Applied Geography最新文献_第3页

The impact of skill-biased technological change on urban-rural income inequality: Evidence from China 技能偏向的技术变革对城乡收入不平等的影响：来自中国的证据

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2026-01-05 DOI: 10.1016/j.apgeog.2026.103891

Yuxin Pan , Feifan Gao , Zuge Xing

As technological progress reshapes the skill demands of regional labor markets, developing countries often exhibit pronounced income inequality. However, existing research on skill-biased technological change (SBTC) primarily focuses on its impact on labor forces with varying skill levels, paying insufficient attention to its role in urban-rural income inequality (URII). This study employs machine learning models to estimate the level of SBTC across 246 prefecture-level cities in China from 2005 to 2015 and analyzes its effect on URII. The results show that China's SBTC intensity increased significantly during the study period, with every 1 % rise in SBTC widening the URII by 0.108 %. This effect remains robust across multiple robustness tests. Furthermore, the widening effect is particularly pronounced in regions with higher foreign direct investment, urbanization level, and non resource-dependent cities. Finally, we find that task biased technological change further amplifies the positive impact of SBTC on URII. Our findings provide policy implications for refining urban-rural development strategies to enhance the inclusivity of technological progress and facilitate skill upgrading among rural laborers.

随着技术进步重塑区域劳动力市场的技能需求，发展中国家往往表现出明显的收入不平等。然而，现有关于技能偏倚技术变革（SBTC）的研究主要集中在其对不同技能水平劳动力的影响上，而对其在城乡收入不平等（URII）中的作用关注不足。本研究采用机器学习模型估算了2005 - 2015年中国246个地级市的SBTC水平，并分析了其对URII的影响。结果表明，研究期间，中国的SBTC强度显著增加，每增加1%，uri就会扩大0.108%。这种效应在多个稳健性测试中保持稳健性。此外，在外国直接投资、城市化水平和非资源依赖型城市较高的地区，这种扩大效应尤为明显。最后，我们发现任务偏向的技术变革进一步放大了SBTC对URII的积极影响。研究结果为完善城乡发展战略以增强技术进步的包容性和促进农村劳动力技能升级提供了政策启示。

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引用次数: 0

Bluetech in Baltimore: Co-creating smart city innovations with local youth 巴尔的摩Bluetech：与当地青年共同创造智慧城市创新

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2026-01-02 DOI: 10.1016/j.apgeog.2025.103887

Michele Masucci , Dillon Mahmoudi , Alan Wiig

This paper presents a case study of a climate-focussed digital internship pilot program intent on increasing engagement with, and jobs in, the “Bluetech” ocean-based economy in Baltimore, Maryland. Understanding participants' lived experiences across multiple strands of alignment–including general interest, digital skills and technology access, environmental knowledge, and perceptions of climate and environmental problems–supported their engagement in the program and advanced their educational and workforce goals. Those experiences are undergirded by the historical setting of Baltimore, which is navigating contemporary interrelated impacts of global change due to climate, economic, and digital infrastructure transformations. Like many cities, Baltimore has developed a policy agenda focused on technological innovation, sustainability, and education as conduits of progress meant to address these concerns. However, the promise of innovation across these policy areas may not be realized if Baltimore's youth do not attain the necessary digital competencies and relevant knowledge to address them. An iterative pedagogy was applied in order to help interns gain conceptual depth, technical skills, and content knowledge, and yielded insights about how youth may traverse the complexities of the modern Bluetech economy. Baltimore's innovation-forward policy agenda continues to require technology, education access and opportunity to connect to the economic directions being designed. And yet, the same advancement of innovation may also exacerbate the longstanding poverty present in the city if more is not done to bridge everyday experiences of youth with opportunities to learn and engage in foundational experiences, skills and knowledge upon which innovation will be based.

本文介绍了一个以气候为重点的数字实习试点项目的案例研究，该项目旨在增加与马里兰州巴尔的摩市“蓝色科技”海洋经济的接触和就业机会。了解参与者在多个方面的生活经历，包括一般兴趣、数字技能和技术获取、环境知识以及对气候和环境问题的看法，有助于他们参与项目，并推进他们的教育和劳动力目标。巴尔的摩的历史背景为这些经验提供了基础，该城市正在应对气候、经济和数字基础设施转型带来的全球变化的当代相互关联的影响。像许多城市一样，巴尔的摩制定了一项政策议程，重点关注技术创新、可持续性和教育，作为解决这些问题的进步渠道。然而，如果巴尔的摩的年轻人没有获得必要的数字能力和相关知识来解决这些问题，那么这些政策领域的创新承诺可能无法实现。为了帮助实习生获得概念深度、技术技能和内容知识，采用了迭代教学法，并就年轻人如何穿越现代蓝科技经济的复杂性产生了见解。巴尔的摩的创新政策议程继续需要技术、教育和机会来连接正在设计的经济方向。然而，如果不采取更多措施，将年轻人的日常经历与学习和参与创新所基于的基础经验、技能和知识的机会联系起来，那么创新的进步也可能加剧城市中长期存在的贫困。

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引用次数: 0

Beyond the core-periphery division: revisiting the location of emerging environmental firms in an urban agglomeration 超越核心-外围划分：重新审视新兴环保企业在城市群中的位置

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-31 DOI: 10.1016/j.apgeog.2025.103890

Tong Shen, Xiyan Mao, Yuchen Li, Hongyu Qian

Despite the emerging role of environmental firms (EFs) in driving urban green transitions, the capacity of cities to develop EFs varies significantly. A profound understanding of EFs locations is required for more cities to benefit from the development of EFs. This paper proposes a new analytical framework that decomposes the location into two components: the local conditions of cities and their locational attractiveness compared to other cities. The framework incorporates the Lotka-Volterra model and Dendrinos-Sonis models to identify local conditions and locational attractiveness, respectively, from a co-evolved perspective. The development of EFs in the Yangtze River Delta (YRD) of China during 2000–2022 serves as a case. The empirical findings have identified five types of locations: glocalized, local-to-global, localized, footloose, and peripheral. Most cities in YRD can offer localized locations for EFs due to the abundance of industrial assets and natural assets and the absence of environmental infrastructures. Fewer than half of the cities can offer an inter-city interaction location based on benefits derived from knowledge creation and technological legitimation and drawbacks experienced in market formation and investment mobilization. The glocalized location for anchoring external resources and collaborating with external actors has an amplification effect on EF's development, while the local-to-global location for exploring external emerging market does not. Overall, this new typology of locations goes beyond the conventional core-periphery structure of an urban agglomeration, and provides a more nuanced spatial framework for the spatial planning of emerging industries.

尽管环境企业在推动城市绿色转型方面的作用越来越大，但城市发展环境企业的能力差异很大。要使更多的城市受益于电子商务的发展，需要对电子商务的位置有深刻的了解。本文提出了一种新的分析框架，将区位因素分解为两部分：城市的本地条件和相对于其他城市的区位吸引力。该框架结合了Lotka-Volterra模型和Dendrinos-Sonis模型，分别从共同进化的角度确定当地条件和地点吸引力。2000年至2022年中国长三角地区的生态系统发展就是一个例子。实证研究结果确定了五种类型的位置：全球定位、本地到全球、本地化、自由流动和外围。由于工业资产和自然资产丰富，而环境基础设施缺乏，长三角的大多数城市都可以提供本地化的电子商务地点。根据知识创造和技术合法化带来的好处以及市场形成和投资动员方面的缺陷，只有不到一半的城市能够提供城际互动地点。锚定外部资源和与外部行动者合作的全球定位对EF的发展具有放大效应，而探索外部新兴市场的从本地到全球定位则没有放大效应。总体而言，这种新的区位类型超越了传统的城市群核心-外围结构，并为新兴产业的空间规划提供了更细致入微的空间框架。

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引用次数: 0

Examining public health crises: Arizona hotspots and neighborhood-level predictors of homicide, suicide, and overdose 检查公共卫生危机：亚利桑那州热点和社区水平的杀人，自杀和过量预测

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-31 DOI: 10.1016/j.apgeog.2025.103888

Jordan Batchelor , Laura Lightfoot , Christi L. Gullion , Charles M. Katz

引用次数: 0

Identifying high-risk roadways using crowdsourced speed variation features from big mobility data 利用来自大移动数据的众包速度变化特征识别高风险道路

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-31 DOI: 10.1016/j.apgeog.2025.103889

Xiao Li

Effectively identifying high-risk roadways is crucial for enhancing road safety; however, traditional crash-based safety assessments are reactive and may underestimate risks. Leveraging emerging big mobility data collected from connected vehicles (CV), this study presents a new approach to proactively identifying high-risk roadways using crowdsourced speed features. Ten speed variation features were generated from one month of CV data to depict speed dynamics on single carriageway ‘A’ roads in Oxfordshire, UK. To capture the temporal dynamics of speed variations, temporally disaggregated features were generated from peak hours, non-peak hours, and weekends. This study employed various statistical and machine learning-based regression models, including Classification and Regression Tree (CART), Random Forest, and eXtreme Gradient Boosting (XGBoost), to analyze the relationship between speed-related features and three safety performance measures based on five years of crash data. The SHapley Additive exPlanation (SHAP) approach was employed to interpret the modelling outputs. Results demonstrated that crowdsourced speed variation features, especially from different temporal windows, are valuable surrogate safety measures for quantifying crash risks. Features related to acceleration (acceleration noise) and speed variance (skewness index) significantly impacted the modelling results. These findings could help transport practitioners better understand how speed variations relate to crash risks and unlock the potential for conducting proactive safety assessments using crowdsourced driving behaviour data.

有效识别高风险道路对加强道路安全至关重要；然而，传统的基于碰撞的安全评估是被动的，可能低估了风险。利用从互联车辆（CV）收集的新兴大移动数据，本研究提出了一种利用众包速度特征主动识别高风险道路的新方法。从一个月的CV数据中生成了10个速度变化特征，以描述英国牛津郡单车道A道路的速度动态。为了捕捉速度变化的时间动态，从高峰时间、非高峰时间和周末生成了时间分解特征。本研究采用各种统计和基于机器学习的回归模型，包括分类与回归树（CART）、随机森林（Random Forest）和极限梯度提升（eXtreme Gradient Boosting），基于五年的碰撞数据，分析速度相关特征与三种安全性能指标之间的关系。采用SHapley加性解释（SHAP）方法来解释建模输出。结果表明，众包速度变化特征，特别是来自不同时间窗口的速度变化特征，是量化碰撞风险的有价值的替代安全措施。与加速度（加速度噪声）和速度方差（偏度指数）相关的特征显著影响建模结果。这些发现可以帮助交通从业者更好地了解速度变化与碰撞风险之间的关系，并释放利用众包驾驶行为数据进行主动安全评估的潜力。

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引用次数: 0

Institutional insights for smart cities and urban innovation: Lessons from building data dashboards 智慧城市和城市创新的制度洞察：从构建数据仪表板的经验教训

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-26 DOI: 10.1016/j.apgeog.2025.103873

B. Donald , N. Lowe , N. Kaza , S. Brail , K. Heatwole , C. DeLoyde , K. Khanal , N. McDonald , D. Planey , O. Wang

Proponents of smart cities often envision a seamless, data-driven utopia where information is continuously collected and used for semi-automated decision-making. This paper offers a counter-narrative based on our experience developing dashboards from public and private data sources across three regional contexts in two North American countries. These projects, initiated by university-led teams, revealed the complex, interpretive, and collaborative nature of data work. Creating these dashboards required harmonising data across spatial, temporal, and institutional boundaries—an effort far more complex than the frictionless processes often promised by smart city advocates. Data collection, analysis, and communication demanded ongoing interpretation and adaptation by scholars, policymakers, and civic leaders. While these efforts did lead to innovations in public service delivery, they also challenge the notion of autonomous, data-driven decision-making central to smart city discourse. Beyond technical outcomes, our projects fostered new and repurposed partnerships, supported work and learning continuity, and enabled collective sense-making. These experiences suggest that rather than striving for fully automated systems, cities should embrace a nuanced form of “smartness”—one that values human judgment, collaboration, and adaptability to build resilience in urban institutions.

智慧城市的支持者经常设想一个无缝的、数据驱动的乌托邦，在这个乌托邦中，信息被不断收集并用于半自动化的决策。本文基于我们在两个北美国家的三个区域背景下从公共和私人数据源开发仪表板的经验，提供了一个相反的叙述。这些项目由大学领导的团队发起，揭示了数据工作的复杂性、解释性和协作性。创建这些仪表板需要跨空间、时间和机构边界协调数据，这比智能城市倡导者经常承诺的无摩擦过程要复杂得多。数据的收集、分析和交流需要学者、政策制定者和公民领袖不断地解释和适应。虽然这些努力确实带来了公共服务提供方面的创新，但它们也挑战了智能城市话语中自主、数据驱动决策的核心概念。除了技术成果之外，我们的项目还培育了新的和重新定位的伙伴关系，支持工作和学习的连续性，并使集体意义得以实现。这些经验表明，城市不应该追求完全自动化的系统，而应该采用一种微妙的“智慧”形式——一种重视人的判断、协作和适应能力的形式，以建立城市机构的弹性。

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引用次数: 0

Mapping precipitation-triggered landslide risks in global human settlements 测绘全球人类住区中降水引发的滑坡风险

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-25 DOI: 10.1016/j.apgeog.2025.103886

Kechao Wang , Xiangyuan Wu , Tzu-Hsin Karen Chen , Wu Xiao

Precipitation-triggered landslides represent a major hazard to human settlements, yet their risks remain unevenly distributed across regions. However, existing studies often lack accurate modeling of precipitation-induced landslide hazards and remain largely hazard-centric, with limited consideration of exposure and socioeconomic vulnerability. This study develops a spatially explicit framework to assess global landslide risks by integrating hazard, exposure, and vulnerability dimensions using multi-source satellite data. Landslide susceptibility was first modeled using extreme precipitation indices, terrain, vegetation, and soil variables within a Max Entropy framework. We then incorporated population density as a measure of exposure, together with indicators of socioeconomic vulnerability, including accessibility to healthcare and transport, economic development, and built-environment quality, to construct an integrated risk index. Results show pronounced spatial disparities: risk hotspots are concentrated in South and Southeast Asia, parts of sub-Saharan Africa, and regions of South America, where rapid urban growth coincides with limited adaptive capacity. In contrast, risks in Europe and North America are characterized by mixed drivers, reflecting both hazard conditions and relatively lower vulnerability. These findings highlight the uneven geography of landslide risks and provide spatial evidence to inform land-use planning, infrastructure investment, and disaster risk reduction strategies. By demonstrating the value of combining hazard, exposure, and vulnerability in a globally consistent framework, this study contributes to the applied geography of natural hazards and offers insights for risk-sensitive planning in diverse settlement contexts.

由降水引发的山体滑坡对人类住区构成了重大威胁，但其风险在不同地区的分布仍然不均匀。然而，现有的研究往往缺乏降水引起的滑坡灾害的准确建模，并且主要以灾害为中心，对暴露和社会经济脆弱性的考虑有限。本研究开发了一个空间明确的框架，利用多源卫星数据综合危害、暴露和脆弱性维度来评估全球滑坡风险。滑坡易感性首先在最大熵框架内使用极端降水指数、地形、植被和土壤变量进行建模。然后，我们将人口密度作为暴露的衡量标准，与社会经济脆弱性指标（包括医疗保健和交通的可及性、经济发展和建筑环境质量）结合起来，构建了一个综合风险指数。结果表明：风险热点地区集中在南亚和东南亚、撒哈拉以南非洲部分地区和南美地区，这些地区的城市快速增长与适应能力有限相吻合。相比之下，欧洲和北美的风险特点是混合驱动因素，反映了危险条件和相对较低的脆弱性。这些发现突出了滑坡风险的不均匀地理分布，并为土地利用规划、基础设施投资和减少灾害风险战略提供了空间证据。通过展示在全球一致的框架中结合危害、暴露和脆弱性的价值，本研究有助于自然灾害的应用地理学，并为不同定居环境下的风险敏感规划提供见解。

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引用次数: 0

Decoding urban park use patterns from a day–night disparity perspective: Evidence from Nanjing using machine learning 从昼夜差异的角度解读城市公园使用模式：基于机器学习的南京证据

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-24 DOI: 10.1016/j.apgeog.2025.103885

Xun Zhang , Junyan Yang , Ao Cui , Yi Shi , Wenlong Li , Chen Zhang , Chenyang Zhang , Zhonghu Zhang , Zhihan Zhang

Urban parks are recognized as important public spaces that support the health and well-being of urban residents. Although georeferenced data have been widely applied in park evaluations, most existing studies address temporal variation by analyzing different time periods separately, rather than adopting a comparative perspective to identify distinct park typologies. This study utilized large-scale mobile phone data from Nanjing, China, to investigate day–night differences in park visitation patterns and their determinants. Using a multi-method analytical approach that integrates principal component analysis, Gaussian mixture model clustering, and random forest models, 169 urban parks were classified into three categories: nighttime local leisure parks, daytime comprehensive leisure parks, and all-day balanced community parks. The results reveal that design and diversity dimensions exerted a consistent influence across park types, ranking among the top two contributors. In contrast, transit accessibility significantly shaped stay duration only in all-day balanced community parks, accounting for 10.64 % of the average relative importance. Moreover, different built-environment elements displayed threshold effects, such as the rapid rise in visiting distance disparities in daytime comprehensive leisure parks when the density of security facilities exceeded 0.4. Conversely, when the number of bus-stops further increased beyond 7, a pronounced accessibility saturation effect emerged, such that additional transit supply no longer influenced day–night stay-duration disparities in all-day balanced community parks. These findings underscore the importance of comparative temporal analysis for characterizing park use and highlight the need for refined, context-sensitive strategies to enhance the effectiveness and inclusiveness of urban park services.

城市公园被认为是支持城市居民健康和福祉的重要公共空间。虽然地理参考数据已被广泛应用于公园评价，但大多数现有研究都是通过单独分析不同时间段来解决时间变化问题，而不是采用比较的视角来识别不同的公园类型。本研究利用来自中国南京的大规模手机数据，调查了公园游客模式的昼夜差异及其决定因素。采用主成分分析、高斯混合模型聚类和随机森林模型相结合的多方法分析方法，将169个城市公园划分为夜间局部休闲公园、白天综合休闲公园和全天平衡社区公园三类。结果显示，设计和多样性维度对公园类型的影响是一致的，排在前两位。相比之下，交通可达性仅在全日平衡型社区公园中显著影响停留时间，占平均相对重要性的10.64%。不同的建成环境要素表现出阈值效应，如白天综合休闲公园安保设施密度超过0.4时，游客距离差异迅速增大。相反，当公交站点数量进一步增加到7个以上时，出现了明显的可达性饱和效应，即额外的交通供应不再影响全天候平衡社区公园的昼夜停留时间差异。这些发现强调了比较时间分析对公园使用特征的重要性，并强调了需要完善的、环境敏感的策略来提高城市公园服务的有效性和包容性。

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引用次数: 0

Exploring traveler roles based on potential group travelers extracted from metro smart card data 基于从地铁智能卡数据中提取的潜在团体旅行者，探索旅行者角色

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-19 DOI: 10.1016/j.apgeog.2025.103872

Xiaoying Shi , Ruixuan Wang , Chao Wu , Dandan Liu , Haitao Xu , Yongping Zhang

Understanding group travel behavior is essential for unraveling the social dynamics underlying urban mobility, supporting more accurate demand forecasting and targeted service planning. Although previous studies have examined the mobility patterns of familiar strangers and group travelers, the spatiotemporal characteristics of group travel across different traveler roles remain underexplored. To fill this gap, this paper proposes an analytical framework for systematically exploring traveler roles in potential group travel behavior. We first identify potential social trips using the concept of spatiotemporal co-existence and then construct a large-scale social network of potential group travelers. By analyzing node features of the network, travelers are classified into distinct groups and the spatiotemporal mobility patterns of each group are subsequently examined. We employ a large-scale smart card dataset from Shanghai's metro system as a case study. The results indicate that potential social trips are more likely to occur during non-commuting hours. Social travelers exhibit high levels of group travel activity, while social isolators show low engagement in group travel except at major transportation hubs, likely for business-related purposes. Weekday- and holiday-preferred group travelers display spatial preferences associated with medical and recreational destinations, respectively. These findings offer valuable insights for socially aware transportation planning and user-centric urban policy-making.

了解团体旅游行为对于揭示城市流动性背后的社会动态、支持更准确的需求预测和有针对性的服务规划至关重要。尽管已有研究考察了熟悉陌生人和团体旅行者的流动模式，但不同旅行者角色的团体旅行时空特征仍未得到充分探讨。为了填补这一空白，本文提出了一个分析框架来系统地探索旅行者在潜在的团体旅行行为中的角色。我们首先利用时空共存的概念识别潜在的社会旅行，然后构建潜在团体旅行者的大规模社会网络。通过对网络节点特征的分析，将出行者划分为不同的群体，并对每个群体的时空迁移模式进行了分析。我们使用上海地铁系统的大规模智能卡数据集作为案例研究。结果表明，潜在的社交旅行更有可能发生在非通勤时间。社交旅行者表现出高水平的团体旅行活动，而社交隔离者在团体旅行中表现出较低的参与度，除了在主要的交通枢纽，可能是出于与商业相关的目的。平日和假日偏好的团体旅行者分别显示出与医疗目的地和娱乐目的地相关的空间偏好。这些发现为具有社会意识的交通规划和以用户为中心的城市决策提供了有价值的见解。

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引用次数: 0

Polycentric urban structure identification and spatial‒temporal evolution analysis using a multisource remote sensing composite network 基于多源遥感复合网络的多中心城市结构识别与时空演化分析

IF 5.4 2区地球科学 Q1 GEOGRAPHY

Applied Geography

Pub Date : 2025-12-19 DOI: 10.1016/j.apgeog.2025.103866

Zhiwei Xie, Zhenkun Weng, Zhonghua Wang, Lishuang Sun, Mingliang Yuan

It is essential to comprehend the polycentric urban structure (PUS) in order to facilitate resource allocation, economic development, and social interactions. Current research mostly uses single-source data such as nighttime light data that reflects social characteristics or optical remote sensing data that reflects natural characteristics, without considering that the formation of PUS is the result of the combined action of social and natural factors. In response to this problem, this paper proposes a method for identifying and analyzing the evolution of PUS using a composite urban network. A network construction method utilizing adaptive fusion weights is employed to integrate nighttime light data and optical remote sensing networks into a composite urban network, facilitating multi-source data fusion. The Louvain algorithm is employed to partition the communities of the composite urban network, while the central nodes are identified using a degree centrality measure based on the Comprehensive Strength Index (CSI). Subsequently, polycentric urban regions (PURs) and urban centers (UCs) are derived by mapping the communities and central nodes to optical image objects. The spatial-temporal evolution of PUS indicates urban development. This study focuses on China's regional center cities, such as Wuhan, Chengdu, Shenzhen, Nanjing, Xi'an and Shenyang, utilizing Visible Infrared Imaging Radiometer Suite/National Polar-orbiting Partnership (VIIRS/NPP) and Landsat 8 data from 2013 to 2020 as the experimental dataset. The experimental results indicate that the mean accuracy of PURs identification utilizing a composite urban network (CUN) is 81.12 %, surpassing the accuracy achieved with the nighttime light urban network (NUN) by 5.09 % and the optical remote sensing urban network (OUN) by 5.13 %. For UCs identification, the traditional method based solely on weighted degree centrality (WDC) achieved a mean accuracy of 56.29 %, while the proposed Comprehensive Strength Index (CSI) method achieved 73.31 %, representing an improvement of 17.02 percentage points (a relative increase of approximately 30.2 %). The expanse of urban region is positively correlated with GDP, while the increase in the distance of urban center displacement indicates a reinforcement of urban polycentricity.

理解多中心城市结构对于促进资源配置、经济发展和社会互动至关重要。目前的研究多采用反映社会特征的夜间灯光数据或反映自然特征的光学遥感数据等单源数据，没有考虑到PUS的形成是社会因素和自然因素共同作用的结果。针对这一问题，本文提出了一种基于复合城市网络的PUS演化识别与分析方法。采用自适应融合权值的网络构建方法，将夜间灯光数据与光学遥感网络整合成复合城市网络，实现多源数据融合。采用Louvain算法对复合城市网络进行社区划分，采用基于综合强度指数（CSI）的度中心性度量来识别中心节点。随后，通过将社区和中心节点映射到光学图像对象，推导出多中心城市区域（PURs）和城市中心（UCs）。PUS的时空演变反映了城市的发展。以武汉、成都、深圳、南京、西安、沈阳等中国区域中心城市为研究对象，利用2013 - 2020年可见光红外成像辐射计套件/国家极轨合作伙伴关系（VIIRS/NPP）和Landsat 8数据作为实验数据集。实验结果表明，利用复合城市网络（CUN）识别的平均精度为81.12%，比夜间灯光城市网络（NUN）和光学遥感城市网络（OUN）的识别精度分别高出5.09%和5.13%。对于UCs的识别，仅基于加权度中心性（WDC）的传统方法的平均准确率为56.29%，而综合强度指数（CSI）方法的平均准确率为73.31%，提高了17.02个百分点（相对提高了约30.2%）。城市区域的扩大与GDP呈正相关，而城市中心位移距离的增加表明城市多中心性的增强。

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引用次数: 0