Evaluating the Spatial Heterogeneity and Driving Factors of Sustainable Development Level in Chengdu with Point of Interest Data and Geographic Detector Model DOI Creative Commons
Yantao Ling,

Yilang Zhao,

Qingzhong Ren

и другие.

Land, Год журнала: 2024, Номер 13(7), С. 1018 - 1018

Опубликована: Июль 8, 2024

Over the past few decades, China has undergone largest and fastest urbanization process in world history. By 2023, Chengdu’s rate had reached 80.5%, significantly higher than national average of 66.16%. Studying experience Chengdu is great significance for optimizing urban planning policies other cities China. Although much literature explored from macro micro perspectives, studies using a top-down approach to examine fringe expansion are relatively scarce. This study first applies entropy weight method analyze spatial-temporal evolution trends development, identifying areas imbalanced development prominent issues. Secondly, K-means machine learning algorithm nightlight data used reconstruct classify regions, comparative analysis conducted with administrative divisions further identify unreasonable spatial distribution structure. Finally, POI geographical detector micro-driving forces major limiting factors solutions. The found that gap between rural narrowing during process, but there severe differentiation second circle Chengdu, where economic accelerating residents’ happiness declining. Moreover, based on land-use reveals urban-rural mainly concentrated Chengdu. Micro-level driving factor western region many small settlements, dense road network scattered functional areas. eastern inefficient extensive use construction land. Additionally, mismatch student status household registration resulted lagging educational resource high entry barriers have hindered progress urbanization, leading low per capita welfare expenditure. These reasons main causing decline happiness, this impact shows significant differences at different temporal scales. Encouraging innovation research or education can serve as long-term effective force promoting sustainable urbanization. provides valuable insights scientifically process.

Язык: Английский

Environmental Protection in the Planning of Large Solar Power Plants DOI Creative Commons
Boško Josimović, Božidar Manić, Ana Niković

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(14), С. 6043 - 6043

Опубликована: Июль 11, 2024

The global trend of reducing the “carbon footprint” has influenced dynamic development projects that use renewable energy sources, including solar in large power plants. Consequently, there is an increasingly pronounced need scientific circles to consider impact these have on space and environment. fact international financial institutions environmental effect be a significant factor when funding one main reasons this topic so important professional circles, particularly among investors. This paper highlights plants can both positive negative impacts Those defined order choose optimal spatial territorial solutions ensure preventive planning active protection. In process, application strategic assessment (SEA) organization becomes important. SEA characterized by holistic approach where complex interactions correlations location planned implementation plant understood at earliest stage project development. By doing this, it possible prevent all potential risks may emerge project’s later stages implementation, which favorable from aspect effective protection point view investors investing projects. Optimal bring about basic role are sought primarily analysis relations with regard land, biodiversity, landscape, factors, highlighted paper. Also, methodological concept applied demonstrated, combining different approaches methods for assessment, as part unique semi-quantitative method multi-criteria evaluation solutions.

Язык: Английский

Процитировано

3
The Association Between Aggressive Driving Behaviors and Elderly Pedestrian Traffic Accidents: The Application of Explainable Artificial Intelligence (XAI) DOI Creative Commons
Minjun Kim, Dongbeom Kim, Jisup Shim

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(4), С. 1741 - 1741

Опубликована: Фев. 8, 2025

This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using Explainable Artificial Intelligence (XAI) method. focuses on Seoul, South Korea, where an aging population urban challenges create a pressing need for safety research. The analysis reveals that behaviors, particularly rapid acceleration, deceleration, speeding, are most influential factors frequency of deaths from accidents. In addition, several built environments demographic such as number crosswalks play varying roles depending spatial match or mismatch risky areas accident spots. findings this underscore importance tailored interventions including well-lit crosswalks, calming measures, driver education, to reduce vulnerabilities pedestrians. integration XAI methods provides transparency interpretability, enabling policymakers make data-driven decisions. Expanding approach other contexts with diverse characteristics could validate refine findings, contributing comprehensive strategy improving globally.

Язык: Английский

Процитировано

0
Moving beyond the physical impervious surface impact and urban habitat fragmentation of Alaska: quantitative human footprint inference from the first large scale 30 m high-resolution Landscape metrics big data quantification in R and the cloud DOI Creative Commons

Moriz Steiner,

Falk Huettmann

PeerJ, Год журнала: 2025, Номер 13, С. e18894 - e18894

Опубликована: Март 24, 2025

With increased globalization, man-made climate change, and urbanization, the landscape–embedded within Anthropocene-becomes increasingly fragmented. wilderness habitats transitioning getting lost, globally relevant regions considered ‘pristine’, such as Alaska, are no exception. Alaska holds 60% of U.S. National Park system’s area is national international importance, considering one wealthiest nations on earth. These characteristics tie into densities quantities human features, e.g ., roads, houses, mines, wind parks, agriculture, trails, etc that can be summarized ‘impervious surfaces.’ Those physical impacts actively affecting urban-driven landscape fragmentation. Using remote sensing data Land Cover Database (NLCD), here we attempt to create first quantification this impact Alaskan its We quantified these using well-established metrics tool ‘Fragstats’, implemented R package “landscapemetrics” in desktop software through interface a Linux Cloud-computing environment. This workflow allows for time overcome computational limitations conventional Fragstats reasonably quick timeframe. Thereby, able analyze land large approx. 1,517,733 km 2 (state Alaska) while maintaining high assessment resolution 30 m. Based traditional methodology, found has reported c. 0.067%. additionally overlaid other features were not included input highlight overall true ( airports, governance boundaries game management park units, .). (human layers), Alaska’s considerably underestimated meaningless estimate. The state more seriously fragmented affected by humans than commonly assumed. Very few areas truly untouched display patch density with corresponding low mean sizes throughout study area. Instead, likely close 100% several metrics. newly created insights, provide state-wide inference considerable importance entities systems overall, especially changing climate. Likewise, methodological framework presented shows an Open Access used reference reproduced virtually anywhere else planet assess realistic large-scale It also sustainable stewardship mitigation policy.

Язык: Английский

Процитировано

0
Evaluating the Spatial Heterogeneity and Driving Factors of Sustainable Development Level in Chengdu with Point of Interest Data and Geographic Detector Model DOI Creative Commons
Yantao Ling,

Yilang Zhao,

Qingzhong Ren

и другие.

Land, Год журнала: 2024, Номер 13(7), С. 1018 - 1018

Опубликована: Июль 8, 2024

Over the past few decades, China has undergone largest and fastest urbanization process in world history. By 2023, Chengdu’s rate had reached 80.5%, significantly higher than national average of 66.16%. Studying experience Chengdu is great significance for optimizing urban planning policies other cities China. Although much literature explored from macro micro perspectives, studies using a top-down approach to examine fringe expansion are relatively scarce. This study first applies entropy weight method analyze spatial-temporal evolution trends development, identifying areas imbalanced development prominent issues. Secondly, K-means machine learning algorithm nightlight data used reconstruct classify regions, comparative analysis conducted with administrative divisions further identify unreasonable spatial distribution structure. Finally, POI geographical detector micro-driving forces major limiting factors solutions. The found that gap between rural narrowing during process, but there severe differentiation second circle Chengdu, where economic accelerating residents’ happiness declining. Moreover, based on land-use reveals urban-rural mainly concentrated Chengdu. Micro-level driving factor western region many small settlements, dense road network scattered functional areas. eastern inefficient extensive use construction land. Additionally, mismatch student status household registration resulted lagging educational resource high entry barriers have hindered progress urbanization, leading low per capita welfare expenditure. These reasons main causing decline happiness, this impact shows significant differences at different temporal scales. Encouraging innovation research or education can serve as long-term effective force promoting sustainable urbanization. provides valuable insights scientifically process.

Язык: Английский

Процитировано

1