서울시 고령자 보행량에 영향을 미치는 물리적 환경 요인에 관한 연구

Abstract

최근 저출산과 고령화가 지속되고 있으며 고령 사회 및 초고령 사회의 진입에 대한 우려가 점차 현실이 되고 있다. 서울시의 고령화 비율은 2015년을 기준으로 12%를 초과하였으며 이는 고령 사회의 기준에 근접한 것을 알 수 있다. 서울시는 이에 대비하기 위한 정책적 시도를 하고 있으나 고령자를 위한 도시환경에 대한 연구와 대비책은 그 속도를 따라가지 못하고 있다. 따라서 본 연구는 고령자 보행량과 물리적 환경 특성의 영향관계를 도출하여 향후 서울시가 고령친화도시로 정착하는데 기여하고자 하였다. 고령보행자의 연령을 60세 이상 65세 미만, 65세 이상 70세 미만, 70세 이상으로 세분화하여 공간적 군집특성을 보이는 지역을 확인하고 다중회귀분석과 공간계량분석을 통해 고령자의 보행량과 물리적 환경 특성의 관계를 분석하였다. 분석결과 각 연령대의 고령보행자는 공간적 군집이 나타나는 것을 확인할 수 있었다. 고령자 보행량에 영향을 미치는 요인을 확인하기 위하여 다중회귀분석과 공간계량분석을 실시하였고 그 결과 세 연령대 모두 최종모형으로 공간오차모형이 채택되었다. 우선, 세 연령대 모두 유의미한 결과를 도출한 경우가 가장 많았으며 보도 폭, 횡단보도 개수, 가로수 개수, 가로등 개수, 공시지가, 주거·상업시설의 면적, 건물층수, 공원까지의 거리, 지하철역까지 거리, 버스 노선 수, 도로면적 비율, 은행·대형마트·슈퍼마켓·종합병원까지의 거리, 환승역세권 여부, 저소득 노인 비중이 유의미한 결과가 도출되었다. 요양병원까지 거리는 모두 유의미한 결과가 도출되었으나 유의확률의 차이가 있었다. 특정 연령대에서만 유의미한 변수로 도출된 경우도 있었는데 이는 업무시설 면적이다. 연구의 결과를 바탕으로 고령자 보행량과 물리적 환경 요인에 대하여 시사점을 도출하였으며 다음과 같다. 첫 째, 고령자를 하나의 동일 집단으로 보는 것이 아니라 세분화하여 살펴볼 필요가 있다. 둘 째, 서울시에서 추진하는 보행친화도시 조성 정책은 고령자 보행을 장려할 수 있을 것으로 기대된다. 본 연구는 고령보행자에 대하여 60세 이상 65세 미만, 65세 이상 70세 미만, 70세 이상 고령자로 세분화하여 차이를 도출했다는 의의를 가지며 향후 보행활동에 대해 연령대별 구분이 가능한 보다 정확한 자료를 구성한다면 구체적인 모형을 제시할 수 있을 것이라 기대된다.|Recently, low birthrate and aging are continuing, and concerns about the entry of aged society and super aged society are gradually becoming reality. The aging rate in Seoul exceeded 12% by 2015, which is close to that of the elderly society. The Seoul Metropolitan Government has made policy attempts to prepare for this, but the research and preparation for the urban environment for the elderly has failed to keep up with the pace. Therefore, this study sought to contribute to the settlement of the Seoul city as an age - friendly city by deriving the influence relationship between the volume of elderly pedestrians and physical environment characteristics. The age of the elderly pedestrians was divided into 60 to 64, 65 to 69 and 70 years old and over. As a result of spatial autocorrelation analysis, it was confirmed that spatial agglomeration appeared in older aged pedestrians. Multiple regression analysis and spatial regression analysis were carried out to identify the factors affecting the pedestrian volume of the aged. As a result, spatial error model was adopted as final model in all ages. First, the most significant results were obtained for all three age groups, and the most significant results were reported. The width of the pavement, the number of crosswalks, street trees, and street lamps, the official land price, the area of ​​residential and commercial facilities, The number of bus routes, the ratio of road area, the distance to a bank, a wholesale and a retial market, a hospital, a transfer station area, and the portion of low-income aged people. There was a significant difference in the probabilities of the distances to the hospitals for the elderly. In some cases, it was derived as a meaningful variable only for a specific age group. This study implies that elderly pedestrians are divided into 60 to 65 years, 65 to 70, and 70 to elderly. It is expected that a concrete model can be presented if more precise data can be classified by age group for future walking activities.; Recently, low birthrate and aging are continuing, and concerns about the entry of aged society and super aged society are gradually becoming reality. The aging rate in Seoul exceeded 12% by 2015, which is close to that of the elderly society. The Seoul Metropolitan Government has made policy attempts to prepare for this, but the research and preparation for the urban environment for the elderly has failed to keep up with the pace. Therefore, this study sought to contribute to the settlement of the Seoul city as an age - friendly city by deriving the influence relationship between the volume of elderly pedestrians and physical environment characteristics. The age of the elderly pedestrians was divided into 60 to 64, 65 to 69 and 70 years old and over. As a result of spatial autocorrelation analysis, it was confirmed that spatial agglomeration appeared in older aged pedestrians. Multiple regression analysis and spatial regression analysis were carried out to identify the factors affecting the pedestrian volume of the aged. As a result, spatial error model was adopted as final model in all ages. First, the most significant results were obtained for all three age groups, and the most significant results were reported. The width of the pavement, the number of crosswalks, street trees, and street lamps, the official land price, the area of ​​residential and commercial facilities, The number of bus routes, the ratio of road area, the distance to a bank, a wholesale and a retial market, a hospital, a transfer station area, and the portion of low-income aged people. There was a significant difference in the probabilities of the distances to the hospitals for the elderly. In some cases, it was derived as a meaningful variable only for a specific age group. This study implies that elderly pedestrians are divided into 60 to 65 years, 65 to 70, and 70 to elderly. It is expected that a concrete model can be presented if more precise data can be classified by age group for future walking activities.