Journal of Iranian Architecture & Urbanism(JIAU)

Abstract Extended Abstract
Background and Objectives: The Today, the city in its various dimensions is rapidly moving towards digitalization. All over the world, various dimensions of the city such as economic, social, political and technological dimensions have been digitalized or are in the process of being digitalized. In recent years, special attention has been paid to the concept of smart city, in this regard, through different approaches to this concept, approaches such as the right to a smart city have been considered. Therefore, the aim of the present article is to examine this approach in the city of Tehran.

Methods: Based on the objective of this research, after reviewing the literature in the selected field, the dimensions and components of the right to the smart city, considering the current conditions of Tehran from the perspective of researchers (in the two areas of the right to the city and the smart city), have been derived using the constructivist paradigm and qualitative content analysis. Semi-structured interviews were used to collect data, and the information obtained was coded, categorized, and then the main and sub-concepts representing the conceptual framework of the right to the smart city in Tehran were formulated based on semantic similarities. Researchers’ identification method was purposive in the first step, and four individuals (two researchers in the right to the city and two in the smart city) were initially selected, followed by snowball sampling to identify others, resulting in theoretical saturation through interviews with 24 researchers. Subsequently, the Tehran Smart City Plan (2021), the comprehensive plan document (2007), and the Fourth Plan for the Development and Progress of Tehran City (2022) were examined through qualitative content analysis to identify the extent of their emphasis on the dimensions of the right to the smart city.

Findings: The fBased on the implementation of the texts, 210 open codes, 58 concepts, 40 subcategories and 11 main categories were extracted, which are as follows:
- Right to smart technology- This category is related to two subcategories: inability to provide hardware technologies and inability to connect to the network.
- Right to smart data- This category is related to three subcategories: access to data, data transparency and data correction and reproduction.
- Right to smart security- This category is related to four subcategories including: information security, transparency in the use and accountability of data monitoring institutions, privacy protection and information control in the direction of the dominant ideology.
- Right to smart infrastructure- This category is related to the subcategories of smart distribution, consumption and monitoring of fuel, electricity, water, smart waste collection and the creation of smart infrastructure.
- Right to Smart Services- Includes the subcategories of Internet services, smart administration, and transparency in Internet services.
- Right to Smart Governance- This category is related to the three subcategories of transparent access to government data, accountability, and administrative services for efficient governance.
- Right to Smart Economy- This right is related to the four subcategories of virtual economic development, decent work and economic inclusion, support for innovation, and smart banking.
- Right to Smart Mobility- This category is related to the seven subcategories of smart urban transport system integration, cost reduction, application development, non-participation, sustainable transport, increased safety, and smart distribution of goods.
- Right to Smart Environment- This category is related to the three subcategories of access to environmental data, environmental resilience, and sustainable development.
- Right to Smart People- This category includes four subcategories of access to digital and smart literacy, education and learning, public space, and development of well-being with a smart city.
- Right to Smart Life- This category is related to three subcategories of home, smart healthcare, and increased security with smart control.
A review of documents showed that in the Tehran Comprehensive Plan (2007), only five dimensions of the right to a smart city were considered, including the right to mobility, environment, people, infrastructure, and the right to a smart economy. In the Fourth Plan for the Transformation and Progress of Tehran Based on the Realization of Urban Justice (2012), the most attention is paid to the right to smart data, and the right to mobility, people, and smart life are not addressed. The local model of Smart Tehran (2012) has paid the most attention to the right to smart infrastructure, but has not paid attention to the right to smart security.

Conclusion: Based on the findings, it seems that the goal of planning a smart city in Tehran should be to realize the right to a smart city and move towards the public interest. The right to a smart city in various dimensions can be realized in Tehran if the components of each dimension are formulated, implemented, and manifested in accordance with the conditions and characteristics of Tehran. Some of the suggestions that can be effective in realizing the right to a smart city in Tehran are as follows:
1. Creating a type of governance that, considering the conditions of Tehran and the demands of citizens, has the necessary legitimacy to advance smart city programs;
2. Clarify the use of surveillance cameras, organizations, and individuals who have direct access to their data;
3. Developing a comprehensive program to teach new technologies to the elderly and less privileged groups so that they can access the smart city;
4. Creating a transparent online platform to hold various surveys in different neighborhoods of Tehran under the supervision of citizen representatives;
5. Formation of local groups selected by citizens as supervisors of Tehran Smart City programs;
6. Allocation of special budget for providing new technologies for less privileged people by Tehran Municipality;
7. Allocation of special budget to provide free access to the network for less privileged people.

Abstract Extended Abstract Background and Objectives: The examination of users’ emotions through social media has developed into an impactful domain across diverse scientific fields, appealing not only to business proprietors and politicians but also to general users. In the meantime, this field has infiltrated urban studies and has been used by urban planners and designers due to its methodology; whether in the form of research that aims solely at emotion analysis or as an integrated layer within broader research endeavors. The aim of this article is to explain this field in the analysis of urban emotions as modeling methods in order to identify the position of this field in urban studies by examining the importance of emotion and the methods of its study in the city.
Methods: This research used the supervised machine learning approach and analyzed the sentiments of tweets related to eight major cities in Iran. The data collection consists of 930 tweets that were collected in a period of 10 years from 2011 to 2022. Initially, over 5000 tweets were collected, and during the tagging process, 80% of them were excluded due to their limited relevance to the city, emphasizing tweets related to urban space. The name of cities and tourist areas were searched to establish a balance between positive and negative data. The tweets are downloaded through Twitter streaming API and the metadata along with the text, including the number of retweets, number of likes and tweet ID, language and location. The data sets have been used for machine training after standard and normalization steps. In this research, the ratio of training data to testing data is 80 to 20. According to the supervised approach, the data were labeled by the researcher with three negative, neutral, and positive labels, and where the researcher had doubts, the opinions of two other experts were used. In general, both machine learning and deep learning have been used. In order to check the validity of the model and to test it, the confusion matrix has been used.
Findings: Firstly, the machine was trained based on 3 algorithms that were used in many research related to text sentiment analysis. Based on the test results presented on the confusion matrix, the accuracy of the trained machine in determining the polarity of the text in three polarities was defined. Among the three used algorithms, support vector machine and random forest have performed better than other algorithms. Given that the model’s highest accuracy was approximately 70%, deep learning was employed to train the machine in order to assess the potential for achieving improved results. In the following, machine learning with a convolutional neural network algorithm and a hybrid algorithm were considered. At first, the machine was trained using a convolutional neural network. The results of the accuracy of the model showed that the model is predictable by up to 75%. Next, an attempt was made to improve the predictive accuracy of the model by writing a hybrid algorithm based on the convolutional neural network. The architecture of this network is such that two types of data are considered as input to the neural network, text data and other features in the data set, including location, number of retweets, number of likes, city codes and searched content (as metadata). Therefore, based on this input and output (classification based on the polarity of the text by the researcher), the machine was trained and finally tested. As depicted in the structure of the hybrid algorithm, the significance of the text is assigned a weight of 90%, while the importance of metadata is weighted 10%. It should be noted that different percentages were given to the importance of each of the inputs and the predictability accuracy of the model was checked. As the model test results show, the designed algorithm has improved the predictability of the machine by 4%.
Conclusion: In this article, sentiment analysis based on model-oriented methods - machine learning and deep learning - was scrutinized, and therefore, while comparing it with traditional methods and lexical methods, the process of urban sentiment analysis was developed and the different levels of the process were described in detail. As stated, these methods have many advantages and can be useful for analyzing the current situation or predicting different urban projects. Besides, compared to traditional methods, they are less expensive, faster and have sufficient accuracy. According to the appropriate capability of the trained machine, this machine can predict the polarity of the data. This means that by using the text data published in social networks, it is possible to analyze the feelings of users. Certainly, when these data are geodatabases, there is also the capability to geolocate emotions. This approach allows for a swift, accurate, and cost-effective general assessment of city spaces. By identifying areas where users perceive negative emotions, the reasons can be investigated and addressed accordingly. This research has been innovative in two aspects, 1) preparing a collection of data related to the sentiment of Persian language users related to the city and 2) analyzing urban sentiment in the country using machine learning in the field of urban planning and design. Some limitations of this research include limited access to all data published on Twitter using Twitter streaming API; the small amount of available and relevant data; low use of Twitter by users due to filtering; and the unavailability of financial resources to prepare and use a larger set of data.

Abstract Urban public spaces, as an inclusive realm, are the spacefor the presence of all users. Despite this, several groups like women due to physical and social factors, are marginalized in this space. Therefore, this article intends to explore the issue of gendered space and define the different aspects of these spaces in the neighborhood scale.The hypothesis of this research has its root in the existence of a meaningful relation between structural and non-structural aspects of urban space and gendered space, which differs within various cultures. Analyzing the theories of gendered space shows that these kinds of spaces are made as the results of gendered activity and representation of space function. And these two criteria lead to the gendered space occupation, gendered time occupation, and gendered function-behavior occupation. In this article structural semiotics and post-structural semiotics were used to examine the different aspects of gendered space. A traditional neighborhood in Tehran –Imamzadeh Ali Akbar in Chizar neighborhood- was chosen as the case study of this research in order to investigate the representation of space and gender relation. Through semiotic method, the signifiers, the signified, and different codes were extracted through analyzing the analyze unit – which was the narrations of 30 women of the neighborhood space. The results of the analysis shows that the space within the house and its private territory is a secure feminine space, and the outside space is a masculine space. In other words, physical codes, besides functional codes (gendered activity) are in direct relation to the socio-cultural codes (representation of space function) and create nonappropriated spaces for women. Therefore, the markedness of space indicates various territories for presence and activity of women and men in urban spaces. This means that women’s attitude towards their identity (from an individual, family, and social aspect) is due to the gendered space, and the dominated ideology and myth-making of a Traditional Woman.