Urban areas have been assessed inconsistently in the past and in previous decades, and this assessment was based on limited, sometimes incomplete, and erroneous data. This approach led to improper assessment and incomplete analysis of urban spaces. In most cases, this type of assessment and analysis is based on the preparation of a questionnaire and field observations by the researcher, even incorporating the researcher’s opinions. However, without questionnaires, field observations, and old data collection techniques, we can collect useful and real data about the living conditions of people in urban areas. This prevents the researcher from commenting on or interfering with the collected data directly or indirectly during data collection and case studies. The question may be how this happens With the development of new and innovative technologies, much of the data needed to analyze urban spaces has been created as unstructured data in the virtual world. Social media is one of the best ways to collect real and pure research data to analyze and evaluate urban spaces. These media have been introduced as a source of real and error-free data for researchers. The researcher’s intelligence and ingenuity in this direction have been introduced as the main leverage and drive in research, and can be very effective. Knowing that social media is considered a source of data, we can refer to the kind of social media investigated in this study. Instagram, a social media platform, is a popular medium worldwide that provides various opportunities for users. By providing these opportunities, users can share many real activities in the virtual world with friends, acquaintances, and people around the world. One of the most attractive features of this medium is hashtag. Hashtags allow users to share the geographic location of an urban space and mark that location as popular and preferred. This can attract people from cyberspace to real space, increase the popularity of desired urban spaces, and introduce people from cyberspace to real space. Human mobility is created by introducing and attracting people from cyberspace to real space. Human mobility is the beginning of people’s activities in a real space. Urban dynamics are based on different types of human mobility and are considered an undeniable necessity in the relationship between human mobility in a city ( Dorostkar, 2025; Dodge et al., 2016; Miller and Shaw, 2015). To evaluate human mobility in a city, we can use the social media Instagram, which provides us with large data sources of people and geographical areas in general. These data do not deprive users of their rights in any way, because before accessing these data, Instagram users must confirm the rules and regulations for the use of Instagram, which means that access to individual data is possible for public use. Based on these interpretations, it is possible to identify and discover patterns of human movement and mobility in the virtual world and on Instagram social media based on the formation of a graph of human mobility ( Gao et al., 2018; Poon and Pandit, 1996; Xu et al., 2015). In addition to being able to pave the way for urban planning and decision-making, Instagram can also be useful in the type of human mobility that identifies data from young or old populations, as young people are very important in the assessment of human mobility. ( Martín et al., 2017; Nara et al., 2017; Panigutti et al., 2017; Wesolowski et al., 2015). An understanding of human mobility is possible by mapping it from different sources. Many studies have been conducted to identify human mobility and determine its pattern of human mobility to find a new way to solve this challenge. Social media data are considered a new source and method to identify these movements, and special attention is paid to the analysis of data from social media ( Dorostkar and Najarsadeghi, 2024; Xia et al., 2011; Yuan and Raubal, 2014; Crooks et al., 2015; Gao et al., 2017; Cheng et al., 2011; Luo et al., 2016; Noulas et al., 2012; Quercia et al., 2015; García-Palomares et al., 2018; Hamstead et al., 2018; Quercia and Saez, 2014; Van Canneyt et al., 2012; Hochman and Manovich, 2013; Lee, Wakamiya, and Sumiya, 2013; Peña-López et al., 2014; Béjar et al., 2016; Chen and Roy, 2009; Dunkel, 2015; Tasse and Hong, 2014).

Demographic change is especially relevant to the resilience of cities, and needs to be examined from a comprehensive perspective that addresses both individual and collective influences ( Wang and Taylor, 2018; Liao et al., 2019). Although a great deal of research on human migration has focused on external drivers, the fundamental drivers of this type of mobility have been less examined ( Wu et al., 2014; Roy et al., 2019). Examining the mobility patterns of humans is critical to comprehending behavioral trends and interactions with the environment ( Luo et al., 2016; Ebrahimpour et al., 2020). Traffic information facilitates the construction of urban mobility models ( Cui et al., 2018), which describe how individuals’ movements influence collective dynamics at the urban scale ( Porcher and Renault, 2021).

Urban and high-density residential areas may experience reduced human movement ( Chan, 2020), whose identification is thus a top priority in city planning and research.

Social media is both a form of communication for citizens and a byproduct of technological progress that facilitates resource accessibility ( Abbasi et al., 2017; Jiang et al., 2019). Moreover, it is a platform for gathering data to evaluate urban areas against strategic importance, physical travel patterns, and activity routines ( Wang and Stefanone, 2013; Longley and Adnan, 2016), providing real-time behavioral insights about humans ( Huang et al., 2020).

This study explores Tehran’s urban spaces by investigating the impact of the hashtag #book garden on Instagram and how it translated online presence into offline presence. The primary research question explores how #bookgarden is making urban spaces more attractive. The Tehran Book Garden, situated in the capital city of Iran (geographical coordinates: 35.7495° N, 51.4316° E) close to the National Library and Shahid Haghani Highway, was used as a case study. As is evident from the temporal popularity graph, peak traffic occurred at 4 PM.

Therefore, this study, with a review of the theoretical basis and some related studies in this field, deals with the process of obtaining data and methods of their analysis, and then the obtained findings are presented. Finally, the results are discussed and concluded.

The data of this study were collected quantitatively through Instagram and with the theme of Book Garden to identify people from different urban areas attracted to this attractive space, and by cross-linking these data on geographic maps we analyzed them. This step is considered the first part. In the second part, the communication between the spatial distribution of publishing posts to the Book Garden Center for urban regions of Tehran is evaluated, and the result is announced. In this section, the method of interpolation of urban space affecting book gardens is used as an important and effective space. Different interpolation methods have been developed according to the nature and type of data to be interpolated, the most important of which is the drawing method. In Map H, we used this method. This method is based on drawing irregular geometric shapes around known points, and is associated with the estimation of unknown values using simple mathematical methods. In map H, we used the triangulation method to measure the drawn irregular area by measuring the distances of each point on each side of the triangle from the center (the location of the book garden) and then calculating the average of these sides. This method shows which level of the city is effective in creating the attractiveness of the urban space of the Book Garden, which is the basis for forming map M, that is, the level of impact of the #book garden in Tehran.

The data for this study were collected in various ways. For the Instagram data collection, the API of this software was used as a graph. Access to this data is complex and requires programming skills (Python software: https://www.python.org/downloads/windows). The code and process of data extraction are presented as follows (the reason for joining Facebook and switching from there to Instagram is the limitation of Instagram from 2020 ( https://github. com/facebookarchive/python-instagram )).

The API allows us to access large amounts of data on the web. The main reason for choosing the social medium Instagram for this study is the high penetration of this software worldwide. According to 2018 statistics, Instagram has approximately 1 billion active users, which shows the strength of penetration and the volume of data collection in this sector. Based on these statistics and unrestricted access, this social medium was used to collect data ( Cortese et al., 2018) and investigate the attractiveness of urban space in this study. The data used in this study consist of public data with unrestricted access extracted from the Internet and Instagram software based on a special hashtag called #bookgarden and the location of Tehran Book Garden urban space (coordinates: 35.7495–51.4316), which can be captured and tracked in Instagram posts from January 21, 2022, to January 21, 2023. The number of hashtags used amounts to 5631 posts with location (linking the location of the sender’s post in the Instagram software), which, of course, is obtained by deleting unrelated posts and hashtags. According to the evaluations, this number was satisfactory for the present study. Hashtags associated with Instagram posts can be used by keywords to support a topic or for advertising, and show different results. Our datasets were fully categorized and accurately extracted from Instagram and included the #bookgarden and the geographic location of its urban space in shared texts, locations, users, related posts, etc., forming a complete but limited dataset in one place. Different methods can be used to analyze hashtags in posts and their texts ( Solis and Webber, 2012; Valderrama-Santomé et al., 2019; Dans and González, 2019; Paül, 2018). However, in this study, to achieve a new and innovative method, we analyzed the hashtags in the form of graphs on the site plan of Tehran and drew the connection with the urban space of the book garden created. Then, using the same graphical connectivity, we ranked the neighborhoods and urban areas according to the influence and attractiveness of urban spaces for the presence of people. This study was conducted in combination to examine a larger area and determine the relationship between the attractiveness of urban spaces and social media Instagram. In another study, the relationship between users and location was determined using Instagram posts to determine the direction of movement of these individuals ( Giordano et al. 2021).

The innovation in this study comes from the method of data collection and analysis, which led to the formation of pure research that can be used as a pattern in other studies.

Figure 1 shows the demographic distribution of Instagram users with open access to their data in the book garden. From this Figure, it can be seen that, particularly between January 21, 2021, and January 21, 2022, users acted with an average and reasonable distribution rate. This shows the effectiveness of the #book garden and the creation of attractiveness of the urban space Book Garden for users. Another point that stands out in this diagram is the normal distribution of data based on this hashtag at different time intervals, which expresses the attention of users following the Book Garden for this urban space. On the left side of Figure 1, there is a chart of the most popular times for using the urban space of the Book Garden. In this chart, it is clear that the most popular time of use for users is 4 p.m. and was introduced as an indicator of the time of day, while 9 to 11 a.m. was the least popular in the morning, and 8 and 7 p.m. were the least popular in the evening. Of course, this low popularity at night has another reason: the geographical location of the book garden and access to it. Access to the Book Garden usually involves cars and subways. At night, these two access roads face high traffic volumes because of the heavy traffic on the highways leading to the Book Garden and the interference with the time when city residents return from work to their residences by subway. This is one of the reasons for the decreasing popularity of the Book Garden at night.

Figure 2 map A shows the penetration rate of Instagram posts with the hashtag #bookgarden in Tehran, taking into account all hashtags associated with the post. As can be seen from this map, the central and southeastern part of Tehran is a very active space in the virtual world and has many differences compared to other neighborhoods and urban areas. In this map, we used the data on #bookgarden based on neighborhoods and urban areas delineated and identified by Tehran Municipality. In this map, the western part of the city faces low and even empty distribution due to its distance from the geographical location of the bookgarden and also due to the location of the industrial part of the city in this area. The opposite is true for the central part of the city, which means that due to the proximity of the city center to the geographical location of the Book Garden, we see the most virtual activity on Instagram from this part. Figure 2 Map B shows the spread of #bookgarden along with its geographical location, and as can be seen in the map, the western part of the city is almost empty, and in this regard, no virtual activity can be seen from this part. In this map, the location of the book garden is marked in light blue. Comparing maps A and B, we can see that a considerable number of users have not indicated their geographical location next to the hashtag. Nevertheless, there are a significant number of posts with the conditions considered in this study that are located and indicated in map B. This map is used as the basis for further data analysis. Using map B and the geographic location of the book garden, we attempted to link the contributions to the book garden in the city; the results are shown in map C. Figure 2 map C shows the progression of the #bookgarden broadcast and its connection to the location of the bookgarden as an attractive urban space in 2021. In this interconnection, we used situational data and the #bookgarden to analyze all activities with open and public access on Instagram, which showed that the central part of the city had the most interconnection of said data. In this area, where bookstores and various scientific and cultural spaces are located, the most virtual activities were carried out, which shows the high human and virtual activity in this area. The main questions for further analysis and calculation of the networked area in this part of the city can be divided into two parts: the impact of users visiting the Book Garden and publishing posts with the #bookgarden and displaying the location on Instagram, and the impact of published posts with these terms on the popularity of the Book Garden urban space. To analyze and accurately measure this section, we used the interpolation method described in map H. Map C shows the neighborhoods and urban areas where the Book Garden received the most popularity over the course of a year.

In Figure 3 Map D, the volume of active users in the neighborhoods and areas of Tehran is shown. This map also shows that the central and southeastern parts of the city have a significant number of active users. However, the most noticeable part of the city is where most of the communication networks for the popularity of the Book Garden’s urban space have emerged, despite the small number of active users. The map states that we will see stronger networks with stronger relationships if there is more virtual activity in the area of communication networking related to the #book garden. Map E shows the activity of active users on Instagram with the #bookgarden, titled Virtual Activity Map and Hidden Urban Layer, and has recorded the user activity. Map F shows the location of the book garden in the urban areas of Tehran and the virtual activities around this area.

By interpreting and reviewing the above six maps, the analyses found are referred to as the results of this study. In Figure 4 map G, which shows the network radiance from the maximum radius of influence of the #bookgarden to the minimum radius, it is clear that the location of the bookgarden is radially and communicatively influential as an impact hole in its environment. This effect is based on the #book garden and the geographical location of the users who mentioned these two components in their posts. Further, we move away from the center of the Book Garden area, the clearer it becomes that the impact of the #book garden decreases and becomes weaker; conversely, the closer we get to the Book Garden area, the greater the impact of the virtual activity. We also reached a new point in our study. That is, the location of the Book Garden is effective and influential in certain areas of the city. By locating cultural, scientific, and artistic activities, these areas are introduced as close poles and connected to the urban space of the Book Garden. In the studies carried out below, we represented this area on map H. In this map, which calculates the area affecting the geographical location of the Book Garden as a three-part area, we process the power of social media Instagram to increase the popularity of the Book Garden’s urban space based on the activities and virtual activity in these social media. In this map, position 2 is the location of the Book Garden, and vertices 3, 4, and 5 are the hypothetical vertices where most virtual mobility on social media Instagram has occurred. Position #1 is the area we are looking at, and we intend to calculate this area to determine the virtual activity. To study the impact of the geographical location of the Book Garden in the cultural, scientific, and artistic fields, we switched to the interpolation calculation method, which allowed us to calculate the desired area, an irregular triangle.

Interpolation calculation (drawing an irregular triangulation): In this method, an irregular triangle is formed by connecting the known points shown in map H, with the known points shown at the vertices of each side. To calculate the colored area inside the triangle, it is sufficient to use the simple average of the known values at each vertex of the triangle (points 3, 4, and 5 at each vertex) to calculate the unknown value. According to the distance of each vertex of the triangle to the location of the Book Garden (location of the Book Garden, number 2), the average colored area of the triangle (colored area number 1) is equal to four, indicating the strong connection of this plane as a plane that influences the attractiveness of the urban space of the Book Garden. Finally, with all the analyses conducted, we came to map M, which shows the effectiveness of #bookgarden in creating a popular urban space among the neighborhoods and urban areas of Tehran in four categories (1: very low impact, 2: low impact, 3: high impact, 4: very high impact). The #bookgarden in this map has a greater impact on the location of the bookgarden and cultural, scientific, and artistic areas; gradually, with the reduction of virtual activities on social media Instagram, the impact on the urban space of the bookgarden has decreased, and with decreasing effect, the color of the map also fades.

In recent years, studies have been conducted on the use of digital data using an urban planning approach ( Martí et al., 2019). These studies have shown that digital resources can be a rich source of raw data, which has led to more applied studies. Meanwhile, social media has emerged as a source of pure data that researchers can use ( Anselin and Williams, 2016; Arribas-Bel et al., 2015; Roick and Heuser, 2013; Shelton et al., 2015). Many medical researchers believe that social media reflects user interests and activities in many ways ( Bawa-Cavia, 2011; Graham et al., 2014; Huang and Wong, 2015). With this theoretical and medical support, we set out to analyze Instagram social media data to evaluate and measure the popularity of the urban space of Tehran’s book garden using the #book garden and the location of active users in this social medium. First, we attempted to access the Instagram data through the API. By categorizing the data and weeding out inefficient data, we obtained a specific set of data on the book garden and user locations. By analyzing these data in terms of geographic location and position on a location map, we created several maps. We found that the percentage of active users on Instagram who registered their location was very low compared to all active users. In addition, the number of people who shared the content in this area with the #book garden and their geographical location was much lower, but this number was satisfactory for our study. It has been found that the area where the book garden is located acts as an attractive center on these social media platforms. Thus, by networking the activities of active users in this area, it became clear that a large part of the users’ activities are related to cultural, scientific, and artistic areas, which play a key role in creating the popularity of the Book Garden urban space on Instagram. We then proceeded to determine whether a certain part of the urban area had irregular networking based on #bookgarden, and we measured the hypothetical area that had the most virtual activity. This hypothetical area, which is the cultural, scientific, and artistic area of the city, has a significant role in virtual activity. Based on the interpolation calculation, we created a zone map of the impact of #bookgarden. In this map, which has four levels (1, very low impact; 2, low impact; 3, high impact; and 4, very high impact), it has been established that the location of the book garden is an influential area, and that cultural, scientific, and artistic areas in the city are affected areas and are introduced as a hidden layer. This hidden layer can be transferred to cyberspace (social media), and then to real space (city level). This three-layer system acts as a system with two invisible layers and one visible layer, and contributes to the popularity of urban spaces. In this study, we developed a new and reliable method to measure the attractiveness of urban spaces in all cities worldwide, which can be used by researchers in urban space studies. This method and the measurement of urban space attractiveness by combining pure and unbiased data and using a combination of simple and complex methods could open new avenues in urban research. The innovation of this study lies in the method of data collection and analysis, which resulted in innovative research that can be used as a model in other studies. Future studies can use the data collection method mentioned in this article and innovative measurement methods in this direction to address issues related to urban space.

Ethics and consent were not required.

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Source code available from https://github.com/facebookarchive/python-instagram .