This study explores gentrification beyond physical displacement and examines it as a new dimension of linguistic and informational inequality. Focusing on Seosulla-gil in Seoul, it compares the amount and accessibility of information in offline (storefront signs) and online (Instagram posts) linguistic landscapes. In the offline sphere, policy restrictions on signboard size have reduced information beyond store names, turning informational absence into aesthetic symbolism. Conversely, online platforms provide extensive contextual information— brand stories, philosophies, and community activities—creating an informational gap between the two spheres. However, this expansion presupposes digital access and excludes groups that lack digital literacy from fully engaging with such information. To mitigate this exclusion, this study proposes two complementary guarantees: the guarantee of digital accessibility, emphasizing institutional responsibility for digital literacy education, and the guarantee of informational accessibility, encouraging shop owners’ voluntary information disclosure and language policies based on the right to access information. Together, these dual guarantees suggest that the exclusion and marginalization caused by gentrification can be transformed into the restoration of publicity and inclusivity.

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.