Identifying Schools for the Fruit in Schools Programme

Transcription

1 1 Identifying Schools for the Fruit in Schools Programme Introduction This report identifies New Zeland publicly funded schools for the Fruit in Schools programme. The schools are identified based on need. Need here is defined from two equally weighted criteria: 1. Schools in high areas of social and material deprivation as indicated by the New Zealand Deprivation Index 2001 (Salmond and Crampton, 2002) 2. Schools in an area where there is a high proportion of the population in the of 5 to 14 years age range Assumptions underpinning the methods used Ideally individual school rolls would be used to determine proxy (socio-economic and population density) characteristics of the school population. However, in absence of individually geocoded school rolls the method used here creates a probabilistic estimate of the local neighbourhood in which the school is located. In others words, as we cannot determine the characteristics of the school population we are instead estimating the characteristics of the neighbourhood around a school. The critical assumption here is that it is highly likely that in most instances children will attend a local school. However, it is fully acknowledged that some children attending some schools will travel to surrounding neighbourhoods. Nevertheless this is considered to have minimal impact on the results for two reasons: a) children who are likely to travel are likely to come from more affluent families. More affluent families are able to exercise, and have access to, greater choice relative to those in more deprived areas (the target for programme are the more deprived areas), and b) those in the more deprived areas that do travel will have a minimal impact on the results as they are likely to be far fewer in number (given that people in lower socio-economic groups have less access to travel and/or alternative non local schooling options). It is important to note that the concept of need can be variously defined. The method used here captures two attributes that are considered important determinants in relation to the programme: deprivation and a relative measure of the number of children in the target age group. Data Exclusions Ethnicity (Māori) has not been specifically included because of the potential for double counting. The NZDep 2001 index does not include ethnicity as one of its component variables, however there is a very strong correlation between highly deprived areas and socio-economic heterogeneity. That is, areas with a high proportion of Māori are also areas of relative high deprivation. Therefore, including ethnicity together with deprivation (that is already a good proxy indicator of ethnicity) would potentially overly bias the calculation of need as it is defined here.

2 2 Data There are two types of data used: Attribute and Spatial Attribute data Census 2001 population Total population 5 to 14 age group population (consisting of 5 to 9 age range and the 10 to 14 age range) NZDep 2001 index of deprivation. A composite index created from the 2001 census (Salmond and Crampton 2002) Meshblock values the calculated (disaggregated) value (the aggregated decile score is not used as the statistical measures used here cannot be used with ordinal grouped data such as the 1 to 10 decile classification) List of publicly funded New Zealand schools (April 2005, Ministry of Education) Spatial Data Statistics New Zealand Meshblocks. These are the lowest level of administrative geography in New Zealand for which census data are released. Geocoded Address points. Used to geocode (map) the school s address data Methods The work was undertaken in four stages using ArcGIS geographical information system (GIS), and GeoDa spatial statistical analysis application (Anselin, 2003): 1. Stage 1 Identify meshblocks with the highest proportion school age group population (census data age range 5 to 14 years) 2. Stage 2 Identify high deprivation meshblocks 3. Stage 3 Geocode and map the schools 4. Stage 4 Identify schools in high deprivation and high proportion school age group areas Stage 1 - Identify meshblocks with the highest proportion school age group Creating the neighbourhood regions The method used to create the proximal school neighbourhood regions is based on the geographical concept of spatial autocorrelation (SA). SA is similar to ordinary correlation in that the aim is to look for the degree of association between variables.

3 3 In the particular case of SA, the aim is to look for the degree of association across space, i.e attribute similarity and locational similarity (Haining 1990, Chrisman 1997, Longley et al 2001). There are a number of established and widely used SA techniques with Moran s I the most commonly used (Bailey and Gatrell 1995, Chrisman 1997, Getis and Ord 1996). The I statistic is a hypothesis test where the null hypothesis is that the data are not correlated and conversely the alternative suggests the presence and degree of autocorrelation. Moran s I is considered to be a global test in that the complete distribution of data are examined collectively. An alternative approach is to examine for spatial autocorrelation at the small area level and in particular, to evaluate the pattern of data around single areas. So called Local Indicators of Spatial Autocorrelation, commonly referred to as LISA have been developed recently, in particular by Luc Anselin (Getis and Ord, 1996; Fotheringham, 1997). Anselin has developed a LISA development of Moran s I. The aim of this statistics is to test for the presence or absence of spatial autocorrelation around fixed areas. The determination of local Moran s I is given as (from Rogerson, 2001, p.173): I i = n ( y y) w ( y y) i i j ij i where n are all data (for example meshblocks) with attribute y (for example NZDep value), and w is a measure of proximity between point i and j. The essential term when considering using statistics of this sort are the weights w ij. In terms of area data (such as meshblocks used here) w ij is expressed as a binary function, either joined or not joined. To create the neighbourhood, third order contiguous spatial autocorrelation is used. That is, for every meshblock SA is assessed not just for those immediately surrounding meshblocks, but also as the meshblocks that surround this first group, and the second group out to the third level are also included. The assumption here is that the more small areas (individual meshblocks) that share similar characteristic the more likely it is that that overall neighbourhood (of many meshblocks) is indicative of these characteristics. Identifying areas of high proportion 5 to 14 years age range The high 5 to 14 years age range areas were identified as the third order spatially autocorrelated meshblocks derived from the product of dividing the 5 to 14 years age range population by the total population. Stage 2 - Identify high deprivation meshblocks The deprived areas were identified as the third order spatially autocorrelated meshblocks (using the same methodology as above) with high NZDep 2001 values. Stage 3 Geocode and map the schools

4 4 The schools were geocoded based on the addresses provided in the data. Stage 4 Identify schools in high deprivation and high proportion school age group areas Once geocoded the GIS point-in-polygon method was used to determine the high deprivation and high population meshblocks containing schools. Results The results consist of a table (table 1) summarising the distribution of schools by spatial autocorrelated deprivation, and proportion of school aged population aged 5 to 14 years, together with a series of three sets of three maps (see figure 1 for examples). Each set of map depicts all of New Zealand, then one map each for the North Island, and the South Island. Both island maps have zoomed in inserts depicting Auckland, Wellington, Christchurch and Dunedin. The maps show: Meshblock thematic map of the proportion of 5 to 14 years age range by quintile Meshblock thematic map of third order spatially auto-correlated NZDep values Meshblock thematic map showing just those meshblocks that contain high proportion 5 to 14 years age range and high spatially auto-correlated NZDep values in which schools are located There 151 schools (7.29 % of total) located within 134 meshblocks (0.35 % of total) as defined by need based on third order spatially autocorrelated meshblock NZDep 2001 values and high proportion 5 to 14 years population (see table 1). Table 1. Summary Results Meshblock Count Count Proportion (%) Total Mainland & Island Meshblocks High NZDep01 Cluster High NZDep01 Cluster - with schools High NZDep01 Cluster - with highest proportion of 5-14yr olds High NZDep01 Cluster - with schools and highest proportion Primary School Count Count Proportion (%) Total No. of 2002 Primary Schools Located within a High NZDep01 Cluster Meshblock Located with a High 5-14yr old Meshblock Located within High NZDep01 and a high proportion of 5-14yr olds

5 5 Map 1. Before (left) and after (right) clustered meshblocks

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7 7 References Anselin, L. (2003) GeoDa 0.9 Users Guide. Spatial Analysis Laboratory, Urbana- Champaign. University of Illinos Bailey, T. C and Gatrell, A. C. (1995) Interactive Spatial Data Analysis Harlow. Longman Chrisman, N. (1997) Exploring Geographic Information Systems. Chichester. John Wiley and Sons Fotheringham, S. (1997) Trends in Quantitative Methods I: Stressing the Local Progress in Human Geography, 21 pp Longley, P.A., Goodchild, M. F., Maguire, D. J., Rhind, D. W. (2001) Geographic Information Systems and Science. Chichester, Wiley Getis, A and Ord, J. K. (1996) Local spatial statistics: an overview, in Longley, P and Batty, M. (eds) Spatial Analysis: Modelling in a GIS Environment Glasgow, GeoInformation International Haining, R. (1990) Spatial data analysis in the social and environmental sciences Cambridge, Cambridge University Press Rogerson, P. A. (2001) Statistical Methods in Geography London, Sage Salmond C., and Crampton, P. (2002) NZDep 2001 index of deprivation user s manual. Wellington. Dept of Public Health, Wellington School of Medicine and Health Sciences, University of Otago