Evaluation of the Equity of Urban Park Green Space Based on Population Data Spatialization: A Case Study of a Central Area of Wuhan, China

Abstract

To measure the equity of urban park green space, spatial matching between service supply and user group demand should be taken into consideration. However, if the demographic data, with the administrative division as the basic unit, are directly applied to characterize the spatial distribution of a user group, it may introduce inevitable deviation into the evaluation results due to the low-resolution nature and modifiable areal unit problem of such data. Taking the central area of Wuhan as an example, the population data spatialization method based on land use modeling was used to build a geographically weighted regression (GWR) model of land cover type and demographic data, and the spatial distribution of the population of the 150 m grid was obtained by inversion. Then, the equity of park green space in Wuhan central city was evaluated by population spatial data and network accessibility. The results showed that (1) the range of park green space in the central urban area of Wuhan was within a walking distance of 15 min, accounting for 25.8% of the total study area and covering 54.2% of the population in the study area; (2) the equity of park green space in Hongshan District was the worst; (3) and the use of population spatial data can measure equity on a more precise scale.

Interaction Analysis of Urban Blue-Green Space and Built-Up Area Based on Coupling Model—A Case Study of Wuhan Central City

“…Many previous studies have mostly carried out macroscopic qualitative analysis from the perspective of the impact of land use change on landscape pattern in rapidly urbanized areas, and have found that in these regions, green space and water area are transformed into construction land, which is prone to form extremely scattered landscapes, but the extent of such impact has not been quantified [31][32][33]. Most of the research lacks conjecture verification and two-step coupling quantitative analysis and evaluation [34,35], or just start from a single aspect of urban green space or blue space, where static studies are conducted on social attributes of space such as public health welfare [36], social equity [37,38], physical attributes such as urban flood [39,40], urban landscape [41,42], and other aspects such as economic development [43]. Relatively few studies analyze the dynamic changes of blue-green space and landscape pattern from the perspective of long time series and multiple nodes, and quantitatively analyze the relationship between urban expansion and blue-green space.…”

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

“…Other studies focused on intensity and the spatial pattern of greenspace demand, which were often compared with greenspace supply. Population density is the most commonly used indicator to describe the level of demand and locate areas that are densely populated with limited greenspace [22][23][24]34]. Furthermore, researchers also found that age and income played a role influencing people's greenspace demand.…”

“…Furthermore, demand for greenspace is not homogenous across a city. Some studies have estimated the demand by population density with the assumption that greenspace demand is higher where there are more people [21][22][23][24]. Other factors, such as age and income, were also found to impact people's greenspace demand [25][26][27].…”

Urban Ecological Corridor Network Construction: An Integration of the Least Cost Path Model and the InVEST Model

“…With a vast population and an 80.2% urbanization rate, Wuhan covers 8569 km 2 and has 13 administrative districts. According to different development orientations and resource distribution, the city is divided into one central area and six suburban areas [25]. The suburban areas include districts of Huangpi, Dongxihu, Caidian, Jiangxia, Hannan, and Xinzhou ( Figure 1).…”