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With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

Pedal misapplications by drivers have received attention as being an underlying factor for the phenomenon known as sudden unintended acceleration (SUA) in vehicles. This research investigates behaviors during a common task for drivers, namely residential parking. Parking has been identified as a maneuver that is often linked with SUA mishaps. Using driving trajectories data from a set of four couples collected as part of a naturalistic driving study, we investigate whether consistent behaviors can be detected when parking at home from a geospatial perspective, i.e., whether deceleration and braking occur in a characteristic way at the end of a driving trajectory, and whether these behaviors vary when the geospatial context of parking changes. An ontology-based approach is used to frame the key behaviors of the naturalistic driving, and big data techniques are applied to extract parking-specific behaviors from driving trajectories. Results show that individuals showed relatively consistent parking behaviors under the same geospatial context and the standard deviation of the deceleration threshold has a larger discrepancy between couples parking at different residences than within couples where parking occurs at the same place.

In this chapter, we present a semantic data modeling framework for representing and analyzing the movement dynamics of individuals that arise from following a schedule or plan of activities in a semantic-enriched environment, i.e., an environment for which an ontology-driven space-time activity knowledgebase has been constructed. The ontology-driven knowledgebase contains spatial, temporal, and semantic information about geospatial entities in the environment. The relations between geospatial entities in the environment are captured in the knowledgebase through the underlying ontology support. Movement by individuals on a university campus according to a semester-based course schedule is employed as a use case to demonstrate this framework. This work demonstrates an RDF-based semantic data model that is used for reasoning about movement, including the movement trajectories of students on campus based on weekly course schedules. Road network information is incorporated to generate movement trajectories more realistically and data from a campus course information system allows us to query and analyze aggregated movement dynamics on campus. This chapter discusses the advantages of a semantic data modeling approach over traditional data models for human activity and movement including the capability to incorporate different data sources into the analysis, and generate geographic visualizations of movement paths as well as the overall movement dynamics for a campus.

The Wikipedia database is a data source of immense richness and variety. Included in this database are thousands of geotagged articles, including, for example, almost real-time updates on current and historic natural hazards. This includes usercontributed information about the location of natural hazards, the extent of the disasters, and many details relating to response, impact, and recovery. In this research, a computational framework is proposed to detect spatial patterns of natural hazards from the Wikipedia database by combining topic modeling methods with spatial analysis techniques. The computation is performed on the Neon Cluster, a high performance-computing cluster at the University of Iowa. This work uses wildfires as the exemplar hazard, but this framework is easily generalizable to other types of hazards, such as hurricanes or flooding. Latent Dirichlet Allocation (LDA) modeling is first employed to train the entire English Wikipedia dump, transforming the database dump into a 500-dimension topic model. Over 230,000 geo-tagged articles are then extracted from the Wikipedia database, spatially covering the contiguous United States. The geo-tagged articles are converted into an LDA topic space based on the topic model, with each article being represented as a weighted multidimension topic vector. By treating each article’s topic vector as an observed point in geographic space, a probability surface is calculated for each of the topics. In this work, Wikipedia articles about wildfires are extracted from the Wikipedia database, forming a wildfire corpus and creating a basis for the topic vector analysis. The spatial distribution of wildfire outbreaks in the US is estimated by calculating the weighted sum of the topic probability surfaces using a map algebra approach, and mapped using GIS. To provide an evaluation of the approach, the estimation is compared to wildfire hazard potential maps created by the USDA Forest service.

This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space-time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space-time and affecting spatiotemporal accessibility for individuals.