Indigenous, urban life — seen through Python
There are lots of cutting edge applications of data science for urban communities, but most of them are led by some combination of universities, public sector, and non-profit research institutes. The MetroLab Network, a City-University collaborative effort, is across 41 cities. Their research projects cover everything from tracking biking routes, community learning hubs, predictive policing, and managing energy use through sensor networks. A professor at NYU identified over 50 research projects in 2015, with many more emerging over the past two years. The University of Chicago launched a Masters in Computational Social Science recently to meld the 21st century tools of data analysis with the 20th century models of social science. There are even a few MOOC’s adding data science to urban planning and social science, like this EdX one from ETH Zurich. The field itself was ‘announced’ via 15 scientists in a February 2009 article.
‘What value might a computational social science — based in an open academic environment — offer society, by enhancing understanding of individuals and collectives?’
Even with all the work over the last decade to meld these disciplines of data analysis and social science for urban environments, it’s nearly impossible to find any research that involves an indigenous perspective. And what a fascinating perspective that might be. Imagine having the benefit of a people with hundreds of years of knowledge about the land you’re studying to help untangle the chaos of trillions of data points. They can trace battles, chieftain’s intermarriages, trading routes, and the growth of settlements through generations.
With the partnership between Ngāti Whātua Ōrākei and Te Pūnaha Matatini, we’re at the beginning stages of asking those questions. The challenge to this promising new field of computational social science is that as models are built — and funding & policy decisions made off those models — that various interests will be silenced. The language of data science is a new currency of power, and just like money and the law have boxed various minorities out of access to resources, so might a lack of their own data scientists lock them out again.
I think it’s really important that we find ways to communicate to each member of Ngāti Whātua Ōrākei what these choices mean — what data sets to include and the ways we slice them to examine them. The implications are tremendous. Auckland Council is developing models to help them understand how to allocate funding for the restoration of watersheds. Eventually their funding for community and social services, for education programs, and policing efforts will go the same way — more data driven than qualitative.
The key question for our team as we work through this small project of analyzing years old survey data from the hapu (sub-tribe, a descriptor for Ngāti Whātua Ōrākei), is to explore how this indigenous tribe might use an internal capability for data science to benefit their people. Is it necessary? Are there beneficial insights that come when the analysts themselves are from the tribe?
The first, simplest lesson that we’ve learned is about the organization of the data collection tools. Because previous New Zealand censuses did not break out the hapu, sub-tribe, of Ngāti Whātua Ōrākei from Ngāti Whātua, there is very little information available to hapu leaders about the particular needs and living conditions for their people. A 2013 survey conducted by the hapu seemed like it got a strong response — over a quarter of the tribe responded — but then we realized it wasn’t representative at all. By far the most common demographic was women 43–54 years old, skewing the analysis.
Our next steps, after laboriously cleaning the survey data, will be to apply basic statistical packages in Python and R, looking for correlations among the hundreds of questions, and the doing that visually. We’ll use OpenANTZ, an open source software program, to visualize the survey answers in 3D, overlaid on a map of Auckland.
The future is really exciting — if you’re willing to work for it; as true for the fate of your New Year’s resolutions as it is of this hapu’s efforts to jump into data science. Eventually I think you’ll see Maori data analysts contributing to Auckland’s environmental regeneration — they’ll combine traditional knowledge of the streams, eels, and volcanoes with sensors from the storm water system to clean the watersheds. You’ll see the indigenous analysts working with local businesses to help neighborhood economies diversify and grow.
