What We Do Who we are Partners and networks
News 2014 News 2015 News 2016 News 2017 News 2018
Events 2014 Events 2015 Events 2016 Events 2017 Events 2018
Articles Working Papers Reports Videos Newsletters Data Statements Books Policy Briefs Featured Publications
Funding Research and learning Work with us Communication Engagement Support us Examples and case studies
The current disconnection between access to increasing amounts of data about urbanization, health, and other global changes and the conflicting meanings and values of that data has created uncertainty and reduced the ability of people to act upon available information which they do not necessarily understand. We see a disconnection between increasing data availability and data processing capability and capacity. In response to this disconnection, modeling has been attributed an important role in international and national research programs in order to predict the future based on past and recent trends. Predictive models are often data heavy and founded on assumptions which are difficult to verify, especially regarding urban health issues in specific contexts. Producing large volumes of data warrants debate about what data are prerequisites for better understanding human health in changing urban environments. Another concern is how data and information can be used to apply knowledge. Making sense of empirical knowledge requires a new transdisciplinary knowledge domain created by a commitment to convergence between researchers in multiple academic disciplines and other actors and institutions in cities.
Read full article here