Title: Reconciling Geospatial Prediction and Retrieval via Sparse Representations
Venue: NeurIPS 2025
Accepted: yes
Keywords: Urban Data Mining

Representation Learning

Geospatial Predictions

Geographic Information Retrieval
Abstract: Urban computing harnesses big data to decode complex urban dynamics and revolutionize location-based services. Traditional approaches have treated geospatial prediction tasks (e.g., estimating socio-economic indicators) and retrieval tasks (e.g., querying geographic objects) as isolated challenges, necessitating separate models with distinct training objectives. This fragmentation imposes significant computational burdens and limits cross-task synergy, despite advances in representation learning and multi-task foundation models.

We present UrbanSparse, a pioneering framework that unifies geospatial prediction and retrieval through a novel sparse-dense representation architecture. By synergistically combining these tasks, UrbanSparse eliminates redundant systems while amplifying their mutual strengths. Our approach introduces two innovations: (1) Bloom filter-based sparse encodings that compress high-sparsity geographic queries and fine-grained text terms for retrieval effectiveness, and (2) a dense semantic codebook that captures granular urban features to boost prediction accuracy. A two-view contrastive learning mechanism further bridges urban objects, regions, and contexts. Experiments on real-world datasets demonstrate 25.16% gains in prediction accuracy and 20.76% improvements in retrieval precision over state-of-the-art baselines, alongside 65.97% faster training. These advantages position UrbanSparse as a scalable solution for large urban datasets. To our knowledge, this is the first unified framework bridging geospatial prediction and retrieval, opening new frontiers in data-driven urban intelligence.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper is related to urban computing. It proposes UrbanSparse, a unified framework addressing the novel challenge of integrating geospatial prediction and retrieval tasks. The system encodes fine-grained text details for retrieval and preserves features for prediction via Bloom filters and a two-view learning mechanism. An extensive experimental evaluation on two real-world datasets shows that UrbanSparse significantly improves performance over existing methods, while achieving significant gains in efficiency, accuracy, and scalability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nS1. The paper tackles a long standing challenge for the urban computing: unifying geospatial prediction and retrieval under a single representation learning framework, which could be essential for future foundation models.\n\nS2. The motivations are clear. Urban computing relies on massive city‐scale data, but existing approaches train separate models for tasks like socioeconomic inference and geographic searches. Combining them into one embedding can save labor and boost efficiency.\n\nS3. The experiments are extensive and detailed. They use two real‐world urban datasets, evaluate both accuracy and runtime, and compare against strong baselines in prediction and retrieval; the reported improvements are meaningful.\n\nS4. The paper is clearly written and easy to follow.\n\nWeakness:\n\nW1. While I find the motivation to be novel, an intuitive running example based on real data inserted in the Introduction may be better for the the reader to understand the importance for the final user to integrate prediction/retrieval urban services.\n\nW2. The chosen datasets (Beijing, Shanghai) reflect large, densely populated cities, so it is unclear if the results extend to cities with different spatial layouts. For example, how would the model perform in cities characterized by sprawling suburban regions? And both datasets contain fewer than 200,000 POIs, which raises the question of whether the approach can handle much higher POI densities. How does varying POI density affect the model’s performance?\n\nW3. While the Bloom filter with two-view contrastive learning empirically works, the paper lacks an intuitive or formal theoretical explanations.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces UrbanSparse, an innovative unified architecture for both geospatial prediction and geographic information retrieval. The core innovation lies in leveraging a Bloom filter-based sparse text encoding, integrated with a two-view learning framework that combines fine-grained token-level and regional context signals. It is evaluated on two real-world urban datasets, demonstrating notable gains in accuracy and also improvement in efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nS1: This paper is well-written and well-motivated to have a unified architecture to tackle two tasks that benefit each other. It provides a detailed explanation to justify the selection of each module of the framework and provides a clear mathematical formulation.\n\nS2: Figures are helpful and informative to understand the overall architecture.\n\nS3: In terms of significance, this work unifies prediction and retrieval tasks that is timely and practically relevant, especially in this urban computing scenario. It demonstrates substantial improvement in both accuracy and efficiency.\n\nW1: The choice of LambdaRank as the loss function for retrieval is not well justified. The added complexity (e.g., tuning, stability) is not clearly offset by demonstrable gains; no ablation against simpler alternatives is provided.\n\nW2: The benefits of the row/column selection optimization are not sufficiently isolated or quantified. It is unclear how much these contribute to the observed efficiency improvements.\n\nW3: Limited Scalability and Generalization Discussion. The evaluation is limited to two cities, with no evidence or discussion of how well UrbanSparse generalizes to other geospatial contexts, data types (e.g., traffic, satellite), or larger-scale urban systems.\n\nQUESTIONS:\n1. Insufficient Justification for LambdaRank Loss (L209): The choice of LambdaRank introduces computational overhead and complexity, but the paper lacks comparison with simpler alternatives (e.g., static ranking weights or margin-based losses). No ablation is provided to demonstrate its necessity or impact.\n\n2. Unclear Benefit of Row/Column Selection Optimization (L250). The proposed row and column selection technique is claimed to accelerate computation, but the mechanism is not well-explained, nor is its contribution isolated in an ablation or runtime analysis.\n\n3. In Table 6: Despite a 50× reduction in parameter size compared to DPR-D, UrbanSparse achieves only ~4× improvement in QPS. Can the authors comment on the remaining bottlenecks? For instance, does hashing, memory access, or bit-level operations dominate the latency?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a unified approach to address both geo-spatial prediction and retrieval tasks in the context of Urban Computing. To enhance computational efficiency, the authors employ Bloom filter-based sparse encoding. They suggest breaking down textual data using a combination of n-gram and dictionary-based tokenizers, which are then transformed into Bloom filter representations via random hashing.\n\nAdditionally, a dense semantic codebook is utilized to capture fine-grained urban features. The method further proposes a joint optimization framework that merges the traditionally separate processes of geographical prediction and retrieval. Experimental results demonstrate that this the suggested model outperforms several baseline methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength:**\nThe paper tries to handle the task of unifying two domains of geo-spatial prediction and retrieval, in Urban Computing. Some ideas in the method, such as using of Bloom Filters, and unified optimization seem to be novel in this domain.\n\n**Concerns and Weaknesses:**\nOne primary concern with this paper is IMO its niche application domain. It is not immediately evident how relevant or appealing the work will be to the broader NeurIPS community. The approach is highly tailored to Urban Computing, and the paper does not clearly articulate how the proposed method could generalize to other, more common NeurIPS-related domains.\n\nBeyond that, several specific issues are noted:\n\n1.\tThe proposed method comprises multiple interconnected components, which introduces significant complexity, IMO. This abundance of detail obscures the central contribution, making the core idea hard follow, in the Method section.\n2.\tA more thorough definition and explanation of Bloom filters would be helpful. Given that Bloom filters are prone to false positives (but not false negatives), it is important to clarify how this characteristic affects the system's performance and reliability\n\nIn terms of Evaluations:\n\n3.\tI believe, that the choice of datasets (Beijing and Shanghai) is not well justified. The paper should explain what datasets are commonly used in Urban Computing and why these two were selected.\n4.\tRegarding baseline comparisons, the retrieval methods used, e.g OpenAI2, BM25, BERT, etc., seems to be  all general-purpose models, not tailored or trained for Urban Computing. I am afraid that the lack of comparison with domain-specific baselines weakens the relevance of the evaluation.\n5.\tThe authors justify excluding methods relying on GPS trajectory by stating that such data are unavailable for their datasets. However, this raises the question: why not use datasets employed by those other studies, especially if aiming for a fair and competitive comparison with recent work in the field?\n5.\tThe paper lacks details on training, validation, and test splits. This is essential for reproducibility and to understand the robustness of the model.\n6.\tAlthough the paper proposes a unified framework, it does not provide evidence or ablation studies demonstrating its advantages over treating the prediction and retrieval tasks separately.\n\nQUESTIONS:\nPlease see above\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces UrbanSparse, a novel method aimed at enhancing the accuracy of population density prediction, house price estimation, and point-of-interest (POI) retrieval by leveraging unified geospatial text encoding and learned embeddings. The approach uses Bloom-filter encoding to transform geographic text data into sparse representations, enabling efficient learning across different urban environments. By sharing useful information between tasks, UrbanSparse improves model performance without relying on complex sequential information.\n\nThe authors validate their method through extensive experiments conducted on datasets from Beijing and Shanghai. These experiments demonstrate that UrbanSparse outperforms existing methods in population density prediction and house price estimation while also showing promising results in POI retrieval tasks. Detailed descriptions of the experimental setup, including training algorithms and hyperparameter tuning processes, are provided to ensure reproducibility and facilitate further research.\n\nIn addition to presenting strong empirical results, the paper discusses the limitations of the current approach and suggests potential areas for future improvement. For instance, the authors consider how to handle noisy data or sparse inputs more effectively. They also explore possible applications and implications of their work, highlighting its potential impact on urban planning, resource allocation, and user-centric services. Overall, UrbanSparse not only advances computational efficiency and reduces resource consumption but also opens new avenues for addressing privacy concerns and fairness challenges in geospatial data analysis. Through thorough data analysis and theoretical exploration, the paper convincingly demonstrates the practical value and broad applicability of UrbanSparse in real-world scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\nThe paper demonstrates high quality through its rigorous experimental setup and detailed documentation, ensuring reproducibility and credibility of the results. The methodology is articulated, making it easy for readers to understand how UrbanSparse integrates Bloom-filter encoding with learned embeddings for geospatial text processing. By showcasing practical applications in population density prediction, house price estimation, and POI retrieval on real-world datasets from Beijing and Shanghai, the authors highlight the significant potential of their approach in urban planning and resource allocation. This combination not only enhances computational efficiency but also opens new avenues for addressing complex geospatial data analysis challenges.\n\nWeaknesses：\nDespite its strengths, the paper could benefit from more robust validation under varied conditions, particularly in handling extremely noisy or highly sparse inputs, which limits its applicability in certain real-world scenarios. Additionally, while the technical details are well-documented, some advanced concepts might be challenging for non-specialist readers, suggesting a need for clearer explanations or simplified background information. Furthermore, a deeper comparison with existing state-of-the-art techniques would help clarify the unique advantages of UrbanSparse and strengthen its claim of originality. Addressing these areas would enhance the overall impact and broad applicability of the paper.\n\nQUESTIONS:\n1、How does UrbanSparse handle extremely noisy or highly sparse inputs, and what strategies could be employed to improve performance under these conditions?\n\n2、Can you provide a more thorough comparison with existing state-of-the-art techniques, highlighting both the advantages and limitations of UrbanSparse relative to other methods?\n\n3、Could you simplify or provide additional background explanations for some of the advanced concepts in the paper to make it more accessible to a broader audience?\n\n4、Have you considered the potential negative societal impacts of deploying UrbanSparse in real-world applications, such as bias in predictions or displacement effects in housing markets? What mitigation strategies do you propose?\n\nLIMITATIONS:\nWhile the authors acknowledge some technical limitations of UrbanSparse—such as its sensitivity to sparse inputs and the need for further optimization—there is no explicit discussion of potential negative societal impacts associated with the deployment of their method in real-world settings. This is an important omission, especially given the application domains such as housing price estimation and population density prediction, which can have significant implications for equity, privacy, and public policy.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-16"}