Spatiotemporal Analysis for Urban Pattern Evolution in Sacred District Mathura of India Through K-Means Classification

The changing real estate investment dynamics in Indian holy cities: effect of spirituality on property buying behavior

“…This was because emotional motivations impacting behavioral real estate in a country (socio-cultural factors such as religious belief and search for inner peace) is a contested concept in India, which can be interpreted in different ways by different respondents, and it is still not a mature concept in India Singh et al (2020) that emotional motivations (intrinsic attributes) impacting behavioral real estate are interpreted differently by people and due to differences in perceptions and religion of the respondent constituting the major diasporic group of the country, the impact of emotional motivations on the gentrification of holy cities may vary from one country to another country (Pokhriyal et al, 2020;Salzman and Zwinkels, 2017;Redden, 2016;Linderman, 2013). Since, India is a multi-religious country where a holy city in India is inclusive of all religions and places of worship (Shinde, 2020;Baranwal et al, 2019). Hence, Tiwari et al (2021) found that a holy city in India attracts tourists/investors to buy second homes from different religions depending on the infrastructure and connectivity of the holy city.…”

“…In addition, many scholarly discussions analyzed that the preference of considering economic reasons (improved infrastructure and connectivity) over emotional reasons (intrinsic attributes) may vary from developed country to developing country setting (Srivastava et al, 2020;Zaban, 2019;Chandan and Kumar, 2019). In a developing country setting (India), people are buying real estate in the Indian holy cities based on spurring infrastructure, connectivity and rising potential of the real estate market (economic reasons) because, in western countries, economic reasons impact behavioral real estate have become a mature concept (Mittal et al, 2021;Shinde, 2020;Baranwal et al, 2019). Hence, talking about the importance of real estate and infrastructure as a significant tool to urbanize the economic pattern of the holy city in India has now become the need of the hour and also this is considered as a timely business decision for the developers to cater for the growing demand of real estate in the Indian holy cities, where real estate and infrastructure demand is spurring and the real estate industry is yet to mature in Indian holy cities (Arora and Singh, 2019;Giudice et al, 2019).…”

“…A holy city is defined as a historical city that is in the proximity of the temple and is centered around pilgrims of a specific religion (Allam, 2020). However, many scholarly discussions found that since India is a multi-religious country, a holy city in India is inclusive of all religions and places of worship (Mittal et al, 2021;Shinde, 2020;Baranwal et al, 2019). The significance of the holy city is measured by the number of pilgrims visiting the temple at religious events (Kumar et al, 2010).…”

“…This change is in the original structure of the city form influenced by the change in the demographic profile of the city. With the rise of population and need for better living standards; the city is extended from core to periphery to fringe, thus presenting different city forms (Baranwal et al, 2019). Many researchers found that due to continuous dynamic growth, migration of people from the inner core to outward areas and urban transformation, the infrastructure of holy cities is developing to provide better connectivity, better standards of living, more development opportunities, thus contributing to major changes in the basic city form, emerging from the core and extending to periphery spreading to the fringes affecting the urban vitality of the city (Tiwari et al, 2021;Srivastava et al, 2020;Zaban, 2019;Chandan and Kumar, 2019).…”

Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China

“…The cluster centers were iteratively optimized to created optimal clusters that minimized the value of d sum . Although it is one of the earliest clustering algorithms, it is simple and effective and still popular for a variety of applications [ 52 , 53 ].…”