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:
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 [1].
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.
For this week’s project, the minimum items you are required to have in your write-up are:
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.