---
title: "SIGSPATIAL (A): papers and universities"
description: "SIGSPATIAL (ACM International Conference on Advances in Geographic Information Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 305 of its papers from 2024–2026 (112 in 2024, 193 in 2025; 2026 not in OpenAlex yet). By the countries of the authors' institutions, the most papers come from the United States (166), China (32) and Japan (27)."
url: https://scholar4ai.com/proceedings/sigspatial
markdown_url: https://scholar4ai.com/proceedings/sigspatial.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "SIGSPATIAL (ACM International Conference on Advances in Geographic Information Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 305 of its papers from 2024–2026 (112 in 2024, 193 in 2025… (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# SIGSPATIAL: ACM International Conference on Advances in Geographic Information Systems — papers and institutions, 2024–2026

Papers of SIGSPATIAL, ACM International Conference on Advances in Geographic Information Systems (CORE rank A; field: Applied computing, Machine learning, Data management and data science), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- SIGSPATIAL (ACM International Conference on Advances in Geographic Information Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 305 of its papers from 2024–2026 (112 in 2024, 193 in 2025; 2026 not in OpenAlex yet).
- By the countries of the authors' institutions, the most papers come from the United States (166), China (32) and Japan (27).
- The institutions with the most papers are University of Maryland, College Park (16), University of Southern California (14) and University of Minnesota (13).
- Its most-cited paper of these years is “Urban Mobility Assessment Using LLMs” (2024), with 24 citations.

## Papers per year

*SIGSPATIAL papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 112 | 110 |
| 2025 | 193 | 191 |
| 2026 | – | – |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S7407088447](https://openalex.org/S7407088447) | The conference's proceedings |

## Who publishes there

*Institutions with the most SIGSPATIAL papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | University of Maryland, College Park | 16 |
| 2 | University of Southern California | 14 |
| 3 | University of Minnesota | 13 |
| 4 | University of California, Riverside | 12 |
| 5 | Emory University | 12 |
| 6 | [UNSW Sydney](https://scholar4ai.com/institutions/unsw-sydney.md) | 12 |
| 7 | University of Florida | 10 |
| 8 | Université Libre de Bruxelles | 8 |
| 9 | Technical University of Munich | 8 |
| 10 | American University in Cairo | 8 |

*Countries with the most SIGSPATIAL papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 166 |
| 2 | China | 32 |
| 3 | Japan | 27 |
| 4 | Australia | 22 |
| 5 | Germany | 22 |
| 6 | Canada | 11 |
| 7 | United Kingdom | 11 |
| 8 | Egypt | 10 |
| 9 | Italy | 10 |
| 10 | Belgium | 9 |

*Keywords with the most SIGSPATIAL papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 34 |
| 2 | human mobility prediction | 14 |
| 3 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 12 |
| 4 | urban mobility | 12 |
| 5 | urban planning | 12 |
| 6 | satellite imagery | 10 |
| 7 | spatio-temporal data | 9 |
| 8 | trajectory prediction | 9 |
| 9 | OpenStreetMap | 8 |
| 10 | human mobility | 8 |

## Most-cited papers

*Most-cited SIGSPATIAL papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 24 | [Urban Mobility Assessment Using LLMs](https://doi.org/10.1145/3678717.3691221) | 2024-10-29 | Prabin Bhandari, Antonios Anastasopoulos, Dieter Pfoser |
| 2 | 21 | [TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model](https://doi.org/10.1145/3678717.3691303) | 2024-10-29 | Shang-Ling Hsu, Emmanuel Tung, John C. Krumm et al. |
| 3 | 19 | [Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories](https://doi.org/10.1145/3678717.3691324) | 2024-10-29 | Zheng Zhang, Hossein Amiri, Dazhou Yu et al. |
| 4 | 17 | [Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning](https://doi.org/10.1145/3678717.3691235) | 2024-10-29 | Junfeng Hu, Xu Liu, Zhencheng Fan et al. |
| 5 | 15 | [GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping](https://doi.org/10.1145/3678717.3691268) | 2024-10-29 | Angel Daruna, Vasily Zadorozhnyy, Georgina Lukoczki et al. |
| 6 | 14 | [Harnessing LLMs for Cross-City OD Flow Prediction](https://doi.org/10.1145/3678717.3691308) | 2024-10-29 | Chenyang Yu, Xinpeng Xie, Yan Huang et al. |
| 7 | 14 | [The Patterns of Life Human Mobility Simulation](https://doi.org/10.1145/3678717.3691319) | 2024-10-29 | Hossein Amiri, Will Kohn, Shiyang Ruan et al. |
| 8 | 13 | [Trajectory Anomaly Detection with Language Models](https://doi.org/10.1145/3678717.3691257) | 2024-10-29 | Jonathan Mbuya, Dieter Pfoser, Antonios Anastasopoulos |
| 9 | 12 | [EFECTIW-ROTER: Deep Reinforcement Learning Approach for Solving Heterogeneous Fleet and Demand Vehicle Routing Problem With Time-Window Constraints](https://doi.org/10.1145/3678717.3691208) | 2024-10-29 | Arash Mozhdehi, Mahdi Mohammadizadeh, Yunli Wang et al. |
| 10 | 12 | [Deep Reinforcement Learning for Multi-Period Facility Location pk-median Dynamic Location Problem](https://doi.org/10.1145/3678717.3691249) | 2024-10-29 | Changhao Miao, Yuntian Zhang, Tongyu Wu et al. |

## Cite this page

Scholar4AI. (2026). SIGSPATIAL: ACM International Conference on Advances in Geographic Information Systems — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/sigspatial

```bibtex
@misc{scholar4ai-proceedings-sigspatial,
  author = {{Scholar4AI}},
  title = {SIGSPATIAL: ACM International Conference on Advances in Geographic Information Systems — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/sigspatial}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).