1. Mapping Global GIS Scholar Mobility: Where Do They Come From and Where Do They Go? 1. GIS人才流动"地图"：全球GIS学者从哪来，到哪去？

Research Overview 研究概述

This study maps the global mobility of GIS scholars by tracking their career paths from doctoral institutions to current academic positions. Using data from GISphere Guide covering nearly 2,000 GIS faculty members across 96 countries and regions, the research analyzes 946 tenure-track GIScience faculty members to understand global academic talent flows and knowledge distribution patterns. 本研究通过追踪GIS学者从博士母校到现任教职的职业路径，绘制了全球GIS学者流动图谱。研究利用GISphere Guide平台的数据，覆盖96个国家与地区、近2000位GIS教师，分析了946位专注于GIScience研究、处于tenure-track（终身教职轨道）上的教师，以理解全球学术人才流动和知识分布模式。

Key Findings 主要发现

• The United States plays a dual role as both a major producer of PhD graduates and a major employer, with approximately 40% of global GIS faculty holding US doctorates.
• 美国在全球GIS人才流动中承担着"双重角色"——既是博士培养大户，又是教职吸纳中心，全球约40%的GIS教职人员拥有来自美国的博士学位。
• Local retention is the norm globally, with scholars often returning to their home regions for academic positions.
• "本地化留任"是全球GIS人才流动的常态，学者们往往最终回到与自己博士阶段同属的地理区域任教。
• GIS academic placement is more dispersed compared to other fields, with the top five PhD-granting institutions accounting for only 15% of global placements.
• 与计算机科学、经济学等高度集中的学科不同，GIS领域的教职安置更为分散，前五大博士培养高校仅占全球GIS教职安置总量的15%。
• Research themes have evolved significantly: from physical geography in the 1990s, to cartography and visualization in the 2000s, to urban intelligence and GeoAI in the 2010s.
• 研究主题随着学术代际发生了显著变化：90年代前以自然地理为主，00年前后地图制图与可视化快速发展，2010年后则明显转向城市智能、GeoAI等方向。

2. Choosing GIS Graduate Programs from Afar: Chinese Students' Perspectives 2. GIS海外研究生项目择校因素：中国学生的视角

Research Overview 研究概述

This study investigates the factors influencing Chinese students' decisions when applying to overseas GIS graduate programs. Based on the push-pull model and three-stage model from educational theory, the research conducted an online survey of 84 Chinese students applying to GIS programs abroad in 2022, analyzing factors including program quality, destination environment, and career prospects. 本研究调查了影响中国学生申请海外GIS研究生项目决策的因素。基于教育学中的推-拉模型和三阶段模型，研究对2022年申请海外GIS项目的84位中国学生进行了在线调查，分析了包括项目质量、目的地环境和职业前景等因素。

Key Findings 主要发现

• The most important factors in program selection are overall university ranking and faculty strength, followed by program discipline ranking, curriculum design, and future career development.
• 选择入学院校时最重要的因素是学校的整体排名和师资力量，其次是项目的学科排名、课程设计和未来职业发展。
• Students pay more attention to course design and research opportunities in program quality, as well as university and discipline rankings in program reputation.
• 在项目教育质量方面，学生更关注课程设计和研究机会；在项目声誉方面，更关注学校排名和学科排名。
• Destination environment factors vary across national, city, and institutional levels, with students particularly concerned about political stability, safety, and cost of living.
• 目的地环境因素在国家、城市和院校三个层次上有不同的关注点，学生特别关注政局稳定、治安水平和生活开销。
• Most applicants (75%) applied to master's programs, with 23.5% planning to pursue academic careers.
• 大多数申请者（75%）申请硕士项目，23.5%的申请者计划从事学术职业。
• GISphere's online resources, including the university guide website and WeChat groups, played a significant role in applicants' decision-making.
• GISphere的线上资源，包括院校指南网站和微信交流群，对申请者的决策起到了显著作用。

3. GISphere Knowledge Graph for Geography Education: Recommending Graduate Geographic Information System/Science Programs 3. GISphere 知识图谱：大语言模型助力地理学教育

Research Overview 研究概述

This paper introduces GISphere-KG, an AI-powered platform that combines knowledge graph (KG) technology with large language models (LLMs) to provide intelligent search and recommendation services for GIS graduate program applicants. The platform integrates data from over 600 GIS programs and 2,000 professors across 97 countries and regions, offering personalized guidance based on research interests and career goals. 本文介绍了GISphere-KG，一个基于人工智能的平台，结合知识图谱（KG）和大型语言模型（LLM）技术，为GIS研究生申请者提供智能搜索和推荐服务。该平台整合了来自97个国家和地区、超过600个GIS项目和2000名教授的数据，根据研究兴趣和职业目标提供个性化指导。

Key Features 核心功能

• Explicit Graph Search: Direct queries for professors' research interests, university locations, and related information.
• 显式图搜索：直接查询教授的研究兴趣、大学地理位置等相关信息。
• Implicit Graph Search: Semantic similarity-based matching of research interests and related professors.
• 隐式图搜索：基于语义相似度的研究兴趣匹配和相关教授发现。
• Complex Query Processing: Natural language understanding of complex student needs and intelligent recommendations.
• 复杂检索场景：自然语言理解复杂的学生需求，提供智能推荐。
• Semantic Similarity Calculation: Using embedding models to calculate similarity between research interests and match students with suitable professors.
• 语义相似度计算：使用嵌入模型计算研究兴趣之间的相似性，为学生匹配合适的教授。

Technical Innovation 技术创新

GISphere-KG addresses three main challenges: information overload (600+ programs and 2000+ professors), lack of personalized guidance, and difficulty understanding research field dynamics. The platform uses Neo4j for knowledge graph visualization and LLMs for natural language processing, enabling interactive dialogue and efficient information retrieval. GISphere-KG解决了三个主要挑战：信息过载（600多个项目和2000多名教授）、缺乏个性化指导，以及难以理解研究领域动态。该平台使用Neo4j进行知识图谱可视化，使用LLM进行自然语言处理，实现交互式对话和高效信息检索。