Accessibility evaluation and multi-scenario optimization of medical services in underdeveloped city driven by multi-source data and latest policies for China DOI Creative Commons
Jinhua Hu,

Chenchen Peng,

Yazhuo Hu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 28, 2024

Equitable and high-quality medical services are more urgent in underdeveloped cities for the higher population ageing demanding social justice. However, there is little attention paid to multi-level services, particularly regarding time indicators under latest policies cities. The improved efforts were hampered partly by single scenario of location optimization, ignoring integrated optimization both road infrastructure institution location. Toward healthy China 2030 rural revitalization policy, this study systematically investigated constructing a evaluation multi-scenario framework with Geographical Information System technology case Xuchang City China. Following goals from policies, hospitals primary health centers evaluated network analysis method further optimized through involving different new facilities roads. Driven urban-rural inequalities, candidate first selected based on multi-source data then determined location‑allocation method, while roads assumed space syntax method. improvement rose rapidly finally slowly increasing number candidates. Few could be suitable, priority was explored local economy planning. findings provide valuable support urban development policies.

Language: Английский

A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data DOI Creative Commons
Yuehong Chen, Congcong Xu, Yong Ge

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(8), P. 3705 - 3718

Published: Aug. 16, 2024

Abstract. China has undergone rapid urbanization and internal migration in the past few years, its up-to-date gridded population datasets are essential for various applications. Existing China, however, suffer from either outdatedness or failure to incorporate data latest Seventh National Population Census of conducted 2020. In this study, we develop a novel downscaling approach that leverages stacking ensemble learning big geospatial produce grids at 100 m resolution using seventh census both county town levels. The proposed employs integrate strengths random forest, XGBoost, LightGBM through fusing their predictions training mechanism, it delineates inhabited areas enhance estimation. Experimental results demonstrate exhibits best-fit performance compared individual base models. Meanwhile, out-of-sample town-level test set indicates estimated dataset (R2=0.8936) is more accurate than existing WorldPop (R2=0.7427) LandScan (R2=0.7165) products Furthermore, with area enhancement, spatial distribution intuitively reasonable two products. Hence, provides valuable option producing datasets. holds great significance future applications, publicly available https://doi.org/10.6084/m9.figshare.24916140.v1 (Chen et al., 2024b).

Language: Английский

Citations

22
Revealing disparities and driving factors in leisure activity segregation of residents and tourists: A data-driven analysis of smart phone data DOI Creative Commons

X. Zhang,

Jin Rui,

Geyang Xia

et al.

Applied Geography, Journal Year: 2025, Volume and Issue: 176, P. 103513 - 103513

Published: Jan. 13, 2025

Language: Английский

Citations

1
Can Rural Digital Economy Mitigate Local Population Outflow: Evidence from China DOI
Sheng Liu, Yuhong Du,

Xiahai Wei

et al.

Journal of Asian Economics, Journal Year: 2025, Volume and Issue: unknown, P. 101942 - 101942

Published: May 1, 2025

Language: Английский

Citations

0
Evaluation Methods for the Human–Land Coupling Coordination Relationship in a Metro Station Area: A Case Study of Chengdu Metro Line 1 DOI Creative Commons
Zhiyong Qiu, Shirui Wen, Hong Yuan

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(3), P. 102 - 102

Published: Feb. 23, 2025

At present, more than 200 cities in the world have developed metro systems. Under agglomeration effect of traffic nodes, rapid population and land development utilization formed around stations cities. However, there is still problem uncoordinated each station area along metro, so it urgent to build an evaluation method coupling coordination relationship between people study laws activities, industrial agglomeration, resources, other aspects analyze its rationality matching. In this study, Chengdu, central city west China, selected as example, Metro Line 1, which has longest history most mature city, taken example. Starting from human activity demand resource supply, indicator system Chengdu 1 constructed. By collecting multi-source data, degree model (CCDM) used quantitatively evaluate human–land area. Then, gray relational analysis (GRA) combined with spatial distribution characteristics are influencing factors relationship, concluded that presents a circular multi-center horizontal structure. Among them, degrees concentration intensity, levels economic development, level service support, contact surrounding areas great influences on Finally, some improvement strategies put forward, such optimizing network layout, building multi-level centers, strengthening functional connections, enhancing intensity. This provides new for coordinated practical significance evaluating construction statuses areas, guiding planning stations, formulating regional stations.

Language: Английский

Citations

0
Risk assessment of seawater intrusion disaster based on FDAHP-EWM method in coastal region of Jinzhou City, China DOI
Haijun Li, Yaowen Zhang,

Jiubo Dong

et al.

Human and Ecological Risk Assessment An International Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: March 3, 2025

Language: Английский

Citations

0
High-resolution population mapping by fusing remote sensing and social sensing data considering the spatial scale mismatch issue DOI Creative Commons
Peijun Feng, Zheng Ma, Jining Yan

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 18, 2025

Language: Английский

Citations

0
Hybrid Learning Model of Global–Local Graph Attention Network and XGBoost for Inferring Origin–Destination Flows DOI Creative Commons

Zhenyu Shan,

Fei Yang, Xian Shi

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(5), P. 182 - 182

Published: April 24, 2025

Origin–destination (OD) flows are essential for urban studies, yet their acquisition is often hampered by high costs and privacy constraints. Prevailing inference methodologies inadequately address latent spatial dependencies between non-contiguous distant areas, which useful understanding modern transportation systems with expanding regional interactions. To these challenges, this paper propose a hybrid learning model the Global–Local Graph Attention Network XGBoost (GLGAT-XG) to infer OD from both global local geographic contextual information. First, we represent study area as an undirected weighted graph. Second, design GLGAT encode correlation feature information into embeddings within multitask setup. Specifically, employs graph transformer capture correlations attention network extract followed fusion ensure validity. Finally, flow performed based on GLGAT-generated embeddings. The experimental results of multiple real-world datasets demonstrate 8% improvement in RMSE, 7% MAE, 10% CPC over baselines. Additionally, produce multi-scale dataset Xian, China, further reveal spatial-scale effects. This research builds existing offers significant practical implications planning sustainable development.

Language: Английский

Citations

0
Spatial Synergy between Tourism Resources and Tourism Service Facilities in Mountainous Counties: A Case Study of Qimen, Huangshan, China DOI Creative Commons
Ying Han,

Yingjie Wang,

Hu Yu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(7), P. 999 - 999

Published: July 6, 2024

Under the influence of mountainous terrain, spatial synergy between tourism resources and service facilities has emerged as a pivotal factor affecting overall efficiency enhancement regional destinations. In order to explore synergistic effect two, taking Qimen County study site, this utilizes Point Interest (POI) data facilities. It constructs fine-scale multidimensional methodology based on grid vectorization conduct scenario-based comparative analyses altitude population density. The objective is elucidate effects development propose strategies. findings are follows: (1) vertical zonation mountains led widespread, decentralized distribution natural in mid-to-high-altitude areas, while humanistic low-altitude urbanized areas exhibit granular, clustered distribution. These contrasting scenarios manifest polarization, making it difficult achieve supply–demand matching layout pattern along transportation routes. (2) gradient two counties significant, with higher level core towns obvious misalignment peripheral areas. (3) Altitude density critical factors influencing supply Through scale aggregation guidance cost–benefit mechanisms, can be classified, stratified, optimized better serve resource development. This provides valuable insights into understanding laws governing utilization within county for academic research purposes.

Language: Английский

Citations

1
Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model DOI Creative Commons
Ortis Yankey, C. Edson Utazi,

Christopher C. Nnanatu

et al.

Applied Geography, Journal Year: 2024, Volume and Issue: 172, P. 103416 - 103416

Published: Sept. 14, 2024

Language: Английский

Citations

1
Uncovering the drivers of gender inequality in perceptions of safety: An interdisciplinary approach combining street view imagery, socio-economic data and spatial statistical modelling DOI Creative Commons

Yu Zhu,

Fenzhen Su, Xin Han

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104230 - 104230

Published: Oct. 24, 2024

Language: Английский

Citations

1