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1. GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

   https://doi.org/10.18653/v1/2023.emnlp-main.317  

   Li, Zekun; Zhou, Wenxuan; Chiang, Yao-Yi; Chen, Muhao (January 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing)

   Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as anchors to connect linguistic information in text corpora with geospatial information extracted from geographical databases. GeoLM connects the two types of context through contrastive learning and masked language modeling. It also incorporates a spatial coordinate embedding mechanism to encode distance and direction relations to capture geospatial context. In the experiment, we demonstrate that GeoLM exhibits promising capabilities in supporting toponym recognition, toponym linking, relation extraction, and geo-entity typing, which bridge the gap between natural language processing and geospatial sciences. The code is publicly available at https://github.com/knowledge-computing/geolm. 

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2. Time-Efficient Locally Relevant Geo-Location Privacy Protection

   https://doi.org/10.56553/popets-2025-0046  

   Qiu, Chenxi; Liu, Ruiyao; Pappachan, Primal; Squicciarini, Anna; Xie, Xinpeng (April 2025, Proceedings on Privacy Enhancing Technologies)

   Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users’ location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geoobfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user’s actual location. Given the potential risk of LR locations disclosing a user’s actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Benders’ decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation. 

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3. EarthCube Data Discovery Studio: A gateway into geoscience data discovery and exploration with Jupyter notebooks

   https://doi.org/10.1002/cpe.6086  

   Valentine, David; Zaslavsky, Ilya; Richard, Stephen; Meier, Ouida; Hudman, Gary; Peucker‐Ehrenbrink, Bernhard; Stocks, Karen (November 2020, Concurrency and Computation: Practice and Experience)

   Abstract EarthCube Data Discovery Studio (DDStudio) is a crossdomain geoscience data discovery and exploration portal. It indexes over 1.65 million metadata records harvested from 40+ sources and utilizes a configurable metadata augmentation pipeline to enhance metadata content, using text analytics and an integrated geoscience ontology. Metadata enhancers add keywords with identifiers that map resources to science domains, geospatial features, measured variables, and other characteristics. The pipeline extracts spatial location and temporal references from metadata to generate structured spatial and temporal extents, maintaining provenance of each metadata enhancement, and allowing user validation. The semantically enhanced metadata records are accessible as standard ISO 19115/19139 XML documents via standard search interfaces. A search interface supports spatial, temporal, and text‐based search, as well as functionality for users to contribute, standardize, and update resource descriptions, and to organize search results into shareable collections. DDStudio bridges resource discovery and exploration by letting users launch Jupyter notebooks residing on several platforms for any discovered datasets or dataset collection. DDStudio demonstrates how linking search results from the catalog directly to software tools and environments reduces time to science in a series of examples from several geoscience domains. URL: datadiscoverystudio.org 

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4. Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping

   https://doi.org/10.4230/LIPIcs.GIScience.2021.I.10  

   Park, Jaehee (September 2020, 11th International Conference on Geographic Information Science (GIScience 2021))

   null (Ed.)

   This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time. 

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5. Analysis of Privacy Protections in Fitness Tracking Social Networks -or You can run, but can you hide?

   Hassan, Wajih Ul; Hussain, Saad; Bates, Adam (August 2018, 27th USENIX Security Symposium)

   Mobile fitness tracking apps allow users to track their workouts and share them with friends through online social networks. Although the sharing of personal data is an inherent risk in all social networks, the dangers presented by sharing personal workouts comprised of geospatial and health data may prove especially grave. While fitness apps offer a variety of privacy features, at present it is unclear if these countermeasures are sufficient to thwart a determined attacker, nor is it clear how many of these services’ users are at risk. In this work, we perform a systematic analysis of privacy behaviors and threats in fitness tracking social networks. Collecting a month-long snapshot of public posts of a popular fitness tracking service (21 million posts, 3 million users), we observe that 16.5% of users make use of Endpoint Privacy Zones (EPZs), which conceal fitness activity near user-designated sensitive locations (e.g., home, office). We go on to develop an attack against EPZs that infers users’ protected locations from the remaining available information in public posts, discovering that 95.1% of moderately active users are at risk of having their protected locations extracted by an attacker. Finally, we consider the efficacy of state-of-the-art privacy mechanisms through adapting geo-indistinguishability techniques as well as developing a novel EPZ fuzzing technique. The affected companies have been notified of the discovered vulnerabilities and at the time of publication have incorporated our proposed countermeasures into their production systems. 

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