This chapter has explored the problem of identifying visits, and subsequently locations, from geospatial trajectories. To this end, a novel algorithm, the Gradient-based Visit Extractor (GVE) has been presented that extracts periods of low mobility from geospatial data while maintaining resilience to noise and overcoming the drawbacks of existing techniques. Specifically, GVE does not place a minimum bound on visit duration, has no assumption of evenly spaced observations, and considers points as they arrive, making it amenable to visit extraction in real-time from a variety of data sources.
In addition to presenting the algorithm, this chapter has provided a compre- hensive analysis of the properties of the visits extracted through an exploration of the parameter space, providing application developers with knowledge to aid in parameter selection. The applicability of GVE to the task of visit extraction has been demonstrated by a comparison to existing approaches through metrics representative of common goals of location extraction. Finally, an investigation into using extracted locations as a basis for prediction has been presented that includes a novel method of parameter selection through a metric that charac- terises the goals of both the extraction and prediction procedures. Through all of these investigations, results demonstrating the suitability of GVE have been achieved, with evidence indicating increased accuracy over existing approaches. This quantitative evaluation, lacking from previous work, demonstrates the ap- plicability of the Gradient-based Visit Extractor (GVE) algorithm to the task of visit extraction and, consequently, as a precursor step to location extraction.
CHAPTER
5
Augmenting Geospatial Trajectories with Land Usage Data
Chapter 4 presented a method of identifying visits using only geospatial trajec- tories as a basis, and clustering these visits into arbitrary shapes that are likely to be meaningful to the users. However, as Section 4.4 showed, the extracted locations are not very representative of the real-world. While this method of identifying locations from trajectories is well-established in the literature, it does not take into consideration properties of the physical world.
Recently, the processing, storage, and connectivity capabilities of location- aware hardware devices have improved, allowing us to consider techniques that require additional data sources, or the ability to query a remote service for information. Making use of these developments, this chapter proposes a novel method of identifying geographical features (e.g. buildings, roads, amenities and areas) that a user has interacted with, creating a mapping between the extracted locations and the real-world. Achieved by augmenting trajectories with land usage data available after trajectory collection has occurred, theLand Usage Identification (LUI) procedure extractsland usage elements, referred to simply as elements, that a person has interacted with, and summarises these interactions in a manner consistent with the visits and locations of previous work. This chapter demonstrates the applicability of this approach through an evaluation and characterisation of the extracted elements, and through a sample application, that of predicting future interactions.
5.1
Introduction
Much existing work has focused on identifying locations from geospatial trajec- tories as a basis for prediction, aiming to determine the likely regions that an
5. Augmenting Geospatial Trajectories with Land Usage Data
individual or other entity will visit in the future. While this is a useful com- ponent of many services, the identified locations do not necessarily correspond to actual geographic features, often spanning multiple buildings or areas. In contrast, this work takes the raw geospatial trajectories and augments them with information about the real-world to identify exactly which building, point of interest or geographical feature a person was likely interacting with, while maintaining compatibility with existing applications.
The Land Usage Identification (LUI) procedure places no additional burden on the user as no additional data is required to be collected from them; instead, additional information can be brought into the system in the form of land usage datasets available after collection has occurred. Throughaugmentation,filtering andsummarisation techniques, the physical features that a user has interacted with are identified and their interactions summarised. This results in elements that replace the locations present in previous work, where each element has associated information describing its location and purpose. This additional information not only provides a foundation for understanding what a person may have been doing, but provides a relationship between the data and the real-world that can be leveraged by applications. This approach has the added benefit of considering periods of time, regardless of whether the person was stationary or moving. Location extraction techniques only consider time when the person was stationary, but land usage elements can be associated with trajectories regardless, identifying time spent on, for example, a road, train track, or sports field.
The utility of the LUI procedure is demonstrated through an exploration of the extracted elements and interactions, a comparison to extracted locations, and an exploration that uses the extracted elements as a basis for prediction, which is a common application of extracted locations. Utilising existing ma- chine learning approaches, we demonstrate increased predictive accuracy over identified elements compared with using extracted locations for the purpose of next location prediction.
5. Augmenting Geospatial Trajectories with Land Usage Data
A discussion of relevant related work is presented in Section 5.2, and the LUI procedure is presented in Section 5.3. Section 5.4 evaluates the procedure and the utility a↵orded by the identified elements, with a conclusion and summary in Section 5.5.