This week's project uses not a GIS program, but a package for exploratory spatial data analysis called GeoDa. GeoDa is a good example of research software. It implements many methods that have been in the academic research literature for several years, some of which have yet to make it into standard desktop GIS tools. Among the methods it offers are simple measures of spatial autocorrelation.
You will use GeoDa to examine the spatial distribution of different ethnic groups in Auckland, New Zealand. In this lesson, you are working with a real dataset.
Until the last 20 years or so, Auckland was a relatively 'sleepy' industrial port. It has been New Zealand's largest city for about a century, but its dominance of the national economy has become even more marked in recent years. This is partly attributable to increasing numbers of immigrants to New Zealand, many of whom have settled in the Auckland region. Today, Auckland accounts for about one third of the total population of the country (about 1.6 million people, depending on where you think the city stops), and for a much larger fraction of the more recent migrant groups. Auckland is the largest Pacific Islander city in the world, and also home to large populations of Māori (the pre-European settlement indigenous people), and Asian peoples, alongside the majority European-descended (or, in Māori, 'Pakeha') 'white' population.
Such rapid change is exciting (it has certainly improved the food in Auckland!), but can also lead to strains and tensions between and within communities. We can't possibly explore all that is going on in a short project like this, but, hopefully, you will get some flavor of the city from this exercise.
The basic analytical approach adopted in this project is very similar to that presented by Andrea Frank in an article:
'Using measures of spatial autocorrelation to describe socio-economic and racial residential patterns in US urban areas' pages 147-62 in Socio-Economic Applications of Geographic Information Science edited by David Kidner, Gary Higgs and Sean White (Taylor and Francis, London), 2002.
This week's project is deliberately more like a short exercise than some of the upcoming projects. This is for two reasons. First, you should be spending a good amount of time starting to develop your term-long project, and producing your project proposal. Second, we will cover some ideas in this project not covered in the readings and also introduce a new tool. If you want to explore these ideas and the GeoDa tool further, then I hope that this exercise will give you an idea where to start!
The zip file you need for Project 4, project4materials.zip, is available in Canvas for download. If you have any difficulty downloading this file, please contact me.
The contents of this archive are as follows:
- ak_CAU01_ethnic shapefiles showing the greater Auckland region delineated by the New Zealand 2001 Census Area Units (CAUs). CAUs are roughly equivalent to tracts in the US census, with a few thousand people in each CAU. There are 355 of these in the greater Auckland region. The data table for this shapefile contains counts and percentages of the population in each of five groups (European, Māori, Pacific Islander, Asian, and 'Other').
- akCity_CAU01_ethnic shapefiles showing the 101 CAUs of the central Auckland 'City' region. This area contains the CBD and many of the more upscale neighborhoods of the city. The ethnicity count and percentage data are repeated in these files.
- akCity_MB01_ethnic shapefiles showing 2001 Census 'Mesh Blocks' for the City area. Mesh Blocks (MBs) are the smallest areal unit used in the New Zealand census with no more than a few hundred people in each. There are almost 3000 MBs in the City area alone.
- ak_DEM_100 raster digital elevation model files that will give you some idea of the topography of the city, although this is for interest only and has no effect on the details of the project.
- nz_coastline shapefiles are also for interest only and will give you some context for Auckland's location relative to the country as a whole (it's 'near the top'!).
- Three GAL files showing contiguity for the census shapefiles. These are used by GeoDa to perform spatial autocorrelation analysis and will be explained in more detail in the project instructions.
You will also need a copy of the GeoDa software in order to run the required analysis for this project.
GeoDa was originally developed at the Spatial Analysis Laboratory (SAL) at the University of Illinois at Urbana-Champaign. The lead researcher on this project has moved now to the University of Chicago. GeoDa can be downloaded there.
The instructions in this project refer to Version 1.14.0 of GeoDa on Windows 10, but things are very similar in the other versions. There are also versions for the Mac and Linux.
Summary of Project Deliverables
For this week’s project, the minimum items you are required to have in your write-up are:
- For a single variable on a single map, describe the results of a global Moran's I spatial autocorrelation analysis. Include a choropleth map and Moran scatterplot along with commentary and your interpretation of the results. In particular, identify any map areas that contribute strongly to the global outcome.
- For a single variable on a single map (but the same variable and a different map from the last one), describe results of a univariate LISA analysis. Include the cluster map and Moran scatterplot in your write-up along with commentary and your interpretation of the results.
Please use the 'Discussion - Lesson 4' forum to ask for clarification on any of these concepts and ideas. Hopefully, some of your classmates will be able to help with answering your questions, and I will also provide further commentary there where appropriate.