The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, fol- lowing the United Nations Sustainable Development Goals (SDGs) framework.

Sub-meter accurate vehicle localization is often the ultimate goal in the automotive industry as well as fleet management today and the Global Navigation Satellite System (GNSS) is one of the most practical ways to achieve this goal. In addition to precise navigation and real-time accurate vehicle location tracking, the high-accuracy location data enables many geospatial and mapping applications. Traditionally, high-accuracy GNSS localization solutions were highly expensive. However, we can now achieve high accuracy localization by combining GNSS and Inertial Measurement Unit (IMU) along with Real-Time Kinematic (RTK) correction at a high availability with low cost. In this paper, we present the evaluation and experimental results of such a solution that we are deploying in a large vehicle fleet. Our experiments show that sub-meter localization accuracy can be achieved in most scenarios. Additionally, we present various geospatial applications of the high-accuracy GPS trajectories, some of which were previously impossible with the data collected from low-accuracy consumer-grade sensors. Finally, we share a dataset with multiple GPS trajectories containing GNSS data from both our low-cost and ground-truth GNSS systems. We believe this dataset will enable further research on various applications of high-accuracy location data as well as GNSS characteristics in different areas.

The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).

Fragile Earth 2021, our annual workshop is taking place as part of the Earth Day events at ACM's KDD 2021 Conference on research in Machine Learning and its applications. The 5th edition of Fragile Earth will bring together the research community, industry, and policymakers to develop radically new technological foundations for advancing and meeting the Sustainable Development Goals in a way that ensures equitable and inclusive progress.

Given a set of GPS trajectories, avoidance region discovery (ARD) finds regions that are avoided by drivers. ARD is important for applications such as sociology, city/transportation planning and crime mitigation, where it can help domain users understand the driver behavior under different concerns (e.g. rush hour, congestion, dangerous neighborhood, etc.). ARD is challenging because of the large number of trajectories with thousands of GPS points, large number of candidate avoidance regions, and the cost of evaluating those. Related work is focused on finding evasive trajectories for a given set of avoidance regions. Distinct from the related work, we propose an Avoidance Region Miner (ARM) approach that can detect both the avoidance regions and evasive trajectories just by using the trajectories in hand without the need of an additional input. A case study on real trajectory data confirms that ARM discovers such regions for further investigation by domain users. Experiments show that ARM yields substantial computational savings compared to a baseline approach.

Given a set of activity points (e.g., crime, disease locations), Ring-Shaped Hotspot Detection (RHD) finds ring-shaped areas where the concentration of activities inside is significantly higher than that outside. RHD is societally important for applications such as environmental criminology, epidemiology, and biology to investigate evasive patterns. RHD is computationally challenging because of the large number of candidate rings, non-monotonic interest measure, and cost of the statistical significance test. Previous approaches (e.g., spatial scan statistics tools) focus on simply-connected shaped areas (e.g., circles, rectangles) and can not detect statistically significant rings. In this paper, a novel algorithm, DGPLMR, is proposed to discover statistically significant ring-shaped hotspots based on the ideas of dual grid based pruning and best enclosing ring refining. Theoretical evaluation proves that the proposed approach is a correct approach (i.e., all outputs satisfy input thresholds) to detect ring-shaped hotspots. Case study on real disease data shows that the proposed approach finds ring-shaped hotspots which were not detected by the existing techniques. Cost analysis and experimental results on synthetic data show that the proposed approach with algorithmic refinements yields substantial computational savings.

Given a collection of geo-located activities (e.g., Crime reports), ring-shaped hotspot detection (RHD) finds rings, where concentration of activities inside the ring is much higher than outside. RHD is important for the applications such as crime analysis, where it may focus the search for crime source's location, e.g. The home of a serial criminal. RHD is challenging because of the large number of candidate rings and the high computational cost of the statistical significance test. Previous statistically significant hotspot detection techniques (e.g., Sat Scan) identify circular/rectangular areas, but can not discover rings. This paper proposes a dual grid based pruning (DGP) approach to detect ring-shaped hotspots. A case study on real crime data confirms that DGP detects novel ring-shaped regions, regions that go undetected by Sat Scan. Experiments show that DGP improves the computational cost of a naive approach substantially.