1. Health and disease are inherently geographic. They emerge, spread, and are experienced through the interactions among individual behaviors, environmental exposures, mobility patterns, and social...

Agentic AI-integrated robots are essential for effective, efficient, and safe Human-Robot collaboration (HRC), where robots must accurately interpret human behavior by understanding the working context. However, current human motion prediction models often focus on task-related context and rarely consider the robot as an influencing factor in HRC. This study navigates different contexts in human motion prediction for Agentic AI-empowered HRC, and proposes a robot-aware deep learning framework that integrates robot and task context into prediction. This framework handles context and human motions separately within a long short-term memory (LSTM)-based two-branch model to predict human motions in HRC tasks. The influence of different contextual information (e.g., robot actions, task-related object location) on prediction performance is also examined. The framework was implemented in a handover task and the results show that the proposed model improved performance by 7.95% in Average Displacement Error (ADE) and 8.74% in Final Displacement Error (FDE), compared to the baseline (i.e., without context). The findings indicate that context integration is critical for anticipating human motions, and the robot is an important context in HRC. This study advances the understanding of context integration in human motion prediction and contributes to the comprehension of AI-integrated robots in real-world HRC.

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results.

Detecting regional co-location patterns on urban road networks is challenging because it is computationally prohibitive to search all potential co-location patterns and their localities, and effective statistical methods for evaluating the prevalence of regional co-location patterns are lacking. To overcome these challenges, this study developed an adaptive method for detecting network-constrained regional co-location patterns. Specifically, an alternate prevalence measure of regional co-location patterns was defined based on the likelihood ratio statistic. A network-constrained k-nearest neighbor method was used to construct instances of candidate co-location patterns, and a heuristic two-phase expansion method was proposed to identify candidate localities of regional co-location patterns. The statistical significance of regional co-location patterns was evaluated using a Monte Carlo simulation. Experiments using extensive simulated datasets showed that our method was superior to three state-of-the-art methods. The proposed method was also applied to a Beijing points of interest (POI) dataset. The identified regional POI co-location patterns could support a better understanding of the spatial organization of urban functions and may be useful for facilitating urban planning.

Regions of anomalous spatial co-locations (ROASCs) are regions where co-locations between two different features are significantly stronger or weaker than expected. ROASC discovery can provide useful insights for studying unexpected spatial associations at regional scales. The main challenges are that the ROASCs are spatially arbitrary in geographic shape and the distributions of spatial features are unknown a priori. To avoid restrictive assumptions regarding the distribution of data, we propose a distribution-free method for discovering arbitrarily shaped ROASCs. First, we present a multidirectional optimization method to adaptively identify the candidate ROASCs, whose sizes and shapes are fully endogenized. Furthermore, the validity of the candidates is evaluated through significance tests under the null hypothesis that the expected spatial co-locations between two features occur consistently across space. To effectively model the null hypothesis, we develop a bivariate pattern reconstruction method by reconstructing the spatial auto- and cross-correlation structures observed in the data. Synthetic experiments and a case study conducted using Shanghai taxi datasets demonstrate the advantages of our method, in terms of effectiveness, over an available alternative method.

Multilevel co-location patterns embedded in spatial datasets are difficult to discern due to the complexity of neighboring relationships among spatial features. The neighboring relationships are used to determine whether instances of different spatial features are located in close geographic proximity. When spatial features are distributed unevenly, the neighboring relationships among spatial features cannot be constructed appropriately. Correspondingly, the instances of co-location patterns cannot be generated correctly, and the prevalence of multilevel co-location patterns cannot be measured accurately. To overcome this challenge, this study develops a method to adaptively detect multilevel co-location patterns based on natural neighborhoods. First, locally adaptive neighboring relationships for instances of different spatial features, called ‘natural neighborhoods’, are defined by considering the formation mechanism of co-location patterns and the local-distribution characteristics of spatial features. Using the natural neighborhoods, we propose a multilevel refining method to identify all global and local co-location patterns algorithmically. We compare the proposed method against three state-of-the-art methods using both simulated and real-life datasets. The comparison shows that the proposed method can discover multilevel co-location patterns from unevenly distributed spatial features more completely and accurately with less a priori knowledge for the construction of the natural neighborhoods.

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.

Building footprints are among the most predominant features in urban areas, and provide valuable information for urban planning, solar energy suitability analysis, etc. We aim to automatically and rapidly identify building footprints by leveraging deep learning techniques and the increased availability of remote sensing datasets at high spatial resolution. The task is computationally challenging due to the use of large training datasets and large number of parameters. In related work, You-Only-Look-Once (YOLO) is a state-of-the-art deep learning framework for object detection. However, YOLO is limited in its capacity to identify small objects that appear in groups, which is the case for building footprints. We propose a LOcally-COnstrained (LOCO) You-Only-Look-Once framework to detect small and densely-distributed building footprints. LOCO is a variant of YOLO. Its layer architecture is determined by the spatial characteristics of building footprints and it uses a constrained regression modeling to improve the robustness of building size predictions. We also present an invariant augmentation based voting scheme to further improve the precision in the prediction phase. Experiments show that LOCO can greatly improve the solution quality of building detection compared to related work.

Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods.

Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of (k + 1)-size co-location patterns from the sub-regions of k-size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns.