Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this paper, we study a number of flood-risk related problems: given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution R, and a volume of rain Ψ, determine which portions of Σ are flooded. We give an overview of efficient algorithms for these problems as well as explore the efficacy and efficiency of these algorithms on real terrains. Flooding can be extremely dangerous and damaging. The United States experienced the wettest 12-month period from June 2018 to May 2019, with major flooding in the Midwest affecting millions of people and causing several billion dollars in damages. Being able to accurately and quickly model flooding can help predict and prepare for the risks. Flood-risk analysis has been studied widely across multiple research communities including environmental science, engineering, machine learning, and GIS communities: see Section 7. Flood risk analysis also has been a focus of a number of companies as well. SCALGO22 is a software development and services company that uses massive terrain dataprocessing technology to provide a flood risk platform for Scandinavian countries. Fathom13 uses high-resolution global data-sets and hydrological modeling to provide flood hazard data for many applications, including insurance and disaster response. Terrain-flood query: given a terrain Σ and a rain pattern, determine which portions of Σ will be flooded. The areas marked in blue are flooded, with regions that water flows over marked in orange. Point-flood query: In some applications, the terrain Σ is fixed and we wish to know whether a query point on Σ will be flooded for a given rain pattern.

Imagine you are considering buying a long-term place with a view of mountains or ocean. For due diligence, your partner asks about flood risk in the area. FEMA maps show the place is outside the 100-year flood zones (1% annual chance). However, you have heard that climate change is making extreme events more extreme and some places have seen multiple 100-year floods within a few years. Next, you browse information about climate change and its impact.

As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities. An accurate prediction method can help BSS schedule resources in advance to meet the demands of users, and definitely improve operating efficiencies of it. However, most of the existing methods for similar tasks just utilize spatial or temporal information independently. Though there are some methods consider both, they only focus on demand prediction in a single location or between location pairs. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Interval Network (STDI-Net). The method predicts the number of renting and returning orders of multiple connected stations in the near future by modeling joint spatial-temporal information. Furthermore, we embed an additional module that generates dynamical learnable mappings for different time intervals, to include the factor that different time intervals have a strong influence on demand prediction in BSS. Extensive experiments are conducted on the NYC Bike dataset, the results demonstrate the superiority of our method over existing methods.

Microsoft's latest Flight Simulator entry doesn't do anything small. It's a title that comes on 10 DVDs and allows you to explore the world in almost its entirety. It turns out that scale even extends to its accidental inclusions. Flight Simulator users recently found an unusual landmark: a 212-story monolith towering over an otherwise nondescript suburb in Melbourne, Australia. In Microsoft Flight Simulator a bizarrely eldritch, impossibly narrow skyscraper pierces the skies of Melbourne's North like a suburban Australian version of Half-Life 2's Citadel, and I am -all for it- pic.twitter.com/6AH4xgIAWg

The enterprise has been talking about Digital Transformation and Industry 4.0 for years. We have seen transformation accelerate and the adoption of artificial intelligence, connected devices, and even virtual reality speed-up over the last few months due to the pandemic. As enterprise digitization continues to be top of mind and data becomes even more critical in this process, we need to look at how all the data created can be better visualized to generate better business outcomes. The Internet of Things (IoT) allows devices to talk to each other through connected sensors - producing real-time data. Companies had to learn how to process large amounts of data from IoT devices.

We propose a novel formal framework (called 3D-nCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set Programming (ASP). 3D-nCDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, and nCDC-ASP to 3D. 3D-nCDC-ASP provides a flexible platform offering different types of reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D-nCDC-ASP, and illustrate its usefulness with applications. This paper is under consideration for acceptance in TPLP.

The enterprise has been talking about Digital Transformation and Industry 4.0 for years. We have seen transformation accelerate and the adoption of artificial intelligence, connected devices, and even virtual reality speed-up over the last few months due to the pandemic. As enterprise digitization continues to be top of mind and data becomes even more critical in this process, we need to look at how all the data created can be better visualized to generate better business outcomes. The Internet of Things (IoT) allows devices to talk to each other through connected sensors - producing real-time data. Companies had to learn how to process large amounts of data from IoT devices.

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

Computational geometry emerged from the?eld of algorithms design and analysis in the late 1970s. It has grown into a recognized discipline with its own journals, conferences, and a large community of active researchers. The success of the?eld as a research discipline can on the one hand be explained from the beauty of the problems studied and the solutions obtained, and, on the other hand, by the many application domains--computer graphics, geographic information systems (GIS), robotics, and others--in which geometric algorithms play a fundamental role. For many geometric problems the early algorithmic solutions were either slow or dif?cult to understand and implement. In recent years a number of new algorithmic techniques have been developed that improved and simpli?ed many of the previous approaches. In this textbook we have tried to make these modern algorithmic solutions accessible to a large audience. The book has been written as a textbook for a course in computational geometry, but it can also be used for self-study.