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Improving the livability and sustainability of the dense urban environments in the old town of cities has been a focus of attention in urban planning, particularly from the perspective of optimizing facility distribution. Given the study about how to understand the relationship between dense urban form and community facility distribution under the premise of satisfying the walking distance is limited, which caused a lack of basis for environment renewal and facility planning. This study uses Fuzhou, China as a case study and proposes a comprehensive spatial analysis framework based on a walking-based measurement, which combines the quantification of urban form metrics, density analysis, network analysis, spatial autocorrelation analysis, and fine-scale multi-source dataset. Results show that there is a close spatial correlation between the dense urban form and community facility distribution; a few correlations between dense urban form metrics and density of community facilities are significant, i.e., community facilities tend to gather in areas with high distribution density and volume density of surrounding buildings and residential areas, and areas with high flow and visibility of surrounding intersections. These findings contribute to making effective planning interventions for enhancing dense urban form and optimizing the distribution of community facilities, so as to attract and promote the site selection of facilities, and ultimately improve the livability and sustainability of the old town’s environment.

Day care centers for the elderly (DC centers) in the Tokyo metropolitan area are urgently needed with the increasing population of elderly people. The departments have tried to make efficient use of existing kindergartens as potential locations of DC centers, considering the shortage of available new sites for DC centers. The present study aims to identify the potential location of DC centers from the existing kindergartens by means of spatial distributive evaluation. Based on the Geographic Information Systems (GIS), an integrated framework consisting of two models has been proposed. The first model is the enhanced spatial equity measurement (ESEM) which is employed to calculate the spatial equity indices (SEI) of DC centers and the second is the potential evaluation (PE) which is employed to calculate the priority values of identified kindergartens, combining the site selection of DC centers and the use of kindergartens. The results show that with the dynamic change of demand population from 2015 to 2030, the distribution of spatial SEI and priority values is diverse. And 21,18, 22 potential locations of DC centers are identified respectively in the year 2015, 2020, and 2030. This study aims to help the departments improve the site selection of health care facilities, especially for those dedicated to the elderly.

金沢大学理工研究域環境デザイン学系, Social welfare and public health departments require reliable assessments to enhance the rationality of phased construction of special elderly nursing homes (SENHs). This paper aims to assess the allocation of SENHs based on a beds-needed index for SENHs (BNIS). This may help departments determine the priority for approving locations of SENHs more accurately with a limited budget. Traditional assessments in Tokyo use the sphere of welfare, ward, and sphere of daily life as spatial units for estimating beds-to-elderly population ratios. We calculate the BNIS by introducing a parameter-improved floating catchment area method (PI-FCA) at a smaller spatial unit, the Chome. In the PI-FCA, the catchment area is generated according to the standard of average population served by SENHs and capacity, the population demand is the population of the elderly requiring care levels 3-5 and is further modified by a coefficient of potential demand via building a multivariate linear model. Improved results were obtained using the PI-FCA. Finally, this study maps the distribution of the degree of BNIS, to provide a basis for the allocation assessment of SENHs. This caters to the needs of departments and is easily applicable in other public healthcare facilities. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.