4  Asking questions

Before we start asking questions of data…

Make a table and write down the data you might want to use:

• What does it show
• What does it not show or is missing
• Does it link to spatial data
• Which column links it to spatial data or does it have latitude and longitude (e.g. points)
• What question an we reasonably ask of the data

4.1 Questions we can ask

We need to suggest how can we incorporate spatial data into our question

Given the data sets above we could seek to answer the following questions:

• Does distance to school affect literacy outcomes

  Data:

  • Census data (literature / illiterate)
  • Sub district polygons (spatial)
  • School locations (coordinates provided)
  • Building outlines (from open street map)

  Analysis:

  • Make building data to points (polygons to centroids)
  • Work out the average distance from each building point to each school per ward / district (whatever the census data is provided at)
  • Plot the average distance to school against the literate column

  Assumptions:

  • Distance might be Euclidean (as the crow flies)
  • Data is from different year
  • Not sure how the census assess literacy ability

• Does the location of public health services appropriately serve the population

  Data

  • Census data (count of people) OR
  • World pop data from a more recent year
  • Public health centers (point data)
  • Polygon outlines (e.g. sub districts)

  Analysis:

  • For each word pop cell (raster cell) work out the closet public health centre (e.g. direct line)
  • Count the number of people assigned to each public health centre
  • Compare the number of people served

  Assumptions:

  • Different health centres might be larger or small and have more or less staff. How can we adjust for that? Number of staff / population?

4.2 Your turn

Can you think of a similar question for:

• Accessing parks and playgrounds across the city
• How can we estimate demand for the Annapurna food scheme locations