Language of Data

Language of Data

Reading Reality in the Language of Data

Our attempt to understand a city holistically through an unconfined perspective on data

We increasingly live in a world of specialized knowledge and fragmentation of data. Understanding a city, however, requires bridging a plurality of data domains: Infrastructure, traffic, demographics, education, culture, health, energy, environment, nature, economics, politics, etc. But an urbanist is not a specialist – or rather: urbanists are specialized in a holistic perspective.

Our data-driven urban analysis intends to provide a dashboard for this understanding. Only by considering a broad spectrum of relevant data constitutive of a city can we properly estimate the impact of changing a parameter. And impacts are not limited to their specific domain: a change in public transport affects worker performance, access to education, public budgets, and quality of life.

While accessing GIS-data in China was comparatively difficult, the large extent of publicly accessible statistical data was surprising.

For decision-makers, this awareness helps with clearing up their priorities under limits of time and money. Our Circularity Optimizer[1] tool can support stakeholders in identifying interventions of desirable impact across multiple urban domains.

However, let us ask a more basic question: What even is “relevant data” to understand a city? To faithfully analyze a city, we at BUSarchitektur are developing a system of indicators with subordinate topics encompassing a wide range of urban data-domains. On the highest level, all parameters integrate into five general indicators, constituting a holistic perspective: Happiness, Profitability, Innovation, Safety, and Sustainability.

But this hierarchy of urban topics must also be met by the actual availability of data. Typical problems include unavailable and incomplete, outdated, or inaccurate data. Very often, though, the problem is not the absence of data but low granularity or so-called data-silos. The former implies that statistics are available for regional performance, but not at the city level. The second addresses “the procedure of keeping data in separate systems that are not able to be integrated or interact”[2]

We encountered these problems in both our cases: Gleisdorf, Austria, and Kunshan, China. However, with our approach we have the flexibility to manage data gaps by scanning all possible sources of information, specific to the case. To feed our indicator-topics with appropriate parameters, we may derive information indirectly from another parameter or facilitate alternative ways of data collection through GIS-data, OpenStreetMap or manual analysis of Satellite Images.

Being aware that there is always room for interpretation in assigning data to our system of indicators and sub-topics, the answer in bridging inevitable gaps rests in transparency about what we measured and how. Nevertheless, our approach can deliver a reasonably accurate picture of the urban case studies due to its flexibility and, importantly, draws upon a solid architecture of data.

Laura P. Spinadel and Tobias Kampl, for URBAN MENUS Decision Intelligence Lab

[1] TECXPORT Bilateral Cooperation Austria-Jiangsu/People’s Republic of China JSTD 2023, co-funded by the Austrian Research Promotion Agency FFG.

[2] Further information can be found in Data Module 4, written by our partners from Akaryon, on elearning.trainingresilience.eu

opinions

category