Implementation of data-driven decisions in urban governance and planning

Abstract

The amount of data produced every day in cities and other human settlements is truly comprehensive. In today's globe, 2.5 quintillion bytes of data are created every single day, and it's quite obvious that it is only going to grow from there. By 2020, it is estimated that 1.7 megabytes of data will be created every second for every person on earth. As there is no way around the fact that big data just keeps getting bigger and therefore it needs to be handled, processed and utilized. On the other hand, the constant expansion of urban big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of data-driven urban governance initiatives. Urban big data offers the potential for cities to obtain valuable insights from a large amount of data gathered through various sources, and the IoT provides a basis for their integration in real world environment taking advantage of highly networked services. The combination of IoT and urban big data in scope of smart city applications is still a very under researched area that has brought new and interesting challenges for achieving the goal of true smart cities in future. In this regard, this thesis aims to shed more light on how city authorities can utilize the constantly growing amount of urban big data to make sustainable, accurate and legitimate decisions based on the main hypothesis that modern urban governance is greatly dependant on real-time rythm (dynamics) of a city and on the feedback of its residents. Hence, the study initially explores the main pillars of the conceptual framework such as urban big data, IoT and smart cities and also the concepts such as data science and urban informatics which are considered as the connection of the abovementioned pillars. The following part of the study investigates the current trends about how data becomes a basis for planning and urban governance decisions. In this part, the initial steps of urban analytics, namely data collection and acquisition, as well as its challenges are studied. The modern methods such as AI based spatial planning, procedural modelling of cities and etc. are also presented in order to give an understanding on the methodological advancements in data-driven decision systems. Subsequently, real-world case studies from countries with different economical and social backgrounds are explored. The study of current trends and case studies in this part reveals the main challenges of data-driven urban governance such as privacy and cyber security concerns, spatial property issues, AI based negative biases for some parts of the city, as well as the big shortage on qualified personnel and lack of necessary standardisation in almost all of the cities. The last part of the thesis focuses on formulating a model based on the theoretical framework and the deductions made from the study of examples. In this scope, the necessity of creating an image for smart urban governance which isn't solely seen as something technical, but also politically and economically interesting notion proves critical as only thus political leaders will also want to be the mediators for this type of systematic reform. Finally, the potentials of facilitating data acquisition, addresing the spatial property issues, establishing a data culture with trained personnel, re-inforcing cyber security measures and the advantages of moving towards interactive/dynamic urban governance becomes apparent towards the goal of data-driven smart urban governance. In conclusion, it is assessed that smart urban governance based on data promises fundamental changes in our cities and the study of this topic should lead to comprehensive subsequent studies on how private information can be protected in this process and in what ways the role of city planners will change during the digitalization of modern cities.