Big Data globalizes urban research
MCC study deals with globalised data collection in urban research.
"First we take Manhattan, then we take Berlin", Leonard Cohen's lyrics chase a strong cold shower down the back of city researchers. The methods used to collect data are by no means uniform - starting with the question of which urban area should be included, through the quantity and quality of the data collected, to contextualization. While Manhattan's residents cause quite few emissions overall, this positive picture is compromised in the larger New York context, with abundant commuters from the surrounding area, where less efficient building densities prevail. Similar problems are encountered when recording greenhouse gas emissions in Berlin: how to limit the geographical area? Should parks and forests be included or only built-up areas? Or should the Berlin hinterland, which produces large amounts of renewable energy for the city, be taken into account?
Overcoming these problems is of central importance for the development of knowledge-based climate solutions that can be individually scaled up to cities worldwide, taking into account the differences between cities. A harmonised and large-scale data infrastructure is needed to pave the way.
"Our study shows that Big Data methods take global urban research to a new level," says Felix Creutzig, head of the study and head of the Land Use, Infrastructure and Transport Working Group. Big data" includes high-resolution satellite data as well as social media data and other urban infrastructure data. The article by Creutzig and an international team of renowned urban researchers, published in the journal Global Sustainability, summarizes data sources as well as methods and advances in global quantitative urban research and highlights their importance for climate protection.
The study presents three ways to expand knowledge about global urban areas: Mainstreaming of data collections, increased use of large amounts of data and further use of calculation methods to analyse qualitative data in order to gain new insights. These data-based approaches have the potential to refine urban climate solutions and bring about change on a global scale.
Creutzig F., Lohrey S., Bai X, Baklanov A., Dawson R., Dhakal S., Lamb W.F., McPhearson T., Minx J., Munoz E., Walsh B., Upscaling urban data science for global climate solutions, Januar 2019, doi: 10.1017/sus.2018.16