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Prediction and monitoring of urban sprawl is a significant urban issue for planners, decision makers, and local authorities in terms of spatial data, strategies, and prediction models. Egyptian cities have expanded rapidly in the last 40 years. This uncontrolled urban growth requires a review of Egyptian urban policies and regulations. The Greater Cairo Region (GCR) is Egypt's economic capital and a significant industrial and commercial city. This study uses spatio-temporal data to suggest an approach based on integrating cellular automata (CA) and multicriteria evaluation (MCE) like analytical hierarchy process (AHP) and running it within the geographical information system (GIS) for monitoring, analyzing, and allocating informal growth in the context of unplanned regions like the GCR. At first, the weights of influencing growth factors were determined and calculated using AHP. Next, the probability map was created according to AHP weights. The historical probability map was then generated based on the historical development. Then a standalone CA macro was developed based on AHP weights to calculate the proximity index for existing urban and transportation features using Moore's extended neighborhood matrix. Finally, the general suitability map was created to identify the most sensitive areas for growth over the next two decades. The prediction process was performed based on the 2012 urban boundary. Then, the prediction accuracy was verified using the Kappa coefficient based on actual urban extents in 2017. The results showed that 676 km2 (35%) of the study area was not sensitive, 386.5 km2 (20%) was slightly sensitive, 522 km2 (27%) was almost sensitive, and 348 km2 (18%) was extremely sensitive to urban sprawl. Finally, we predicted the urban growth for the period from 2022 to 2052 for every 5 years. The outcomes showed a linear scattered sprawl pattern along transportation features and an annular pattern around existing urban spaces. Therefore, fertile farming land and natural resources in the GCR are set to become almost extinct over the next two decades, threatening food production and security. These results indicate the necessity to modify the region's urban policies to protect the agricultural areas and control the unplanned urban sprawl. [ABSTRACT FROM AUTHOR]

Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city's virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database. [ABSTRACT FROM AUTHOR]