Abstract
Urban systems design arises from disparate current planning approaches (urban design, Planning Support Systems, and community engagement), compounded by the reemergence of rational planning methods from new technology (Internet of Things (IoT), metric based analysis, and big data). The proposed methods join social considerations (Human Well-Being), environmental needs (Sustainability), climate change and disaster mitigation (Resilience), and prosperity (Economics) as the four foundational pillars. Urban systems design integrates planning methodologies to systematically tackle urban challenges, using IoT and rational methods, while human beings form the core of all analysis and objectives. Our approach utilizes an iterative three-phase development loop to contextualize, evaluate, plan and design scenarios for the specific needs of communities. An equal emphasis is placed on feedback loops through analysis and design, to achieve the end goal of building smart communities.
Highlights
Modern planning, as a unified practice of coherent theory, is an invention of the late 19th to early 20th century which arose to meet the growing challenges and complexities of urbanity
The paper seeks to outline a conceptual framework planning smart communities based on the practice of urban systems design
Accomplishing this task requires expounding core principles upon which this approach is developed: historic practices, the controversies associated with data, their applications, and system complexity behind. This Urban systems design (USD) method is built upon three phases: contextualization, evaluation, and iterative change that are further explored in this paper
Summary
As a unified practice of coherent theory, is an invention of the late 19th to early 20th century which arose to meet the growing challenges and complexities of urbanity. The paper seeks to outline a conceptual framework planning smart communities based on the practice of urban systems design. Accomplishing this task requires expounding core principles upon which this approach is developed: historic practices, the controversies associated with data, their applications, and system complexity behind. This USD method is built upon three phases: contextualization, evaluation, and iterative change that are further explored in this paper
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