy this level of access to the existing 5 schools. Even with 2 additional schools added at good locations, this only increases to 46.7%. A distance of 1600 m would serve as a more achievable goal: 50.2% of our pupils are within that distance of the 5 existing schools and 81.2% would be with 2 more well located schools. Results from the Minimizing Facilities model suggest, however, that to cover all pupils within 1600 m would require 12 schools, which is not feasible practically.

The LAMs applied produced results on a range of criteria which gave insight into how well the 5 existing schools serve the area, as well as where substantial areas of poorly served demand lie and also helped to identify and evaluate some good locations for siting one or two new schools. Several pairs of candidates seemed to merit particular consideration and further evaluation in this context, namely candidates 4 and 18 or 4 and 14 or 4 and 24. Candidates 4 and 18 also have the advantage of being located near new residential areas, largely developed since 2010, and therefore not included in our demand data.

Conclusion

The best solution to improve the accessibility is by adding two more schools in poorly served areas with high number of pupils without closing any of the existing schools. The government should also consider increasing the distance requirements to build new school (at least, double the number to 1600 m).

The results from the LAMs used here could be further enhanced if demand could be estimated more accurately using data on EBs or even smaller user defined units plus data on new residential areas, as well as information on how many children from Rawang go to schools elsewhere (e.g. in Kuala Lumpur or the rural areas where their grandparents live) and on how many children travel to schools in Rawang from areas outside. Further ahead, more sophisticated models could be developed taking account of a wider range of variables such as actual modes of travel to school and preferences for different schools.

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