University College Ghent, Faculty of Applied Engineering Sciences, Belgium

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1 USING GIS TO FACE PROBLEMS RELATED TO SPATIAL AND SOCIAL INEQUALITY - CASE STUDY: CAPACITY ISSUES OF PRE- SCHOOLS IN GHENT, BELGIUM FRANSEN Koos, VERRECAS Niels University College Ghent, Faculty of Applied Engineering Sciences, Belgium Abstract The growing popularity of the urban fabric as qualitative living environment has apparent effects on all Flemish regional cities. Social and spatial inequality is perceptible in many city functionalities, manifested amongst others in the scholar system. Pupils of primary schools (in Flanders children from 2.5 to 12 years) living in the proximity of a suitable school are forced to attend schools at a greater distance because the capacity of nearby schools is exceeded. The research at hand aims to provide an automated and adaptable tool for local authorities to visualise and analyse the current school constellation and support policy decisions concerning capacity extensions of existing schools, implantation of new schools or suppression of non- essential school locations. In the general applicable model, GIS and network analysis were used to determine the catchment area for each school. Furthermore, the model was used to produce a coverage map based on the theoretical catchment areas for the current demography, which was then compared to the actual situation, thus pinpointing and identifying problem areas for which appropriate measures have to be taken. Finally the model was used to predict the impact of future demographic evolutions on the current school constellation, analyse modifications on the datasets and determine the validity of certain decision policies. As so, the model was proven to be adaptable to other input datasets. The model was validated for pre- schools in the city of Ghent, Flemish Region, Belgium and proved to be a valuable tool to support local policy in education. Keywords: GIS, pre- school, education, accessibility, catchment area, location- allocation, network analysis, prediction models, spatial inequality 1 Introduction The growing migration to the city since the beginning of the 21 st century leads to an increase of the population in the city centres and the outer city rims [1]. These dynamics strain the public facilities which are not calculated for these recent evolutions. An example can be found in the capacity of schools, which in a lot of the major cities in Western Europe is not a fit for the increase of the number of children in the urban agglomerations. In Flanders (Belgium) the 1

2 capacity issues of schools are, amongst others, expressed by the periodical returning phenomenon of parents camping in front of the school gates during the enrolment periods in order to be sure to get hold of place for their children. Another symptom of the school capacity problems is that children have to travel over greater distances because there is not enough place in the schools in their neighbourhood. Although a vast amount of research has already been done concerning the accessibility of schools and their service area [2], [3], [4], [5], [6], solutions concerning the capacity of schools which are directly applicable to the educational system are still lacking. This is especially the case for elementary schools in Flanders (Belgium). The research described in this paper offers a ready to use tool for local governments and school communities to help them adapt their policy to demographic and spatial evolutions and face today s and tomorrow s challenges. 2 Methodology The research at hand presents a method for locating areas or schools with accessibility and/or capacity issues by using a set of indicators determined through the use of a GIS (Geographical Information System), thus allowing efficient budget allocations for capacity extensions of existing schools, implantation of new schools or suppression of non- essential school locations. Two sets of eleven indicators were determined, the first set applies to the level of statistical or spatial areas while the second set describes the schools. Both sets were then used as input for a choice- driven model. This automated GIS model contains a set of tools and is based upon the closest network path calculated with Esri ArcGIS 10.1 Network Analyst. The model allows the assessment of the present situation and the prediction of future evolutions. The datasets needed as input were [7], [9]: a geospatial dataset containing the borders of the statistical areas, a geospatial dataset containing the address and the age of the inhabitants of all statistical areas, a geospatial dataset containing for each school the name, the address and the educational system of the school and for each age group of the school the capacity, the actual number of pupils and the number of pupil rejections, a table containing the relationship between the statistical area of the pupil s domicile and the statistical area of the school he or she attends, a spatial network dataset of all the roads. 2

3 The indicators were generated with (automated) sub- models. Each indicator can also be used outside the choice- driven model, as an independent analysis or in combination with other indicators. The indicators on the level of statistical areas can be used to determine in which areas an under- or overcapacity exists. The set of indicators on the school level can be used for decisions on budget allocation within a school community 1. Apart from the basic input data sets, some sub- models for the calculation indicators also need the theoretical catchment area of the school. The theoretical catchment area is the area for which the maximal capacity of each school is reached and is calculated by allocating inhabitants of a certain age category to the school based upon the minimal network distance. Overlaps of these catchment areas result in a theoretical overcapacity whereas areas that are not covered, indicate a theoretical shortage in capacity. The theoretical catchment areas of the schools are also generated from the basic input data sets using an automated model. The indicators for the statistical areas are [7], [9]: the absolute number of a certain age category in the statistical area and the percentage of inhabitants of a certain age category relative to the total number of inhabitants of the statistical area, the number of schools in the statistical area, the percentage of inhabitants of a certain age category that attend a school in their own statistical area relative to the total number of inhabitants of that age category in the statistical area, the percentage of inhabitants of a certain age category that attend a school in an adjacent statistical area relative to the total number of inhabitants of that age category in the statistical area, the percentage of inhabitants of a certain age category that attend a school in a statistical area which is not their own or an adjacent statistical area, relative to the total number of inhabitants of that age category in the statistical area, the percentage of inhabitants of a certain age category that attend a school located in the same statistical area of their domicile, relative to the total number of inhabitants of that age category that attend a school in that statistical area, the percentage of inhabitants of a certain age category that attend a school in a certain statistical area, but live in an adjacent statistical area, relative to the total number of inhabitants of that age category that attend a school in that statistical area, 1 A school community consists of more than one school settlement on different locations. 3

4 the percentage of inhabitants of a certain age category that attend a school in a certain statistical area and do not live in that or an adjacent statistical area, relative to the total number of inhabitants of that age category that attend a school in that statistical area, the absolute number of inhabitants of a certain age category, living outside the theoretical catchment area of that age category per statistical area (B u), the multiplication of the number of overlaps minus one (O 1) and the absolute number of inhabitants of a certain age category domiciled in the theoretical catchment area of that age category per statistical area (B i), the theoretical overcapacity or shortage of the statistical area as result of the operation: R = B i x (O 1) - B u The indicators for the schools are [7], [9]: the school capacity of a certain age category, the educational network to which the school belongs, the actual number of pupils of a certain age category per school, the percentage of pupils of a certain age category in relation to the school capacity per school, the number of refusals of a certain age category per school, the percentage of inhabitants of a certain age category that attend the school and live in the same statistical area that school is located in, relative to the total number of pupils attending that school, the percentage of inhabitants of a certain age category that attend the school and live in a statistical area adjacent to the area the school is located in, relative to the total number of pupils attending that school, the percentage of inhabitants of a certain age category that attend the school and live outside the same or an adjacent statistical area that school is located in, relative to the total number of pupils attending that school, the minimal distance of the theoretical catchment area of the school, the average distance of the theoretical catchment area of the school, the maximum distance of the theoretical catchment area of the school. All the models were created using Esri Modelbuilder. 4

5 3 Case study: The city of Ghent To validate the models, the city of Ghent was used as test case. Geographically, Ghent is characterized by a historical city centre encircled by an area of 19 th century urban expansion. This 19 th century belt is surrounded by a peripheral area with a village- like structuring [7]. Ghent is the capital of East- Flanders and is the city that attracts the largest number of pupils and students in Belgium. Ghent counts 98 pre- schools. The overall capacity shortage for pre- schools in the year was resolved by implementing temporary solutions such as the use of container classes [8]. However, these ad hoc solutions are not sufficient to face the global capacity problems to be expected in the years to come. For 41 of the 98 pre- schools, the actual service area was computed based on the closest network path between the home of each pupil and the school. To assess the usability of the choice- driven model on the level of the school, the outcome of the model was evaluated in detail for two schools [7], [9]. 4 Results In what follows, the most important results of the developed sub- models will be discussed as well as the outcome for both choice- driven models (statistical area and school). Finally, changing the model s input, thus indicating the usability of the model for predicting future developments, proves the adaptability of the model. An overview of the complete analysis can be found in our master s thesis and in a previously published article [7], [9]. The specific input datasets for the case study of Ghent are: spatial dataset with the borders of the 201 statistical sectors 2 in Ghent, the characteristics of the entire Ghent population (age, address, ), the characteristics of all pre- schools (location, capacity for each age group, actual number of pupils for each age group, ), a table featuring the allocation of each child to the school it attends, a spatial network dataset of all the roads of Ghent. The age for children going to pre- schools is two to five year. 2 A statistical sector is the smallest geographical unit available in Belgium. 5

6 Indicators figure 1: The percentage of children attending a school in their own statistical area (Ghent ) figure 2: The percentage of children that attend a school and do not live in the same or an adjacent statistical area (Ghent ) 1. The percentage of children attending a school in their own statistical area (figure 1) The population of children attending a school in their own statistical area is highest in the peripheral areas containing one or more schools, indicating a high degree of self- sufficiency. All these statistical areas can be marked as peripheral village centres with a high sense of community. Before the fusion of 1976 they were independent villages. In the area just outside the city centre some statistical areas with two or three schools also have a high degree of self- sufficiency, but in general the percentage of pupils attending a school in their own statistical area is low in this area [9]. 2. The percentage of children that attend a school and do not live in the same or an adjacent statistical area according to the statistical area of the school (figure 2) 6

7 This indicator is a measure for the supra- local attractiveness of the schools in a certain statistical area, relative to the capacity. Low percentages can therefore indicate local capacity issues. The highest percentages are found in the city centre and in the environment of the Gent- Sint- Pieters railway station, south from the city centre. This is in accordance with the city centre s high degree of facilities and emphasizes the import nature of these schools and their local overcapacity. Moreover, these areas are well served by public transportation. figure 3: The theoretical overcapacity or shortage (Ghent ) figure 4: Capacity and education portal of the school (Ghent ) 3. The theoretical overcapacity or shortage based upon the children of 2 to 5 years living outside and inside the theoretical catchment areas of the schools (figure 3) This indicator is also used to determine local capacity issues, be it now on a theoretical level. North of the city centre, the apparent local shortage is problematic, because of the clustering of high ratios of shortage in the surroundings. Other theoretical local under capacities are countered by neighbouring theoretical local overcapacities. The centre and the Gent- Sint- Pieters railway station surroundings, have a high local overcapacity, which confirms the existence of import schools [7], [8]. 7

8 4. Capacity and educational portal3 of the school (figure 4) The concentration of schools with the highest capacity (more than 120 pupils) are located in the city centre and some peripheral areas. Although the school density is higher just outside the city centre, the capacities are mainly lower. Nearly all neighbourhoods are characterized by the combination of one school subsidized by the city and one or more adjacent bigger schools of the catholic network (portal). figure 5: Amount of refusals (Ghent ) figure 6: The percentage of children that attend the school and live outside the same or an adjacent statistical area (Ghent ) 5. The number of refusals (figure 5) Most schools with a high ratio of actual pupils in relation to their capacity, also have a high number of refusals. This indicates the popularity of a school, especially for the ones in the centre of the city. In the area just outside the city centre, the high number of refusals indicates a local shortage of capacity. 3 The following educational portals are possible for the choice in primary schools in Ghent: Education Secretariat of Cities and Municipalities (OVSG), Community Education (GO!), the free Subsidized Catholic Education (VSKO) and Small Talk Education Providers (OKO) 8

9 6. The percentage of children that attend the school and live outside the same or an adjacent statistical area in accordance to that school (figure 6) High percentages are an indicator for a high degree of supra- local attractiveness, relative to the capacity. In the city centre, the high percentages can be explained by the popularity of these schools, while in the environment of the Gent- Sint- Pieters railway station, the high degree of supra- local attractiveness can be ascribed to local overcapacity. Low percentages can also indicate local capacity issues, especially in densely populated areas, as for example in the north of the city centre. Choice- driven model On the level of the statistical area, the model was applied using values for the indicators in accordance with a policy aimed at statistical areas in which a local shortage is to be expected. Four statistical sectors were selected as a result of the choice- driven model (figure 7). Afterwards, the statistical sectors were arranged by increasing theoretical shortage in capacity, thus pinpointing the most problematic areas. The selected areas are regions in which locally situated capacity issues are currently imminent, thus validating the model as a useful query tool [9]. The choice driven model was also applied on the figure 7: Selection of the choice- driven model at the level of the statistical sector and the level of the school (Ghent ) level of the schools, but this time in accordance with a policy aimed at locating schools with large travel distances for the children attending these schools. Applying the model resulted in the selection of two schools (figure 7): one school is located in the peripheral area and the other in the city centre. Comparing the theoretical to the actual data on address level, indicates that both schools have a widely spread average service area. Studying the actual relation between the location of the school and pupils addresses more closely, leads to conclude that the school located in the city center attracts a lot of pupils from the entire urban tissue due to its popularity, while the school in the peripheral area especially attracts pupils from areas with local capacity shortages. 9

10 The adaptability of the model for evaluating future developments figure 8: Changes in the theoretical overcapacity or shortage for the school years and (Ghent) By changing the age category as input for the theoretical models (1 to 4 and 0 to 3 year olds), it is possible to make predictions concerning over- and under capacity for the near future. The prediction of the theoretical overcapacity or shortage for the next two years, indicates that the overall overcapacity in the city centre gradually reduces or disappears, especially in the north (figure 8). figure 9: Changes in the school catchment areas for the school years and (Ghent) 10

11 The spread of the school catchment areas diminishes for most areas, with some exceptions. By applying the automated models for the near future in relation to the current demographic evolutions, urgent interventions can be planned more easily (figure 9). Finally, the geospatial dataset containing the schools was altered, in order to validate the applicability of the model for the simulation of the impact of future interventions. This was tested by adding a school with a certain capacity to the dataset and running the different theoretical automated models. figure 10: Changes in the theoretical overcapacity or shortage by implantation of an extra school (simulation for Ghent ) Adding a school in the north of the 19th century belt, characterized by a cluster of high degrees of under capacity, resulted in local switch to theoretical overcapacity (figure 10). 5 Conclusion Validation of the outcome of the automated model results in a usable tool for educational decision policies. Not only the selections of the BOS- models (Beleidsondersteunend Selectie- model or Policy Supporting Selection Model), but also the individual indicators generate a valuable output. By developing the models on two levels (statistical sector and school), local decision- making is supported, both for interventions regarding a particular area or a specific school. The tool is already approved by the local government and will be used for determining the location of a new school or budget allocation in accordance to the current school constellation. 11

12 The general applicability of the models indicate that they are adaptable for use in analyzing different urban dynamics. The models are transferable to other policies, aimed at different stakeholders. Therefor, using a different dataset as input can lead to an analysis of other urban phenomena, for example the critical shortage of kindergartens or the allocation of homes for the elderly. A further elaboration of the models in combination with a detailed survey of the educational system, will lead to a more thorough study of the gathered outcomes. Mainly socio- economic aspects that play a critical role in this study should be further analysed. Also, the impact of public transport on the accessibility of schools should be taken in to consideration. 6 References [1] Deboosere P. België en de transitie van krimp naar groei, Geron tijdschrift over ouder worden & samenleving, The Netherlands, vol. 14/issue 3, pp 33-36, [2] Pearce J. Techniques for defining school catchment areas for comparison with census data, Computers, Environment and Urban Systems, United Kingdoms, pp , [3] Talen E. School, community, and spatial equity: An empirical investigation of access to elementary schools in West Virginia, Annals of the Association of American Geographers, United States of America, vol. 91/issue 3, pp , [4] Bejleri I., Steiner R. L., Fischman A. & Schmucker J. M. Using GIS to analyze the role of barriers and facilitators to walking in children's travel to school, Urban Design International, vol. 16/issue 1, pp 51-62, [5] Mulaku G. C. & Nyadimo E. GIS in Education Planning: the Kenyan School Mapping Project, Survey Review, vol. 43/issue 323, pp , [6] Singleton A. D., Longley P. A., Allen R. & O'Brien O. Estimating secondary school catchment areas and the spatial equity of access, Computers Environment and Urban Systems, vol. 35/issue 3, pp , 2011 [7] Deruyter, G., Fransen, K., Verrecas, N., De Maeyer, Ph., (2013), Evaluating spatial inequality in preschools in Ghent, Belgium, 13th International Multidisciplinary Scientific Geoconference - SGEM 2013, Cartography and GIS, June 2013 [8] Apostel K. Capaciteitsprobleem: over Vraag en Aanbod, School in de Stad, Stad in de School, ed. ASP, Belgium, pp ,

13 [9] Fransen, K., Verrecas, N. (2013). Evaluating spatial and social inequality in pre- schools in Ghent, Belgium - An accessibility and service area analysis using GIS, Master s thesis (unpublished), University College Ghent, Faculty of Applied Engineering sciences. 7 Acknowledgements We would like to thank the people of the Department Strategy and Coordination Data Analysis and GIS City of Ghent, for their valuable and insightful comments and suggestions. 13