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Automating geospatial Analysis Workflows Using ChatGPT-4 24

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Automating Geospatial Analysis Workflows Using ChatGPT-4

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

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

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

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

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

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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

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

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

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

关键词：

Artificial Intelligence and Human Geography

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arXiv 2023年

作者： Gao, Song Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison 550 N Park Stret MadisonWI53706 United States

This paper examines the recent advances and applications of AI in human geography especially the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design. AI technologies have enabled deeper insights into complex human-environment interactions, contributing to more effective scientific exploration, understanding of social dynamics, and spatial decision-making. Furthermore, human geography offers crucial contributions to AI, particularly in context-aware model development, human-centered design, biases and ethical considerations, and data privacy. The synergy beween AI and human geography is essential for addressing global challenges like disaster resilience, poverty, and equitable resource access. This interdisciplinary collaboration between AI and geography will help advance the development of GeoAI and promise a better and sustainable world for all. © 2023, CC BY.

关键词： Decision making

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

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

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

Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions

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arXiv 2022年

作者： Liang, Yunlei Zhu, Jiawei Ye, Wen Gao, Song Geospatial Data Science Lab University of Wisconsin Madison WI United States School of Geosciences and Info-Physics Central South University Changsha China

Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help reveal the spatial interaction patterns, understand the hidden regional structures and support regional development decision-making. Given the recent development of Graph Convolutional Networks (GCN) and its powerful performance in identifying multi-scale spatial interactions, we proposed an unsupervised GCN-based community detection method region2vec on spatial networks. Our method first generates node embeddings for regions that share common attributes and have intense spatial interactions, and then applies clustering algorithms to detect communities based on their embedding similarity and spatial adjacency. Experimental results show that while existing methods trade off either attribute similarities or spatial interactions for one another, region2vec maintains a great balance between both and performs the best when one wants to maximize both attribute similarities and spatial interactions within communities. © 2022, CC BY.

关键词： Embeddings

Bringing Spatial Interaction Measures into Multi-Criteria Assessment of Redistricting Plans Using Interactive Web Mapping

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arXiv 2023年

作者： Kruse, Jacob Gao, Song Ji, Yuhan Szabo, Daniel P. Mayer, Kenneth R. Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison United States Eötvös Lóránd Tudományos Egyetem Department of Operations Research Budapest Hungary Department of Political Science University of Wisconsin-Madison United States

Redistricting is the process by which electoral district boundaries are drawn, and a common normative assumption in this process is that districts should be drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness metrics and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the failings of such proxy measures and the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction communities to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), which can be used for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), and to explore the range of IR values found in valid districting plans, we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans, and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. Using the ensembles produced from these three methods, we perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regards to seats allocated to each of the two major parties. © 2023, CC BY.

关键词： Zoning

A Modified Spectral Remote Sensing Index to Detect Spatiotemporal Changes of Plastic Greenhouses in Fragmented Terrains

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A Modified Spectral Remote Sensing Index to Detect Spatiotem...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yijia Chen Shanshan Chen Song Gao Chun Li Ninglv Li Liding Chen School of Ecology and Environmental Science Yunnan University Kunming Yunnan China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing PR China Department of Geography Geospatial Data Science Lab University of Wisconsin-Madison Madison WI Southwest United Graduate School Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

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 agricultural sustainability. However, extracting PGs in fragmented terrains 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 based on the differences in spectral signatures using the Landsat-8 Operational Land Imager (OLI), and it was used to detect PG's spatiotemporal changes. After precision validation (the F1 score is 92.8% and 88.60% in 2015 and 2020, respectively), the results indicate that MPGI is suitable for extracting PGs in fragmented terrains. Its application to PGs mapping in the Southwest Plateau region demonstrates its potential for heterogeneous surface digital mapping.

关键词： Earth Green products Production Spatiotemporal phenomena Plastics Indexes Sustainable development