Researchers and practitioners have been modeling the street networks of metropolitan and geographical regions using space syntax or configurational analysis since the late 1990s and early 2000s. Some models even extend to a national scale. A few examples include the island of Great Britain, within the national boundaries of England, over half of the Combined Statistical Area of Metropolitan Chicago and the entirety of Chatham County, Georgia and the City of Savannah in the USA, and the Chiang-rai Special Economic Zone in northern Thailand bordering Myanmar and Laos. Researchers at Qatar University constructed a space syntax model of Metropolitan Doha in 2018. It covered a land area of 650 km2 , encompassing over 24,000 streets, and approximately eighty-five percent (~85%) of the total population (~2.8 million) in Qatar. In a short time, this model led to a deeper understanding of spatial structure at the metropolitan and neighborhood level in Doha compared to other cities of the world, especially in the Gulf Cooperation Council region. The paper presents the initial results of expanding this model to the State of Qatar, which provides ideal conditions for this type of large-scale modeling using space syntax. It occupies the Qatari Peninsula on the Arabian Peninsula adjacent to the Arabian/Persian Gulf, offering natural boundaries on three sides. Qatar also shares only a single border with another country to the southwest, which Saudi Arabia closed due to the current diplomatic blockade. The expanded model includes all settlements and outlying regions such as Al Ruwais and Fuwayriţ in the far north, Al Khor and the Industrial City of Ras Laffan in the northeast, and Durkan and Zekreet in the west. Space syntax is serving as the analytical basis for research into the effect of the newly opened rail transportation systems on Doha's urban street network. Researchers are also utilizing space syntax to study micro-scale spatial networks for pedestrians in Souq Waqif, Souq Wakra, and other Doha neighborhoods. The paper gives a brief overview of this research's current state with an emphasis on urban studies.
Identification of critical node clusters for consensus in large-scale spatial networks