Almost every activity in our modern life leaves a digital trace, typically including location and time. Either captured by a sensor, manually input, or extracted from a social media post, the increase in the volume, variety, and velocity of spatiotemporal data is unprecedented. The ability to manage and analyze this data is important for many application domains, including smart cities, health, transportation, agriculture, sports, biodiversity, et cetera. It is critical to not only effectively manage and analyze the data but also to uphold privacy and ethical considerations. Since the civilian use of GPS was allowed in 1980, followed by the technological advances in other location tracking systems – wifi, RFID, bluetooth, etc., it is becoming more and more easy to track moving objects. The Mobility Data Science Summit was an opportunity to discuss the challenges of managing this data and making sense of it, with a focus on the tooling and standardization requirements.

Data science is commonly known as the pipeline of methods and tools from data acquisitions, until the delivery of useful insights, going through data cleaning, integration, management, and analysis. Many tools exist for helping data scientists in every step in this pipeline. Yet mobility data has its own characteristics that cannot be handled by common data science tools. Mobility data is typically available in the form of sequences of location points with time stamps that are generated by location tracking devices. So the is both multidimensional and time series, a structure that requires special data science tools and methods.

OGC has proactively envisioned the need for specialized data models and exchange formats, and formed working groups including moving features SWG and Temporal DWG. It is also natural that the temporal concepts found their way to the work of other working groups, such as GeoPose. This summit aimed to synchronize across working groups, and to align the concepts.

It is not surprising that mobility data can find important applications in a broad range of domains such as maritime, public transport, and logistics. In some sense, it can have a fundamental impact in many aspects of real-life.

Different from other types of data, such as spatial data and time series data, mobility data has several challenging characteristics.

As shown in Figure 2 Mobility Data Computation Stack, mobility data science brings requirements to several layers, from the low level of computation infrastructure to the high level of data modeling and various tasks.

Computation Infrastructure determines where and how mobility data can be generated, collated, derived, swapped, and archived.

Next, mobility data tasks face more challenges than other types of data.

In terms of general mobility data challenges, one special concern is privacy issues. Privacy can block analysis or can enable better analysis by using more restrictive data. This issue concerns not only humans, but also some commercial and endangered animals, which may also have security concerns. For example, cows can be tracked to provide business-sensitive analyses. For humans, due to the highly predictable nature of human behaviors, even small pieces of mobility data can lead to the leakage of identity information. So it is important to find the balance between utility of data and privacy preservation. Unfortunately, most privacy preservation methods restrict analysis ability and de-anonymization is always a concern. Notably, in some cases, the privacy issue may not exist when the question of interest can be answered at a mass level and data analysis does not need to focus on individuals.

Last but not least, evaluation of mobility data needs more effort in the future. Due to the particularities of mobility data, people need better characterization of data quality and more means to assess data quality, so that people can know whether the datasets at hand are suitable for the target analysis. Also, interoperability is an important aspect of, for example, integration of mobility data from different systems requires that those systems can talk to each other and understand each other’s data semantics.

The reader is referred to additional community publications that elucidate the differentiation between mobility data science and general data science, and could thus complement the discussion in this section [1-3].

Currently, there are not enough common tools for mobility data science, because both mobility datasets and use-cases are so diverse. Existing analysis methods and tools are often not transferable across multiple types of data. This lack of widely-used tools is slowing down the community effort towards collaboratively building a mobility data science eco-system and tool-box. In terms of handling massive datasets, the existing big data tools are designed for general purposes and limited in ability to specifically handle mobility data, as a result, mobility data is not the first-class citizen in these tools.

Then a natural question is: which kind of tools are expected for mobility data science? Well, a first requirement is the capability of rapid processing of large mobility datasets, and the critical point is to make proper analysis using data reduction. For example, just plotting everything for visualization makes a big mess, so it is important to design visual summaries that make sense for visualization. A recent work reflecting this point is presented in [4], where the work models movement locations, directions, and speeds using “prototypes,” and supports exploration and anomaly detection.

Another example is Mapbox vector tiles, which can carry time information such that the returned tiles are temporal-aware, instead of the spatial-only ones served by traditional tile servers. To better fulfill the requirement of mobility data, the generation strategy of vector tiles can also take into account factors such as zoom level, viewport, and the amount of data being processed. Then secondly, consider the question “what can be done with the existing OGC Standards to enable richer queries and analysis?” For example, it is not enough to just answer questions based on a single trajectory, but we also need to think about use cases that go beyond a single trajectory to a group of trajectories. Afterwards, when some widely-used tools appear, an attempt can be made to structure/classify mobility datasets to derive some metadata that can help define use cases and give guidelines for certain types of analysis (Figure 3).

During the summit, invited speakers brought examples of their work about creating tools for mobility data science, including QARTA [6], MobilityDB [5], and SensorUp [10].

QARTA is an open source map service featuring high accuracy and scalability. The main motivation behind QARTA is that both researchers and industry practitioners have put much effort into the efficiency of map services, so currently efficiency is no longer a bottleneck. Instead, the accuracy is becoming a bigger concern in such services. For example, even if the most efficient shortest path algorithm is available at hand, the query results would still be as inaccurate as the input map. With the idea that mobility data can be leveraged to boost the accuracy of map services, QARTA includes a Match or Make module (see Figure 4). Given a road network G and trajectory points P, this module will do map matching when G is more accurate than P, and vice versa. This module will perform map making to update G based on P when P is more accurate than G. In summary, QARTA’s success is due to two features: (1) QARTA uses machine learning to build its own highly accurate map, in terms of map topology, and more importantly, in terms of dynamic metadata like edge weights of the road network; and (2) QARTA employs machine learning to calibrate its query answers based on various contextual information. Currently, QARTA has been deployed in all taxis and the third largest food delivery company in the state of Qatar and performed as well, or even better than, commercial map services.

MobilityDB is an open source geospatial trajectory management and analysis platform, which is built on top of PostgreSQL and PostGIS. With the aim to be a mainstream system for industry use, MobilityDB provides many benefits including:

To support efficient management of mobility data, MobilityDB implements multiple temporal types, such as tgeogpoint for a temporal geography point and tfloat for dynamic attributes including speed, heading, and so on. Currently, MobilityDB is in active development, and more functionalities will be provided or enhanced.

The SensorUp software [10] serves as the data fabrics between an organization’s real-time data, its prediction capabilities, and operational execution. By stitching dynamic data sources from various places, the software makes it possible to build interoperable digital infrastructure for real-time data-driven operations and decision making. Many use cases can benefit from this kind of infrastructure, such as digital assistance for firefighters before flashover, dispatch inspection, and repair before equipment failure, et cetera.

Cross-scale aggregation, visualization and analytics are useful when handling big trajectory data. Inspired by this, a quad-tree based trajectory simplification approach is presented in [7], where the spatial distribution of POIs determines the degree of trajectory simplification. So in areas with a higher density of POIs, a trajectory will be less simplified.

Because OGC standards focus mainly on static geospatial data, the support of mobility data may be disruptive to the existing infrastructure. In the future, more standards will be needed that take into account the dynamic nature of mobility data. A good example of such standards is the OGC SensorThings API [8], which provides an open, geospatial-enabled, and unified way to interconnect the Internet of Things (IoT) devices, data, and applications over the Web. Good standards like this can bring many benefits, such as promoting best practices in industry, saving time for collaboration, reducing communication cost, enabling interoperability among multiple systems, et cetera.

Although many categories of relevant standards exist as shown in Figure 8, such as public transport, ticketing, traffic, spatial data, and so on, ‘mobility data standards’ is still in its infancy. Much of what is needed is understood but just getting started. Figure 9 outlines the on-going standardization work for mobility data, which currently focus more on expressiveness but not data size issues (e.g., caused by the capturing of highly-dynamic features like orientation).

Specifically, the following points are identified and agreed to be worthwhile for further standardization work.

[1] Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Aref, W., …​ & Zimányi, E. (2023). Towards Mobility Data Science (Vision Paper). arXiv preprint arXiv:2307.05717.

[2] Sakr, M., Ray, C., & Renso, C. (2022). Big mobility data analytics: recent advances and open problems. GeoInformatica, 26(4), 541-549

[3] Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Anderson, T., …​ & Zimányi, E. (2022). Mobility data science (dagstuhl seminar 22021). In Dagstuhl reports (Vol. 12, No. 1). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.

[4] Graser. A., Widhalm, P., & Dragaschnig, M. (2020). The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science. doi:10.1080/13658816.2020.1776293.

[5] Zimányi, E., Sakr, M., & Lesuisse, A. (2020). MobilityDB: A mobility database based on PostgreSQL and PostGIS. ACM Transactions on Database Systems (TODS), 45(4), 1-42.

[6] Musleh, M., Abbar, S., Stanojevic, R., & Mokbel, M. (2021). QARTA: an ML-based system for accurate map services. Proceedings of the VLDB Endowment, 14(11), 2273-2282.

[7] Fu, Cheng; Huang, Haosheng; Weibel, Robert (2021). Adaptive simplification of GPS trajectories with geographic context – a quadtree-based approach. International Journal of Geographical Information Science, 35(4):661-688.

[8] https://www.ogc.org/standards/sensorthings

[9] https://www.iso.org/standard/53798.html

[10] https://sensorup.com/