Search Results (7)

Crime prediction in urban environments is a complex and pressing challenge driven by the intricate interplay of spatiotemporal dependencies, hierarchical geographic patterns, and socio-environmental determinants. We propose a multi-scale geo-temporal crime embedding (MSG-TCE) framework, which hierarchically models these dynamics via three novel components: a hierarchical residual temporal encoder (HRTE), a periodic transformer Encoder (PTE), and a hyperbolic spatial pooler (HSP). The HRTE captures multi-scale temporal trends by combining dilated convolutions with residual connections, while the PTE explicitly encodes periodic crime patterns using self-attention conditioned on cyclical positional encodings. The HSP maps spatial crime hotspots into hyperbolic space to better represent their inherent hierarchical structure, spanning city–district–neighbourhood–street-segment scales, and aggregates neighbourhood information via graph convolutions. These components are fused through a gated cross-attention mechanism, yielding a unified embedding for crime prediction. Experiments on real-world datasets from Chicago, Los Angeles, and New York City demonstrate that MSG-TCE achieves consistent improvements over five competitive baselines across RMSE, Precision@20, and DTW metrics, with statistically significant gains at longer prediction horizons. Ablation studies confirm the contribution of each component. Spatial visualisation maps, robustness analyses, and an exploratory covariate-augmented variant further substantiate the empirical validity of the framework. This paper also discusses limitations, including data reporting biases, the need for full covariate integration, and ethical considerations, pertaining to algorithmic fairness in crime prediction. Full article

Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road network distances. We term this range-feasibility blindness and derive its analytical radius $\Delta =R_{max}(\alpha -1)/\left(2\alpha \right)$, where $\alpha$ is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms $\alpha \in [1.40,1.52]$ and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road network range constraints simultaneously with depot opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments on Dongli District (Tianjin) show cost reductions of 20.6–28.2% over greedy sequential baselines across three demand scenarios, with gains increasing monotonically with instance scale; cross-city experiments in Beijing and Shanghai confirm consistent improvement averaging 11.4% (Chaoyang, Beijing) and 10.2% (Pudong, Shanghai) over greedy initialisation across diverse urban morphologies. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. Full article

Public health emergencies (PHEs) test the crisis response capacity of grassroots organizations like China’s Residential Committees (RCs). While existing research attributes this capacity to factors like resource mobilization or state-society relations, its deeper structural foundations in land regimes and spatial configurations remain underexplored, particularly in Global South urbanization contexts. To fill this gap, this study develops a “Grassroots Organizational Capacity” (GOC) analytical framework, which disaggregates capacity into four dimensions: information, implementation, mobilization and cooperation, and coercion. We then employ this framework in a comparative case study of urban (Jiayi) and rural (Hedong) neighbourhoods in Nanhai, China, during the 2022 lockdown. Drawing on semi-structured interviews with ten key stakeholders in 2022, the findings reveal divergent types of governance. In the rural case, collective land ownership and open spaces foster an “Embedded Autonomy” type, enabling a proactive response through dense social networks. In the urban case, state land dependency and spatial fragmentation lead to a “Reactive Co-Governance” type, which relies on top-down state intervention. This study’s contribution is to provide a case-based illustration of how land and space structures are actively associated with grassroots crisis response effectiveness, rather than serving as passive backdrops. Full article

Person re-identification (Re-ID) is a key technology used in the field of intelligent surveillance. The existing Re-ID methods are mainly realized by using convolutional neural networks (CNNs), but the feature information is easily lost in the operation process due to the down-sampling structure design in CNNs. Moreover, CNNs can only process one local neighbourhood at a time, which makes the global perception of the network poor. To overcome these shortcomings, in this study, we apply a pure transformer to a video-based Re-ID task by proposing an adaptive partitioning and multi-granularity (APMG) network framework. To enable the pure transformer structure better at adapting to the Re-ID task, we propose a new correlation-adaptive partitioning (CAP) of feature embedding modules that can adaptively partition person images according to structural correlations and thus retain the structure and semantics of local feature information in the images. To improve the Re-ID performance of the network, we also propose a multi-granularity (MG) module to better capture people feature information at different levels of granularity. We performed validation trials on three video-based benchmark datasets. The results show that the network structure based on the pure transformer can adapt to Re-ID tasks well, and our APMG network outperforms other state-of-the-art methods. Full article

Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. A known limitation of GNNs is that, as the number of layers increases, information gets smoothed and squashed and node embeddings become indistinguishable, negatively affecting performance. Therefore, practical GNN models employ few layers and only leverage the graph structure in terms of limited, small neighbourhoods around each node. Inevitably, practical GNNs do not capture information depending on the global structure of the graph. While there have been several works studying the limitations and expressivity of GNNs, the question of whether practical applications on graph structured data require global structural knowledge or not remains unanswered. In this work, we empirically address this question by giving access to global information to several GNN models, and observing the impact it has on downstream performance. Our results show that global information can in fact provide significant benefits for common graph-related tasks. We further identify a novel regularization strategy that leads to an average accuracy improvement of more than $5\%$ on all considered tasks. Full article

The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate graph classification. Previous pooling methods were based on assigning a score to each node and then pooling only the highest-scoring nodes, which might throw away whole neighbourhoods of nodes and therefore information. Here, we proposed a novel pooling method UGPool with a new point-of-view on selecting nodes. UGPool learns node scores based on node features and uniformly pools neighboring nodes instead of top nodes in the score-space, resulting in a uniformly coarsened graph. In multiple graph classification tasks, including the protein graphs, the biological graphs and the brain connectivity graphs, we demonstrated that UGPool outperforms other graph pooling methods while maintaining high efficiency. Moreover, we also show that UGPool can be integrated with multiple graph convolution networks to effectively improve performance compared to no pooling. Full article

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation. Full article