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Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, including high-dimensionality, noise, outliers, and time distortions. This oversight can lead to potentially large computational costs and inaccurate patterns. To address these challenges, this paper proposes a novel neural network-based method integrating temporal autoencoder and dynamic time warping-based K-means clustering algorithm to mutually promote each other for mining spatiotemporal mobility patterns. Comparative results showed that our proposed method outperformed several time series clustering techniques in accurately identifying mobility patterns on both synthetic and real-world data, which provides a reliable foundation for data-driven decision-making. Furthermore, we applied the method to monthly county-level mobility data during the COVID-19 pandemic in the U.S., revealing significant differences in mobility changes between rural and urban areas, as well as the impact of public response and health considerations on mobility patterns. 

Abstract This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts. 

Crystallization of the lunar magma ocean yielded a chemically unique liquid residuum named KREEP. This component is expressed as a large patch on the near side of the Moon and a possible smaller patch in the northwest portion of the Moon’s South Pole-Aitken basin on the far side. Thermal models estimate that the crystallization of the lunar magma ocean (LMO) could have spanned from 10 and 200 My, while studies of radioactive decay systems have yielded inconsistent ages for the completion of LMO crystallization covering over 160 My. Here, we show that the Moon achieved >99% crystallization at 4,429 ± 76 Ma, indicating a lunar formation age of ~4,450 Ma or possibly older. Using the¹⁷⁶Lu–¹⁷⁶Hf decay system (t_1/2= 37 Gy), we found that the initial¹⁷⁶Hf/¹⁷⁷Hf ratios of lunar zircons with varied U–Pb ages are consistent with their crystallization from a KREEP-rich reservoir with a consistently low¹⁷⁶Lu/¹⁷⁷Hf ratio of 0.0167 that emerged ~140 My after solar system formation. The previously proposed younger model age of ~4.33 Ga for the source of mare basalts (240 My after solar system formation) might reflect the timing of a large impact. Our results demonstrate that lunar magma ocean crystallization took place while the Moon was still battered by planetary embryos and planetesimals leftover from the main stage of planetary accretion. The study of Lu–Hf model ages for samples brought back from the South Pole-Aitken basin will help to assess the lateral continuity of KREEP and further understand its significance in the early history of the Moon. 

Abstract Maintaining educational resources and training materials as timely, current, and aligned with the needs of students, practitioners, and other users of geospatial technologies is a persistent challenge. This is particularly problematic within CyberGIS, a subfield of Geographic Information Science and Technology (GIS&T) that involves high‐performance computing and advanced cyberinfrastructure to address computation‐ and data‐intensive problems. In this study, we analyzed and compared content from two open educational resources: (1) a popular online web resource that regularly covers CyberGIS‐related topics (GIS Stack Exchange) and (2) existing and proposed content in the GIS&T Body of Knowledge. While current curricula may build a student's conceptual understanding of CyberGIS, there is a noticeable lack of resources for practical implementation of CyberGIS tools. The results highlight discrepancies between the attention and frequency of CyberGIS topics according to a popular online help resource and the CyberGIS academic community. 

The Moon has had a complex history, with evidence of its primary crust formation obscured by later impacts. Existing U-Pb dates of >500 zircons from several locations on the lunar nearside reveal a pronounced age peak at 4.33 billion years (Ga), suggesting a major, potentially global magmatic event. However, the precision of existing geochronology is insufficient to determine whether this peak represents a brief event or a more protracted period of magmatism occurring over tens of millions of years. To improve the temporal resolution, we have analyzed Apollo 14, 15, and 17 zircons that were previously dated by ion microprobe at ~4.33 Ga using isotope dilution thermal ionization mass spectrometry. Concordant dates with sub-million-year uncertainty span ~4 million years from 4.338 to 4.334 Ga. Combined with Hf isotopic ratios and trace element concentrations, the data suggest zircon formation in a large impact melt sheet, possibly linked to the South Pole–Aitken basin.