A review of spatially-explicit GeoAI applications in Urban Geography

Liu, Pengyuan; Biljecki, Filip

doi:10.1016/j.jag.2022.102936

Cited by 50 publications

(38 citation statements)

References 86 publications

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“…However, the application of GNNs in urban studies is relatively new and has been yet limited. Many relevant applications can be imagined for GNNs such as urban dynamics, which includes the development of cities and the dynamic flow of socio-economic activities; social segregation analysis, which concerns the differentiation of different urban areas from the social and demographic points of view; and urban sensing, which aims at identifying different land uses and social activities based on social media or volunteered user data [194].…”

Section: F Urban Planningmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: F Urban Planningmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Scale is an innate concept in GIScience and plays a pivotal role in GeoAI (Kang et al, 2019;Liu & Biljecki, 2022).…”

Section: Challenges In Accommodating Spatial Scalementioning

confidence: 99%

“…Scale is an innate concept in GIScience and plays a pivotal role in GeoAI (Kang et al, 2019; Liu & Biljecki, 2022). The challenge is whether we can introduce multiscale analysis into XAI.…”

Section: Challenges Introduced By Geoaimentioning

confidence: 99%

“…Scope represents the spatial extent of study areas or data analytic windows that, in DNN layers, could be considered as the size of receptive fields (Jacobsen et al, 2016). Liu and Biljecki (2022) pointed out how variations in scale could heavily impact model performance in GeoAI. They further explored the Modifiable Areal Unit Problem (MAUP), which is considered a geocoding issue in GeoAI but could influence the effectiveness of GeoXAI as well.…”

Section: Challenges Introduced By Geoaimentioning

confidence: 99%

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The challenges of integrating explainable artificial intelligence into GeoAI

Jin

Sieber

2023

Transactions in GIS

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Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs). We summarize these as three major challenges, related generally to XAI computation, to GeoAI and geographic data handling, and to geosocial issues. XAI computation includes the difficulty of selecting reference data/models and the shortcomings of attributing explanatory power to gradients, as well as the difficulty in accommodating geographic scale, geovisualization, and underlying geographic data structures. Geosocial challenges encompass the limitations of knowledge scope—semantics and ontologies—in the explanation of GeoAI as well as the lack of integrating non‐technical aspects in XAI, including processes that are not amenable to XAI. We illustrate these issues with a land use classification case study.

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“…From a geospatial point‐of‐view, Geospatial AI (GeoAI), as an interdisciplinary field combining Geography, GIScience, and AI, advocates the idea of developing and utilizing AI techniques in geography and earth science research (Janowicz et al, 2020). This, in turn, benefits downstream tasks in health (Kamel Boulos et al, 2019), urban studies (Liu & Biljecki, 2022), traffic prediction (Polson & Sokolov, 2017), earth system science (Ham et al, 2019), and so on. GeoAI can also be categorized into two branches: Symbolic GeoAI and Subsymbolic GeoAI.…”

Section: Symbolic and Subsymbolic Geoaimentioning

confidence: 99%

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

Mai

Gao

et al. 2022

Transactions in GIS

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The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic AI and Connectionist AI (or the so-called Subsymbolic AI). Symbolic AI focuses on research based on classical logic and higher-level symbolic (human-readable) knowledge representations. It posits the use of declarative knowledge in reasoning and learning as critical to producing intelligent behavior (Goel, 2022). Examples are logical inference, symbolic reasoning, ontol-

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A review of spatially-explicit GeoAI applications in Urban Geography

Cited by 50 publications

References 86 publications

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph Neural Networks for Intelligent Transportation Systems: A Survey

The challenges of integrating explainable artificial intelligence into GeoAI

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

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