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Open Data Day 2016: Data Expedition

Measuring Provision of Public Services for Education

Details

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In Malaysia, Sinar Project with the support of OKFN organized a one day data expedition based on the  guide from the School of Data to look for data related to government provisioning of health and education services.  This brought a group with diverse skills to ask questions of public interest, to look for data and to analyse and visualize the data to try find the answers. 

Data Expedition

A data expedition are quests to explore uncharted areas of data and report back. The participants comprising several people with different skillsets throughout the day at the Sinar Project office, explored if there was data related to schools and clinics to see if there was data and analysis methods available to try gain insights on public service provision for education and health.

We used the guides and outlines for data expedition from the School of Data. The role playing guides worked as a great ice breaker. There was healthy competition on who could draw the best giraffes for those wanting to prove their mettle as a designer for the team.

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Deciding what to explore, education or health?

The storyteller in the team, who was a professional journalist started out with a few questions to explore.

  • Can we know if there are any villages or towns which are far away from schools?
  • Can we know if there are any villages or towns which are far away from clinics and hospitals?
  • How about population density and provision of clinics and schools?

The scouts then went on a preliminary exploration for whether data for this exists.

 

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Looking for the Lost City of Open Data

Scouts with the aid of the rest of the team  looked for data to be able to answer the questions,  and found a lot of usable data from the Malaysian government open data portal data.gov.my.  Lists of all public schools and clinics with addresses were found, as well as number of teachers for each district.

It was decided by the team that given the time limitation, to focus on trying to answer the questions on education data. An additional question was to also look for class sizes to see if schools are overcrowded or not.

Education

Open Data

 

Data in Reports

Definitions

  • Not all schools are created equal, there are different types, some are considered as high achieving schools or Sekolah Berprestasi Tinggi


Health

Open Data

GIS

Other Data

Budgets

Sinar Project had some budgets as open data, at state and federal levels that could be used as additional reference point. These were created as part of the Open Spending project.

Selangor State Government

http://data.sinarproject.org/dataset/selangor-state-government-2015-budget

 

Federal Government

Higher education

 

Education

 

Methodology

The team opted to focus on seeing if they can work with the available datasets to answer the questions on education provision by first converting all school addresses into geocode, and then looking at joining up data to try find out relationships between enrollments, school and teacher ratios.

Joining up data

To join up data; the different data sets such as teacher numbers and schools, VLOOKUP function in Excel was used to join by Schoolcode.

Converting Address to geolocation (latlong)

To convert Address to Lat, Lng use the cleaned up address along with a geocoding tool like csvgeocode 

./node_modules/.bin/csvgeocode ./input.csv ./output.csv --url "https://maps.googleapis.com/maps/api/geocode/json?address={{Alamat}}&key=<GOOGLE_GEOCODING_KEY>" --verbose

Convert the completed CSV to GeoJSON points

Use the  csv2geojson

csv2geojson --lat "Lat" --lon "Lng" Selangor_Joined_Up_Moe.csv

To get population by PBT

Look at data from the state economic planning unit agency site for socio-economic data specifically the section Jadual 8

To get all the schools separated by individual PBT (District)

Take the GeoJSON of Schools data and PBT Boundaryloaded into QGIS; and use the Vector > Geo-processing > Intersect.  

A top from Stack Exchange suggests  it might be better to use Vector > Spatial Query > Spatial Query option.

Open Datasets Generated

The cleaned up and joined up datasets created during this expedition are made available on GitHub. While the focus was on education, due to the similarity in available data, the methods were also applied to clinics also.

https://github.com/Sinar/SinarODD2016

Visualizations

All Primary and Secondary Schools on a Map with Google Fusion Tables

https://www.google.com/fusiontables/DataSource?docid=1lVyjIIEm_McqmiSEfQY5vecrhqRjmaJ1wzdiEo1q#map:id=7

Teacher to Students per school ratios

 

https://www.google.com/fusiontables/DataSource?docid=18ieB8OqzpK3Ch9KcD4BiiADdmk8SXnS0x_IINxHc#map:id=3

Discovery

  • Teachers vs enrollment couldn’t provide class sizes or data related to overcrowding
  • Needed demographic datasets to measure schools to eligible population
  • Needed more school datasets for teachers by subjects and class ratios
  • Methods used for location of schools can also be applied for clinics & hospital data

It was discovered that additional data was needed to provide useful information on quality of education. There was not enough demographic data found to check against number of schools in a particular district. Teacher to student ratio was also not a good indicator of problems reported in the news. The teacher to enrollment ratios were generally very low with a mean of 13 and median of 14. What was needed was ratio by subject teachers, class size or against population of eligible children of each area, to provide better insights.

Automatically calculating distance from points was also considered and matched up with whether there were school bus operators in the area. This was discussed because distance from schools may not also be insightful for rural areas, where there was not enough children to warrant a school within distance policy. A tool to check distance from a point to the nearest school though could be built with the data made available. This could be useful for civil society to use data as evidence to prove that distance was too far or transport not provided for some communities.

Demographic data was found within local council boundaries, this could be used by others to match whether there was enough schools by local council boundaries. Interestingly in Malaysia however, education is under Federal government and despite having state and local bodies, these do not match up with local council boundaries or electoral boundaries. Administrative boundary data was made available as open data thanks to the efforts of another civil society group Tindak Malaysia which scanned and digitized the electoral and administrative boundaries manually.

Running future expeditions

This was a one day expedition so it was time limited. For running these brief expeditions we learned the following lessons.

  • Focus and narrow down expedition to specific issue
  • Be better prepared, scout for available datasets beforehand and determine topic
  • Maybe working on central repository or wiki of available data may be a good idea to work on first, rather than go on expedition given that it is hard to find data in Malaysia

 

Appendix I: Re-use of the found and created datasets at Magic Data Analyst Course following day

One of the members of the team on the first day, applied additional tools and analysis for the data created during the open data day expedition.

The objective was a continuation exploration to answer the questions posed during Open Data Day to discover which areas are underserved by schools.  To analyze further, QGIS along with another open and freely released data set from another civil society organization Tindak Malaysia  which was the Administrative Boundaries of the Local Authorities (PBT) in Selangor.  

The goal was to separate out the schools by Local Authority as there was population data by Local Authority using the Vector Intersection analysis function in QGIS. After plotting out the data, we could see that Primary schools are much smaller in sizes but distributed more widely.  High schools are more focused in urban areas and are of larger size.

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Another observation on possible imbalance can be seen by comparing number of schools against local council borders Ampang Jaya (MPAJ) and Subang Jaya (MPSJ)  for urban areas; and Sabak Bernam, Hulu Selangor for semi-urban areas. The data shows that there are more schools in semi-urban areas against population, but it also shows that are also some discrepancies in school distribution between different urban areas with similar population numbers. It would seem that a minimal school distance to communities may be in place. A proper conclusion would likely need further segmenting as urban/rural distribution will need to be taken into account.

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Source:

Story Type: Update

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Contributors

Exploration team 

  • Lim Hui Ying (Storyteller)
  • Haris Subandie (Engineer)
  • Jack Khor (Designer)
  • Chow Chee Leong (Analyst)
  • Donaldson Tan (Engineer)
  • Michael Leow (Engineer)
  • Sze Ming (Designer)
  • Swee Meng (Engineer)
  • Hazwany (Nany) Jamaluddin (Analyst)
  • Loo (Scout)

Funding & Support

Mini Grant

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Additional Support from

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