초록

Abstract

The purpose of this study is to ascertain librarian’s perceptions on their users’ library access and its patterns. Interviews with seven library staff reveal physical accessibility issues including spatial relationships between libraries and other facilities such as shopping malls and schools, travel distance, transportation mode, and temporal perspectives of library access. The results from the interviews corroborate the findings from secondary analysis using household travel data. However, interviewees do not have a clear picture of how much their users travel and spend to access their libraries. This is true, even though these factors are critical to analyze physical accessibility. Based on the findings, this study makes recommendations to improve the accessibility of public libraries. Suggestions include the concept of sponsorships with other facilities, the placing of material drop-boxes on the main route, and providing library programs during lunch time.

초록

이 연구의 목적은 어린이도서관의 공간특성과 이용자의 행위특성을 규명하고, 이용행태에 따른 공간특성의 선호도 차이를 실증 분석하는 것이다. 이를 위해 이 연구는 전국에 분포한 16개 어린이도서관을 표집기관으로 선정하고, 영유아, 저학년, 고학년 이용자를 대상으로 661부의 자료를 수집하여 분석하였다. 분석결과는 다음과 같다. 첫째, 공간특성과 이용자특성간의 관계에서 연령, 성별에 따른 차이가 있는 반면, 발달단계에 따른 차이는 없었다. 둘째, 공간특성과 이용행태와의 관계에서 차이가 나타났다. 구체적 내용은 다음과 같다. 방문목적: 독서목적의 이용자는 쾌적성과 편안함을 선호하는 반면 비독서목적의 이용자는 소통성과 영역성을 선호하였다. 방문형태: 단독방문보다 동반방문시 다양성, 편안함, 소통성을 더 선호하였다. 활동패턴: 동적활동의 이용자보다 정적활동의 이용자가 다양성을 더 선호하였다. 이용빈도: 방문횟수가 증가할수록 심미성, 역동성, 융통성, 휴식성, 소통성, 영역성에 대한 선호도는 증가하는 반면 안전성에 대한 선호도는 감소하였다. 체류시간: 체류시간이 길수록 다양성, 자존감, 편안함, 역동성, 융통성에 대한 선호도가 높았다.

Abstract

The purpose of this study is to clarify the spatial characteristics of the children's library and user's behavioral characteristics and to empirically analyze the difference in preference of the spatial characteristics based on the using behaviors. For doing this, this study selected 16 children's libraries in the country as a sampling institute and collected and analyzed 661 questionnaires targeting infants, and the lower and upper graders. The results of analysis are as follows. First, while there was a difference in the relation between the spatial characteristics and the user's characteristics according to ages and sexes, there was no difference in the developing stage. Second, there was a difference in the relation between the spatial characteristics and the using behaviors. Detailed results are as follows. While users who visit the library for reading prefer amenity and peacefulness, those who visit the library for other reasons prefer communication and territoriality. Children prefer diversity, peacefulness and communication when they visit in group rather than to visit individually. In terms of behavioral patterns, users who act quietly prefer diversity than those who act actively. Reading use frequency, as the number of times to visit increased, preference on esthetic sense, dynamics, flexibility, leisure, communication and territoriality increased, but preference on safety decreased. As the length of visit became longer, preference on diversity, self-esteem, peacefulness, dynamics and flexibility increased.

초록

개체들 사이의 관계를 저차원 공간에 매핑하는 다차원척도법을 수행하기 위한 다양한 방법과 알고리즘이 개발되어왔다. 그러나 PROXSCAL이나 ALSCAL과 같은 기존의 기법들은 50개 이상의 개체를 포함하는 데이터 집합을 대상으로 개체 간의 관계와 군집 구조를 시각화하는데 있어서 효과적이지 못한 것으로 나타났다. 이 연구에서 제안하는 군집 지향 척도법 CLUSCAL(CLUster-oriented SCALing)은 기존 방법과 달리 입력되는 데이터의 군집 구조를 고려하도록 고안되었다. 50명의 저자동시인용 데이터와 85개 단어의 동시출현 데이터에 대해서 적용해본 결과 제안한 CLUSCAL 기법은 군집 구조를 잘 식별할 수 있는 MDS 지도를 생성하는 유용한 기법임이 확인되었다.

Abstract

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.