The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applications heavily operate on this third dimension, i.e., land surface. Hence, for the field of databases to stay relevant, it should be able to efficiently process spatial queries given this constrained third dimension. However, online processing of the surface k Nearest Neighbor (skNN) queries is quite challenging due to the huge size of land surface models which renders any accurate distance computation on the surface extremely slow. In this paper, for the first time, we propose an index structure on land surface that enables exact and fast responses to skNN queries. Two complementary indexing schemes, namely Tight Surface Index (TSI) and Loose Surface Index (LSI), are constructed and stored collectively on a single novel data structure called Surface Index R-tree (SIR-tree). With those indexes, we can process skNN query efficiently by localizing the search and minimizing the invocation of the costly surface distance computation and hence incurring low I/O and computation costs. Our algorithm does not need to know the value of k a priori and can incrementally expand the search region using SIR-tree and report the query result progressively. It also reports the exact shortest surface paths to the query results. We show through experiments with real world data sets that our algorithm has better performance than the competitors in both efficiency and accuracy.

As geo-realistic rendering of land surfaces is becoming commonplace in geographical information systems (GIS), games and online Earth visualization platforms, a new type of k Nearest Neighbor (kNN) queries, "surface" k Nearest Neighbor (skNN) queries, has emerged and been investigated recently, which extends the traditional kNN queries to a constrained third dimension (i.e., land surface). All existing techniques, however, assume a static environment, limiting their utility in emerging applications (e.g., Location-based Services) where objects move. In this paper, for the first time, we propose two exact methods that can continuously answer skNN queries in a highly dynamic environment which allows for arbitrary movements of data objects. The first method, inspired by the existing techniques in monitoring kNN in road networks [7] maintains an analogous counterpart of the Dijkstra Expansion Tree on land surface, called Surface Expansion Tree (SE-Tree). However, we show the concept of expansion tree for land surface does not work as SEtree suffers from intrinsic defects: it is fat and short, and hence does not improve the query efficiency. Therefore, we propose a superior approach that partitions SE-Tree into hierarchical chunks of pre-computed surface distances, called Angular Surface Index Tree (ASI-Tree). Unlike SE-tree, ASI-Tree is a well balanced thin and tall tree. With ASI-Tree, we can continuously monitor skNN queries efficiently with low CPU and I/O overheads by both speeding up the surface shortest path computations and localizing the searches. We experimentally verify the applicability and evaluate the efficiency of the proposed methods with both real world and synthetic data sets. ASI-Tree consistently and significantly outperforms SE-Tree in all cases.

T here is a critical need for advanced geospatial decision-making tools for countless geospatial applications, such as urban planning, emergency response, military intelligence, simulator training, and serious gaming. With the abundance of available geospatial data-such as satellite and aerial imagery-the most effective approach to geospatial decision-making is through sophisticated virtualization. An ideal application designed to formulate and evaluate decision-making questions should contain realistic modeling, a host of geotagged and timestamped data sets, and efficient presentation of a basic set of spatiotemporal queries on top of the information-rich virtual geolocation. A three-part virtualization process like this could simulate, say, New York city and provide answers to complex questions in a completely virtual environment. For example, by embedding the road data, building data, elevation data, and microclimate data of New York, then using a combination of range queries, nearest-neighbor queries, and visibility queries, it would be possible to determine what windvector impact the Freedom Tower might have on south Manhattan.To be effective, such a decision-making technology must satisfy the requirements known as RAISE, which includes Realistic simulation, Accurate information fusion, Interactive query and access, Scalable infrastructure, and Efficient time-to-build. Considering RAISE, as our objective, we designed and developed an end-toend system dubbed GeoDec for geospatial decision-making. GeoDec blends several techniques developed independently in the fields of databases, artificial intelligence, computer graphics, and computer vision into a threetier architecture to facilitate the decisionmaking process. The system allows users to interact with a virtualized geolocation through a set of spatiotemporal queries to access layers of GIS data seamlessly integrated into GeoDec's database server.Current geospatial visualization systems fall into three main categories: Earth visualization (EV), game systems, and GIS. EV platforms, as envisioned by Al Gore, 1 include applications such as Microsoft Virtual Earth or Google Earth. These platforms display realistic 3D worlds created from aerial imagery. Game systems, such as Half-Life 2, render simulated urban environments realistically. Other examples include SimCity, a city-planning simulation, and Spore, an interactive world in which fictional data analysis is used to support the gameplay. The third category, GIS, includes Environmental Systems Research Institute (see http://www.esri.com) products, which offer query and analysis of static worlds. In addition, there are numerous research projects that use geospatial visualization. For example, UrbanSim focuses on socioeconomic modeling for city planning, 2 the GeoVISTA Center develops visualization tools for decision making (see http:// www.geovista.psu.edu), and the UCI Rescue (at the University of California, Irvine) targets crisis-response management. 3 Despite all of this, no single system that we are aware...

The growing popularity of online Earth visualization tools and geo-realistic games and the availability of high resolution terrain data have motivated a new class of queries to the interests of the GIS and spatial database community: spatial queries (e.g., kNN) over land surface. However, the fundamental challenges that restrict the applicability of these studies to real world applications are the prohibitive time complexity and storage overhead to precompute the shortest surface paths. In this paper, for the first time, we propose an approximate solution to address both challenges and allow browsing the shortest surface paths in O() time, where N is the size of the terrain. With this method, the time and space requirements for an exhaustive all-pair pre-computation have been reduced from O(N 3) to O(N 1.5) and O(N) respectively. The substantial savings in both time and storage are gained by taking advantage of the fact that the O(N 2) surface paths only deviate from approximate straight lines at O() points, termed rough vertices. As a result, we propose a linear time shortest surface path computation algorithm between two arbitrary vertices and a linear size storage structure, which captures all the shortest surface paths between any pair of vertices. We experimentally verified the applicability and scalability of the proposed methods with large real world and synthetic data sets and showed that accuracy higher than 97% can be obtained in most cases.

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