Computers Environment and Urban Systems最新文献

{"title":"Strategic bi-objective optimization for electric vehicle fleet replacement leveraging shared charging facilities","authors":"Shouzheng Pan ,&nbsp;Ran Wei ,&nbsp;Xiaoyue Cathy Liu ,&nbsp;Jeff Phillips ,&nbsp;Bei Wang","doi":"10.1016/j.compenvurbsys.2025.102353","DOIUrl":"10.1016/j.compenvurbsys.2025.102353","url":null,"abstract":"<div><div>Electrification of vehicle fleets has advanced significantly in recent years to achieve net-zero greenhouse gas (GHG) emissions. As a cost-effective strategy, shared charging facilities are increasingly used by public and private sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge other electric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizations considering transitions to electrified mobility. It is especially difficult when considering the variability in daily fleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimization model designed to strategically guide the replacement of vehicle fleets with EV. The model aligns the spatial-temporal dynamics of vehicle routes with the availability of shared charging facilities. It is particularly relevant for organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals of minimizing GHG emissions from fuel consumption and vehicle idling, and reducing operational delays (e.g. detour and charging time for the EV fleet). We applied this model to the University of Utah campus fleet, utilizing shared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies for replacing vehicles with varied operational characteristics, offering detailed plans and schedules that balance GHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assess the effects of different battery sizes, station disruptions, and traffic delays on the model's outcomes and a feasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation for fleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage shared charging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenance costs. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102353"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design","authors":"Lingyao Li ,&nbsp;Songhua Hu ,&nbsp;Yinpei Dai ,&nbsp;Min Deng ,&nbsp;Parisa Momeni ,&nbsp;Gabriel Laverghetta ,&nbsp;Lizhou Fan ,&nbsp;Zihui Ma ,&nbsp;Xi Wang ,&nbsp;Siyuan Ma ,&nbsp;Jay Ligatti ,&nbsp;Libby Hemphill","doi":"10.1016/j.compenvurbsys.2025.102329","DOIUrl":"10.1016/j.compenvurbsys.2025.102329","url":null,"abstract":"<div><div>As urban populations grow, the need for accessible urban design has become urgent. Traditional methods for assessing public perceptions of accessibility, such as surveys and interviews, are often resource-intensive and geographically limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models (LLMs) can facilitate their use. In this study, we examine over one million Google Maps reviews from points of interests (POIs) across the United States and fine-tune the Llama 3 model using the Low-Rank Adaptation (LoRA) technique to identify public sentiment toward accessibility. At the POI level, most categories – restaurants, retail, hotels, and healthcare – show negative sentiments, indicating persistent barriers across key sectors. Socio-spatial regression analysis reveals that more positive sentiment is associated with areas that have higher proportions of white residents and greater socioeconomic advantage. Conversely, more negative sentiment is expressed in areas with higher concentrations of elderly and highly-educated populations. Interestingly, no clear link is found between the presence of disabilities and public sentiments, but a significant positive relationship does exist between disability-friendly scores and public perception. Overall, our findings demonstrate the value of crowdsourcing with LLM-enhanced analysis in identifying accessibility challenges and informing inclusive urban design, offering actionable insights for planners, policymakers, and advocates striving toward more equitable cities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102329"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The association of subjective physical disorder and pedestrian volume: A big urban data and machine-learning approach","authors":"Fangqi Liu ,&nbsp;Yi Lu ,&nbsp;Qiwei Song ,&nbsp;Waishan Qiu ,&nbsp;Dongwei Liu","doi":"10.1016/j.compenvurbsys.2025.102348","DOIUrl":"10.1016/j.compenvurbsys.2025.102348","url":null,"abstract":"<div><div>Physical disorder in an urban area is characterized by visible damage, decay, and deterioration in its built environment, such as broken windows, graffiti, and litter. While its adverse effects on mental health, crime rates, and life satisfaction are well-documented, its impact on pedestrian volume–an essential indicator of urban vibrancy and livability–remains poorly discussed. Moreover, previous studies have predominantly relied on objective measures of physical disorder, overlooking subjective perceptions and potentially leading to biased interpretations. To address these crucial research gaps, we developed an online visual survey to evaluate the perceived physical disorder in Shanghai, China, across five dimensions: architectural disorder, commercial disorder, road disorder, greenery disorder, and infrastructure disorder. Then, we leveraged diverse machine learning algorithms to predict citywide spatial patterns of physical disorder based on both high-level street elements and low-level features. Finally, we examined the associations between urban physical disorder and pedestrian volumes, categorized by age and gender. Our findings reveal disparities in the influence of different types of subjective physical disorder on pedestrian volumes by demographic groups. Moreover, the subjective physical disorder provides a valuable supplement to existing built environment factors in explaining collective walking behavior. Notably, greenery disorder exhibits a significant negative association with walking behavior among female, adult, and elderly pedestrians, whereas infrastructure disorder predominantly impacts young pedestrians. Leveraging big data, this subjective measurement framework enables demographically sensitive evaluation systems of physical disorder as well as targeted interventions to reduce perceived physical disorder and improve walkability for different population groups.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102348"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperlocal heat stress around bus stops in Philadelphia: Insights from spatio-temporal microclimate modeling and explainable AI","authors":"Shengao Yi ,&nbsp;Xiaojiang Li ,&nbsp;Donghang Li ,&nbsp;Xinyu Dong ,&nbsp;Ruoyu Wang ,&nbsp;Qian Xu","doi":"10.1016/j.compenvurbsys.2025.102341","DOIUrl":"10.1016/j.compenvurbsys.2025.102341","url":null,"abstract":"<div><div>The Urban Heat Island (UHI) effect significantly impacts public transit users, particularly those waiting at bus stops, where prolonged exposure to extreme heat poses health risks. Despite increasing attention to climate resilience, limited research has focused on hyperlocal, pedestrian-level thermal stress at bus stops or its relationship with the surrounding urban environment. To address this gap, we generated hourly 1-meter resolution Universal Thermal Climate Index (UTCI) maps for Philadelphia using high-resolution, multi-source geospatial data and microclimate modeling, capturing detailed summer daytime spatio-temporal heat stress patterns around more than 8,000 bus stops. We further developed an explainable machine learning framework, combining Random Forest (RF) and SHAP analysis to uncover complex, nonlinear relationships and threshold effects between heat stress and both built environment and socioeconomic variables. Key findings include: (1) Significant spatio-temporal variation in heat stress, with consistently high levels at midday across the city; (2) Higher heat stress around bus stops located in low-income neighborhoods, while more affluent areas (e.g., higher median household value) exhibit reduced thermal exposure; (3) Green View Index (GVI) and Enclosure emerged as the most effective heat-mitigating features, and (4) complex threshold effects across key urban indicators highlight the importance of targeted and equitable interventions to reduce heat stress in vulnerable areas.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102341"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying street multi-activity potential (SMAP) and local networks with MLLMs and multi-view graph clustering","authors":"Jiatong Li ,&nbsp;Mingyi Ma ,&nbsp;Yuan Lai","doi":"10.1016/j.compenvurbsys.2025.102350","DOIUrl":"10.1016/j.compenvurbsys.2025.102350","url":null,"abstract":"<div><div>Streets are essential public spaces hosting a variety of social, cultural, and economic activities that collectively form urban vitality. However, due to limitations in research methodology and data, existing studies often oversimplify street activities by focusing solely on pedestrian flows. This study introduces a novel approach using Multimodal Large Language Models (MLLMs) and multi-view graph-based community detection to systematically evaluate street multi-activity potential (SMAP). Utilizing diverse urban data, we quantified the SMAP based on six common pedestrian activities (sitting, standing, walking, jogging, exercising, and street vending) in Beijing's central urban area. Results reveal significant spatial disparities in the suitability scores of different activity types, challenging the conventional reliance on walking as a proxy for street activities. By applying community detection algorithm with multi-view graph fusion and reinforcement learning, we identified 245 SMAP areas and uncovered their underlying spatial network patterns in Beijing. Assessment of SMAP areas' total potential and diversity of potential reveals the complex relationship between the two dimensions. By further identifying high total potential SMAP areas with varied levels of diversity, we discovered their distinct patterns in semantic features and spatial distributions. Overall, this study develops a novel and scalable framework for evaluating street spaces and observing their potential for diverse activities, which will guide future planning to support activity diversity and enhance urban vitality.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102350"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban visual uniqueness: A landmark-free framework to quantify city's identity and distinctiveness from everyday scenes","authors":"Song Guo ,&nbsp;Kee Moon Jang ,&nbsp;Fábio Duarte ,&nbsp;Yuhao Kang ,&nbsp;Carlo Ratti","doi":"10.1016/j.compenvurbsys.2025.102351","DOIUrl":"10.1016/j.compenvurbsys.2025.102351","url":null,"abstract":"<div><div>The visual appearance of a city is shaped by a complex interplay of factors, including cultural backgrounds, geographical features, historical developments, and policy decisions. But measuring cities' visual uniqueness remains a challenge. Previous studies often focused on iconic landmarks, neglecting everyday scenes that people are likely to encounter. By examining how and to what extent different visual patterns build up unique characteristics of cities, we propose a data-driven framework to measure visual uniqueness in terms of identity and distinctiveness. We performed bottom-up visual clustering on Google Street View (GSV) images in the six most visited Japanese cities. We found that 8 representative visual clusters explain each city's visual identity and relative distinctiveness. This research demonstrates how artificial intelligence applied to visual data can reveal subtle differences in urban environments. In the era of growing globalization, with frequent tourism and intercity visits, the cultivation of a city's unique visual characteristics can help avoid the homogenization of urban landscapes, and stimulate the development of urban tourism by shaping an imageable city.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102351"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ScaleFC: A scale-aware geographical flow clustering algorithm for heterogeneous origin-destination data","authors":"Huan Chen ,&nbsp;Zhipeng Gui ,&nbsp;Dehua Peng ,&nbsp;Yuhang Liu ,&nbsp;Yuncheng Ma ,&nbsp;Huayi Wu","doi":"10.1016/j.compenvurbsys.2025.102338","DOIUrl":"10.1016/j.compenvurbsys.2025.102338","url":null,"abstract":"<div><div>Exploring the cluster pattern of geographical flow facilitates the understanding of the underlying process of geographical phenomena among spatial locations. Despite recent advancements in identifying flow clusters, challenges remain when handling flow data with uneven length, heterogeneous density and weak connectivity. To solve the issues, this study proposes a Scale-aware Flow Clustering algorithm (ScaleFC). It identifies flow clusters of arbitrary lengths by employing an analytical scale to generate an adjustable neighborhood range of each flow. Meanwhile, inspired by the idea of boundary-seeking clustering, ScaleFC introduces partitioning flows to identify flow clusters with different densities, and separate the weakly-connected clusters. To validate the effectiveness, we compared ScaleFC with three mainstream baselines, i.e., AFC, FlowLF and FlowDBSCAN, on six synthetic datasets. The results presented that ScaleFC can accurately identify the clusters with complex structures, achieving an average accuracy improvement of 27 %, 17 %, and 15 % over the three competitors, respectively. The application on bike-sharing data with 16,140 flow pairs from Shanghai City demonstrated that ScaleFC is capable to capture both long-distance and short-distance movements, thereby providing a more comprehensive understanding to multi-scale human mobility patterns in geographical space.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102338"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI for urban planning: Synthesizing satellite imagery via diffusion models","authors":"Qingyi Wang ,&nbsp;Yuebing Liang ,&nbsp;Yunhan Zheng ,&nbsp;Kaiyuan Xu ,&nbsp;Jinhua Zhao ,&nbsp;Shenhao Wang","doi":"10.1016/j.compenvurbsys.2025.102339","DOIUrl":"10.1016/j.compenvurbsys.2025.102339","url":null,"abstract":"<div><div>Generative AI offers new opportunities for automating urban planning by producing site specific urban layouts and enabling flexible design exploration. However, existing approaches often struggle to produce realistic and practical designs at scale. Therefore, we adapt a state-of-the-art stable diffusion model, extended with ControlNet, to generate high-fidelity satellite imagery conditioned on land use descriptions, infrastructure, and natural environments. To overcome data availability limitations, we spatially link satellite imagery with structured land use and constraint information from OpenStreetMap. Using data from three major U.S. cities, we demonstrate that the proposed diffusion model generates realistic and diverse urban landscapes by varying land-use configurations, road networks, and water bodies, facilitating cross-city learning and design diversity. We also systematically evaluate the impacts of varying language prompts and control imagery on the quality of satellite imagery generation. Our model achieves high FID and KID scores and demonstrates robustness across diverse urban contexts. Qualitative assessments from urban planners and the general public show that generated images align closely with design descriptions and constraints, and are often preferred over real images. This work establishes a benchmark for controlled urban imagery generation and highlights the potential of generative AI as a tool for enhancing planning workflows and public engagement.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102339"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling shared e-micromobility as a label propagation process for detecting overlapping communities","authors":"Peng Luo ,&nbsp;Chengyu Song ,&nbsp;Hao Li ,&nbsp;Di Zhu ,&nbsp;Songhua Hu ,&nbsp;Fábio Duarte","doi":"10.1016/j.compenvurbsys.2025.102336","DOIUrl":"10.1016/j.compenvurbsys.2025.102336","url":null,"abstract":"<div><div>Shared micro-mobility such as electric scooters (e-scooters) has gained significant popularity in many cities. While many studies have analyzed the spatiotemporal patterns of shared micro-mobility using individual-level trip data, the spatial structure of e-scooter mobility networks and their socio-economic implications remain underexplored. Examining these mobility networks through the lens of network science — such as analyzing their community structures — can provide valuable insights for urban policy and planning. For example, allocating e-scooters at the overlapping locations of two communities may improve the operational efficiency of e-scooter distribution. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns.We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. Additionally, we discovered significant social segregation within the overlapping communities: areas belong to multiple communities tend to be wealthier with shorter commute times. Our results provide a potential explanation for the community structure in human mobility networks and may offer insights for urban planning and policymaking aimed at creating a more equitable and accessible mobility system.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"122 ","pages":"Article 102336"},"PeriodicalIF":8.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban planning in the age of large language models: Assessing OpenAI o1's performance and capabilities across 556 tasks","authors":"Xukai Zhao ,&nbsp;He Huang ,&nbsp;Tao Yang ,&nbsp;Yuxing Lu ,&nbsp;Lu Zhang ,&nbsp;Ruoyu Wang ,&nbsp;Zhengliang Liu ,&nbsp;Tianyang Zhong ,&nbsp;Tianming Liu","doi":"10.1016/j.compenvurbsys.2025.102332","DOIUrl":"10.1016/j.compenvurbsys.2025.102332","url":null,"abstract":"<div><div>Integrating Large Language Models (LLMs) into urban planning presents significant opportunities to enhance efficiency and support data-driven city development strategies. Despite their potential, the specific capabilities of LLMs within the urban planning context remain underexplored, and the field lacks standardized benchmarks for systematic evaluation. This study presents the first comprehensive evaluation focused on OpenAI o1's performance and capabilities in urban planning, systematically benchmarking it against GPT-3.5 and GPT-4o using an original open-source benchmark comprising 556 tasks across five critical categories: urban planning documentation, examinations, routine data analysis, AI algorithm support, and thesis writing. Through rigorous testing and manual analysis of 170,627 words of generated output, OpenAI o1 consistently outperformed its counterparts, achieving an average performance score of 84.08 % compared to 69.30 % for GPT-4o and 45.27 % for GPT-3.5. Our findings highlight o1's strengths in domain knowledge mastery, basic operational competence, and coding capabilities, demonstrating its potential applications in information retrieval, urban data analytics, planning decision support, educational assistance, and LLM-based agent development. However, significant limitations were identified, including inability in urban design, susceptibility to fabricating information, moderate academic writing quality, challenges in high-level professional examinations, and spatial reasoning, and limited support for specialized or emerging AI algorithms. Future optimizations should prioritize enhancing multimodal integration, implementing robust validation mechanisms, adopting adaptive learning strategies, and enabling domain-specific fine-tuning to meet urban planners' specialized needs. These advancements would enable LLMs to better support the evolving demands of urban planning, allowing professionals to focus more on strategic decision-making and the creative aspects of the field.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"121 ","pages":"Article 102332"},"PeriodicalIF":8.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}