Reconciling Geospatial Prediction and Retrieval via Sparse Representations
Authors: YI LI
Yuanlong Chen
Weiming Huang
Xiaoli Li
Gao Cong
Venue: NeurIPS 2025 poster ()
Keywords: Urban Data Mining
Representation Learning
Geospatial Predictions
Geographic Information Retrieval
Primary area: deep_learning

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.

Reconciling Geospatial Prediction and Retrieval via Sparse Representations
Li
Yuanlong Chen
College of Computing and Data Science Nanyang Technological University 1 liyi0067 Ce. www.ntu.eeee edu.
College of Computing and Data Science Nanyang Technological University yuanlong001 Ce. www.tttueeee edu..edu... sg
Weiming Huang School of Geography, University of Leeds
Xiaoli Li
Institute for Infocomm Research, A*STAR College of Computing and Data Science
W. Huang@leeds ac uk
Nanyang Technological University x1li 1iirr.irr.. a-star sg
Gao Cong
College of Computing and Data Science Nanyang Technological University gaocongEntu, edu. sg
Abstract
Urban computing harnesses big data to decode complex urban dynamics and revo- lutionize location-based services. Traditional approaches have treated geospatial prediction tasks (e.g., estimating socio-economic indicators) and retrieval tasks (e.g., querying geographic objects) isolated challenges, necessitating separate models with distinct training objectives. This fragmentation imposes significant computational burdens and limits cross-task synergy, despite advances in represen- tation learning and multi-task foundation models.
We present UrbanSparse, pioneering framework that unifies geospatial predic- tion and retrieval through a novel sparse-dense representation architecture. By synergistically combining these tasks, UrhanSparse 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 se- mantic codebook that captures granular urban features boost prediction accuracy. A two-view contrastive learning mechanism further bridges urban objects. regions, and contexts. Experiments on real-worla datasets demonstrate 26.16% gains in prediction accuracy and 20.76% improvements in retrieval precision over state- of of-the-art baselines, alongside 65.97% faster training. These advantages position SirbanSparse 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
* Cooresppndiig author.
2 Data and code available at
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Over the past decade, we have witnessed a surge of urban data from a variety of sources, c.g., remote sensing images, points of interest (POIs), and human trajectories. This presents unprecedented opportunities for developing data-driven solutions to address various long-standing challenges, where various machine learning models have been developed for many tasks, such as economic growth prediction [26], air quality analysis [72], transport planning 77. and trajectory search [64]. Such tasks fall under two categories: prediction and retrieval.
Prediction tasks, also known as Geospatial Predictions [45) estimate holistic urban socio-economic indicators, either from data-rich areas unknown areas or from the past to the future [38]. Rep- resentative tasks in this strand include predicting land use, population density, crime rates, and transportation [ 24
68). Retrieval tasks, also known as Geographic Information focus on identifying relevant geographic entities by considering both keyword relevance and geographic proximity. For example, users searching for "coffee" along with GPS coordinates receive a list of nearby coffee shops. In this process, a GIR model computes the relevance scores between user queries and geographic objects to determine the order in which results are displayed to users. The effectiveness of retrieval can be evaluated and enhanced using labeled queries, where each query is labeled with one or more user-selected geographic objects. Prediction and retrieval tasks have traditionally been studied independently, driven by the long-held assumption that they require fundamentally different features (i.e., retrieval tasks emphasize low-frequency text terms [56], whereas prediction or classification tasks prioritize common or aggregated features [44)). With the development of representation learning and multi-task methods in both domains 14 27), which train one foundation model for multiple downstream tasks, it naturally leads us to critical question: can we develop a unified model to tackle and enhance both geospatial prediction and retrieval tasks?
25
33
40
45
Retrieval (GIR) 5 32
42
58
In this work, we reveal the great complementary advantages of jointly tackling the two tasks. For example, conventional prediction methods usually rely on POI density to estimate population density, which may fail in regions with a few large residential buildings (each suggesting a high number of residents). A unified model can utilize the abundant search queries from these residents to improve predictions. Likewise, traditional retrieval sorts geographic objects solely by fine-grained linguistic similarities and geographic proximity [8] 21 33], whereas unified model considers POI associations across the whole city and recommends similar areas that may meet user's needs.
Despite these potential benefits, reconciling the inherent conflicts between the two tasks poses signifi- cant challenges. The first challenge is preserve fine- tre-grained textual features. Existing methods for prediction tasks generally use region-level data aggregation to extract geospatial proximity (50] [59]
and regional associations 17 70). Such aggregation can ruin the textual details, leading to poor retrieval effectiveness. The second challenge is to extract predictive information from various text terms. While some distinctive, low-frequency text terms for retrieval tasks enhance predictions, many "Postoode 101011" 1") don't have semantics and may introduce noise or outliers into predictions. The third challenge is to leverage labeled queries. Though studies [14] 29, 39) demonstrate that fine-tuning on labeled queries improves retrieval performance, achieving such improvements in pre- diction tasks remains non-trivia. and unexplored. Finally, existing prediction models require capturing complex spatial relationships, and retrieval methods often involve fine-tuning large language models, both face efficiency challenges on large datasets.
To address these challenges, we propose UrbanSparse, a unified framework for geospatial prediction and retrieval that employs two-view learning mechanism capturing both fine-grained textual details and holistic regional context. First, in Individual View, we preserve fine-grained features by splitting texts with multiple tokenizers and encoding them as Bloom filter bits, evaluating and recording term-level importance with neural networks to mitigate information loss. Second, in Collective View, we maximize mutual information between regions and their geographic context extract key predictive features while filtering out noise. Third, both views share a dense codebook trained with a novel warm-up strategy: we start with prediction tasks and then interleave training on both tasks, ensuring a smooth task transition. Finally, we introduce row- and column-selecte leverage Bloom filter sparsity to boost efficiency. Experiments on real-world datasets demonstrate that UrbanSparse outperforms state-of-the-an baselines, achieving up to 25.16% improvement in prediction and 20.76% in retrieval effectiveness while reducing training time by 65.97 65.97% and memory requirements by 86.49% compared to traditional BERT-basce embeddings.
techniques that

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In summary, our contributions are at least threefold:
We tackle the critical yet underexplored challenge of unifying geospatial prediction and retrieval within single framework. By showing that these traditionally separate tasks can be co-optimized for mutual benefit, our work paves the way for next-generation geospatial foundation models.
A Novel Research Problem:
A Two-View Learning Mechanism: We propose a two-view learning process that combines Bloom filter-based sparse representations for fine-grained textual encoding with graph contrastive learning for local and contextual geographic encoding. By maximizing the mutual information between regions and their surroundings, our approach learns useful features from Bloom filter bits without extensive pre-training.
Extensive experiments validate the supe- riority of UrbanSparse over tftt-of-tthear baselines, demonstrating significant improvements effectiveness and efficiency. This framework not only advances task performance but also establishes new standard for scalable and resource-efficient urban computing solutions.
Comprehensive Performance and Efficiency Gains:
2 Related Work
2.1 Geospatial Predictions
Geospatial predictions aim to estimate key urban characteristics by leveraging statistics and associa- tions across urban regions. Early works like Yuan et al. [65] analyzed human mobility and POIs to identify functional zones, while Zheng et al. [72] combined meteorological data, road networks, and taxi movements to predict air quality. Street-level imagery has been used to assess urban safety (4) and social media data has uncovered urban patterns [66. POI and check-in data are also used to classify urban zones 663. However, these task-specific models lack generalizability.
Recent work explores unsupervised methods get rid of task-specific labels and learn generalized urban representations. Wang et al. [60 introduced mobility graphs with human movements as edges, while Fu et al. [17] enhanced these with POIs via graph auto-encoders. Zhai et al. (6 modeled POI co-occurrence, and Niu and Silva[50) incorporated spatial proximity. Recent approaches include adversarial training for multi-modal data (70). attention mechanisms for cross-modal features [69], contrastive learning on multi-view [68] or hierarchical graphs (24) aggregation, and pre-trained foundation models 27 for general-purpose region embeddings. However, these methods prioritize holistic features and neglect fine- teeegrrined details, unfeasible for retrieval tasks. In contrast, our method preserves both granular textual terms and holistic geospatial correlations to support both tasks.
2.2 Geographic Information Retrieval
Geographic Information Retrieval (GIR) handles text-based queries with geographic distances [52] Early research (6 12
43 use a linear combination of distances [54] and unsupervised text similarities including IDF [5 and BM25 (53) to identify relevant objects for queries. These methods use ofg-of-worde (BOW) to represent texts, which lacks deep semantics and limits their retrieval effectiveness and prediction accuracies. The integration of deep learning models into GIR marked a significant shift, using dense representations to compute text similarities. Early learning- based models [66] 71] encode texts with lightweight neural networks, yet they lack inherent semantic knowledge and rely on extensive labeled queries. Some text-based IR models [8, 21 are adapted to GIR tasks, which use Word2Vec [48] embeddings to estimate semantic relevance. However, empirical studies (39] demonstrate that these approaches [18] [1] fail to compete with classical methods in retrieval effectiivnnes.. Moreover, these methods don't generate reusable representations and struggle on large-scale GIR databases. Pre-trained Language Models (PLMs) like BERT [3] and ERNIE [22] advanced GIR by embedding semantic understanding. DrW (39 aligned BERT-based representations with query-aware geographic preferences, while MGeo [4] and ERNIE-GeOL [22 pre-trained on
city-specific datasets, integrating mobility data and multi-modality features. Despite strong retrieval performance, these methods lacked holistic urban representations needed for prediction tasks and were computationally intensive. Our approach bridges this divide by learning individual-level relevance from labeled queries while incorporating collective- urban context to support predictions.

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3 Preliminaries
Definition (Representation Learning for Geographic Information Retrieval). Given spatial keyword query q and a geo-textual object o, each with location and text, representation learning encodes their texts into vectors f() R so that
Relevance q. o) = F (DistSimmqq o).
where DistSim and TextSim measure spatial proximity and text similarity, and F combines them. The top-k relevant objects then retrieved from S = (o1....,ON]
Definition 2 (Representation Learning for Geospatial Predictions). Given an urban region u E U, representation learning encodes its spatial, functional, and containing objects into f(u, o) E RR A predictor 9 I + R then estimates altributes Y (u) (e.g, socio-economic indicators).
Problem Statement. Given geo-textual objects S (OI,...,ON]] and regions U {ul,... learn unified mapping such that
f()) Rdd f(u) E RR.
where d and l the dimensions for queries/objects and regions, respectively.
4 Method
Individual View
Quer Bloom filers
Pooei Layer
Sparse Representations
Relevance
Query Text
Hranting
Retrieval Tasks Loss
Codebook
Object Text Token
10 Evoluction Lagar
Object
Collective View
Graph
Region Muticl Informaionn
GCN
Pcoling
Goographic Locations
Dense
Elwermmntton
Region Represercators
Prediction
meeking
Tasks
GCN
Pooling
Conteet graph representaiinee
Downstream
Contest Graph
Predictor
Figure Overview of the UrbanSparse framework. (1) The Individual View encodes query and object text into Bloom filters, transforming them into sparse representations with fine-grained text importance. (2) The Collective View maximizes the mutual information between regions and their context, thereby learning meaningful geographic associations from Bloom filters.
We present UrbanSparse, unified framework for geospatial prediction and retrieval that addresses four key challenges: (1) preserving fine-grained textual details for retrieval, (2) learning effective urban associations for prediction, (3) leveraging labeled queries for mutual task enhancement, and (4) improving computational efficiency. As shown in Figure | our system processes textual queries, geographic objects, and their spatial locations through two complementary views: First, the Individual View encodes text terms into Bloom filters via multiple hash functions. A neural network converts them into weighted sparse representations, avoiding information loss of dense vector aggregation. Second, the Collective View models urban regions through dual graph contrastive learning: an urban region graph capturing spatial proximity and context graph encoding broader spatial influences. The mutual information maximization between regions and their contexts extracts geographic associations and filters noise. Third, shared codebook connects the view, with warm-up training strategy for seamless task fusion. In addition, we propose row- and column-selection techniques that exploit Bloom filter sparsity to improve computational efficiency.
4.1 Text Encoding with Bloom filters
Textual descriptions of geographic objects are crucial for geospatial retrieval and prediction tasks. Traditional retrieval methods like BM25 use Bag-of- Words (BOW) representations, which effectively

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identify key terms but struggle with scalability due to the vast vocabulary of geographic texts. Most
prediction methods employ one-hot encoding of object categories [24 33
68 which neglect fine-
grained text information. While recent pre-trained language models (PI.M) [
have 14
29
39
improved semantic understanding, they incur high computational costs and slow inference speeds.
We observe that geographic texts contain distinctive city-specific terms like addresses, landmarks, and local business names. PLM-based approaches often underperform on these terms they appear infrequently in pre-training corpora, requiring substantial fine-tuning data to match classical methods' performance (Table D. This suggests deep semantic understanding from PLMs may be unnecessary. and that representing city- sipeccficiic terms through finer lexical granularity could suffice. We therefore propose to decompose texts with multiple n-gram and dictionary-basse -base tokenizers and then encode them to Bloom filters 2 via random hashing. This strategy preserves critical lexical patterns similar to BOW while maintaining constant dimensionality for efficient computation. We use vanilla Bloom filters in implementation, detailed in Appendix A
Two key concerns arise in using Bloom filters: (1) Hash Collision, which means that distinct text terms may sometimes share the same hash value, leading to inaccurate representations. However, we empirically find that such collision doesn't 't significantly affect the holistic urban features important for predictions. In retrieval tasks, we can leverage membership test (as in in Appendix (A) to exclude query terms not present in the object's Bloom filters, reducing the negative impact of hash collisions. (2) Loss of Token Order, as Bloom filters only record the existence of terms without capturing their order. This may lead inaccuracies in handling order-sensitive queries in retrieval tasks (e.g. treating "Unit 123456" and "Unit 654321" as the same term). However, the spatial keyword queries handled in GIR tasks are typically concise, and the n-gram tokenizer (as in DSSM (33) is generally sufficient. A 3-gram tokenizer will turn "123456" into "12#", "#34#", "#56", encoding the local order of tokens.
4.2 Relevance Learning with Neural Networks
We then handle retrieval tasks with neural networks, computing a relevance score to determine the order in which results are displayed to users, as in [ We empirically found that methods based on term-mateee matching can achieve strong effectiveness without training (c.g., BM25-D in Table. A. as they effectively match city-specific terms without prior knowledge. Hence, we propose to leverage the inherent cerm-matching capabilities of Bloom filters, i.e., the non-zero bits in each Bloom filter correspond to text terms from the query or objects. Specifically, we keep the representation sparse, performing bit-by-bil bit evaluation on Bloom filter bits:
TextSim(q, = (B, o F,(B,)) (B. 3 Fo(.))
(I)
In Eq. D B, and B, are the Bloom filters (length m) for the query and object, respectively. We compute two sparse representations by reweighting the bits within the query and object Bloom filters separately. As in Figure T the encoders Fg and F. contain a large, shared codebook matrix to encode the Bloom filters into dense embeddings, followed by two non-linear hidden layers that further compress them into oow-dimensional space, extracting potential semantic relevance, Finally, the embeddings are expanded back via an Evaluation Layer to the dimension of the input Bloom filters, where each dimension is regarded as the importance of the bit at the corresponding position. The object Bloom filters can be evaluated offline, reducing online computations. The neural network F(B) can be defined as:
m W. WR
= (W.B/ Σ Bd), h = =b(B)=
(2)
h2 = aW2h, + b2), W₂ E R
(3)
hg = (Wah2 + bz), W3 E ghaxha
(4)
F(B)- ar (W, + 1, W, E ma +0
(5)
Here, h; denotes the output of i-th layer with dimension h; weight W., and bias b, a denotes the activation function. W, is the codebook matrix shared between the query and object encoder F, T and F., and Σn B, is the number of non-zero bits within the Bloom filters, ensuring a consistent hy across the varying amount of text terms. Eq 5 is an evaluation layer tailored for Bloom filters, where W, E xma is set to zero, ensuring each intersecting bit has equal initial importance of 1.
5

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This preserves the term matehiig capabilities of Bloom filters, which gives a good starting point in optimization that leads faster convergence. Finally, we normalize and combine the text similarities with geographic distances:
T(g, o) Sigmoid (3i TextSim(qq o) + 82)
(6)
D(g.0) - -log(1 + Dist(g, o))
(7)
Relevance n) T(q, m) + 7 D(g,)) + 72 D)g
(8)
Here, we use the logarithm function to align the distance with human spatial perceptions, i.e., individuals are more sensitive to differences proximity with nearby objects, while this sensitivity diminishes for objects further apart. The normalization of the text similarities facilitates its smooth combination with geographic distances. 81, 32, 71, 72 rescale and balance the influence of two similarities and their first-order interaction, which better excludes proximate objects with little text similarities. We initially set 82 = 7/2 0 and B1 "1 = and train these parameters together with the neural networks via LambdaRank
B
loss.
4.3 Extracting Collective Features
Our objective is to learn geospatial associations critical for prediction tasks. Following most geospatial models 191 we employ Graph Neural Networks (GNNs) to preserve POI spatial relationships ( However, common self-suuervised approaches like graph reconstruction struggle with Bloom filters, as they inherently mix informative text terms with useless terms. We posit that informative terms are those shared across regions but exhibit diverse spatial distributions. Unique terms like "Postcode 11 114514" are unhelpful because they only exist in one place and cannot be leveraged by downstream predictors. The density useful terms like "Starbucks" helps identify commercial zones.
Inspired by Tobler's Second Law of Geography ("the phenomenon external to a geographic area of interest affects what goes on inside"), we learn useful information from Bloom filters by maximizing the mutual information between city regions and their surroundings. Specifically, we construct a city-wise graph with Delaunay Triangulation following [24 33 and perform contrastive learning on two graphs following 220 733 The two graphs include: 1) A region graph consisting of objects within a region, which captures intra-region bit distribution patterns, and 2) context graph incorporating K-hop neighborhoods of the region graph. We utilize two 2-layer Graph Convolutional Networks (GCNs) 30 to compute object-level and graph-level representations:
E. MLP, (o(A,H,Wī bDD + bb),
Z MLP; (PrgPoAllll 2.)),
(9)
2 (IP. 10W + bG), Zg MI.Px(AvgPoo((25)) (2.).
(10)
where Ar, A. are adjacency matrices, H,, He are the features from the dense codebook in Eq. 2 WT, Wf are learnable weights, and 6,, by are biases in the i-th GCN layer. AvgPool denotes the graph-level average pooling. We then maximize the mutual information (MI) between the region and context graphs, defined as:
1 M
MI(Z,, 2,) + MI(Z; }
Cpred
(11)
where the MI estimation is computed as:
MI Z,, Z2) = =E,,c [log D(Z,, z,)] + Ep,c log (1 DD(22 22))
MI(25. 2g) = EE [log D(Z zj) + Ear log (1 2 z3))
where D(a, b) = a WTb is bilinear discriminator, and f, â are negative samples generated by randomly removing a portion of bits (e.g., 20%) from the Bloom filters before encoding them with the dense codebook in Eq 2 This design enhances the fine-grained text terms encoded by Bloom filters. By maximizing the mutual information between regions and their context while discriminating negative inputs with any missing bits, extract critical geographic associations from shared bits.

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4.4 Training Strategy and Optimizations
The shared codebook must balance coarse region-level features (for prediction) and fine query-object matches (for retrieval). Direct joint training causes codebook overfitting to retrieval data due to scale disparity: prediction tasks generally uses only thousands of regions, while retrieval tasks can involve millions of user queries. To resolve this, we employs two-phase training: (1) Warm-up Phase: Train exclusively on prediction tasks for some (i.e., 2-3) epochs, and (2) Alternating Phase: Iteratively training on prediction and retrieval data batches. This effectively balances holistic region features while absorbing object specifics (full algorithm in Appendix B
In addition, we propose to accelerate the computation leveraging Bloom fil- ter sparsity. As shown in Figure 2 we propose row and column selection in the codebook and the evaluation layer, significantly accelerating training and inference computations.
Iloom Silees
Hirdden
Entraader Lovel
Dense Codebook
Condidate Object Bloom
Evoridion
Doll Senedeenii A Pasking
Intersection
5
Experiments
Figure 2: Illustration of the proposed optimization technique.
In this section, we evaluate the output representations on geographic prediction and retrieval tasks following previous literature 39 We also perform efficiency and ablation studies.
5.1 Experimental setups
Table 1: Dataset Statistics
Datasets We use data from two cities, i.e. Bei- jing and Shanghai. The datasets include Point- of-Interests (POIs) from Meituan [39). leading consumer service platform in China. The statistics of the datasets are shown in Table
City
POIs
Labeled Queries
Regions
Beijing
122420
168,998 127,183
1010
Shanghai
116859
1358
Downstream tasks and Evaluation Protocols We evaluate the learned representations three
downstream tasks: POI retrieval, Population Density Prediction, and House Price Prediction. POI
retrieval is one of the most common tasks in the location-based service. We evaluate following [39].
where the user-selected objects on the Meituan platform are labeled as ground truth and the results are
measured with Recall and Normalized Discounted Cumulative Gain (NDCG). Population density and
house price prediction are two common tasks in the literature [9], and we measure the results with
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). More details for downstream tasks and metrics are provided in Appendix D
Baselines
The proposed method is compared with strong GIR and Urban Region Representation
Learning baselines, including GraphS [99, DGI [59]. MVGRL 20) SpaBERT
35
HGI
24
and CityFM I for prediction tasks, and BM25 53 BERT B.. OpenA DRMM
18
DrW
39
MGeo [14]. and DPR (29) for retrieval tasks. Many other recent prediction methods
28
34
62
69
rely on other data types (e-g., GPS trajectory data of vehicles) as inputs. However, GPS trajectory
data are only available in very few cities, and we did not find them for our datasets. Thus, we cannot run these methods for comparison in our experiments. It's noteworthy that we omit Pl.M-based retrieval methods with sparse representations 4 [5] due to their common failure on common GIR addresses and numbers. More details of these baselines can be found in Appendix
5.2 Experimental Results in Prediction Tasks
We evaluate the effectiveness of UrbanSparse on Population Density Prediction (population per square kilometer) and on House Price Prediction (CNY per square meter) as in Table and The results give important insights: (1) Graph contrastive learning methods (DGI, MVGRL), while incorporating geospatial proximity within region graphs, perform similar or worse than a direct
7

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feature reconstruction over input features (i.e., GraphSAGE) due to the loss of fine- -hniind textual features. (2) Competitive baselines HGI and CityFM have mitigated the problem via rule-based geographic context learning, where CityFM pre-trains on extensive OpenStreetMap data to achieve the second-best performance. (3) UrbanSparse stands out by leveraging Bloom filters as fine-graines text features with its novel contrastive learning at multiple granularities. Particularly, we compare it with its variant UrbanSparse w/o Individual (where labels from retrieval tasks are removed). The results show that improvements from labeled queries are small (but statistically significant, i.e., T-test p-value < 0.05). Without these labels, our framework still outperform other baselines.
Table 2: Population Density Prediction, with the best in bold and the second best underlined
Beijing
Shanghai RMSEL
Method
MAE]
RMSEL
R²T
MAE|
R²T
GraphSage
4566 + 361
7113 + 811
0.60 + 0.06
11020 + 807
15142 + 1827
0.34 + 0.05
DGI
5703 = 259
8403 + 234
0.47 :: 0.04
13022 + 567
17482 + 371
0.17 +: 0.04
MVGRL
5675 = 248
8397 + 378
0.47 0.05
12266 - 633
16739 + 774
0.24 0.01
SpuBERT
6494 = 432
9088 + 856
0.34
11586 - 672
15401 = 1333
0.31 0.07
HGI
4547 = 349
7210 + 754
0.05
8942 + 755
13606 = 1512
0.46 0.09
CityPM
4420 = 348
6496 + 694
0.66 + 0.04
6930 + 633
10751 + 1184
0.67 + 0.05
Sparne
3307 + 14
5772 + 31
0.75 + 0.003
5343 + 55
8958 + 169
0.78 0.01
w/o Individual
3368 + 53
5871 + 78
0.74 0.01
5467 + 81
9189 + 177
0.76 + 0.01
(Gains)
25.16%
11.14%
13.64%
22.90%
16.68%
16.42%
Table 3: House Price Prediction, with the best in bold and the second best underlined
Beijing RMSEJ
Shanghai
Method
MAE
R²t
MAE!
RMSEL
R²t
SpaBERT
21015 1837
28083 + 2289
0.55 0.05
16772
1253
25340 + 3405
0.31 = 0.08
GruphSage
14239 1824
19947 + 2836
0.77 1 0.05
17408
1065
25118 + 3217
0,32 = 0.05
DGI
16203 1352
22399 + 1796
0.70 0.05
18415 1 426
25876
0.14 0.04
MVGRL
16799 2035
23792 # 2753
0.66 0.09
17833 + 211
25011 237
0.20 0.01
HGI
14974 1 1251
21833 1: 2022
0.72 0.06
16095 1140
24022 1: 3478
0.38 0.06
CityFM
17721 + 2178
24123 + 2884
0.66 166+00 0.06
15694
1727
24862 + 4444
0.33 0.15
UrbanSparsc
11983 + 507
17155 767
0.82 002=0.00
13281 + 325
20610 - 671
0.46 = 0.04
w/o Individual (Gains)
12881 1. 569
18326 691
0.02
13756 1 166
21669 535
0,40 1 0.03
15.84%
14.00%
6.49%
15.38%
13.90%
17.95%
5.3 Experimental Results in Retrieval Tasks
We evaluate UrbanSparse in POI retrieval tasks against strong retrieval baselines, where standard deviations are omitted as they are very small (< 0.003). As the vanilla BM25, BERT, OpenAI, and DPR only consider text similarity, we supplement BM25-D, BERT-D, OpenAI-D, and DPR-D to incorporate geographic distances following [39] by defining Relevance g, o) = (1 a1
Dnorm (0, ())0)) +a (q, o), where D Duorm o) denotes the geographic distances, Tnorm denotes the text similarity from the vanilla baseline, both are normalized to [0. 1]. a is a hyper-parameter balancing the text and the distance similarities, set by grid searching on the dev set. In addition, DRMM, DrW, DPR, UrbanSparse are fine-tumed on labeled queries while the rest are not. The results in Table&proovic several key insights: (1) The classical term-matching method BM25-D significantly outperform vector-based methods BERT-D and the leading commercial product, OpenAI-D. This underscores the critical importance of term-matching capabilities in retrieval tasks. (2) UrbanSparse surpasses heavy-weigh BERT-based methods such as DrW and DPR-D, showcasing the effectiveness of evaluating Bloom filter bits. Furthermore, its superiority over its variant without the Collective View (denoted as w/o Collective) validates the benefits of incorporating prediction tasks.
5.4 Efficiency Studies
Training Time We evaluate the training time of UrbanSparse against top-performing baselines on 1 NVIDIA V100 32GB. As shown in Table 5 DPR, GraphSAGE, HGI, CityFM, and DrW require
8

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Table 4: Point-of-Interee Retrieval, with the best in bold and the second best underlined
Method
Beijing NDCG@5
Shanghai NDCG@5
Recall 10
NDCG@1
Recall @ 10
NDCG@1
BM25
0.3401
0.2199
0.1634
0.3274
0.1913
0.1260
BM25-D
0.5477
0.4263
0.3569
0.6484
0.5215
0.4380
BERT
0.1602
0.1169
0.0979
0.1277
0.0853
0.0662
BERT-D
0.2400
0.1614
0.1298
0,2622
0.1687
0.1233
OpenAl
0.3265
0.2157
0,1637
0,3213
0.1875
0.1258
OpenAI-D
0.5206
0.3803
0.3078
0.6313
0.4852
0.3864
DRMM
0.1773
0.1105
0.0758
0.1921
0.1287
0.0804
DRMM-D
0.4357
0.2378
0.1566
0,4380
0.2433
0.1595
DrW
0.6316
0.4814
0.3791
0.7159
0,5394
0.4114
DPR
0.4183
0,2775
0.2121
0.4087
0.2498
0.1746
DPR-D
0.6688
0,4980
0.4132
0.7281
0.5641
0.4554
UrbanSparse
0.7062
0,5734
0,4990
0.7589
0.6209
0,5315
w/o Collective
0.6988
0,5695
0,4991
0.7526
0.6157
0.5289
(Gains)
5.59%
15.14%
20.76%
4.23%
10.07%
16.71%
Table 5: Training Time (Minutes) and Inference Memory (MB)
(a) Prediction Tasks
(b) Retrieval Tasks
Method
Training Time
Memory Usage
Method
Triining Time
Memory Usage
Beijing
Shanghai
Beijing
Shanghai
Beijing
Shanghai
Beijing
Shanghai
GraphSAGE
24
3.95
3.95
OpenAl
N/A
N/A
717.30
684.72
HGI
281
510
0.25
0.33
DrW
142
97
11806.29
10210.42
CityFM
510
355
3.95
3.95
DPR-D
448
282
507.80
491.60
UrbanSparse
22
0.25
0.33
UrhanSparse (Saves)
33
71.92
66.40
(Saves)
8.33%
42.11%
0.00%
0.00%
64.08%
65.97%
85.84%
86.49%
considerably longer training time. This is particularly evident for DPR and CityFM as they rely fine-tuning BERT, resulting in substantial training overhead. UrbanSparse leverages the sparsity of Bloom filters significantly reduce computational demands.
Inference Memory Usage We also report the memory usage in inference, with all embeddings stored in 32-bit float numbers. For prediction tasks, as the trained models can be offloaded and urban regions are relatively few, the memory usage across methods exhibits negligible differences. In retrieval tasks, however, significant differences emerge due to the model parameters needed to process user queries and a large amount of POIs. UrbanSparse produces sparse representation with fixed dimension of 8192 and density of only 2-3%, achieving dramatically reduced memory usage, making it resource-efficiem. choice for retrieval tasks.
We further evalu- the query processing speed of UrbanSparse against DPR-D by running brute- search. not evaluate DrW as requires > 24 hours for evaluation. As shown in Table [ UrbanSparse significantly outperforms DPR-D, achieving approximately 3.6x and 3.8x higher Queries Per Second (QPS) in Beijing and Shang-
Query Processing Speed
Table 6: Query Per Second Comparison
Method
#Params (M)
Beijing
Shanghai
DPR-D
110
133.05
133,94
UrbanSparse
2.72
476.29
505.20
hai, respectively. This substantial improvement is attributed to UrbanSparse's small model size and high representation sparsity
*While we achieve a reduction parameters, the 4x QPS gain bounded by our custom CUDA kernels for sparse representation calculations: the non-coue coalessee memory accesses in our kernel and an insufficiently optimized kernel dispatch strategy incur significantly higher latency than vendor-optimizee dense kernlls from experts. We anticipate that expert-tuned kernels will further narrow this gap.

---

Scalability UrbanSparse is lightweight and can theoretically scale up due to low computational complexity. To empirically verify this, we evaluate on established large datasets GeoGLUE [31, public GIR benchmark with 2,849,754 POIs. However, the benchmark doesn't support prediction tasks as it contains over 50% fake POIs for anonymity. As shown in Table 7 our methods show competitive retrieval effectiveness while trains much faster than PLM- method DPR-D. DrW and DRMM are omitted as they reports OOM on this dataset.
Table 7: Point-of-Interees Retrieval and Training Time on GeoGLUE
Method
Recall @ 10
NDCG@5
NDCG@1
Training Time (Min)
MGeo [14
N/A
N/A
0.5270
Unknown
DPR-D
0.7611
0.6318
0.5350
131
UrarnS
0.7621
0.6344
0.5310
54
5.5
Ablation Studies
While UrbanSparse's prediction advantages stem from Bloom filter-contrastive learning integration, can other contrastive learning methods replicate this by simply adopting Bloom filters? Our evaluation shows fundamental limitations: Table T reveals Bloom filters' inconsistent impact. While producing 150%+ improvements for Shanghai population prediction with DGI/MVGRL. other tasks show mixed results (-11.4% +36.3%). In conclusion, standard contrastive objectives appear poorly suited to extract Bloom filters' encoded bit patterns, while UrbanSparse achieves consistent gains. We also studied other hyperparameters (e.g., tokenizers, hash functions, Bloom filter length), and put these technical details in Appendix
Table 8: Effect of Bloom Filters BERT Embeddings
Method
Pop. Pred. R²t
House Pred. R²1
Beijing
Shanghai
Beijing
Shanghai
DGI w/ Bloom filters
0.4682
0.1877
0.7240
0.1511
0.4807
0.4752
0.7521
0.2060
(Gains)
2.7%
153.2%
3.9%
36.3%
MVGRL
0.4483
0.1716
0.6686
0.1866
w/ Bloom filters
0.3974
0.4416
0.7085
0.2130
(Gains)
-11.4%
157.2%
6.0%
14.2%
UraaseSpareeuaage
0.3830
0.3046
0.3388
0.1010
UrbanSparse
0,7480
0,7805
0.8234
0.4612
(Gains) 95.3%
156.2%
143.0%
356.6%
6 Conclusion
In this work, we introduced UrbanSparse, a unified framework integrating the traditionally separate geospatial prediction and retrieval tasks via two-view learning mechanism that combines Bloom filter-based sparse representations and graph contrastive learning. Extensive experiments demonstrate its ability outperform state-of-the-art baselines while achieving significant gains in efficiency, accuracy, and scalability. This study pioneers a new research direction in urban computing, offering transformative solution to unify task frameworks and address complex urban challenges with greater resource efficiency.
10

---

Acknowledgment
This research/project is supported by the National Research Foundation, Singapore, under its Al Singapore Programme (AISG Award No: 10122-hh--2221-8--0000 and A* RIE2025 Manufacturing, Trade and Connectivity (MTC) Programmatic Fund (M24N660043) administered by A*STAR. Yuanlong Chen's work is supported by the Singapore International Graduate Award.
References
[1] Pasquale Balschre, Weiming Huang, Gao Cong, and Yi Li. City foundation models for learning general purpose representations from openstreetmap. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA. October 21-25, 2024, pages 87-97, 2024.
[2] Burton H. Bloom. Space/time trade-offs in hash coding with allowable errors. Commun. ACM, 13(7): 422 426, 1970. ISSN 0001-0788 -0722
[3] Christopher Burges, Robert Ragno, and Quoc Le. Learning rank with nonsmooth cost functions. In Advances in Neural Information Processing Systems, volume 19, 2006.
[4] Jianly Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-embedding: Multi-lingual, multi-funntionalliyy multi-granularity text embeddings through self-knowledge distillation, 2023.
[5] Lisi Chen, Gao Cong, Christian S. Jensen, and Dingming Wu. Spatial keyword query processing: An experimental evaluation. Proc. VLDB Endow. 6(3):217-228, 2013.
[6] Yen- Yu Chen, Torsten Suel, and Alexander Markowetz. Efficient query processing in geographic web search engines. In Proceedings the 2006 ACM SIGMOD international conference Management of data, pages 277-288 2006.
Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long, Yiding Liu, Arun Kumar Chandran, and Richard Ellison. Robust road network representation learning: When traffic patterns meet traveling semantics. In Proceedings of the 30th ACM International Conference on Information & Knowledge Munagement, CIKM 22,, page 211-220, New York, NY, USA, 2021. Association for Computing Machinery. ISBN 97814503.
[8] Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, and Fuzheng Zhang. Pniteessf-itterest relationship inference with spatial-enriched graph neural networks. Proc. VLDB Endow. 15(3):504-511 November 2021. ISSN 2150-8097.
[9] Yile Chen, Weiming Huang, Kaiqi Zhao, Yue Jiang, and Gao Cong. Self supf-superviee representation learning for geospatial objects: A survey. Information Fusion, 123: 103265, 2025. ISSN 1 1566-2535.
[10] Maria Christoforaki, Jinru He, Constantinos Dimopoulos, Alexander Markowetz, and Torsten Suel. Text space: efficient geo-search query processing. In Proceedings of the 20th ACM International Conference Information and Knowledge Management, CIKM 11. page 423-432, 2011. ISBN 9781450307178.
[11] Gao Cong, Christian S. Jensen, and Dingming Wu. Efficient retrieval of the top-k most relevant spatial web objects. Proc. VLDB Endow., 2(1):337 -488 2009, ISSN 2150-8097.
[12] De Felipe, Vagelis Hristidis, and Naphtali Rishe. Keyword search on spatial databases. 2008 IEEE 24th International conference on data engineering, pages 656-665. IEEE, 2008.
[13] Jacob Devlin, Ming-Wo Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume (Long and Short Papers), pages 4171-4186, 2019.
[14] Ruixue Ding, Boli Chen, Pengjun Xie, Fei Huang, Xin Li, Qiang Zhang, and Yao Xu. Mgeo: A multi- modal geographic pre- training method. Proceedingy of the 46th International ACM SIGIR Conference on Rexearch and Development in Information Retrieval, 2023.
[15] Thibault Formal, Benjamin Piwowarski, and Stéphane Clinchant. SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking. page 2288-2292. Association for Computing Machinery, New York, NY, USA, 2021. ISBN 4781499383.

---

[16] Vanessa Frias-Martinn Martinez, Victor Soto, Heath Hohwald, and Eurique Frias-Martinez. Characterizing urban landscapes using geolocated tweets. In Proceedings of the 2012 Internaiional Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pages 239-248, 2012.
[17] Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, and Xiaolin Li. Efficient region embedding with multi- view spatial networks: A perspective of locality-consi constraed spatial autocorrelations. In Proceedings of the 32rd AAAI Conference on Artificial Intelligence, pages 906 913, 2019.
[18] Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. A deep relevance matching model for ad-hoc retrieval. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 16, page 55-64, 2016. ISBN 9781 178443034
[19] William L. Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 1024-1034, 2017.
[20] Kaveh Hassani and Amir Hosein Khasahmadi. Contrastive multi-view representation learning on graphs. In Proceedings of International Conference on Machine Learning, pages 3451-3461. 2020.
[21] Baotian Hu, Zhengdong Lu, Hang Li, and Qingeai Chen. Convolutional neural network architectures for matching natural language sentences. In Proceedings of the 27th International Conference on Neural Information Processing Systems- Volume 2, NIPS' 14, page 2042-2202 2050,, 2014.
[22] Jizhou Huang, Haifeng Wang. Yibo Sun, Yunsheng Shi, Zhengjie Huang, An Zhuo, and Shikun Feng. Ernie- geol: A geography-and-language pre-trained model and its applications in baidu maps. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22, page 3029-3039, 2022. ISBN 9781450393850.
[23] Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM '13, page 2333-2338, 2013. ISBN 9781450322638.
[24] Weiming Huang. Daokun Zhang, Gengchen Mai, Xu Guo, and Lizhen Cui. Learning urban region representations with pois and hierarchical graph infomax. ISPRS Journal of Photogrammetry and Remote Sensing, 196: 145, 2023. ISSN 0924-2716.
[25] Weiming Huang, Jing Wang, and Gao Cong. Zero-shot urban function inference with street view images through prompting pretrained vision-language model. International Journal of Geographical Information Science, (8(7):1414-1443 14-1442, 2024.
[26] Bo Hui, Da Yan, Wei-Shinn Ku, and Wenlu Wang. Predicting economic growth by region embedding: A multigraph convolutional network approach. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pages 555-564, 2020.
[27] Krzysztof Janowicz. Gengchen Mai, Weiming Huang, Rui Zhu, Ni Lao, and Ling Cai. Geofm: how will geo-foundation models reshape spatial data science and geoai? International Journal of Geographical Information Science, 39(9): 186811155..1.111111 2025.
[28] Porter Jenkins, Ahmad Farag, Suhang Wang, and Zhenhui Li. Unsupervised representation learning of spatial data via multimodal embedding. In CIKM, pages 1993-2002, 2019.
[29] Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769-6781, 2020.
[30] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In P Proceedings of the 5th International Conference on Learning Representations. 2017.
[31] Dongyang Li, Ruixue Ding, Qiang Zhang, Zheng Li, Boli Chen, Pengjun Xie, Yao Xu, Xin Li, Ning Guo, Fei Huang, and Xiaofeng He. Geoglue: A geographic language understanding evaluation benehmark. CoRR, abs/2305.06545. 2023.
[32] Yi Li and Gao Cong. Geobloom: Revisiting lightweight models for geographic information retrieval. Proc. VLDB Endow., 18(5):1348-1533 2025.
12

---

[33] Yi Li, Weiming Huang, Gao Cong. Hao Wang, and Zheng Wang. Urban region representation learning with openstreetmap building footprints. In Proceedings the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023, pages 1363-1373. 2023.
[34] Zechen Li, Weiming Huang. Kai Zhao, Min Yang. Yongshun Gong, and Meng Chen. Urban region embedding via multi-view contrastive prediction. In AAAI, pages 1724-8732, 2024.
[35] Zekun Li, Jina Kim, Yao-Yr Yi Chiang, and Muhao Chen. SpaBERT: A pretrained language model from geographic data for geo-entity representation. In Findings of the Association for Computational Linguisties: EMNLP 2022, pages 2757-2769, 2022.
[36] Chenxi Liu, Hao Miao, Qianxiong Xu, Shaowen Zhou, Cheng Long, Yan Zhao, Ziyue Li, and Rui Zhao. Efficient multivariate time series forecasting via calibrated language models with privileged knowledge distillation. In 2025 IEFF st International Conference on Data Engineering (ICDE), pages 3165-3178, 2025.
[37] Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, and Rui Zhao. Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 18780-18788, 2025.
[38] Chenxi Liu, Shaowen Zhou, Qianxiong Xu, Hao Miao, Cheng Long, Ziyue Li. and Rui Zhao. Towards cross- mooss-mod modeling for time series analytics: A survey in the LLM era. In Proceedings of the Thirty- Fourth International Join! Conference on Artificial Intelligence, IJCAI 2025, Montreal, Canada, August 16-22, 2025, pages 10564-10572. ijeai.org, 2025.
[39] Shang Liu, Gao Cong, Kaiyu Feng. Wanli Gu, and Fuzheng Zhang. Effectiveness perspectives and deep relevance model for spatial keyword queries. Proceedings of the ACM on Management of Data, 1(I): 222, 2023.
[40] Shuai Liu, Guojic Song, and Wenhao Huang. Real-time transportation prediction correction using reconstruction error in deep lcarning. ACM Trams. Knowl. Discov. Data, 14(2), February 2020. ISSN 1556-4681.
[41] Shuai Liu, Xiucheng Li, Yile Chen, Yue Jiang, and Gao Cong. Disentangling dynamics: Advanced, scalable and explainable imputation for multivariate time series. IEEE Transactions on Knowledge and Data Engineering. 2025.
[42] Ahmed Mahmood and Walid G. Aref. Query processing techniques for big spatial-keywon data. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, page 1777-1782, 2017. ISBN 9781450341974.
[43] Thomas Mandl, Paula Carvalho, Giorgio Maria Di Nunzio, Fredric Gey. Ray R Larson, Diana Santos, and Christa Womser-Hacker Geoclef 2008: The clef 2008 tross-langaage geographic information retrieval track overview. Evaluating Systems for Multilingual and Multimodal Information Access: 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17-19, 2008, Revised Selected Papers 9, pages 808-821. Springer, 2009.
[44] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. 2008. ISBN 978-0-521-86571--3
[45] Rohin Manvi, Samar Khanna, Gengehen Mai, Marshall Burke, David B. Lobell, and Stefano Ermon. Geollm: Extracting geospatial knowledge from large language models. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, 2024.
[46] Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Kai Zheng, Feiteng Huang, Jiandong Xie, and Christian S. Jensen. A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data. In 40th IEEE International Conference Data Engineering, ICDE 2024, Utrecht, The Netherlands, May 13-16, 2024, pages 1050-1062. IEEE, 2024.
[47] Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang. Kai Zheng, and Christian S. Jensen. Spatio-temporal prediction on streaming data: A unified federated continuous learning framework. IEEE Transactions on Knowledge and Data Engineering, (7(22221 226622140,, 2140, 2025.
[48] Tomas Mikolov, llya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. volume 26, 2013.
13

---

[49] Nikhil Naik, Jade Philipoom, Ramesh Raskar, and César Hidalgo. Streetscore predicting the perceived safety of one million streetscapes. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 793-799, 2014.
[50] Haifeng Niu and Elisabete A. Silva. Delineating urban functional use from points of interest data with neural network embedding: A case study in greater london. Computers, Environment and Urban Systems, 88:10 101651,, 2021.
[51] Paolo Palmieri, Luca Calderoni, and Dario Maio. Spatial bloom filters: Enabling privacy in location-aware applications. In Information Security and Cryptology, pages 16-36, 2015. ISBN 978-3-319-1674599
[52] Ross S Purves, Paul Clough, Christopher B Jones, Mark H Hall, Vanessa Murdack, al. Geographic information retrieval: Progress and challenges spatial search of text. Foundations and Trends in Information Retrieval, (2(-3))164-3111 2018.
[53] S. E. Robertson and S. Walker, Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR '94, pages 232-241, 1994. ISBN 978-1-4471-20999
[54] Angela Schwering. Approaches to semantic similarity measurement for geo-spatial data: survey. Transactions GIS, 12(1):5-29, 2008.
[55] Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. A latent semantic model with palpluttiioa--pollllg structure for information retrieval. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM "14, page 101-110, 10, 2014, ISBN 9781450325981.
[56] Karen Sparck Jones. A statistical interpretation of term specificity and its application in retrieval, Journal of Documentation, 28(1):1 1-21,, 1972.
[57] Karen Sparck Jones. A statistical interpretation of term specificity and its application retrieval, page 132-142. 1988. ISBN 0947568212.
[58] Jiabao Sun, Jiajie Xu, Kai Zheng, and Chengfei Liu. Interactive spatial keyword querying with semantics. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM '17, page 1727-1736, 2017. ISBN 9781450349188,
[59] Petar Velickovic. William Fedus, William L. Hamilton, Pietro Liò. Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In Proceedings of the 7th International Conference on Learning Representations, 2019.
[60] Hongjian Wang and Zhenhui Li. Region representation learning via mobility flow, In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, page 237-246, 2017. ISBN 9781 450349185.
[61] Tonglong Wei, Youfang Lin, Yan Lin. Shengnan Guo, Lan Zhang, and Huaiyu Wan. Micro-macro spatial- temporal graph-based encoder-decoder for map-constrained trajectory recovery. IEEE Transactions on Knowledge and Data Engineering, 36(1 1):6544-658) 2024.
[62] Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, and Cheng Wang. Multi graph fusion networks for urban region embedding, In Proceedings of the 31st International Joint Conference Artificial Intelligence, pages 2312-2318, 2022. 2022.
[63] Zijun Yao. Yanjie Fu, Bin Liu, Wangsu Hu, and Hui Xiong. Representing urban functions through zone embedding with human mobility patterns. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pages 3919-3925, 2018
[64] Haitao Yuan and Guoliang Li. Distributed in mn-memory trajectory similarity search and join on road network. In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019, pages 1 1262-1273. IEFF, 2019.
[65] Jing Yuan, Yu Zheng, and Xing Xie. Discovering regions of different functions in city using human mobility and pois. Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 186-194, 2012.
[66] Zixuan Yuan, Hao Liu, Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu, and Hui Xiong. Spatio- temporal dual graph attention network for query- poi matching. SIGIR '20, page 629 638, 2020. ISBN 978 4503800384
14

---

[67] Wei Zhai, Xueyin Bai, Yu Shi, Yu Han. Zhong-Ren Peng, and Chaolin Gu. Beyond word2vec: An approach for urban functional region extraction and identification by combining place2vec and pois. Computers, Environment and Urban Systems, 74:1-12, 2019.
[68] Liang Zhang. Cheng Long, and Gao Cong. Region embedding with intra and inter-view contrastive learning. IEEE Transactions on Knowledge and Data Engineering, pages 1-6, 2022.
[69] Mingyang Zhang, Tong Li, Yong Li, and Pan Hui. Multi-view joint graph representation learning for urban region embedding, In Proceedings of 29th International Joint Conference on Artificial Intelligence. pages 4431 4437, 2020.
[70] Yunchao Zhang, Yanjie Fu, Pengyang Wang, Xiaolin Li, and Zheng. Unifying inter-region autocorrela- tion and intra-region structures for spatial embedding via collective adversarial learning. In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1700-1708, 2019.
[71] Ji Zhao, Dan Peng. Chuhan Wu, Huan Chen, Meiyu Yu, Wanji Zheng, Li Ma, Hua Chai, Jieping Ye, and Xiaohu Qie. Incorporating semantic similarity with geographic correlation for query-poi relevance learning. Proceedings of the AAAI Conference Artificial Intelligence, 33(01): 127001::77 2019.
[72] Yu Zheng, Furui Liu, and Hsun- Hsun-Ping Hsieh. U-air: when urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference Knowledge Discovery and Data Mining. pages 1436 1444, 2013.
[73] Haicang Zhou, Weiming Huang, Yile Chen, Tiantian Hc, Gao Cong, and Yew Soon Ong. Road network representation learning with the third law of geography. Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Uirich Paquct, Jakub M. Tomezak, and Cheng Zhang, editors, Advances Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurlPS 2024, Vancouver, BC, Canada, December 10 15, 2024, 2024.
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A Bloom Filters and Membership Tests
probabilistic data structure designed for set membership testing. Given a set A {ai, .2...... an} } of elements, Bloom filter encodes A using bit vector B of length m, initially filled with zeros. It relies on independent hash functions H1, H2,...; Hks each mapping an input element to position in {l....,
Bloom filter is a space
To insert an element
A, the bits at indices (a),), H2())..... Ha( the bit vector B are set to 1. To check whether query element belongs to the set, the Bloom filter examines the bits at positions H1 (q), (g).)..... ..... (g) If any of these bits O. q is definitely not in A. If all are the Bloom filter reports that ( may belong to A, introducing false positive probability but guaranteeing no false negatives.
This tradeoff makes Bloom filters particularly useful in large-scale applications where space efficiency and fast membership queries are critical. In our implementation, k 2 2 sufficient for small false positive rates (See table [T]. We use SHA-256 as random hash functions, leaving more sophisticated designs to future work.
empirically found m 8192,
B Training Algorithm
We hereby detail the training algorithm used balance the training on retrieval and prediction tasks in our framework.
Algorithm 1 Two-Phase Training of UrbanSparse
1: Input: Pprdd (region data), Dieir (query-objec pairs), Ewann 3, Exotal 20
2: Parameter: Model fo with codebook C
3: procedure WARM-UP PHASE
4: for epoch to Evarm do
5: for each batch B E prod do
6:
Compute Cpeed
7:
Update A &- 0 V ppedd
8: end for
9: end for
end procedure
11:
procedure ALTERNATING PHASE
12:
for epoch Ewarm + to Evotal do
13:
Shuffle Pred and Dretr for i = max (iex((reeal) [Drerr] do
15:
Sample batch Bp D pred B, Diet Compute Cpred on B, via Eq.3
16:
17:
Compute Lier on Bp via LambdaRank
18:
Update + - 17 (Cpred + Crew)
19:
end for
20:
end for
21:
end procedure
C Complexity Analysis
We analyze the time complexity of the proposed UrbanSparse in retrieval tasks. The prediction tasks are not analyzed they vary significantly with downstream predictors. Let h2 be the output dimension of the i-th model layer, u, the count of non-zero bits the user query and object Bloom filters, the time complexity of model forward process during training is given by O(uu e)hy h1/12 - h2ha + min u, e) As hi, 112, and hg are small constants, (i.e., 256 and 32 in our implementation), the training time is dominated by + which is only affected by the query and object text lengths, and the number of hash functions. The time complexity during inference Olu(h, ha) + h + figh),, which is dominated by the query length.
16

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D Downstream Tasks and Evaluation Protocols
To evaluate the quality of the learned representations, we consider three downstream tasks:
POI Retrieval. Given a user query, retrieve relevant points interest (POIs). We follow the
dataset split and protocol of [39), where Meituan user-selected POIs serve ground truth.
Population Density Prediction. Predict the population density of a geographic region based on learned embedding.
House Price Prediction. Estimate the average house price in region using its representa- tion.
It is noteworthy that land use, population density, and house price prediction are the top-3 common tasks according to the recent survey [9]. However, we fail to find high-quality land use ground truth in the two studied cities, so we only evaluate the latter two tasks.
Evaluation Metrics
We employ the following metries for each task:
@ecall@ and NDCG@K (Normalized Discounted Cumulative Gain) for POI retrieval. Recall@K K. measures the fraction of ground-truth POIs appearing in the top- K results, while NDCG@K K accounts for both relevance and ranking position.
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of De- termination (R²) ) for the regression tasks (population density and house price). MAE and RMSE quantify absolute and squared deviations, respectively; R² indicates the proportion of variance explained by the model.
For retrieval tasks, requested and got the established benchmark from 39] which has fixed split with the train/dev/val ratio 0.81 1.81:000::00 10. As the splits are fixed without randomness, the standard deviations appear to be very small (< 0.003 for all methods) and we omit the standard deviations in our table. For prediction tasks, we follow common practice of unsupervised representation learning, evaluating the learned representations with scikit-learn FandommoorestRggresoo using f-lold cross-validation We strictly repeat all experiments 10 times, report the average results and standard deviations without cherry-picking.
all urban regions
Data Sources All datasets (or their corresponding embeddings/Bloom filters) used in this paper are publicly available. Table 9 lists each data type along with its source and download link.
Table 9: Data sources and download links
Data Type
Source
Link
POI datasets and queries
Mcituan
Population density
WorldPop
https://hub.wott
House prices
Beike
sttps://ke.com
Administrative boundaries
GADM
https://gadn.org
Due to licensing constraints, the raw Meituan query and POI text data cannot be shared. Instead, we provide the corresponding Bloom filters and geographic coordinates in our GitHub repository, along with BERT, OpenAI, and our trained DPR embeddings to enable full replication of all experiments.
E Baselines and Implementation Details
We compared with the following baselines from the prediction and retrieval tasks respectively:
Prediction Methods
GraphSage [9:: This classical graph learning algorithm samples and aggregates neighbor nodes to compute node embeddings. It commonly used as a geospatial representation learning baseline with node feature graph structure reconstruction objectives. It is noteworthy that we have tested both vanilla GCN and GraphSAGE as representative graph learning baselines. However, GCN suffers from scalability issue and reports OOM in our datasets.
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DGI [59]: This method maximizes the mutual information between node and graph embeddings.
We take its graph embedding as the region representation. It doesn't explicitly learn geospatial correlations.
MVGRL [20]: Inspired by DGI, this method maximizes the mutual information between the node and graph embedding from the original graph and an augmented graph constructed by graph diffusion. We use its graph embedding as the region representation. It doesn't It explicitly learn geospatial correlations.
SpaBERT 35|: This method utilizes pre-trained BERT to learn geographic object repre- sentations with text and geospatial proximity. We average its object embeddings region representation.
HGI [24]: Inspired by DGI, this method incorporates geospatial domain knowledge by hierar- chically maximizing the mutual information between POI, region, and city representations. It proposes a novel rule-based strategy of positive and negative sampling to preserve fine-grained and holistic information simultaneously.
CityFM M:
This method learns ceneral-purpose geospatial representations from multimodal OpenStreetMap node, polyline, and polygon data. We use its node encoder to encode POI representations and average them the region representation.
(2) Retrieval Methods
BM25 [:3]
This classical information retrieval method computes text similarities based on bag-of-words (BOW) representations and ferm-matching.
BERT 13 BERT is a representative pre-trained language model that excels in capturing deep semantics. We use the cosine similarity between queries and object representations to assess text similarities.
OpenAr OpenAl's sestt-emedding---eee 3-mmllll generates high-quality text embedding effective for retrieval tasks. Its technical details remain proprietary.
DRMM 18] This model evaluates text similarities based on pairwise local interactions at the term level. doesn't account for geographic proximity.
DrW [39] This method utilizes BERT to capture term-level text similarities and propose a novel query-aware combination strategy with geospatial distances.
DPR 20 : This method finetunes BERT on labeled queries, shortening the distance between
textual similarities between relevant query-object pairs.
MGeo [4] This method applies multi-task pre- pre-training on a BERT- based encoder and fine- tunes it by user queries. As we unable to replicate the results of MGeo with their official code, we don't ion'ted evaluate on the two datasets in the main content of the paper, and only reference the evaluation results on GeoGLUE in Table 7 as presented by the authors.
(3) UrbanSparse Variants
UrbanSparse w/o Individual, where we remove the proposed Individual View in Figure 1 UrbanSparse w/o Collective, where we remove the proposed Collective View in Figure I
It is worth noting that although many recent methods for urban region representation learning rely on human mobility data (c.g.. vehicle trajectories), such data are available for only limited number of cities, so we do not include them in our comparisons. Instead, to ensure relevance, we compare against the most recent versions of HGI (2023) and CityFM (2024). On the other hand, sparse retrieval methods such as SPLADE [1) and BGE-M3 4 rely on PLM tokenizers that split each digit of an address number into a separate token. As result, they cannot properly match street or house numbers and cannot work properly in our datasets.
For retrieval baselines BM25, BERT, OpenAI, and DPR only consider text similarity, we supplement BM25-D, BERT-D, OpenAT-D, and DPR-D to incorporate geographic distances following (39
by defining Relevance(q.. o) (1 - a1 Dnorm(g. o)) + a - Toorm (q, o), where 1) Dnorm (q,
denotes the geographic distances, T Torm: denotes the text similarity from the vanilla baseline, both are normalized to J0, 1]. is a prree-pprraeter balancing the text and the distance similarities, set by
grid searching on the dev dataset as in Table [0]
The representation dimension d varies among different baselines. We set d = 64 for HGI, d = 512 for DGI and MVGRL, d = 768 for BERT SpaBERT, and DPR, d 1024 for CityFM and GraphSAGE,
18

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Table 10: value for baselines
Method
Beijing
Shanghai
BM25-D D
0,4
0.4
BERT-D
0.4
0.4
OpenAI-D
0.3
0.3
DRMM-D
0.7
0.7
DPR-D
0.3
0.3
and d 1536 for OpenAI, following the settings recommended the corresponding paper. For the proposed UrbanSparse, we fix the Bloom filter length to m 8192 with & = 2 SHA-256 hash functions. In prediction tasks, we set the output region representation dimension d 64. For retrieval tasks, all methods run brute-force search over all POIs unless otherwise specified. All experiments are conducted on NVIDIA V100 GB.
F
Additional Ablation Studies
E1
Bloom Filter Length & Number of Hash functions
We analyze the effect of Bloom filter length and the number of hash functions À using NDCG@5 on the POI retrieval in Beijing, chosen for its low standard deviation (below 0.002) and sensitivity to lead worse performance due
Bloom filter changes. As shown in Table IT m < 2048 and insufficient capacity, while m > 8192 or k > 2 yields negligible gains, suggesting that Bloom filters reach their optimal capacity when and are sufficient to encode the geographic vocabulary, and further increases offer no additional benefits.
Table 11: Effect of l and Beijing POI Retrieval
m
512
2048
8192
32768
k
0.5392
0,5382
0.5569
0.5464
2
0.5578
0.5689
0.5724
0.5738
0.5530
0.5689
0.5717
0.5730
0.5312
0.5679
0.5717
0.5727
F.2
Effect of Tokenizers
We analyze how the choice of tokenizers affects the retrieval effectiveness of UrbanSparse. We tested n-gram tokenizers as DSSM (55) and dictionary- saason tokenizer Jieba (https://githr com/ jonnffxsjy//// j//jjbba)......... Table 12 shows that the combination of I-gram, 2-gram, and dictionary- tokenizers achieves the best Recall 20 and NDCG@5, enhancing the matchint capability of Bloom filters.
Table 12: Effect of Tokenizers on Beijing POI retrieval
Tokenizer
Recall@ 20
NDCG@5
1-gram
0.7022
0.5547
2-gram
0.7175
0.5623
3-gram
0.6642
0.4511
1.2.3-gram
0.7133
0.5551
Dict. (Jieba)
0.7264
0.5633
1,2 1.2ggramDDict.
0.7427
0.5740
E3 Effect of Context Graph Construction
The context graph, constructed by randomly sampling from the K-hop neighbors of objects within the region graph, plays critical role in the proposed Collective View. Larger K values create more
19

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diverse and comprehensive context graphs, which can enhance the model's 's ability to capture complex relationships. As shown in Table 13 K and K achieve the highest effectiveness for both population and house price prediction tasks. K > 4 yields no significant gains in effectiveness.
Table 13: Effect of K -hoop Context Graphs
K
Pop. Pred. R²t
House Pred. R²t
Beijing
Shanghai
Beijing
Shanghai
0.6035
0.6614
0.7656
0.3751
2
0.6818
0.7228
0,7910
0.4470
3
0.7399
0.7857
0,8200
0.4530
4
0.7480
0.7805
0.8234
0.4612
F.4 Effect of Training Algorithms
We evaluate the effect of the proposed training algorithm as in Algorithm | by (1) training on two datasets separately each epoch instead of interweaving each data batch and (2) removing the warm-up epochs. The results in Table [4 demonstrate that these modifications lead to reduced effectiveness in either the prediction or retrieval task. This suggests that the proposed training algorithm successfully trained codebook to share useful information between the two tasks.
Table 14: Effect of Training Algorithms
Method
Pop. Pred. R²
Retrieval NDCG@51
Beijing
Shanghai
Beijing
Shanghai
UraanSparse
0.7480
0.7805
0.5734
0.6209
Train separately
0.7001
0.7450
0.5459
0.6140
No warm-up up
0.7290
0.7670
0.5553
0.6162
F5 Effect of Row/Column Selection
We evaluate the efficiency gain of the two sparsification optimizations described in Figure 2 i.c., row selection and column selection, by analyzing the training time (minutes) and training memory usage (MB) on both cities, As shown in Table both optimizations reduce memory footprint via sparsifying dense computations. Column selection alone yields > 4x speedup (214 51 min) and matched entries. Row selection brings a smaller but complementary gain (72 + 51 min). When POI Bloom filters contain many bits, the sparse dense kernel in py PyTorhh does not significantly outperform dense multiplication due to memory access patterns, so column selection delivers the major efficiency gains while row selection acts as secondary refinement.
> 2x memory reduction (166.5 + 71.9 MB) by computing only query-bii buery-bit-matched
Table 15: Effect of Row and Column Selection Optimizations on Training Efficiency.
Method
Training Time (min)
Training Memory (MB).
Beijing
Shanghai
Beijing
Shanghai
UrbanSparse
51
33
71.92
66.40
w/o Row Selection
72
40
127.38
112.21
w/o Column Selection
214
133
166.51
147.75
G Further Discussions
G.1 Limitations and Future Work
First, UrbanSparse unifies prediction and retrieval through Bloom-filter encodings and learned em- beddings, but it presumes sufficiently rich geo-textual inputs and discards text sequence information,
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which may degrade performance sparse or highly noisy data. In future work, two straightforward strategies may be useful strengthen robustness: (1) Designing off tet-ttee-shlff query rewriting modules to formalize queries and eliminate noises. (2) Explicitly marking empty areas with special markers (e.g., random points) better inform the model [3 Second, all our experiments rely on Meituan data from Beijing and Shanghai, and the model's hyperparameters (e.g. filter size, hash count, training schedule) were tuned for these cities, potentially limiting generalization to other urban environments. Third, the prediction tasks in this paper only involve the prediction from known areas to known areas. Future work should consider the spatio-temporal prediction tasks 44 471, such as time series forecasting [36] 37,, imputation 41, and recovery [6]]. Finally, UrbanSparse may inadvertently reflect or amplify existing biases in spatial data sources (i.e.. POIs in this work), thereby reinforcing socioeconomic disparities across urban regions. Future work should adopt fairess--wwan sampling and iojse--njected query augmentation during training, and include systematic bias auditing and fairness calibration across cities and demographic groups.
G.2 Broader Impact
By improving population density and house-price estimates and enhancing POI retrieval, UrbanSparse aid urban planning, resource allocation, and user-facing location services while reducing com- putational costs. However, the use of fine-grained user queries and POI data risk privacy concerns, models trained major-city data may underperform in underserved regions, and high-precision retrieval could be misused for targeted marketing or surveillance.
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Reviews:
[{"review_id": "vzQGUvd7Lp", "reviewer": "Reviewer_Yh1G", "summary": "This 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.", "questions": "1、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?", "limitations": "While 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths：\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.", "was_revised": true, "final_justification": "The authors' responses have sufficiently addressed my concerns.", "rebuttal": "Thank you for your constructive feedback. We would like to address each of the specific questions as follows:\n\n**Q1:** We clarify that handling extremely noisy or sparse inputs is a very different challenge out of the scope of this work. However, we agree that further validation would clarify UrbanSparse's robustness in such scenarios.\n\nTo address this, we have conducted additional evaluations using the established GeoGLUE [1] benchmark, containing 2,849,754 POIs, with over 50% deliberately introduced fake POIs and queries with shuffled coordinates. Results demonstrate that UrbanSparse achieves competitive POI retrieval performance comparable to strong PLM-based methods (detailed results in Appendix F.1). For the population density prediction task under noisy conditions, UrbanSparse still delivers superior performance (\\$R^2=0.5344\\$) compared to the second-best method, CityFM (\\$R^2=0.4774\\$). We attribute this to the integration of Bloom filters and contrastive learning, which effectively mitigates the impact of noise through mining useful text bits and ignoring useless information. As for sparse input, we clarify that the Beijing dataset does contain regions with highly sparse POIs, and our method performs the best among all tested methods, indicating a certain degree of robustness. We plan to add the corresponding case studies in these data-sparse regions to clarify the detailed impact of sparse input.\n\nIn addition, two straightforward strategies may be useful to strengthen robustness:\n(1) Integrating off-the-shelf query rewriting modules to reduce query noise.\n(2) Explicitly marking empty areas with special markers (e.g., random points) to better inform the model.\n\nWe will present the full results and detailed ablation studies on these strategies in the revised Appendix.\n\n**Q2:** We have rigorously compared UrbanSparse with publicly available state-of-the-art baselines in both geospatial prediction and retrieval domains, including prominent methods such as HGI (IJGIS 2023), CityFM (CIKM 2024), and DrW (VLDB 2023). We omitted certain recent methods due to either reproducibility limitations or their exclusive reliance on specific data types (e.g., human-mobility data, taxi origin-destination pairs), which are not generally accessible for a fair comparison. In the revision, we will clearly articulate these points to better contextualize our comparisons.\n\n**Q3:** Thanks for your feedback. We will clarify advanced concepts such as Bloom filters, contrastive learning, and mutual information maximization. In the revised manuscript, these concepts will be thoroughly defined with intuitive explanations and complemented by visual illustrations to facilitate understanding among readers unfamiliar with these technical details.\n\n**Q4:** We recognize the importance of explicitly discussing potential negative societal impacts, especially considering the sensitivity of application domains like housing and urban planning. We will move the limitation section from the appendix to the main content, addressing possible issues such as prediction biases and unintended reinforcement of socioeconomic disparities. \n\nWe hope our clarifications and additional experiments can address your concern.\n\n[1] Li et, al. Geoglue: A geographic language understanding evaluation benchmark. abs/2305.06545, Arxiv 2023."}, {"review_id": "AMqhgj5GBj", "reviewer": "Reviewer_Xk76", "summary": "The 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.", "questions": "Please see above", "limitations": "Yes", "rating": 3, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "While the authors mention potential broader applications of their method, these extensions are well beyond the scope of the current paper, and there is no clear evidence provided to demonstrate their applicability or potential benefits. Therefore, in my view, the scope of the paper remains focused on a single urban computing application. That said, based on the responses of other reviewers who appear to be more domain-expert, it seems they find merit in the contribution.\n\nRegarding to answer to W4 (Baseline comparisons) the authors claim in the rebuttal \"While we fail to test ERNIE-GeoL (KDD 2022) and MGeo (SIGIR 2024) on our datasets due to reproducibility issue,\" – It's not clear to me what this means. It's also not clear why they place a \"strong baseline\" in the Appendix and not in the main paper.\n\nOverall, while the rebuttal addresses some of my concerns through clarification, as a non-expert in this area, I remain uncertain whether the paper passes the acceptance threshold. So, I remain with my score, but I won't be disappointed if the paper is accepted.", "rebuttal": "Thank you for the careful reading and constructive feedback. We would like to address each weak point as follows:\n\n**Overall concern:** We clarify that Urban Computing is not a niche application; it has become a cornerstone of real‑world AI applications, powering services such as Google Maps, Uber, and food‑delivery platforms that billions rely on daily. The technical challenges studied in our work, i.e., fusing large‑scale text search with spatio‑temporal forecasting, also appear in areas such as temporal recommender systems and video‑event search. More importantly, our unique integration of Bloom‑filter‑based representation and two-level contrastive learning are not confined by urban computing. Such strategy can be applied to the general text-based retrieval/recommender systems, unlocking the joint benefits of retrieval and prediction and bringing broader impact to the NIPS community.\n\n**W1. Method clarity.**\nThank you for your feedback. We will restructure Section 3 to center on our core innovation: the use of Bloom filters and tailored contrastive learning at both bit‑ and region‑level. We will also introduce a more intuitive figure to illustrate the overall framework, highlighting its core components and avoiding overwhelming training/optimization details.\n\n**W2. Bloom filters and false positives.**\nWe apologize for the oversight in the main text. A comprehensive study is already provided in Appendix E.1, including a definition of Bloom filters and ablation experiments varying bit‑vector length (*m*) and number of hash functions (*k*), which show that false positives are empirically mitigated when $m \\geq 8192$, $k \\geq 2$. We will add a concise reference in Section 4.1 to guide readers directly to these results.\n\n**W3. Dataset choice justification.**\nOur primary goal is to explore the joint benefit of retrieval and prediction tasks, which requires datasets that support both. For this reason, we selected the Beijing and Shanghai benchmarks (from Liu et al. [2]), which provide over 160,000 realistic user queries paired with 120,000 POIs that uniquely enable industry-scale joint evaluation. In contrast, commonly used POI datasets (e.g., from OpenStreetMap or Foursquare) typically contain fewer than 30,000 POIs and lack user queries, making them unsuitable for retrieval tasks.\n\nHowever, we understand that evaluating on additional benchmarks can further demonstrate generality. Therefore, we also assess UrbanSparse on the widely used GeoGLUE \\[1] benchmark, where UrbanSparse achieves retrieval performance comparable to strong PLM‑based models (see Appendix F.1). For population prediction, UrbanSparse also achieves the best $R^2 = 0.5344$, outperforming the next best method, CityFM ($R^2 = 0.4774$). Full results will be included in the revised manuscript.\n\n**W4. Baseline comparisons.**\nIn addition to general‑purpose models, we have already compared UrbanSparse against domain‑specific methods. Specifically, we compare with the latest versions of POI-based prediction methods HGI (ISPRS 2023) and CityFM (CIKM 2024), and retrieval methods DrW (SIGMOD 2023). While we fail to test ERNIE-GeoL (KDD 2022) and MGeo (SIGIR 2024) on our datasets due to reproducibility issue, we found that both methods are adapted from DPR (EMNLP 2020) with geospatial components. Hence, we combine DPR with geospatial distance to form DPR-D, which we believe to be a strong baseline (As demonstrated in Appendix F.1, DPR-D performs better than MGeo on GeoGLUE). \n\n**W5. GPS-based methods and datasets.**\nMobility-based methods often rely on multi-modal fusion, which is orthogonal to our primary objective of bridging retrieval and prediction. Datasets used in those works (e.g., NYC) contain rich mobility traces (>2,000,000 taxi records) but only a limited number of POIs (<30,000). Applying our method to such datasets would require major architectural changes to handle the mobility signal, which is beyond our current scope. For a fair comparison, we focus only on methods that use the same input modalities, leaving exploration of mobility-enhanced models to future work.\n\n**W6. Train/validation/test splits.**\nWe apologize for the lack of detail in the main text. The full settings are described in Appendix D:\n\n* For retrieval, we adopt a fixed train/dev/test split of 0.81:0.09:0.10, following Liu et al. \\[2], and make our data and splits publicly available.\n* For prediction, we follow standard unsupervised representation learning practice by evaluating embeddings with a scikit-learn RandomForestRegressor using 5-fold cross-validation across all urban regions.\n \n We will bring these details into Section 4.3 for clarity and reproducibility.\n\n**W7. Ablation on unified vs. separate tasks.**\nTables 2–4 already report “w/o individual” (i.e., training only on prediction) and “w/o collective” (i.e., training only on retrieval), showing clear performance degradation when either task is omitted. We will highlight these rows and the corresponding discussion to emphasize the necessity and effectiveness of joint training.\n\nWe hope the revisions and additions can address your concern and improve the quality, robustness, and clarity of our work.\n\n\\[1] Li et al. GeoGLUE: A geographic language understanding evaluation benchmark. arXiv:2305.06545, 2023.\n\n\\[2] Liu et al. Effectiveness perspectives and a deep relevance model for spatial keyword queries. Proc. ACM on Management of Data, 1(1):1–25, 2023."}, {"review_id": "VLDd5uTJsj", "reviewer": "Reviewer_7EV1", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "S1: 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.", "was_revised": true, "final_justification": "Thanks to the authors for the rebuttal content, and my questions are resolved properly. I suggest including these ablation and discussion about performance bottleneck into camera camera-ready version or the appendix.", "rebuttal": "Thank you for the careful reading. We would like to address your concern as follows:\n\n**W1 and Q1. Clarification of LambdaRank choice.**\n\nWe clarify that loss‑function design is not our primary contribution, and we adopt LambdaRank (NIPS, 2016) because it is a well-known one in learning‑to‑rank practices. Nevertheless, we fully agree that ablation studies on loss functions would help us identify the source of performance gain. Hence, we conduct an ablation study on Beijing datasets. While a static ranking‑weight loss (true=1, false=0) fails to converge, a simple contrastive loss (the same as in DPR) leads to very close results, with 1.2\\% performance drop in NDCG@10 and 0.4\\% gain in NDCG@1. We are still investigating other loss functions and will provide additional results in the appendix.\n\n**W2 and Q2. Isolation of row/column selection benefits.**\n\nWe apologize for the missing ablation studies. We have now conducted ablations on both optimizations:\n\n| Method                               | Training Time (min) |              | Training Memory Usage (MB) |              |\n| ------------------------------------ | ------------------- | ------------ | -------------------------- | ------------ |\n|                                      | **Beijing**         | **Shanghai** | **Beijing**                | **Shanghai** |\n| **UrbanSparse**                      | 51                  | 33           | 71.92                      | 66.40        |\n| **UrbanSparse w/o row selection**    | 72                  | 40           | 127.38                     | 112.21       |\n| **UrbanSparse w/o column selection** | 214                 | 133          | 166.51                     | 147.75       |\n\nThe results show that both optimization leads to significantly less memory usage due to the sparsification of the dense matrix. As for training time, column selection alone yields a >4× speedup (214 -> 51 min) and >2× memory reduction (166.5 -> 71.9 MB) by computing only query-bit‑matched entries. Row selection only adds a smaller benefit (72 -> 51 min), as when POI Bloom filters have many bits, the PyTorch sparse‑dense matrix multiplication is not significantly faster than dense counterparts (due to memory access patterns). Thus, column selection provides the lion’s share of our efficiency gains, with row selection as a complementary, secondary optimization.\n\n**W3. Scalability and generalization.**\n\nWe clarify that we have tested UrbanSparse's scalability on the GeoGLUE [1] benchmark (with 2.85 M POIs) in Appendix F.1. As shown in Table 10, UrbanSparse matches or slightly exceeds the strong multimodal baseline MGeo and trains in **54 min** versus **131 min** for DPR‑D :\n\n| Method          | Recall@10  | NDCG@5     | NDCG@1     | Training Time (min) |\n| --------------- | ---------- | ---------- | ---------- | ------------------- |\n| MGeo            | N/A        | N/A        | 0.5270     | —                   |\n| DPR‑D           | 0.7611     | 0.6318     | 0.5350     | 131                 |\n| **UrbanSparse** | **0.7621** | **0.6344** | **0.5310** | **54**              |\n\nWe apologize for the lack of clarity in the main text and will add a concise reference in Section 5.5 to guide readers directly to these results.\n\n**Q3. Remaining inference bottlenecks**\n\nThank you for pointing this out. Our detailed profiling shows that Bloom‑filter hashing adds negligible overhead. The primary cost arises from **our custom CUDA kernels for sparse representation calculations**: the non‑coalesced memory accesses in our kernel and an insufficiently optimized kernel dispatch strategy incur significantly higher latency than vendor‑optimized dense kernels from NVIDIA experts. Consequently, while we achieve a 50× reduction in parameters, the 4× QPS gain is bounded by these CUDA kernel inefficiencies. We anticipate that expert‑tuned CUDA kernels (particularly, leveraging shared memory with better parallelism) will further narrow this gap.\n\n[1] Li et al. GeoGLUE: A geographic language understanding evaluation benchmark. arXiv:2305.06545, 2023."}, {"review_id": "o8laDMb8tS", "reviewer": "Reviewer_vg91", "summary": "The 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.", "questions": "Please refer to the weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your careful reading and constructive feedback. We would like to address each point in turn.\n\n**W1. Running Example.**\nWe have added a concise running example to the Introduction to show how prediction and retrieval interact in practice: Suppose our system analyzes recent land‑use and economic data across a city and flags a particular neighborhood, home to several hundred restaurants and cafés, as an emerging “culinary hotspot”. When a user searches for “best sushi nearby”, the retrieval module does not simply return the most similar sushi bars city‑wide; it prioritizes those within the hotspot that have seen a surge in visits and searches over the past month. Likewise, a query for “top‑rated vegan restaurants” surfaces vegan venues in the same area that are gaining traction. By combining prediction (detecting dynamic hotspots) with retrieval (serving context‑aware results), the app delivers recommendations that are both timely and closely aligned with the city’s evolving landscape.\n\n**W2. Different spatial layouts and POI density.**\nHandling spatial diversity and POI density is orthogonal to our core contribution, yet we appreciate the your concern and respond as follows:\n\n1. Our Beijing dataset already spans more than 16 000 km² and includes urban, peri‑urban, and suburban zones, providing naturally diverse patterns. We will add a dedicated case study on a low‑density suburban region of Beijing in the revised Appendix.\n2. We further evaluate UrbanSparse on the GeoGLUE benchmark (2 849 754 POIs) and show in Appendix F.1 that our model matches strong PLM‑based retrieval baselines (Recall\\@10 = 0.760 vs. 0.761 for DPR) while training in 54 minutes, more than twice as fast as DPR.\n\n**W3. Intuitive explanation for the Bloom‑filter contrastive learning.**\nTo clarify why our method works, we will insert the following intuitive explanation into Section 4:\n\n1. *Bit‑level InfoMax.* A discriminator‑based (InfoMax) loss operates on individual Bloom filter bits. Negative POI pairs are discriminated on whether any bits are missing, mimicking the Bloom filter’s membership test, ensuring the detection of important bits. \n2. Region‑level InfoMax: We group POIs into regions via administrative boundaries, and train a second discriminator to judge whether one region’s aggregated representation is contained within a larger region’s, also analogous to the Bloom filter’s membership test, ensuring spatial coherence across scales.\n\nWe hope this intuitive explanation clarifies the roles of the two contrastive objectives and resolves your concern."}]

Author rebuttal:

Meta-review: This paper proposes UrbanSparse, a new method that unifies geospatial prediction and retrieval through a sparse-dense representation architecture. By synergistically combining these tasks, UrbanSparse eliminates redundant systems while amplifying their mutual strengths.

Pros:
- The idea of unifying geospatial prediction and retrieval is interesting. The proposed method is novel
- The paper demonstrates high quality through its rigorous experimental setup and detailed documentation
- The paper is clearly written and easy to follow

Cons:

Most of the reviewers’ concerns are addressed, except for the one below.

- As pointed out by Reviewer Xk76, one primary concern with this paper is 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.

The paper can be accepted if Senor AC or Program Chairs think the topic fits NeurIPS well.
Decision: Accept (poster)