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SSRN 2025年

作者： Chen, Shanshan Chen, Yijia Gao, Song Li, Chun Li, Ninglv Chen, Liding School of Ecology and Environmental Science Yunnan University Kunming650091 China Southwest United Graduate School Kunming650092 China Geospatial Data Science Lab Department of Geography University of Wisconsin–Madison MadisonWI53706 United States State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China

Plastic greenhouse (PG), as a new type of modern agricultural measure, has been used widely due to its significant benefits for agricultural production. However, it also raises concerns about its potential environmental impact. Timely monitoring of PG is necessary for the agricultural sustainability. However, extracting PGs in fragmented terrains based on remote sensing images is difficult due to the variety of types of PGs and high environmental heterogeneity. In this study, a modified plastic greenhouse index (MPGI) was proposed to monitor PG based on the differences on spectral signatures using Landsat-8 Operational Land Imager. Four study sites, including Weifang (China), Nantong (China), Kunming (China), and Dalat (Vietnam), were selected for index applications. And the effectiveness and robustness of the MPGI were examined by comparing with the exiting PG indices. The results indicated that MPGI improved extraction accuracy in fragmented terrains. The F1 score was 85.7%-87.9% in four sites. The MPGI demonstrated its capability across various season and datasets, highlighting it had the potential for the PGs mapping in heterogeneous regions. This index provides solutions of farm management and supports the development of smart agriculture. © 2025, The Authors. All rights reserved.

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ChatGPT for Intelligent Spatial Analysis Workflow Construction 24

ChatGPT for Intelligent Spatial Analysis Workflow Constructi...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Nie, Ying Gao, Song Geospatial Data Science Lab University of Wisconsin MadisonWI United States

ISBN: (纸本)9798400711077

The capability of constructing executable scientific workflows that integrate data, models, and domain-specific knowledge using AI tools remains quite limited. This study utilizes large language model (LLM)-powered chatbots such as ChatGPT to streamline the process of automating spatial analysis workflow construction. By exploring prompt engineering techniques in ChatGPT, we aim to generate scientific workflows that address spatial analysis problems using ArcGIS geoprocessing tools. We identify key challenges and propose strategies involving geo-knowledge and structured prompts to enhance GIS workflow generation. Our preliminary results show that the accuracy and effectiveness of ChatGPT-generated GIS workflow are improved by adopting these strategies. © 2024 Copyright held by the owner/author(s).

关键词： GeoAI

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Automating geospatial Analysis Workflows Using ChatGPT-4 24

Automating Geospatial Analysis Workflows Using ChatGPT-4

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Zhang, Qianheng Gao, Song Geospatial Data Science Lab University of Wisconsin MadisonWI United States

ISBN: (纸本)9798400711077

The field of geospatial Artificial Intelligence (GeoAI) has significantly impacted domain applications such as urban analytics, environmental monitoring, and disaster management. While powerful geoprocessing tools in geographic information systems (GIS) like ArcGIS Pro are available, automating these workflows with Python scripting using AI chatbots remains a challenge, especially for non-expert users. This study investigates whether ChatGPT-4 can automate GIS workflows by generating ArcPy functions based on structured instructions. We tested prompt engineering’s ability on helping large language models (LLMs) understand spatial data and GIS workflows. The overall task success rate reaches 80.5%. It is a valid and easy to implement approach for domain scientists who want to use ArcPy to automate their workflows. © 2024 Copyright held by the owner/author(s).

关键词： Disaster prevention

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A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin–destination flows prediction

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International Journal of Applied Earth Observation and Geoinformation 2025年 136卷

作者： Zhao, Yibo Cheng, Shifen Gao, Song Lu, Feng State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison Madison 53706 WI United States The Academy of Digital China Fuzhou University Fuzhou 350002 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China

Accurately predicting truck origin–destination (OD) flows is essential for optimizing logistics systems and promoting coordinated regional development. Existing methods typically assume a monotonic decrease in truck OD flows with increasing geospatial distance, which oversimplifies the complex non-monotonic distribution patterns observed in practice. Moreover, these methods overlook interregional socioeconomic distances and their interaction with geospatial distances, thereby limiting the prediction accuracy and reliability. This study introduces a gravity-inspired model that integrates both geospatial and socioeconomic distances (GSD-DG) to explicitly represent their combined influence on truck OD flows. Specifically, we 1) develop a geospatial distance relation graph using the Weibull function to model the complex spatial distribution patterns of truck OD flows with varying geospatial distances;2) propose a gravity-inspired representation learning method based on graph attention mechanism to quantify the influence of socioeconomic distance on truck OD flows;and 3) construct a deep gravity model that integrates these distances and their interactions to capture their non-linear relationship with truck OD flows. Extensive experiments on four datasets with varying spatial scale and economic development levels demonstrate that the GSD-DG model improves the robustness and prediction accuracy across diverse spatial distribution patterns, reducing RMSE by 14.2%–85.8% and MSE by 23.5%–92.5% compared to the six baseline models. Incorporating socioeconomic distance and its interaction with geospatial distance further reduces RMSE by 8.5%–36.0%. Additionally, explainable artificial intelligence techniques highlight how these distances affect truck OD flows, providing valuable policy insights for logistics planning and coordinated regional development. © 2024 The Authors

关键词： geospatial distance Gravity-inspired model Heavy trucks Origin–destination flows Socioeconomic distance Spatial interaction

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Measuring Access Inequality in A Hybrid Physical-Virtual World: : A Case Study of Racial Disparity of Healthcare Access During CoVID-19 30

Measuring Access Inequality in A Hybrid Physical-Virtual Wor...

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30th International Conference on Geoinformatics, Geoinformatics 2023

作者： Wu, Meiliu Huang, Qunying Gao, Song University of Wisconsin-Madison Spatial Computing and Data Mining Lab Department of Geography MadisonWI United States University of Wisconsin-Madison Geospatial Data Science Lab Department of Geography MadisonWI United States

ISBN: (纸本)9798350344240

The disparity of resource access (e.g., food and healthcare) among different population groups essentially reflects social inequalities. Emerging information and communications technology (ICT) has facilitated the teleactivities that can replace or complement traditional physical visits, yet existing approaches for measuring access disparity still fail to consider virtual interactions. To this end, this study proposes a unified framework to measure access inequality in both physical and virtual spaces simultaneously, using the POIs' visit patterns from mobile phone data to capture spatial unevenness of accessibility among different groups (physical space), as well as the Household Pulse Survey data to reveal the group disparity of teleactivity access (virtual space). In particular, a novel Access Inequity Index is proposed based on the Information Theory Index and the Theil Index, to reveal the access inequality in physical and virtual spaces respectively. Next, to demonstrate the feasibility of the proposed framework, we examine racial groups and their disparity in physical-virtual healthcare access during the pandemic (April-July 2021) in U.S. 15 most populated metropolitans. Our results indicate: (1) race is a significant risk marker for underlying conditions that affect health, including telehealth access;(2) the usage of telehealth access aligns with the risk for COVID-19 infection, hospitalization, and death by race (e.g., the minority groups Black and Others are more vulnerable and in the higher demand of telehealth service);and (3) residential segregation impacts on the segregated pattern of physical healthcare access by race (e.g., the Black-dominant healthcare access zone highly matches the Black residential cluster in the south of Chicago), while such impacts may differ in different kinds of healthcare services (e.g., physicians, mental health practitioners, and dentists). Compared with traditional single-space approaches, the proposed hybrid-spaces appro

关键词： Information theory

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VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and Visualization Platform Using Deep Reinforcement Learning 4

VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and...

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4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021

作者： Rao, Jinmeng Gao, Song Zhu, Xiaojin Geospatial Data Science Lab University of Wisconsin-Madison Madison United States Department of Computer Sciences University of Wisconsin-Madison Madison United States

ISBN: (纸本)9781450391207

Trajectory data is among the most sensitive data and the society increasingly raises privacy concerns. In this demo paper, we present a privacy-preserving Vehicle Trajectory Simulation and Visualization (VTSV) web platform (demo video: https://***/NY5L4bu2kTU), which automatically generates navigation routes between given pairs of origins and destinations and employs a deep reinforcement learning model to simulate vehicle trajectories with customized driving behaviors such as normal driving, overspeed, aggressive acceleration, and aggressive turning. The simulated vehicle trajectory data contain high-sample-rate of attributes including GPS location, speed, acceleration, and steering angle, and such data are visualized in VTSV using ***, an autonomous driving data visualization framework. Location privacy protection methods such as origin-destination geomasking and trajectory k-anonymity are integrated into the platform to support privacy-preserving trajectory data generation and publication. We design two application scenarios to demonstrate how VTSV performs location privacy protection and customize driving behavior, respectively. The demonstration shows that VTSV is able to mitigate data privacy, sparsity, and imbalance sampling issues, which offers new insights into driving trajectory simulation and GeoAI-powered privacy-preserving data publication. © 2021 ACM.

关键词： data visualization

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Forecasting Inter-Destination Tourism Flow via a Hybrid Deep Learning Model

arXiv

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arXiv 2023年

作者： Fang, Hanxi Gao, Song Zhang, Feng School of Earth Science Zhejiang University China Geospatial Data Science Lab University of Wisconsin-Madison United States

Tourists often go to multiple tourism destinations in one trip. The volume of tourism flow between tourism destinations, also referred to as ITF (Inter-Destination Tourism Flow) in this paper, is commonly used for tourism management on tasks like the classification of destinations' roles and visitation pattern mining. However, the ITF is hard to get due to the limitation of data collection techniques and privacy issues. It is difficult to understand how the volume of ITF is influenced by features of the multi-attraction system. To address these challenges, we utilized multi-source datasets and proposed a graph-based hybrid deep learning model to predict the ITF. The model makes use of both the explicit features of individual tourism attractions and the implicit features of the interactions between multiple attractions. Experiments on ITF data extracted from crowdsourced tourists' travel notes about the city of Beijing verified the usefulness of the proposed model. Besides, we analyze how different features of tourism attractions influence the volume of ITF with explainable AI techniques. Results show that popularity, quality and distance are the main three influential factors. Other features like coordinates will also exert an influence in different ways. The predicted ITF data can be further used for various downstream tasks in tourism management. The research also deepens the understanding of tourists' visiting choice in a tourism system consisting of multiple attractions. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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GIscience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIscience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

arXiv

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arXiv 2020年

作者： Rao, Jinmeng Gao, Song Kang, Yuhao Huang, Qunying Geospatial Data Science Lab University of Wisconsin-Madison United States Department of Geography University of Wisconsin-Madison United States

1 The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection. Copyright © 2020, The Authors. All rights reserved.

关键词： Trajectories

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Measuring Access Inequality in A Hybrid Physical-Virtual World: : A Case Study of Racial Disparity of Healthcare Access During CoVID-19

Measuring Access Inequality in A Hybrid Physical-Virtual Wor...

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International Conference on Geoinformatics

作者： Meiliu Wu Qunying Huang Song Gao Department of Geography Spatial Computing and Data Mining Lab University of Wisconsin-Madison Madison WI USA Department of Geography Geospatial Data Science Lab University of Wisconsin-Madison Madison WI USA

The disparity of resource access (e.g., food and healthcare) among different population groups essentially reflects social inequalities. Emerging information and communications technology (ICT) has facilitated the teleactivities that can replace or complement traditional physical visits, yet existing approaches for measuring access disparity still fail to consider virtual interactions. To this end, this study proposes a unified framework to measure access inequality in both physical and virtual spaces simultaneously, using the POIs’ visit patterns from mobile phone data to capture spatial unevenness of accessibility among different groups (physical space), as well as the Household Pulse Survey data to reveal the group disparity of teleactivity access (virtual space). In particular, a novel Access Inequity Index is proposed based on the Information Theory Index and the Theil Index, to reveal the access inequality in physical and virtual spaces respectively. Next, to demonstrate the feasibility of the proposed framework, we examine racial groups and their disparity in physical-virtual healthcare access during the pandemic (April-July 2021) in U.S. 15 most populated metropolitans. Our results indicate: (1) race is a significant risk marker for underlying conditions that affect health, including telehealth access; (2) the usage of telehealth access aligns with the risk for COVID-19 infection, hospitalization, and death by race (e.g., the minority groups Black and Others are more vulnerable and in the higher demand of telehealth service); and (3) residential segregation impacts on the segregated pattern of physical healthcare access by race (e.g., the Black-dominant healthcare access zone highly matches the Black residential cluster in the south of Chicago), while such impacts may differ in different kinds of healthcare services (e.g., physicians, mental health practitioners, and dentists). Compared with traditional single-space approaches, the proposed hybrid-spaces app

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