Geospatial data plays a central role in modeling our world, for which
OpenStreetMap (OSM) provides a rich source of such data. While often spatial
data is represented in a tabular format, a graph based representation provides
the possibility to interconnect entities which would have been separated in a
tabular representation. We propose in our paper a framework which supports a
planet scale transformation of OpenStreetMap data into a Spatial Temporal
Knowledge Graph. In addition to OpenStreetMap data, we align the different
OpenStreetMap geometries on individual h3 grid cells. We compare our
constructed spatial knowledge graph to other spatial knowledge graphs and
outline our contribution in this paper. As a basis for our computation, we use
Apache Sedona as a computational framework for our Spatial Temporal Knowledge
Graph construction

基于开放街道地图 (OpenStreetMap) 数据，我们提出了一个支持全球范围的 OpenStreetMap 数据转化成时空知识图谱的框架，同时利用 Apache Sedona 作为计算框架来构建该图谱。

基于 OpenStreetMap 和 H3 网格的全球规模时空知识图谱

A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap  And H3 Grid

Mobile user profiling refers to the efforts of extracting users'
characteristics from mobile activities. In order to capture the dynamic varying
of user characteristics for generating effective user profiling, we propose an
imitation-based mobile user profiling framework. Considering the objective of
teaching an autonomous agent to imitate user mobility based on the user's
profile, the user profile is the most accurate when the agent can perfectly
mimic the user behavior patterns. The profiling framework is formulated into a
reinforcement learning task, where an agent is a next-visit planner, an action
is a POI that a user will visit next, and the state of the environment is a
fused representation of a user and spatial entities. An event in which a user
visits a POI will construct a new state, which helps the agent predict users'
mobility more accurately. In the framework, we introduce a spatial Knowledge
Graph (KG) to characterize the semantics of user visits over connected spatial
entities. Additionally, we develop a mutual-updating strategy to quantify the
state that evolves over time. Along these lines, we develop a reinforcement
imitative graph learning framework for mobile user profiling. Finally, we
conduct extensive experiments to demonstrate the superiority of our approach.

本研究提出了一种基于模仿的移动用户建模框架，该框架使用强化学习任务，以空间知识图和图学习相结合的方法来更准确地预测用户的移动行为，本文通过多项实验验证了其优越性。