• Journal of the Korean Geographical Society
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• v.31 no.1
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• pp.1-18
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• 1996

This study examines the spatial characteristics of postal office patronage in rural areas. in the light of future possible relocation and closures of the postal facilities. Most of private services have flown out small rural central places due to the decrease of supporting population, and there consequently remain only a few public services including government-run post offices at the Myon seats, the lowest level among rural central places in Korea. The small local population and its further decline undermine the rationale for maintaining such public services in depleted rural areas. For the worse of it, the government recently plans to transform the postal system to a quasi-private, corporational structure. One can fear that the profit-seeking nature of the new postal corporation will inevitably force to close many of such small rural facilities. The study first analysed nation-wide censuses of postal offices for the years of 1986 and 1992. The postal services examined are per capita number of postal stamps and revenue stamps sold, and letters, parcels, telegrams and monetary transactions handled at the post offices. It is found that, while the usage of postal services has increased substantially throughout the nation during the period of 1986-1992, the increment has largely been occurred by urban post offices rather than by those in Gun seats (i.e., rural counties); and that the gap of the service levels between urban and rural post offices is ever widening. The study further examined the service differentials among the post offices within rural counties to find that those post offices adjacent to the county (Gun) seats and larger urban centers rendered less amount of services than remote rural post offices, indicating that rural residents tend to partonize larger centers more and more than local Myon seats. At the second stage of the study, questionnaire surveys were conducted in Muju, Kimpo, and Hongsung-Gun's. These three counties are meant to represent respectively the remote, suburban, and intermediary counties in Korea. The analyses of survey data reveal that the postal hinterlands of the county seats extend to much of nearby Myons, the subdivisions of a Gun. It is also found that the extent of postal hinterlands of the three counties and the magnitude of patronage and quite different from each other depending upon the topography, population density, and the propinquity of the counties to metropolitan centers. The findings suggest to reappraise the current flat allocation scheme of public facilites to each of rural subdivisions throughout the nation. A detailed analysis on the travel behavior of the survey respondents yields that age is the most salient variable to distinguish activity spaces of rural residents. The activity spaces of older respondents tend to be more limited within their Myon, whereas those of younger respondents extend across the Myon boundary, toward the central towns and even distant larger cities. The very existence of several activity spaces in rural areas calls for an attention in the future locational decisions of public facilities. The locational criteria, employed by the Ministry of Communication of Korean government to establish a post office, are the size of hinterland population and the distance from adjacent postal facilities. The present study findings suggest two additional criteria: the order in rural central place hierarchy and the propinquity to the upper-level centers of the central hierarchy. These old and new criteria are complementary each other in that the former criteria are employed to determine new office locations, whereas the latter are appropriate to determine facility relocation and closures.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.