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. Also as Markdown.

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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	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	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	34
2	human mobility prediction	14
3	graph neural networks	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	2024-10-29	Prabin Bhandari, Antonios Anastasopoulos, Dieter Pfoser
2	21	TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model	2024-10-29	Shang-Ling Hsu, Emmanuel Tung, John C. Krumm et al.
3	19	Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories	2024-10-29	Zheng Zhang, Hossein Amiri, Dazhou Yu et al.
4	17	Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning	2024-10-29	Junfeng Hu, Xu Liu, Zhencheng Fan et al.
5	15	GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping	2024-10-29	Angel Daruna, Vasily Zadorozhnyy, Georgina Lukoczki et al.
6	14	Harnessing LLMs for Cross-City OD Flow Prediction	2024-10-29	Chenyang Yu, Xinpeng Xie, Yan Huang et al.
7	14	The Patterns of Life Human Mobility Simulation	2024-10-29	Hossein Amiri, Will Kohn, Shiyang Ruan et al.
8	13	Trajectory Anomaly Detection with Language Models	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	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	2024-10-29	Changhao Miao, Yuntian Zhang, Tongyu Wu et al.

Conferences in the same field#

AAAI · NeurIPS · IJCAI · KDD · WWW · SIGIR · VLDB · ICDE · SIGMOD · ICDM · PODS · MICCAI · CIKM · ECAI · ECML PKDD · RecSys · AIED · ECIR · WSDM · ICWSM · ICDAR · MSR · LAK · PPSN · ISLPED · ISMB · ISWC · SDM · CAiSE · ICDT · ER

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

@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}
}

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