Reem Y. Ali

Spatiotemporal Data Mining: A Computational Perspective

Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey

Given a definition of change and a dataset about spatiotemporal (ST) phenomena, ST change footprint discovery is the process of identifying the location and/or time of such changes from the dataset. Change footprint discovery is fundamentally important for the study of climate change, the tracking of disease, and many other applications. Methods for detecting change footprints have emerged from a diverse set of research areas, ranging from time series analysis and remote sensing to spatial statistics. Researchers have much to learn from one another, but are stymied by inconsistent use of terminology and varied definitions of change across disciplines. Existing reviews focus on discovery methods for only one or a few types of change footprints (e.g., point change in a time series). To facilitate sharing of insights across disciplines, we conducted a multi‐disciplinary review of ST change patterns and their respective discovery methods. We developed a taxonomy of possible ST change footprints and classified our review findings accordingly. This exercise allowed us to identify gaps in the research that we consider ripe for exploration, most notably change pattern discovery in vector ST datasets. In addition, we illustrate how such pattern discovery might proceed using two case studies from historical GIS. WIREs Data Mining Knowl Discov 2014, 4:1–23. doi: 10.1002/widm.1113

Spatial computing perspective on food energy and water nexus

In the coming decades, the increasing world population is expected to grow the demand for food, energy, and water resources. In addition, these resources will be under stress due to the climate change and urbanization. Previously, more problems were caused by piecemeal approaches analyzing and planning those resources independent of each other. The goal of the food, energy, and water (FEW) nexus approach is to prevent such problems by understanding, appreciating, and visualizing the interconnections and interdependencies of FEW resources at local, regional, and global levels. The nexus approach seeks to use the FEW resources as an interrelated system of systems, but data and modeling constraints make it a challenging task. In addition, the lack of complete knowledge and observability of FEW interactions exacerbates the problem. Related work focused on physical science solutions (e.g., desalination, bio-pesticides). No doubt these are necessary and worthwhile for FEW resource security. Overlooked in these work is that spatial computing may help domain scientists achieve their goals for the FEW nexus. In this paper, we describe our vision of the spatial computing’s role in understanding the FEW nexus using a Nexus Dashboard analogy. From a spatial data lifecycle perspective, we provide more details on the spatial computing components behind the Nexus Dashboard vision. In each component, we list new technical challenges that are likely to drive future spatial computing research.

Discovering Non-compliant Window Co-Occurrence Patterns: A Summary of Results

Given a set of trajectories annotated with measurements of physical variables, the problem of Non-compliant Window Co-occurrence (NWC) pattern discovery aims to determine temporal signatures in the explanatory variables which are highly associated with windows of undesirable behavior in a target variable. NWC discovery is important for societal applications such as eco-friendly transportation (e.g. identifying engine signatures leading to high greenhouse gas emissions). Challenges of designing a scalable algorithm for NWC discovery include the non-monotonicity of popular spatio-temporal statistical interest measures of association such as the cross-K function. This challenge renders the anti-monotone pruning based algorithms (e.g. Apriori) inapplicable. To address this limitation, we propose two novel upper bounds for the cross-K function which help in filtering uninteresting candidate patterns. Using these bounds, we also propose a Multi-Parent Tracking approach (MTNMiner) for mining NWC patterns. A case study with real world engine data demonstrates the ability of the proposed approach to discover patterns which are interesting to engine scientists. Experimental evaluation on real-world data show that MTNMiner results in substantial computational savings over the naive approach.

Future connected vehicles: challenges and opportunities for spatio-temporal computing

Modern vehicles are increasingly being equipped with rich instrumentation that enables them to collect location aware data on a wide variety of travel related phenomena such as the real-world performance of engines and powertrain, driver preferences, context of the vehicle with respect to others nearby, and--indirectly--traffic on the transportation network itself. Combined with their increased access to the Internet, these connected vehicles are opening up vast opportunities to improve the safety, environmental friendliness, and the overall experience of urban travel. However, significant spatial computing challenges need to be addressed before we can realize the full potential of connected vehicles. This paper presents some of the open research questions under this theme from the perspectives of query processing, data science and data engineering.

Discovering non-compliant window co-occurrence patterns

Given a set of trajectories annotated with measurements of physical variables, the problem of Non-compliant Window Co-occurrence (NWC) pattern discovery aims to determine temporal signatures in the explanatory variables which are highly associated with windows of undesirable behavior in a target variable. NWC discovery is important for societal applications such as eco-friendly transportation (e.g. identifying engine signatures leading to high greenhouse gas emissions). Challenges of designing a scalable algorithm for NWC discovery include the non-monotonicity of popular spatio-temporal statistical interest measures of association such as the cross-K function which renders the anti-monotone pruning based algorithms (e.g. Apriori) inapplicable for such interest measures. In our preliminary work, we proposed two upper bounds for the cross-K function and a top-down multi-parent tracking approach that uses these bounds for filtering out uninteresting candidate patterns and then applies a minimum support (i.e. frequency) threshold as a post-processing step to filter out chance patterns. In this paper, we propose a novel bi-directional pruning approach (BDNMiner) that combines top-down pruning based on the cross-K function threshold with bottom-up pruning based on the minimum support threshold to efficiently mine NWC patterns. Case studies with real world engine data demonstrates the ability of the proposed approach to discover patterns which are interesting to engine scientists. Experimental evaluation on real-world data show that the proposed approach yields substantial computational savings compared to prior work.

Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities

Recent years have seen the emergence of many new and valuable spatial datasets such as trajectories of cell-phones and Global Positioning System (GPS) devices, vehicle engine measurements, global climate models simulation data, volunteered geographic information (VGI), geo-social media, and tweets. The value of these datasets is already evident through many societal applications including disaster management and disease outbreak prediction. However, these location-aware datasets are of a volume, variety, and velocity that exceed the capability of current CyberGIS technologies. We refer to these datasets as Spatial Big Data. In this chapter, we define spatial big data in terms of its value proposition and user experience which depends on the computational platform, use-case, and dataset at hand. We compare spatial big data with traditional spatial data and with other types of big data. We then provide an overview of the current efforts, challenges and opportunities available when spatial big data is enabled via next-generation CyberGIS. Our discussion includes current accomplishments and opportunities from both an analytics and an infrastructure perspective.

Supply and Demand Aware Synthetic Data Generation for On-demand Traffic with Real-world Characteristics

The on-demand economy has attracted significant attention in recent years, with a rapid growth in on-demand services ranging from ride-hailing to package delivery and grocery pickup. However, real-world spatio-temporal data that can be used for evaluating research on on-demand brokers design and supply-demand regulation are either not publicly available or are very limited in their spatial coverage. Research efforts in generating synthetic spatio-temporal datasets such as traffic generators have only focused on one side of the business model, particularly the demand side, and thus are not convenient for studying market variations such as the problem of supply-demand imbalance. In addition, many of these generators do not accurately reflect real-world data characteristics. In this paper, we propose a supply and demand aware framework for generating synthetic datasets for the purpose of designing on-demand spatial service brokers, while also capturing real-world data characteristics by leveraging multiple publicly available data sources. We also present an evaluation of the quality and performance of our proposed framework.

Transdisciplinary Foundations of Geospatial Data Science

Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.