Methods of Population Spatialization Based on the Classification Information of Buildings from China's First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

High-Precision Population Spatialization in Metropolises Based on Ensemble Learning: A Case Study of Beijing, China

Accurate spatial population distribution information, especially for metropolises, is of significant value and is fundamental to many application areas such as public health, urban development planning and disaster assessment management. Random forest is the most widely used model in population spatialization studies. However, a reliable model for accurately mapping the spatial distribution of metropolitan populations is still lacking due to the inherent limitations of the random forest model and the complexity of the population spatialization problem. In this study, we integrate gradient-boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM) and support vector regression (SVR) through ensemble-learning algorithm-stacking to construct a novel population-spatialization model we name GXLS-Stacking. We integrate socioeconomic data that enhance the characterization of the population’s spatial distribution (e.g., point-of-interest data, building outline data with height, artificial impervious surface data, etc.) and natural environmental data with a combination of census data to train the model to generate a high-precision gridded population density map with a 100 m spatial resolution for Beijing in 2020. Finally, the generated gridded population density map is validated at the pixel level using the highest resolution validation data (i.e., community household registration data) in the current study. The results show that the GXLS-Stacking model can predict the population’s spatial distribution with high precision (R2 = 0.8004, MAE = 34.67 persons/hectare, RMSE = 54.92 persons/hectare), and its overall performance is not only better than the four individual models but also better than the random forest model. Compared to the natural environmental features, a city’s socioeconomic features are more capable in characterizing the spatial distribution of the population and the intensity of human activities. In addition, the gridded population density map obtained by the GXLS-Stacking model can provide highly accurate information on the population’s spatial distribution and can be used to analyze the spatial patterns of metropolitan population density. Moreover, the GXLS-Stacking model has the ability to be generalized to metropolises with comprehensive and high-quality data, whether in China or in other countries. Furthermore, for small and medium-sized cities, our modeling process can still provide an effective reference for their population spatialization methods.

Literature Review Reveals a Global Access Inequity to Urban Green Spaces

Differences in the accessibility to urban resources between different racial and socioeconomic groups have exerted pressure on effective planning and management for sustainable city development. However, few studies have examined the multiple factors that may influence the mitigation of urban green spaces (UGS) inequity. This study reports the results of a systematic mapping of access inequity research through correspondence analysis (CA) to reveal critical trends, knowledge gaps, and clusters based on a sample of 49 empirical studies screened from 563 selected papers. Our findings suggest that although the scale of cities with UGS access inequity varies between countries, large cities (more than 1,000,000 population), especially in low- and middle-income countries (LMICs), are particularly affected. Moreover, the number of cities in which high socioeconomic status (high-SES) groups (e.g., young, rich, or employed) are at an advantage concerning access to UGS is substantially higher than the number of cities showing better accessibility for low-SES groups. Across the reviewed papers, analyses on mitigating interventions are sparse, and among the few studies that touch upon this, we found different central issues in local mitigating strategies between high-income countries (HICs) and LMICs. An explanatory framework is offered, explaining the interaction between UGS access inequity and local mitigating measures.

Evaluation of the Fairness of Urban Lakes' Distribution Based on Spatialization of Population Data: A Case Study of Wuhan Urban Development Zone

Lake reclamation for urban construction has caused serious damage to lakes in cities undergoing rapid urbanization. This process affects urban ecological environment and leads to inconsistent urban expansion, population surge, and uneven distribution of urban lakes. This study measured the fairness of urban lakes’ distribution and explored the spatial matching relationship between service supply and user group demand. The interpretation and analysis of Wuhan’s remote sensing images, population, administrative area, traffic network, and other data in 2018 were used as the basis. Specifically, the spatial distribution pattern and fairness of lakes’ distribution in Wuhan urban development zone were investigated. This study establishes a geographic weighted regression (GWR) model of land cover types and population data based on a spatialization method of population data based on land use, and uses population spatial data and network accessibility analysis results to evaluate lake service levels in the study area. Macroscopically, the correlation analysis of sequence variables and Gini coefficient analysis method are used to measure the fairness of the Wuhan lake distribution problem and equilibrium degree, and the location entropy analysis is used to quantitatively analyze the fairness of lakes and Wuhan streets from the perspective of supply and demand location entropy. Levels improve the accuracy of the research. Results showed that (1) the area covered by lakes in Wuhan urban development zone is 1007.96 km² within 60-min of walking, accounting for 30.6% of the total area of the study area. This area can house 5,050,275 people, accounting for 60.8% of Wuhan’s total population. (2) The lakes in the central city area are less fair than the lakes outside the Third Ring Road. (3) The service level of North Lake is the highest among all the lakes in the study area, and that of Hou Lake is the lowest. (4) The spatial layout of the fairness of the lakes’ distribution is roughly distributed in circles. The fairness level collapses toward the city center, indicating that the closer to the city center, the lower the fairness level.

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

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.

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and socioeconomic statistics. In particular, an index, Land Resource Pressure (LRP), is proposed to evaluate the land carrying capacity, and a case study was carried out in Wuhan. The LRP of Wuhan was calculated on 250 m * 250 m grids, and showed a circularly declining pattern from central to outer areas. We collected its influencing factors in terms of nature resources, economy, transportation and urban construction, and then analyzed its causes via geographically weighted (GW) models. Firstly, pair-wise correlations between LRP and each influencing factor were explored via the GW correlation coefficients. These local estimates provide an important precursor for the following quantitative analysis via the GW regression (GWR) technique. The GWR coefficient estimates interpret the influences on LRP in a localized view. Results show that per capita gross domestic product (PerGDP) showed a higher absolute estimate among all factors, which proves that PerGDP has a relieving effect on LRP, especially in the southwestern areas. Overall, this study provides a technical framework to evaluate land carrying capacity with multi-source data sets and explore its localized influences via GW models, which could provide practical guidance for similar studies in other cities.