Abstract
Based on network analysis, different trip modes were integrated into an improved potential model, and the geography of the spatial equity of nursing homes in Changchun is explored in 5-min, 10-min and 15-min scenarios, respectively. Results show that: (1) trip modes have significant influence on spatial equity and that the geography of spatial equity varied with trip modes; (2) the spatial equity value in Changchun is overall kept to a very low level. Most areas in urban fringes and urban core areas belong to underserved areas, and the capacity of nursing home, travel cost and the number of seniors, are the main influencing factors; (3) the geography of spatial equity in different scenarios show a very similar ring structure; namely, the spatial equity value within the urban core and at the most urban periphery is lower than that in intermediate areas. The hot spot analysis showed that the southwest urban fringes and east of the urban core are hot spot areas, while the urban core itself has cold spot areas.
Highlights
China is in a period of rapid demographic, social, and economic transformation
This paper found that the landscapes of spatial equity vary with trip modes and scenarios
This paper found that the spatial competition coming from the neighboring regions affect spatial equity, so the competition factor, especially in a macro-spatial scale, matters
Summary
China is in a period of rapid demographic, social, and economic transformation. Omer (2006) believed that spatial equity refers to the degree to which the services or facilities were distributed in different economic, ethnic, or age groups [7]. Some scholars believe that trip modes have significant influence on spatial equity of service facilities, because different modes of transport can produce different accessibility landscapes [14,15,16] What is more, integrating trip modes into traditional accessibility models can better reflect different social groups’ ability to access certain services [17]. Shen (2017) explored the spatial equity of public green space among different resident groups by a Gaussian-based two-step floating catchment area method [31].
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