Increased temperatures in urban areas due to high energy consumption and greenhouse gas emissions are some of the problems of today's cities. Urban open spaces in hot and arid climates experience this problem more in summer. The purpose of this study is to show the relationship between morphological components of the urban fabric and thermal comfort through integrated analysis. In this study, urban fabric types were extracted by considering ground space index, floor space index, open space ratio, average height, streets orientation, streets organization, and type of plots according to their configuration of mass and space. To study thermal comfort, a field study was carried out in five neighborhoods in the historical context, for five continuous days in summer. Environmental variables including air temperature, humidity, radiant temperature, and wind speed were measured and the physiological equivalent temperature index was calculated using ENVI-met software and thermal comfort in the neighborhoods with different morphological characteristics was analyzed. The results showed that more than half of the data during the day in the hot season in all five neighborhoods are in conditions of extreme heat stress. Comparison of neighborhoods with different morphological features indicated that two neighborhoods with higher open space ratios, despite the difference in the orientation of the streets and the type of plots, have lower thermal comfort compared to other types. A neighborhood with a higher ground space index and a lower open space ratio has a lower average physiological equivalent temperature.

The current research is based on the process of algorithmic design and simulation of village boundaries using the Grasshopper plugin in the Rhino software. The proposed algorithm utilizes the image simulator component and its importer in the Grasshopper plugin to input two-dimensional images taken from samples into this plugin. Before entering the data, the input images are converted to black and white to allow the system to distinguish between the background texture and the subject of analysis. Additionally, the proposed algorithm identifies the village boundary by comparing the subject and the text in the pixels of the two-dimensional image. By calculating the internal area of the identified boundary, the algorithm estimates the proportion of textures relative to the background. The main objective of this research is to examine the impact of green and dry textures as natural factors on the density of residential texture in terraced villages in Iran, across various climates. In addition to identifying the density of village textures, emphasizing the comparison of parameters will provide new insights into the texture of terraced villages. The simulation analysis tool, known as the density measurement algorithm, is developed based on aerial maps created by the researchers for this study. By using inverted aerial maps, this algorithm can determine the density of natural texture in the formation of villages and residential texture. This capability reduces the need for physical presence and enhances the accuracy of outputs in analyzing village texture. Furthermore, the identification of parameters will provide the next generation of rural housing with the density pattern of previous generations.