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    Geological Informatization
  • Geological Informatization
    Modernization construction of geological survey in the era of big data
    WANG Bin, LI Jingchao, SHI Junfa, SONG Guoxi, GAO, Zhenji
    2025, 32(03): 223-230. https://doi.org/10.20117/j.jsti.202503006
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    [Objective] In today's rapidly evolving world, emerging information technologies such as big data, artificial intelligence, the Internet, blockchain, and remote sensing are driving significant changes in current work methodologies and shaping a new paradigm for earth science research. Big data, serving as both a foundational infrastructure and a novel component, offers fresh opportunities and technical support for modernizing geological surveys. Precision monitoring, accurate forecasting, and refined services have become essential requirements for achieving high-quality development in geological survey work. The United States Geological Survey and other advanced countries' geological survey institutions are vigorously pursuing the informatization, digitalization, and intelligent transformation of their operations. These efforts have yielded substantial progress, with many having essentially achieved geological survey modernization.
    [Method] Information technology stands as a pivotal force behind the transformation and evolution of geological survey endeavors. The integration of contemporary information technologies is revolutionizing traditional geological survey models, markedly enhancing efficiency, capacity, and the overall level of geological survey work. This paper commences by defining the fundamental essence of geological survey modernization, encompassing the establishment of a three-dimensional survey monitoring and observation system, an analysis, prediction, and evaluation framework, an information service system, business informatization support systems, a geological science and technology innovation ecosystem, and a comprehensive geological survey management structure. It further delineates the trajectory and orientation of geological survey modernization, aiming at digitization of surveys, automation of monitoring, quantification of predictions, intelligence in evaluations, and wisdom in services.
    [Result] Drawing upon the meteorology sector as a representative example, which heavily relies on earth observation technologies, the paper outlines strategies for advancing meteorology modernization through satellite remote sensing and digital analysis simulation techniques. These include automating data collection, enhancing the sophistication of numerical forecasting, centralizing information resource management, refining social services, and standardizing operational procedures. Lastly, grounded in the practical context of geological surveys, the paper proposes strategies and recommendations for China's geological survey modernization. These encompass building infrastructural foundations for geological surveying and monitoring, developing business informatization capabilities, expanding geological information products, establishing standards for geological informatization, and conducting research into basic theories and technological equipment within the realm of informatization.
    [Conclusion] Looking ahead, automation, informatization, and intelligence will characterize the new era of geological survey work. Consequently, accelerating the construction of geological survey modernization is not only imperative but also urgently needed.
  • Geological Informatization
    Unified data model for geological survey management based on metadata and semantic modeling
    WEN Min, YUE Yi, LIU Rongmei, REN Wei, ZHANG Huaidong, WANG Xianghong, SHI Yan, ZHAO Mingming, YU Hailong
    2025, 32(03): 231-244. https://doi.org/10.20117/j.jsti.202503007
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    [Objective] The rapid proliferation of information technologies has engendered unprecedented volumes of heterogeneous data across disparate systems and sensors. A primary challenge in multi-source data integration lies in reconciling divergent data models and organizational frameworks. This issue is particularly acute in geological survey domains, where massive, spatiotemporally correlated datasets are managed across fragmented systems with heterogeneous models and standards. Such structural and semantic discrepancies hinder data management efficiency, exacerbating redundancy, inconsistency, and dispersion.
    [Method] We propose a metadata-driven, semantic modeling approach to construct a unified data model for geological survey business management. The methodology comprises five sequential stages: (1) requirements analysis and data organization, (2) metadata extraction,(3) domain semantic analysis, (4) hierarchical model construction, and (5) iterative evaluation and refinement.
    [Result] To address challenges associated with multi-source data, inconsistent standards, semantic ambiguity, and insufficient correlation in geological survey business management systems, the proposed methodology systematically organizes data sources, extracts and analyzes metadata from relevant systems, and performs semantic integration. This process yields a standardized data framework comprising unified entities, attributes, and relational structures. By leveraging geospatial coordinates and master reference data, a hierarchical organizational model is developed to harmonize heterogeneous datasets, enabling consistent data description and domain-specific abstraction. Bidirectional mapping protocols and synchronization mechanisms are established between the unified model and disparate sources. The resulting conceptual, logical, and physical data models have been validated through implementation in a geological survey business management data center.
    [Conclusion] The proposed approach successfully constructs a unified data model for geological survey business management, integrating data from 20 heterogeneous sources. This model enables centralized data description and holistic, one-stop management services, resolving issues of standard inconsistency, semantic ambiguity, and multi-source data-induced redundancy. By facilitating interoperability, shared analytics, and advanced applications, the framework supports optimized workflows and evidence-based decision-making. This work provides a scalable technical solution to reconcile structural and semantic discrepancies in multi-source heterogeneous data, including spatiotemporal datasets.
  • Geological Informatization
    Design and implementation of digital surface substrate survey system
    LIU Yuanyuan, LI Fengdan, ZHANG Jinlong, LIU Chang, LYU, Xia
    2025, 32(03): 245-256. https://doi.org/10.20117/j.jsti.202503005
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    [Objective] Ground substrate surveying, as an emerging field in the survey and monitoring of natural resources, has seen extensive exploratory research and demonstration efforts by numerous domestic experts and scholars in recent years. These endeavors have focused on the classification and surveying of ground substrates. Consequently, a preliminary technical system for ground substrate survey methods has begun to form. Simultaneously, advancements in information technology related to ground substrate surveys have progressed in tandem. Research and exploration by multiple teams have paved the way for the informatization of ground substrate surveys. However, there is currently a lack of an integrated information system to support the digitization and standardization of the entire investigation process. There is an urgent need to establish a digital software system to achieve the digitization of the entire business process and the full lifecycle management of business data. This would support the creation of a unified foundation and a single set of data for surface substrate investigations.
    [Method] To address this issue, this paper designs and develops a digital surface substrate investigation system, which includes the following aspects: (1) Based on the analysis of data content and characteristics during the ground substrate survey process, and considering the features of current mature databases, an integrated hybrid database storage model was developed. This model combines “cloud storage + PostgreSQL relational database + MinIO object storage + MongoDB database.” (2) Centered on the concept of “one cloud, one database, one platform, three application terminals, two integrations, and two safeguards,” a five-layer architecture was adopted to construct the overall technical framework of the ground substrate survey system. (3) Drawing from the technical route of “indoor research, field survey, database modeling, and platform services” for ground substrate layer surveys, the digital workflow and system functional composition for ground substrate surveys were designed. (4) Key technologies were developed, such as online services for public base map data and big data storage based on hybrid databases. (5) The digital ground substrate survey system was developed. This system includes: A mobile subsystem for digital ground substrate survey field data collection; A desktop subsystem for digital ground substrate survey data editing and mapping; and A web subsystem for digital ground substrate survey data management and services.
    [Result] The digital ground substrate survey system effectively supports the main process informatization of ground substrate surveying, including project management, data collection, data editing, data aggregation, mapping, database construction, and services. It enhances the efficiency of base map data services, field data collection, and data editing and mapping, achieving unified storage and management of data throughout the entire workflow.
    [Conclusion] As compliance software for ground substrate survey standards, it lays the foundation for the standardization of data and the unified construction of a national ground substrate survey database.
  • Geological Informatization
    Key technologies and applications of multi-modal data collaborative cloud services for geological surveys in difficult terrains
    LI Fengdan, Lyu Xia, TAO Liufeng, WEN Xingping, GAO Bo, LIU Yuanyuan, LIU, Chang
    2025, 32(03): 257-265. https://doi.org/10.20117/j.jsti.202503004
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    [Objective] Conducting geological surveys in challenging and hazardous terrains, particularly in remote plateau regions, poses significant difficulties when integrating “remote sensing + geology” multi-modal data. Given these complexities, this article focuses on innovating the storage, representation, intelligent extraction of geological remote sensing information, and automated services for geological survey data, leveraging advanced technologies like cloud computing, big data, and artificial intelligence. These innovations collectively establish a collaborative cloud service technical framework for multimodal geological survey data, catering to the demand for intelligent high-resolution data services and integrated multi-source data in difficult and dangerous geological survey areas.
    [Method] (1) To address the challenge of unified expression and storage for multimodal geological survey data, we propose a novel, multi-dimensional, and multidisciplinary “remote sensing + geology” data storage model. This model constructs a centralized database that harmonizes data across various levels and scales throughout its lifecycle, allowing dynamic updates. It facilitates comprehensive, area-wide, and element-wise data support tailored to the arduous conditions of geological surveys. (2) In response to inefficiencies in image data utilization and service distribution, we introduce an automatic extraction technique for linear features from high-resolution remote sensing images, combining deep learning with wavelet transform technology. Additionally, we present a service distribution strategy for geological survey image base maps optimized for multi-platform operation. This dual approach automates linear feature extraction from intricate images and enhances the distribution efficiency of large-scale, multi-type, high-precision image base maps.(3) Addressing shortcomings in the existing data storage model, our proposal entails developing a unified database encompassing multiple hierarchical levels, scales, lifecycle stages, with continuous updates. This ensures coherent, full-coverage data management crucial for geological surveys in demanding environments. (4) To overcome low automation levels and the inability to promptly meet field survey demands, we have designed a framework centered around field geological survey location sensing, knowledge discovery, and proactive knowledge services. This framework propels the dissemination of geological survey information in challenging territories, resolving key issues related to timeliness, precision, and comprehensiveness of such services. Collectively, these technological advancements constitute a collaborative cloud service ecosystem for multimodal geological survey data, forming an “Intelligent Spatial Platform for Geological Surveys in Difficult and Dangerous Areas” underpinned by a “cloud + terminal” service paradigm.
    [Result] The research findings have been successfully implemented and validated across over 400 regional geological and mineral surveys, including Quaternary geology and ophiolite belt surveys in formidable regions like Qinghai-Tibet.
    [Conclusion] The outcomes of this research effectively fulfill the operational needs of geological surveys in difficult terrains, bolstering the enhancement of geological surveying capabilities.
  • Geological Informatization
    A study on text extraction and graphic retrieval from raster geological maps based on textual elements
    ZHANG Xingyi, ZHANG Yaxin, CHEN Lu, XU Shiguang, WANG Xinrui, ZHENG Kun, ZHAO Fei
    2025, 32(03): 266-275. https://doi.org/10.20117/j.jsti.202503010
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    [Objective] Raster geological maps constitute vital data resources in geological research and mineral exploration. However, these maps are often stored in non-standardized, unstructured raster formats, posing significant challenges for large-scale data retrieval, integration, and intelligent utilization. Traditional geological data management systems frequently struggle with fragmented storage, inefficient querying, weak inter-data relationships, and inadequate support for semantic-level searches. These limitations hinder the comprehensive exploitation of geological information. To address these challenges, this study explores large-scale textual element extraction from raster geological maps and proposes a novel map-text retrieval framework to enhance semantic accessibility and intelligent processing of unstructured geological data.
    [Method] This study proposes a method for text extraction and map-text retrieval from raster geological maps. A distributed architecture leveraging HBase and the Hadoop distributed file system (HDFS) is constructed to efficiently manage large-scale unstructured raster geological maps and associated documents. For text extraction from geological maps, a deep learning-based optical character reader (OCR) pipeline is implemented, combining a differentiable binarization network (DBNet) for text region detection with a convolutional recurrent neural network (CRNN) for sequence-based text recognition. This approach substantially improves text detection and recognition accuracy under complex map backgrounds. In processing geological reports, the term frequency-inverse document frequency (TF-IDF) algorithm is employed for semantic similarity analysis, establishing meaningful associations between map elements and document content. Building on this, a sequence labeling model integrating bidirectional encoder representations from transformers (BERT) and conditional random field (CRF) is utilized to automatically extract geological entities and domain-specific keywords. Additionally, a full-text retrieval module based on the Apache Solr search engine is incorporated, enabling high-efficiency, semantic-aware retrieval of geological documents.
    [Result] The experimental results indicate that the proposed method substantially enhances the usability of textual information and the efficiency of keyword extraction in real-world applications. The proportion of usable text increased from 51.7% to 82.3%, reflecting a marked improvement in the accuracy and completeness of text extraction. Furthermore, the efficiency of keyword extraction achieved a 426% improvement compared to the TextRank algorithm. The proposed framework demonstrates strong scalability and adaptability, enabling efficient processing of large-scale geological datasets, supporting real-time storage and rapid retrieval across datasets of various sizes, and significantly advancing the utilization efficiency of unstructured geological data.
    [Conclusion] This study proposes a method for text extraction and map-text retrieval tailored to unstructured geological data, integrating distributed big data infrastructure, deep learning-based OCR technologies, and advanced semantic extraction models. The proposed method significantly strengthens the connection between raster geological maps and textual geological knowledge, thereby improving data utilization efficiency, research productivity, and geological decision-making. Looking ahead, future work will focus on incorporating cutting-edge Transformer-based architectures, constructing domain-specific geological knowledge graphs, and enhancing user interaction via intuitive interfaces. This integrated approach introduces a novel technological paradigm for geological data processing and knowledge discovery, with potential applications in scientific research, natural resource management, and digital geoscience services.
  • Geological Informatization
    Large-scale landslide monitoring in Qinzhou district based on SBAS-InSAR
    WANG Hongling, HU Xiangxiang, SHI Yaya, WU Chengyong
    2025, 32(03): 276-287. https://doi.org/10.20117/j.jsti.202503011
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    [Objective] Landslides represent a significant geological hazard in mountainous areas of western China. Particularly, the Qinzhou district of Tianshui city, Gansu province—characterized by steep terrain, complex geological structures, and uneven precipitation distribution—experiences frequent landslides that pose severe risks to ecological stability and infrastructure safety. However, limited long-term, high-precision landslide monitoring data and insufficient analysis of multifactorial triggers hinder effective risk management. This study aims to identify the spatiotemporal evolution characteristics of large-scale landslides in this region and elucidate the dominant environmental drivers and their interactions.
    [Method] A total of 50 Sentinel-1A descending orbit SAR images, acquired between June 2021 and June 2024, were processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to derive surface deformation time series. Seventeen representative large-scale landslides were identified based on deformation features. Subsequently, the Geographical Detector model was employed to quantify the influence of eight environmental variables—elevation, slope, relief amplitude, aspect, precipitation, humidity, seismic activity, and anthropogenic disturbance—on landslide distribution. The spatial explanatory power (q-values) of each factor and their pairwise interactions were systematically analyzed.
    [Result] Surface deformation rates in Qinzhou district exhibited significant spatial heterogeneity, ranging from -5.08 mm/a to 13.7 mm/a. Landslides were predominantly concentrated in zones with moderate elevation (1300-1750 m), moderate slope gradients (10°-15°), and annual precipitation between 535-550 mm. Landslides were further classified into three types: high-speed active, moderate-to-low-speed, and stable. Elevation (q = 0.374), precipitation (q = 0.252), and soil moisture (q = 0.216) emerged as the most influential single factors. Notably, multifactor interactions demonstrated strong nonlinear enhancement effects, such as precipitation interacting with topographic relief (q = 1) and elevation interacting with human activity (q = 0.91), substantially improving explanatory power for landslide distribution.
    [Conclusion] This study reveals that landslide occurrence in Qinzhou district is governed by the interplay of multiple environmental factors, exhibiting distinct spatial clustering, threshold responses, and nonlinear coupling effects. The integrated approach combining SBAS-InSAR monitoring and the Geographical Detector framework provides a robust methodology for capturing spatiotemporal dynamics and driving mechanisms of landslides. These findings offer scientific guidance for early warning systems, spatial planning, and ecological risk mitigation in mountainous regions with similar geological and climatic conditions.
    • Spatio-temporal Perception
  • Spatio-temporal Perception
    Dynamic monitoring of water area changes in lake Victoria based on SAR imagery
    ZHU Yutian, XU Jia, GE Ying, WANG Hongyan, ZHAO Bingkun
    2025, 32(03): 288-298. https://doi.org/10.20117/j.jsti.202503008
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    [Objective] Lake Victoria, the largest freshwater lake in Africa, plays a critical role in regional water resource management, flood preparedness, and ecological conservation across the Nile River Basin. However, conventional methods relying on optical remote sensing are severely limited by persistent cloud cover and heavy rainfall in the tropical climate, resulting in fragmented temporal observations long-term monitoring consistency. These atmospheric conditions often lead to discontinuities in temporal observations, limiting the ability to conduct consistent and reliable long-term monitoring. Therefore, there is an urgent need for a more stable, weather-independent, and temporally consistent method for large-scale inland water body mapping.
    [Method] We advanced water body extraction techniques by exploiting the all-weather imaging capability of Sentinel-1 SAR datasets. A rigorous comparative analysis identified an optimal SAR-based water index, which was coupled with an improved Edge-Otsu algorithm to replace conventional methods that rely on manually set initial thresholds. This innovation enables adaptive, automated water body segmentation, minimizing subjectivity and enhancing methodological reproducibility. The framework was applied to generate a 10-meter resolution monthly water surface area dataset for lake Victoria from 2017 to 2023. This dataset facilitated a comprehensive analysis of the spatiotemporal dynamics of lake Victoria.
    [Result] The proposed method demonstrated exceptional stability and automation across multi-temporal SAR imagery, achieving an overall water extraction accuracy of 98.9%. Compared to global products such as the JRC Global Surface Water (GSW) dataset (R = 0.1) and Dynamic World (R = 0.29), our dataset exhibited a significantly higher correlation of 0.76 with in situ water level records, reflecting superior temporal consistency and reliability. Notably, the algorithm captured dynamic water boundary fluctuations without manual intervention, outperforming traditional threshold-dependent approaches.
    [Conclusion] The high-resolution dataset revealed distinct temporal and spatial patterns in lake Victoria's surface area. From 2017 to 2022, the lake exhibited a gradual expansion trend, followed by a minor contraction in 2023. More specifically, water surface area increased rapidly during the long rainy season (March to May), typically peaking in June, then gradually receding through the dry season (July to September). Spatial heterogeneity was most evident in the northeastern and southern basins, particularly in Kenya's Winam Gulf and Tanzania's Mwanza Gulf. This study provides comprehensive and long-term monitoring of the dynamic changes in lake Victoria's water surface area, offering a solid scientific basis for transboundary water resource management. It plays a vital role in supporting ecological conservation and promoting regional sustainable development. Furthermore, the SAR-based methodology pioneered here is transferable to other tropical regions, supporting hydrological modeling, climate adaptation strategies, and cross-border environmental management initiatives.
  • Spatio-temporal Perception
    An intelligent extraction method of feature points in map registration
    WEI Yuanbiao, REN Fu, DU Qingyun
    2025, 32(03): 299-306. https://doi.org/10.20117/j.jsti.202503009
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    [Objective] Mathematical foundations are integral to maps, enabling users to precisely interpret spatial relationships, feature positions, and geometric configurations. Restoring these foundations is critical for maps lacking coordinate information, particularly historical or digitized scans. Map registration serves as the primary means to align map images with standard geographic coordinates, facilitating their integration into geospatial analyses. However, existing registration methods prioritize image feature extraction over leveraging inherently associated geographic coordinates, compromising accuracy and robustness. This limitation is pronounced for maps with complex projections, variable scales, divergent symbology, or significant imaging distortions. Consequently, there is a pressing need for an adaptable, automated approach that harnesses semantic feature points with embedded geographic coordinates to restore cartographic mathematical frameworks.
    [Method] This study introduces a map registration framework combining deep learning-based semantic keypoint detection with a cubic transformation model. A YOLOv8-pose architecture is trained on annotated data to efficiently identify visually discernible semantic keypoints while preserving their geographic coordinates. These paired image-geographic coordinates are then input into a weighted least squares algorithm to derive cubic transformation parameters, effectively modeling the spatial-to-geographic transformation. This process automates the recovery of mathematical foundations for unreferenced maps, minimizing manual intervention and enhancing resilience across diverse cartographic conditions.
    [Result]Experiments validated the method's performance on six maps with varying projections, scales, symbology, and imaging artifacts (rotation, perspective distortion, overexposure, texture interference). The approach achieved over 90% precision and recall in semantic keypoint matching, demonstrating strong adaptability to challenging scenarios. By reconciling recovered mathematical frameworks with standard geographic data, the method successfully integrated unreferenced maps into hybrid geospatial datasets, vector, and raster formats.
    [Conclusion] By integrating semantic features with geographic coordinates within a deep learning paradigm, this study achieves efficient, accurate, and robust restoration of map mathematical foundations. The proposed method addresses limitations of traditional approaches, such as rigid transformation models, heavy manual reliance, and poor generalizability. This work advances applications in historical cartography, thematic mapping, geospatial data fusion, and semantic geographic space analysis.
  • Spatio-temporal Perception
    Mapping public responses to natural disasters in China across text mining
    QU Zheng, WANG Juanle, ZHAO Jie
    2025, 32(03): 307-318. https://doi.org/10.20117/j.jsti.202503001
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    [Objective] An increasing number of the public tend to share information via social media posts during disaster events. This is significant for supporting disaster risk reduction decision-making by capturing timely public response information. This study proposes a method for generating public disaster response maps through mining social media text data, demonstrating its application by constructing such maps for major earthquakes, typhoons, and cold waves in China using Weibo data.
    [Method] Weibo text data related to earthquakes, typhoons, and cold waves (2018 - 2022) were collected via the Sina Weibo API and subjected to preprocessing steps, including deduplication, filtering, and word segmentation. Based on administrative division data, a comprehensive gazetteer containing national township-level (street) administrative names was developed. By integrating Python's requests and pandas libraries with jieba word segmentation technology, geographic entities in Weibo texts were precisely identified and extracted. For different disaster types, keyword frequency statistics were conducted, followed by spatial analysis.
    [Result] The results reveal that: (1) Earthquake responses clustered in Sichuan province, reflecting its status as a seismically active region;(2) Typhoon-affected areas predominantly included coastal provinces (Guangdong, Zhejiang, Fujian, Shandong), with Guangdong exhibiting the highest impact proportion, consistent with typhoon track distributions; (3) Cold waves howed higher prevalence in central/southern China (Jan-Mar) and primarily affected northern regions (Inner Mongolia, Beijing) during Nov-Dec. These findings align well with statistical validation, confirming methodological reliability. The generated maps, visualize spatiotemporal distribution patterns and public response hotspots for different disaster types.
    [Conclusion] This approach effectively reveals spatial-temporal distribution patterns of public responses to various disasters across large regions. It provides decision-making support for precise disaster prevention and mitigation, addressing spatial information gaps in traditional surveys. Despite challenges from data bias and quality inconsistencies in social media, the methodology demonstrates significant potential for disaster monitoring. Future work should focus on: (1) Refining geolocation extraction algorithms; (2) Enhancing emotional information analysis; (3) Integrating multi-source data to build a comprehensive disaster monitoring system.
    • Spatio-temporal Cognition
  • Spatio-temporal Cognition
    Exploring surface water resource distribution in Egypt based on land surface temperature inversion
    LUO Jingqiu, FENG Li, GE Ying, WANG Hongyan, LI Yong
    2025, 32(03): 319-329. https://doi.org/10.20117/j.jsti.202503002
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    [Objective] Land surface temperature(LST) variations significantly affect surface water resource distribution, particularly in heavily dependent on these resources. Egypt, located in a tropical desert climate, is highly dependent on its surface water resources, primarily supplied by the Nile river. Therefore, accurate surface temperature inversion is crucial for formulating long-term water resource response strategies in the country. This study aims to: (1) investigate the impact of LST on surface water distribution in Egypt, (2) enhance single-channel algorithm accuracy for LST inversion, and (3) establish an inversely proportional functional model linking LST to surface water distribution.
    [Method] Due to limited temperature measurement data in Egypt, this study employed a combination of variable normalized difference vegetation index (NDVI) thresholds and high-precision atmospheric water vapor content data to calculate a high-precision surface temperature dataset for Egypt using the Google Earth Engine (GEE) platform. By creating buffer zones at varying distances from the Nile River, quantitative characteristics of LST in riverine and surrounding areas were analyzed. A mathematical model was developed by assigning weighted coefficients to water resource distribution patterns, enabling analysis of the relationship between LST and surface water dynamics in Egypt.
    [Result] LST inversion results, validated against Landsat 8 temperature products, achieved root mean square errors (RMSE) below 1.0 for two study areas—exceeding World Meteorological Organization (WMO) accuracy standards—and below 1.5 for the third. High linear fit accuracy was observed between inverted LST and meteorological station measurements (R2 = 0.828), closely matching historical long-term correlation (R2 = 0.849). Analysis revealed: (1) In water-abundant regions, LST reduction effects diminished rapidly with distance from water bodies; (2) In sparse, braided water distribution areas, LST effects decreased gradually. The inversely weighted proportional function model demonstrated strong correlation (R2 = 0.96) in regions with significant surface water resources.
    [Conclusion] This research validates the efficacy of variable NDVI thresholds and atmospheric water vapor data for LST inversion in tropical desert regions. The inversely weighted proportional model provides a quantitative framework for understanding LST-water resource interactions in Egypt, offering critical insights for water management in arid regions. The model is broadly applicable to mid-low-latitude deserts facing water scarcity. Future work will refine NDVI threshold optimization (e.g., via genetic algorithms), integrate higher-resolution LST data, advance surface water distribution modeling, and employ machine learning to enhance model precision.
  • Spatio-temporal Cognition
    Structural characteristics and evolution of population flow networks in the Chengdu-Chongqing economic circle
    CAO Weiwei, CHEN Xiaohan, CHU Mengtao, JING Chongyi
    2025, 32(03): 330-340. https://doi.org/10.20117/j.jsti.202503003
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    [Objective] Population movement reflects the complex interplay between human activities and geographical dynamics, facilitating the spatial diffusion and concentration of resources, capital, and technology. To deepen the understanding of its characteristics, patterns, and development trends, this study analyzes the structural features and evolutionary dynamics of population flow networks.
    [Method] This research utilizes Amap migration big data to examine the spatiotemporal patterns and structural evolution of the population flow network within the Chengdu-Chongqing economic circle over the past five years. By integrating complex network analysis and GIS methods, the study provides a comprehensive investigation.
    [Result] The results show that population inflows and outflows in most cities within the Chengdu-Chongqing Economic Circle have generally increased over the past five years, though fluctuations with periodic patterns persist. Chengdu, Chongqing, and Leshan exhibit a net daily population inflow pattern, contrasting with the other thirteen cities. A clear weekly rhythm characterizes population flows: in Chengdu and Chongqing, inflow peaks occur on Sundays and outflow peaks on Saturdays, while in the remaining fourteen cities, inflow peaks fall on Saturdays and outflow peaks on Sundays. Rather than forming a typical “Chengdu-Chongqing” dual-center structure, the population flow evolves into a single-hub network centered on Chengdu. The flow network demonstrates distinct spatial proximity and hierarchy, with larger flows primarily occurring between Chengdu-Chongqing and their satellite cities. Over five years, inter-tier-one-city flows (Chengdu-Chongqing) exhibit an increasingly polarized trend, accounting for 48%, 45%, 49%, 51%, and 56% of the total annually. Smaller flows dominate interactions among non-core cities. Three cohesive subgroups emerge: a western cluster around Chengdu, an eastern cluster around Chongqing, and a southern Sichuan cluster, collectively representing around 70% of intercity mobility. Additionally, population flow networks derived from Amap data versus railway data reveal significant spatial discrepancies, with Amap-based networks highlighting stronger hub-and-spoke dynamics in core cities.
    [Conclusion] Through analyzing five-year Amap migration datasets, this study systematically elucidated the spatiotemporal dynamics, distribution, network structure, and evolutionary trends of population flows in the Chengdu-Chongqing economic circle. The findings enhance regional population flow theory, offering insights for regional planning and governance. Additionally, the comparison of Amap and railway data challenges single-source research paradigms, providing a methodological reference for future studies.