ALBERT

Description: This paper presents a transportation spatio-temporal system that efficiently converts traffic video data into vehicular motion information in spatio-temporal databases for a variety of transportation applications. The proposed transportation spatio-temporal system interpolates the vehicle trajectory data (i.e., time, location, and speed), which are extracted from video, and integrates them with spatial road information for storage of dynamic transportation environments. The proposed transportation spatio-temporal system can mitigate data storage and retrieval issues related to storing large amounts of traffic video. Moreover, users can manage and operate multiform and multidimensional traffic data in a spatio-temporal transportation environment. The proposed approach is demonstrated for typical transportation applications. The experimental results show that the proposed transportation spatio-temporal system has excellent potential for addressing issues related to storage of large amounts of traffic video data.

Description: Recent rapid development of wireless communication, mobile computing, global navigation satellite systems (GNSS), and spatially enabled sensors are leading to an exponential growth of available mobility data produced continuously at high speed. Due to these advancements, a new class of monitoring applications has come to the focus, including real-time intelligent transportation systems, traffic monitoring and mobile objects tracking. These new information flow processing (IFP) application domains need to process huge volume of mobility data arriving in the form of continuous data streams from mobile objects. IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity . Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. A prototype implementation is rooted in a new stream processing model that overcomes the challenges of current distributed stream processing models and enable seamless integration with batch and interactive processing like MapReduce.

Description: Business professionals are increasingly mobile and should be supported by suitable mobile Decision Support Systems (DSS). In our previous work, we have established that such suitable mobile DSS should be (i) GeoBI(Geospatial Business Intelligence)-enabled and (ii) context-based, and have addressed issues regarding context characterization and context modeling. The present paper deals with mobile GeoBI context-based reasoning. Through realistic scenarios, it highlights (i) the requirement for context-based reasoning to enhance mobile GeoBI experience, (ii) the need for contextual metrics/statistics to help mobile business professionals discover their local context, (iii) the need for crossing business performance metrics with contextual metrics to help mobile business professionals in discovering the context hidden behind business performance figures, and proposes convenient solutions to tackle these needs.

Description: Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches.

Description: Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In order to explore these information spaces, navigation techniques for controlling the virtual camera are required to facilitate wayfinding and movement. However, navigation is not a trivial task and many available navigation techniques do not support users effectively and efficiently with their respective skills and tasks. In this article, we present an assisting, constrained navigation technique for multiscale virtual 3D city models that is based on three basic principles: users point to navigate, users are lead by suggestions, and the exploitation of semantic, multiscale, hierarchical structurings of city models. The technique particularly supports users with low navigation and virtual camera control skills but is also valuable for experienced users. It supports exploration, search, inspection, and presentation tasks, is easy to learn and use, supports orientation, is efficient, and yields effective view properties. In particular, the technique is suitable for interactive kiosks and mobile devices with a touch display and low computing resources and for use in mobile situations where users only have restricted resources for operating the application. We demonstrate the validity of the proposed navigation technique by presenting an implementation and evaluation results. The implementation is based on service-oriented architectures, standards, and image-based representations and allows exploring massive virtual 3D city models particularly on mobile devices with limited computing resources. Results of a user study comparing the proposed navigation technique with standard techniques suggest that the proposed technique provides the targeted properties, and that it is more advantageous to novice than to expert users.

Description: Providing an adequate route description requires in-depth spatial knowledge of the route in question. In this article we demonstrate that despite having travelled a route recently and having much experience of the area in question, an individual may lack such a degree of knowledge. Previous research and experience informs us that a map is an effective tool for bridging gaps in one’s spatial knowledge. In this article we propose an approach, known as an Interactive Route Description , for defining and interpreting route descriptions interactively with a map. This approach is based on the concept of annotating the map in question and allows the aforementioned gap in one’s spatial knowledge to be bridged. An additional benefit of defining route descriptions in this way is that it facilitates automatic parsing and in turn offers many potential applications. One such application, illustrated in this paper, is the automatic transformation to other representations of the description such as turn-by-turn instructions.

Description: Geometric footprints, which delineate the region occupied by a spatial point pattern, serve a variety of functions in GIScience. This research explores the use of two density-based clustering algorithms for footprint generation. First, the Density-Based Spatial Clustering with Noise (DBSCAN) algorithm is used to classify points as core points, non-core points, or statistical noise; then a footprint is created from the core and non-core points in each cluster using convex hulls. Second, a Fuzzy-Neighborhood (FN)-DBSCAN algorithm, which incorporates fuzzy set theory, is used to assign points to clusters based on membership values. Then, two methods are presented for delineating footprints with FN-DBSCAN: (1) hull-based techniques and (2) contouring methods based on interpolated membership values. The latter approach offers increased flexibility for footprint generation, as it provides a continuous surface of membership values from which precise contours can be delineated. Then, a heuristic parameter selection method is described for FN-DBSCAN, and the approach is demonstrated in the context of wildlife home range estimation, where the goal is to a generate footprint of an animal’s movements from tracking data. Additionally, FN-DBSCAN is applied to produce crime footprints for a county in Florida. The results are used to guide a discussion of the relative merits of the new techniques. In summary, the fuzzy clustering approach offers a novel method of footprint generation that can be applied to characterize a variety of point patterns in GIScience.

Description: Contour lines are important for quantitatively displaying relief and identifying morphometric features on a map. Contour trees are often used to represent spatial relationships between contours and assist the user in analysing the terrain. However, automatic analysis from the contour tree is still limited as features identified on a map by sets of contours are not only characterised by local relationships between contours but also by relationships with other features at different levels of representation. In this paper, a new method based on adjacency and inclusion relationships between regions defined by sets of contours is presented. The method extracts terrain features and stores them in a feature tree providing a description of the landscape at multiple levels of detail. The method is applied to terrain analysis and generalisation of a contour map by selecting the most relevant features according to the purpose of the map. Experimental results are presented and discussed.

Description: Histograms have been widely used for estimating selectivity in query optimization. In this paper, we propose a new histogram construction method for geographic data objects that are used in many real-world applications. The proposed method is based on analyses and utilization of clusters of objects that exist in a given data set, to build histograms with significantly enhanced accuracy. Our philosophy in allocating the histogram buckets is to allocate them to the subspaces that properly capture object clusters. Therefore, we first propose a procedure to find the centers of object clusters. Then, we propose an algorithm to construct the histogram buckets from these centers. The buckets are initialized from the clusters’ centers, then expanded to cover the clusters. Best expansion plans are chosen based on a notion of skewness gain. Results from extensive experiments using real-life data sets demonstrate that the proposed method can really improve the accuracy of the histograms further, when compared with the current state of the art histogram construction method for geographic data objects.

Description: Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the S patio- T emporal P olygonal C lustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.

Description: Geosensor networks present unique resource constraints to spatial computation, including limited battery power, communication constraints, and frequently a lack of coordinate positioning systems. As a result, there is a need for new algorithms that can efficiently satisfy basic spatial queries within those resource constraints. This paper explores the design and evaluation of a family of new algorithms for determining the topological relations between regions monitored by such a resource-constrained geosensor network. The algorithms are based on efficient, decentralized (in-network) variants of conventional 4-intersection and intersection and difference models, with in-network data aggregation. Further, our algorithms operate without any coordinate information, making them suitable applications where a positioning system is unavailable or unreliable. While all four algorithms are shown to have overall communication complexity O( n ) and optimal load balance O(1), the algorithms differ in the level of topological detail they can detect; the types of regions they can monitor; and in the constant factors for communication complexity. The paper also demonstrates the impact of finite granularity observations on the correctness of the query results. In the conclusions, we identify the need to conduct further fundamental research on the relationship between topological relations between regions and limited granularity sensor observations of those regions.

Description: The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms.

Description: Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.

Description: This paper addresses the problem of continuous aggregate nearest-neighbor (CANN) queries for moving objects in spatio-temporal data stream management systems. A CANN query specifies a set of landmarks, an integer k , and an aggregate distance function f (e.g., min, max, or sum), where f computes the aggregate distance between a moving object and each of the landmarks. The answer to this continuous query is the set of k moving objects that have the smallest aggregate distance f . A CANN query may also be viewed as a combined set of nearest neighbor queries. We introduce several algorithms to continuously and incrementally answer CANN queries. Extensive experimentation shows that the proposed operators outperform the state-of-the-art algorithms by up to a factor of 3 and incur low memory overhead.

Description: Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple environments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car , Walk , Bus ). A generic and precise location representation is proposed that can apply in all environments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to represent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road networks to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.

Description: In this paper, we present a novel method for fast lossy or lossless compression and decompression of regular height fields. The method is suitable for SIMD parallel implementation and thus inherently suitable for modern GPU architectures. Lossy compression is achieved by approximating the height field with a set of quadratic Bezier surfaces. In addition, lossless compression is achieved by superimposing the residuals over the lossy approximation. We validated the method’s efficiency through a CUDA implementation of compression and decompression algorithms. The method allows independent decompression of individual data points, as well as progressive decompression. Even in the case of lossy decompression, the decompressed surface is inherently seamless. In comparison with the GPU-oriented state-of-the-art method, the proposed method, combined with a widely available lossless compression method (such as DEFLATE), achieves comparable compression ratios. The method’s efficiency slightly outperforms the state-of-the-art method for very high workloads and considerably for lower workloads.

Description: With the trend of cloud computing, outsourcing databases to third party service providers is becoming a common practice for data owners to decrease the cost of managing and maintaining databases in-house. In conjunction, due to the popularity of location-based-services (LBS), the need for spatial data (e.g., gazetteers, vector data) is increasing dramatically. Consequently, there is a noticeably new tendency of outsourcing spatial datasets by data collectors. Two main challenges with outsourcing datasets are to keep the data private (from the data provider) and to ensure the integrity of the query result (for the clients). Unfortunately, most of the techniques proposed for privacy and integrity do not extend to spatial data in a straightforward manner. Hence, recent studies proposed various techniques to support either privacy or integrity (but not both) on spatial datasets. In this paper, for the first time, we propose a technique that can ensure both privacy and integrity for outsourced spatial data. In particular, we first use a one-way spatial transformation method based on Hilbert curves, which encrypts the spatial data before outsourcing and, hence, ensures its privacy. Next, by probabilistically replicating a portion of the data and encrypting it with a different encryption key, we devise a technique for the client to audit the trustworthiness of the query results. We show the applicability of our approach for both k -nearest-neighbor queries and spatial range queries, which are the building blocks of any LBS application. We also design solutions to guarantee the freshness of outsourced spatial databases. Finally, we evaluate the validity and performance of our algorithms with security analyses and extensive simulations.

Description: Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical models and computer algorithms that infer or estimate specific forest metrics. For these outputs to be useful, algorithms must be validated and/or calibrated using a sub-sample of ‘known’ metrics measured using more detailed, reliable methods such as field sampling. In this paper we describe a novel method for delineating and deriving metrics of individual trees from LiDAR data based on watershed segmentation. Because of the costs involved with collecting both LiDAR data and field samples for validation, we use synthetic LiDAR data to validate and assess the accuracy of our algorithm. This synthetic LiDAR data is generated using a simple geometric model of Loblolly pine ( Pinus taeda ) trees and a simulation of LiDAR sampling. Our results suggest that point densities greater than 2 and preferably greater than 4 points per m2 are necessary to obtain accurate forest inventory data from Loblolly pine stands. However the results also demonstrate that the detection errors (i.e. the accuracy and biases of the algorithm) are intrinsically related to the structural characteristics of the forest being measured. We argue that experiments with synthetic data are directly useful to forest managers to guide the design of operational forest inventory studies. In addition, we argue that the development of LiDAR simulation models and experiments with the data they generate represents a fundamental and useful approach to designing, improving and exploring the accuracy and efficiency of LiDAR algorithms.

Description: In this paper we propose a fundamental approach to perform the class of Range and Nearest Neighbor (NN) queries, the core class of spatial queries used in location-based services, without revealing any location information about the query in order to preserve users’ private location information. The idea behind our approach is to utilize the power of one-way transformations to map the space of all objects and queries to another space and resolve spatial queries blindly in the transformed space. Traditional encryption based techniques, solutions based on the theory of private information retrieval, or the recently proposed anonymity and cloaking based approaches cannot provide stringent privacy guarantees without incurring costly computation and/or communication overhead. In contrast, we propose efficient algorithms to evaluate K NN and range queries privately in the Hilbert transformed space. We also propose a dual curve query resolution technique which further reduces the costs of performing range and K NN queries using a single Hilbert curve. We experimentally evaluate the performance of our proposed range and K NN query processing techniques and verify the strong level of privacy achieved with acceptable computation and communication overhead.

Description: We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high quality urban texture documentation. We propose to implement PTD in two steps. At the first step, termed viewpoint selection, a minimum number of viewpoints in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) with a desirable quality can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we define each of these steps and prove that both are NP-hard problems. Accordingly, we propose efficient algorithms to implement the viewpoint selection and assignment problems. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

Description: Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap.

Description: Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like “what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?”, and (2) a probability-threshold query like “what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?”. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G t a ( V , E ) by analyzing uncertain trajectories of moving objects. Based on G t a ( V , E ), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data.

Description: We are witnessing a significant growth in the number of smartphone users and advances in phone hardware and sensor technology. In conjunction with the popularity of video applications such as YouTube, an unprecedented number of user-generated videos (UGVs) are being generated and consumed by the public, which leads to a Big Data challenge in social media. In a very large video repository, it is difficult to index and search videos in their unstructured form. However, due to recent development, videos can be geo-tagged (e.g., locations from GPS receiver and viewing directions from digital compass) at the acquisition time, which can provide potential for efficient management of video data. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). This effectively converts a challenging video management problem into a spatial database problem. This paper attacks the challenges of large-scale video data management using spatial indexing and querying of FOVs, especially maximally harnessing the geographical properties of FOVs. Since FOVs are shaped similar to slices of pie and contain both location and orientation information, conventional spatial indexes, such as R-tree, cannot index them efficiently. The distribution of UGVs’ locations is non-uniform (e.g., more FOVs in popular locations). Consequently, even multilevel grid-based indexes, which can handle both location and orientation, have limitations in managing the skewed distribution. Additionally, since UGVs are usually captured in a casual way with diverse setups and movements, no a priori assumption can be made to condense them in an index structure. To overcome the challenges, we propose a class of new R-tree-based index structures that effectively harness FOVs’ camera locations, orientations and view-distances, in tandem, for both filtering and optimization. We also present novel search strategies and algorithms for efficient range and directional queries on our indexes. Our experiments using both real-world and large synthetic video datasets (over 30 years’ worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms.

Description: Private transport accounts for a large amount of total CO 2 emissions, thus significantly contributing to global warming. Tools that actively support people in engaging in a more sustainable life-style without restricting their mobility are urgently needed. How can location-aware information and communication technology (ICT) enable novel interactive and participatory approaches that help people in becoming more sustainable? In this survey paper, we discuss the different aspects of this challenge from a technological and cognitive engineering perspective, based on an overview of the main information processes that may influence mobility behavior. We review the state-of-the-art of research with respect to various ways of influencing mobility behavior (e.g., through providing real-time, user-specific, and location-based feedback) and suggest a corresponding research agenda. We conclude that future research has to focus on reflecting individual goals in providing personal feedback and recommendations that take into account different motivational stages. In addition, a long-term and large-scale empirical evaluation of such tools is necessary.

Description: 〈h3〉Abstract〈/h3〉 〈p〉Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The ever-increasing size of data emanating from mobile devices and sensors, dictates the use of distributed systems for storing and querying these data. Typically, such data sources provide some spatio-temporal information, alongside other useful data. The RDF data model can be used to interlink and exchange data originating from heterogeneous sources in a uniform manner. For example, consider the case where vessels report their spatio-temporal position, on a regular basis, by using various surveillance systems. In this scenario, a user might be interested to know which vessels were moving in a specific area for a given temporal range. In this paper, we address the problem of efficiently storing and querying spatio-temporal RDF data in parallel. We specifically study the case of SPARQL queries with spatio-temporal constraints, by proposing the 〈em〉DiStRDF〈/em〉 system, which is comprised of a Storage and a Processing Layer. The DiStRDF Storage Layer is responsible for efficiently storing large amount of historical spatio-temporal RDF data of 〈em〉moving objects〈/em〉. On top of it, we devise our DiStRDF Processing Layer, which parses a SPARQL query and produces corresponding logical and physical execution plans. We use Spark, a well-known distributed in-memory processing framework, as the underlying processing engine. Our experimental evaluation, on real data from both aviation and maritime domains, demonstrates the efficiency of our 〈em〉DiStRDF〈/em〉 system, when using various spatio-temporal range constraints.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents 〈em〉GeoTrend+〈/em〉; a system approach to support scalable local trend discovery on recent microblogs, e.g., tweets, comments, online reviews, and check-ins, that come in real time. 〈em〉GeoTrend+〈/em〉 discovers top-〈em〉k〈/em〉 trending keywords in arbitrary spatial regions from recent microblogs that continuously arrive with high rates and a significant portion has uncertain geolocations. 〈em〉GeoTrend+〈/em〉 distinguishes itself from existing techniques in different aspects: (1) Discovering trends in arbitrary spatial regions, e.g., city blocks. (2) Considering both exact geolocations, e.g., accurate latitude/longitude coordinates, and uncertain geolocations, e.g., district-level or city-level, that represents a significant portion of past years microblogs. (3) Promoting recent microblogs as first-class citizens and optimizes different components to digest a continuous flow of fast data in main-memory while removing old data efficiently. (4) Providing various main-memory optimization techniques that are able to distinguish useful from useless data to effectively utilize tight memory resources while maintaining accurate query results on relatively large amounts of data. (5) Supporting various trending measures that effectively capture trending items under a variety of definitions that suit different applications. 〈em〉GeoTrend+〈/em〉 limits its scope to real-time data that is posted during the last 〈em〉T〈/em〉 time units. To support its queries efficiently, 〈em〉GeoTrend+〈/em〉 employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last 〈em〉T〈/em〉 time units. The index also materializes top-〈em〉k〈/em〉 keywords in different spatial regions so that incoming queries can be processed with low latency. In peak times, the main-memory optimization techniques are employed to shed less important data to sustain high query accuracy with limited memory resources. Experimental results based on real data and queries show the scalability of 〈em〉GeoTrend+〈/em〉 to support high arrival rates and low query response time, and at least 90+% query accuracy even under limited memory resources.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, Transportation Network Companies (TNC) such as Uber and Lyft have embraced ridesharing: a passenger who requests a ride may decide to save money in exchange for the inconvenience of sharing the ride with someone else and incurring a delay. When matching passengers, these services attempt to optimize cost savings. But a possible scenario is that while passenger A is matched to passenger B, if matched to passenger C then both A and C would have saved more money. This leads to the concept of “fairness” in ridesharing, which consists of finding the Nash equilibrium in a ridesharing plan. In this paper we compare the optimum plan (i.e., benefit maximized at a global level) and the fair plan in both static and dynamic contexts. We show that in contrast to the theoretical indications, the fair plan is almost optimum. Furthermore, the fairness concept may help attract more passengers to rideshare and thus further reduce vehicle miles traveled. If social preferences are included in the total benefit, we demonstrate that the optimum ridesharing plan may be unboundedly and predominantly unfair in a sense that will be formalized in this paper.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort and trust issues, have not been fully considered in the existing works. In this paper, we formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP-hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Location-based social networks (LBSNs) have become popular platforms that allow users to share their check-in activities with friends. Annotating semantic tags of locations, as one of the hottest research topics in LBSNs, has attracted considerable attention. Semantic annotation requires sufficient location features to train classifiers. Based on the analysis of LBSN data, we find that users’ check-in activities have similarities that can promote the extraction of location features and improve the accuracy of semantic annotation. However, the existing studies ignored the use of the similarities of users’ check-in activities for extracting suitable location features. Therefore, in this paper, a new location feature, called the similar user pattern (SUP), is first extracted by capturing the similarities among the different users’ check-in activities. Second, annotating semantic tags of locations is treated as a multi-label classification problem. Thus, multi-label semantic annotation with an extreme learning machine (ELM) is proposed, called MSA-ELM. The MSA-ELM algorithm trains a binary ELM classifier for each tag in the tag space to support multi-label classification. Finally, a series of experiments are conducted to demonstrate both the accuracy and efficiency of annotating semantic tags of locations. The experimental results show that the SUP is a proper location feature, and the MSA-ELM algorithm has a good performance for multi-label semantic annotation.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one hyperspectral image at a time. In practice, we may often collect two or more similar hyperspectral images, and the end of the hyperspectral images of the signal could be only slightly different. Traditional NMF-based hyperspectral unmixing methods cannot take advantage of the fact that different hyper-spectral images may have similar or even the same end-element signals. Accordingly, in order to improve the performance of NMF-based hyperspectral unmixing, we present an algorithm in this paper that can process two hyperspectral images, simultaneously, and makes full use of the available information when most of the signals at the two end-points are similar. This improves the effect of end-element extraction in hyperspectral unmixing evidenced by experimental results on both synthetic and real-world data.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S〈sup〉2〈/sup〉R-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the S〈sup〉2〈/sup〉R-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Spatial keyword search has been playing an indispensable role in personalized route recommendation and geo-textual information retrieval. In this light, we conduct a survey on existing studies of spatial keyword search. We categorize existing works of spatial keyword search based on the types of their input data, output results, and methodologies. For each category, we summarize their common features in terms of input data, output result, indexing scheme, and search algorithms. In addition, we provide detailed description regarding each study of spatial keyword search. This survey summarizes the findings of existing spatial keyword search studies, thus uncovering new insights that may guide software engineers as well as further research.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features. Fine-grained places are often significantly more frequent and more similar to each other, and disambiguation heuristics developed for larger places, such as those based on population or containment relationships, are often not applicable in these cases. In this research, we address the disambiguation of fine-grained place names from everyday place descriptions. For this purpose, we evaluate the performance of different existing clustering-based approaches, since clustering approaches require no more knowledge other than the locations of ambiguous place names. We consider not only approaches developed specifically for place name disambiguation, but also clustering algorithms developed for general data mining that could potentially be leveraged. We compare these methods with a novel algorithm, and show that the novel algorithm outperforms the other algorithms in terms of disambiguation precision and distance error over several tested datasets.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this 〈em〉thought-piece〈/em〉 to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decision-making processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Connected planar graphs are of interest to a variety of scholars. Being able to simulate a database of such graphs with selected properties would support specific types of inference for spatial analysis and other network-based disciplines. This paper presents a simple, efficient, and flexible connected planar graph generator for this purpose. It also summarizes a comparison between an empirical set of specimen graphs and their simulated counterpart set, and establishes evidence for positing a conjecture about the principal eigenvalue of connected planar graphs. Finally, it summarizes a probability assessment of the algorithm’s outcomes as well as a comparison between the new algorithm and selected existing planar graph generators.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Choosing the best location for starting a business or expanding an existing enterprize is an important issue. A number of location selection problems have been discussed in the literature. They often apply the 〈em〉Reverse Nearest Neighbor〈/em〉 as the criterion for finding suitable locations. In this paper, we apply the 〈em〉Average Distance〈/em〉 as the criterion and propose the so-called 〈em〉k〈/em〉-most suitable locations (〈em〉k〈/em〉-MSL) selection problem. Given a positive integer 〈em〉k〈/em〉 and three datasets: a set of customers, a set of existing facilities, and a set of potential locations. The 〈em〉k〈/em〉-MSL selection problem outputs 〈em〉k〈/em〉 locations from the potential location set, such that the average distance between a customer and his nearest facility is minimized. In this paper, we formally define the 〈em〉k〈/em〉-MSL selection problem and show that it is NP-hard. We first propose a greedy algorithm which can quickly find an approximate result for users. Two exact algorithms are then proposed to find the optimal result. Several pruning rules are applied to increase computational efficiency. We evaluate the algorithms’ performance using both synthetic and real datasets. The results show that our algorithms are able to deal with the 〈em〉k〈/em〉-MSL selection problem efficiently.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The paper presents the details of designing and developing GeoSpark, which extends the core engine of Apache Spark and SparkSQL to support spatial data types, indexes, and geometrical operations at scale. The paper also gives a detailed analysis of the technical challenges and opportunities of extending Apache Spark to support state-of-the-art spatial data partitioning techniques: uniform grid, R-tree, Quad-Tree, and KDB-Tree. The paper also shows how building local spatial indexes, e.g., R-Tree or Quad-Tree, on each Spark data partition can speed up the local computation and hence decrease the overall runtime of the spatial analytics program. Furthermore, the paper introduces a comprehensive experiment analysis that surveys and experimentally evaluates the performance of running de-facto spatial operations like spatial range, spatial K-Nearest Neighbors (KNN), and spatial join queries in the Apache Spark ecosystem. Extensive experiments on real spatial datasets show that GeoSpark achieves up to two orders of magnitude faster run time performance than existing Hadoop-based systems and up to an order of magnitude faster performance than Spark-based systems.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The volume of spatio-temporal data is growing at a rapid pace due to advances in location-aware devices, e.g., smartphones, and the popularity of location-based services, e.g., navigation services. A number of spatio-temporal access methods have been proposed to support efficient processing of queries over the spatio-temporal data. Spatio-temporal access methods can be classified according to the type of data being indexed into the following categories: (1) indexes for historical spatio-temporal data, (2) indexes for current and recent spatio-temporal data, (3) indexes for future spatio-temporal data, (4) indexes for past, present, and future spatio-temporal data, (5) indexes for spatio-temporal data with associated textual data, and (6) parallel and distributed spatio-temporal systems and indexes. This survey is Part 3 of our previous surveys on the same subject (Mokbel et al. IEEE Data Eng Bull 26(2):40–49, 〈span〉2003〈/span〉; Nguyen-Dinh et al. IEEE Data Eng Bull 33(2):46–55, 〈span〉2010〈/span〉). In this survey, we present an overview and a broad classification of the spatio-temporal access methods published between 2010 and 2017.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉〈em〉k〈/em〉-nearest neighbor (〈em〉k〈/em〉-NN) queries are well-known and widely used in a plethora of applications. However, in the original definition of 〈em〉k〈/em〉-NN queries there is no concern regarding diversity of the answer set with respect to the user’s interests. For instance, travelers may be looking for touristic sites that are close to where they are, but that would also lead them to see different parts of the city. Likewise, if one is looking for restaurants close by, it may be more interesting to learn about restaurants of different categories or ethnicities which are nonetheless relatively close. The interesting novel aspect of this type of query is that there are two competing criteria to be optimized: closeness and diversity. We propose two approaches that leverage the notion of linear skyline queries in order to find the 〈em〉k〈/em〉 diverse nearest neighbors within a radius 〈em〉r〈/em〉 from a given query point, or (〈em〉k, r〈/em〉)-DNNs for short. Our proposed approaches return a relatively small set containing 〈em〉all〈/em〉 optimal solutions for 〈em〉any〈/em〉 linear combination of the weights a user could give to the two competing criteria, and we consider three different notions of diversity: spatial, categorical and angular. Our experiments, varying a number of parameters and exploring synthetic and real datasets, in both Euclidean space and road networks, respectively, show that our approaches are several orders of magnitude faster than a straightforward approach.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore’s 〈em〉Crossing the Chasm〈/em〉 as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Query processing is an important challenge for spatial databases due to the use of complex data types that represent spatial attributes. In particular, due to the cost of spatial joins, several optimization algorithms based on indexing structures exist. The work in this paper proposes a strategy for semantic query optimization of spatial join queries. The strategy detects queries with empty results and rewrites queries to eliminate unnecessary spatial joins or to replace spatial by thematic joins. This is done automatically by analyzing the semantics imposed by the database schema through topological dependencies and topological referential integrity constraints. In this way, the strategy comes to complement current state-of-art algorithms for processing spatial join queries. The experimental evaluation with real data sets shows that the optimization strategy can achieve a decrease in the time cost of a join query using indexing structures in a spatial database management system (SDBMS).〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The increasing popularity of location-based applications creates new opportunities for users to travel together. In this paper, we study a novel spatio-social optimization problem , i.e., Optimal Group Route, for multi-user itinerary planning. With our problem formulation, users can individually specify sources and destinations, preferences on the Point-of-interest (POI) categories, as well as the distance constraints. The goal is to find a itinerary that can be traversed by all the users while maximizing the group’s preference of POI categories in the itinerary. Our work advances existing group trip planning studies by maximizing the group’s social experience. To this end, individual preferences of POI categories are aggregated by considering the 〈em〉agreement〈/em〉 and 〈em〉disagreement〈/em〉 among group members. Furthermore, planning a multi-user itinerary on large road networks is computationally challenging. We propose two efficient greedy algorithms with bounded approximation ratio, one exact solution which computes the optimal itinerary by exploring a limited number of paths in the road network, and a scaled approximation algorithm to speed up the dynamic programming employed by the exact solution. We conduct extensive empirical evaluations on two real-world road network/POI datasets and our results confirm the effectiveness and efficiency of our solutions.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Query reformulation, including query recommendation and query auto-completion, is a popular add-on feature of search engines, which provide related and helpful reformulations of a keyword query. Due to the dropping prices of smartphones and the increasing coverage and bandwidth of mobile networks, a large percentage of search engine queries are issued from mobile devices. This makes it possible to improve the quality of query recommendation and auto-completion by considering the physical locations of the query issuers. However, limited research has been done on location-aware query reformulation for search engines. In this paper, we propose an effective spatial proximity measure between a query issuer and a query with a location distribution obtained from its clicked URLs in the query history. Based on this, we extend popular query recommendation and auto-completion approaches to our location-aware setting, which suggest query reformulations that are semantically relevant to the original query and give results that are spatially close to the query issuer. In addition, we extend the bookmark coloring algorithm for graph proximity search to support our proposed query recommendation approaches online, and we adapt an A* search algorithm to support our query auto-completion approach. We also propose a spatial partitioning based approximation that accelerates the computation of our proposed spatial proximity. We conduct experiments using a real query log, which show that our proposed approaches significantly outperform previous work in terms of quality, and they can be efficiently applied online.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data types and operations. In the indexing layer, ST-Hadoop spatiotemporally loads and divides data across computation nodes in Hadoop Distributed File System in a way that mimics spatio-temporal index structures, which result in achieving orders of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. In the operations layer, ST-Hadoop shipped with support for three fundamental spatio-temporal queries, namely, spatio-temporal range, top-k nearest neighbor, and join queries. Extensibility of ST-Hadoop allows others to extend features and operations easily using similar approaches described in the paper. Extensive experiments conducted on large-scale dataset of size 10 TB that contains over 1 Billion spatio-temporal records, to show that ST-Hadoop achieves orders of magnitude better performance than Hadoop and SpaitalHadoop when dealing with spatio-temporal data and operations. The key idea behind the performance gained in ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The widespread availability of GPS and the growing popularity of location based social networking applications such as Flickr, Yelp, etc., enable more and more users to share their route activities or trajectories. At the same time, the recent advancement in large-scale 3D modeling has inspired applications that combine visibility and spatial queries, which in turn can be integrated with user trajectories to provide answers for many real-life user queries, such as 〈em〉“How can I choose the route which provides the best view of a historic site?”〈/em〉. In this work, we propose and investigate the 〈em〉k〈/em〉 Aggregate Maximum Visibility Trajectory (〈em〉k〈/em〉 AMVT) query and its variants. Given sets of targets, obstacles, and trajectories, the 〈em〉k〈/em〉 AMVT query finds top-k trajectories that provide the best view of the targets. We extend the 〈em〉k〈/em〉 AMVT query to incorporate different weights (or preferences) with trajectories and targets. To provide an efficient solution to our problem, we employ obstacle and trajectory pruning mechanisms. We also employ an effective target ordering technique, which can further improve query efficiency. Furthermore, we extend the proposed queries to introduce preferences on trajectories in situations where smaller trajectories are preferred due to time constraints, or trajectories closer to the query user are preferred. To verify the efficiency and effectiveness of our solutions, we conduct an extensive experimental study using large synthetic and real datasets.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents a novel Multi-criteria Optimal Location Query (MOLQ), which can be applied to a wide range of applications. After providing a formal definition of the novel query type, we propose an Overlapping Voronoi Diagram (OVD) model that defines OVDs and Minimum OVDs (MOVDs), and an OVD overlap operation. Based on the OVD model, we design advanced approaches to answer the query in Euclidean space. Due to the high complexity of Voronoi diagram overlap computation, we improve the overlap operation by replacing the real boundaries of Voronoi diagrams with their Minimum Bounding Rectangles (MBR). Moreover, if there are changes to a limited number of objects, re-evaluating queries over updated object sets would be expensive. Thus, we also propose an MOVD updating model and an advanced algorithm to incrementally update MOVDs to avoid the high cost of query re-evaluation. Our experimental results show that the proposed algorithms can evaluate the novel query type effectively and efficiently.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Recently, researches on smart phones have received attentions because the wide potential applications. One of interesting and useful topic is mining and predicting the users’ mobile application (App) usage behaviors. With more and more Apps installed in users’ smart phone, the users may spend much time to find the Apps they want to use by swiping the screen. App prediction systems benefit for reducing search time and launching time since the Apps which may be launched can preload in the memory before they are actually used. Although some previous studies had been proposed on the problem of App usage analysis, they recommend Apps for users only based on the frequencies of App usages. We consider that the relationship between App usage demands and users’ recent spatial and temporal behaviors may be strong. In this paper, we propose 〈em〉Spatial and Temporal App Recommender〈/em〉 (〈em〉STAR〈/em〉), a novel framework to predict and recommend the Apps for mobile users under a smart phone environment. The 〈em〉STAR〈/em〉 framework consists of four major modules. We first find the meaningful and semantic location movements from the geographic GPS trajectory data by the 〈em〉Spatial Relation Mining Module〈/em〉 and generate the suitable temporal segments by the 〈em〉Temporal Relation Mining Module〈/em〉. Then, we design 〈em〉Spatial and Temporal App Usage Pattern Mine〈/em〉 (〈em〉STAUP-Mine〈/em〉) algorithm to efficiently discover mobile users’ 〈em〉Spatial and Temporal App Usage Patterns〈/em〉 (〈em〉STAUP〈/em〉s). Furthermore, an 〈em〉App Usage Demand Prediction Module〈/em〉 is presented to predict the following App usage demands according to the discovered 〈em〉STAUP〈/em〉s and spatial/temporal relations. To our knowledge, this is the first study to simultaneously consider the spatial movements, temporal properties and App usage behavior for mining App usage pattern and demand prediction. Through rigorous experimental analysis from two real mobile App datasets, 〈em〉STAR〈/em〉 framework delivers an excellent prediction performance.〈/p〉

Description: 〈p〉The original version of this article contained two errors.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Map reading is a visual task that can strongly vary between individuals and maps of different characteristics. Aspects such as where, when, how long, and in which sequence information on a map is looked at can reveal valuable insights for both the map design process and to better understand cognitive processes of the map user. Contrary to static maps, for which many eye tracking studies are reported in the literature, established methods for tracking and analyzing visual attention on interactive maps are yet missing. In this paper, we present a framework called FeaturEyeTrack that allows to automatically log the cartographic features that have been inspected as well as the mouse input during the interaction with digital interactive maps. In particular, the novelty of FeaturEyeTrack lies in matching of gaze with the vector model of the current map visualization, therefore enabling a very detailed analysis without the requirement for manual annotation. Furthermore, we demonstrate the benefits of this approach in terms of manual work, level of detail and validity compared to state-of-the-art methods through a case study on an interactive cartographic web map.〈/p〉

Description: Due to the high uptake of location-based services (LBSs), large spatio-temporal datasets of moving objects’ trajectories are being created every day. An important task in spatial data analytics is to service range queries by returning trajectory counts within a queried region. The question of how to keep an individual user’s data private whilst enabling spatial data analytics by third parties has become an urgent research direction. Indeed, it is increasingly becoming a concern for users. To preserve privacy we discard individual trajectories and aggregate counts over a spatial and temporal partition. However the privacy gained comes at a cost to utility: trajectories passing through multiple cells and re-entering a query region, lead to inaccurate query responses. This is known as the distinct counting problem. We propose the Connection Aware Spatial Euler (CASE) histogram to address this long-standing problem. The CASE histogram maintains the connectivity of a moving object path, but does not require the ID of an object to distinguish multiple entries into an arbitrary query region. Our approach is to process trajectories offline into aggregate counts which are sent to third parties, rather than the original trajectories. We also explore modifications of

Description: This paper studies the constrained-space probabilistic threshold range query (CSPTRQ) for moving objects, where objects move in a constrained-space (i.e., objects are forbidden to be located in some specific areas), and objects’ locations are uncertain. We differentiate two forms of CSPTRQs: explicit and implicit ones. Specifically, for each moving object o , we model its location uncertainty as a closed region, u , together with a probability density function. We also model a query range, R , as an arbitrary polygon. An explicit query can be reduced to a search (over all the u ) that returns a set of tuples in form of ( o , p ) such that p ≥ p t , where p is the probability of o being located in R , and 0≤ p t ≤ 1 is a given probabilistic threshold. In contrast, an implicit query returns only a set of objects (without attaching the specific probability information), whose probabilities being located in R are higher than p t . The CSPTRQ is a variant of the traditional probabilistic threshold range query (PTRQ). As objects moving in a constrained-space are common, clearly, it can also find many applications. At the first sight, our problem can be easily tackled by extending existing methods used to answer the PTRQ. Unfortunately, those classical techniques are not well suitable for our problem, due to a set of new challenges. Another method used to answer the constrained-space probabilistic range query (CSPRQ) can be easily extended to tackle our problem, but a simple adaptation of this method is inefficient, due to its weak pruning/validating capability. To solve our problem, we develop targeted solutions that are easy-to-understand and also easy-to-implement. Our central idea is to swap the order of geometric operations and to compute the appearance probability in a multi-step manner. We demonstrate the efficiency and effectiveness of the proposed methods through extensive experiments. Meanwhile, from the experimental results, we further perceive the difference between explicit and implicit queries; this finding is interesting and also meaningful especially for the topics of other types of probabilistic threshold queries.

Description: In recent years, with the upgrading of mobile positioning and the popularity of smart devices, location related research gets a lot of attentions. One of popular issues is the trip planning problem. Although many related scientific or technical literature have been proposed, most of them focused only on tourist attraction recommendation or arrangement meeting some user demands. In fact, to grasp the huge tourism opportunities, more and more tour operators design tourist packages and provide to users. Generally, tourist packages have many advantages such as cheaper ticket price and higher transportation convenience. However, researches on trip planning combining tourist packages have not been mentioned in the past studies. In this research, we present a new approach named Package-Attraction-based Trip Planner ( PAT-Planner ) to simultaneously combine tourist packages and tourist attractions for personalized trip planning satisfying users’ travel constraints. In PAT-Planner , we first based on user preferences and temporal characteristics to design a Score Inference Model for respectively measuring the score of a tourist package or tourist attraction. Then, we develop the Hybrid Trip-Mine algorithm meeting user travel constraints for personalized trip planning. Besides, we further propose two improvement strategies, namely Score Estimation and Score Bound Tightening , based on Hybrid Trip-Mine to speed up the trip planning efficiency. As far as we know, our study is the first attempt to simultaneously combine tourist packages and tourist attractions on trip planning problem. Through a series of experimental evaluations and case studies using the collected Gowalla datasets, PAT-Planner demonstrates excellent planning effects.

Description: Trajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technologies, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-to-points trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top- k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid nearest neighbor-based method while also proposing an alternative, more efficient spatial range-based approach. Second, we investigate the continuous counterpart of distance-to-points trajectory search where the query is long-standing and the set of returned trajectories needs to be maintained whenever updates occur to the query and/or the data. Third, we propose and study two practical variants of distance-to-points trajectory search, which take into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our range-based approach outperforms previous methods by at least one order of magnitude for the snapshot and up to several times for the continuous version of the queries.

Description: Twitter has become an important data source for detecting events, especially tracking detailed information for events of a specific domain. Previous studies on targeted-domain Twitter information extraction have used supervised learning techniques to identify domain-related tweets, however, the need for extensive manual labeling makes these supervised systems extremely expensive to build and maintain. What’s more, most of these existing work fail to consider spatiotemporal factors, which are essential attributes of target-domain events. In this paper, we propose a semi-supervised method for Automatical Targeted-domain Spatiotemporal Event Detection (ATSED) in Twitter. Given a targeted domain, ATSED first learns tweet labels from historical data, and then detects on-going events from real-time Twitter data streams. Specifically, an efficient label generation algorithm is proposed to automatically recognize tweet labels from domain-related news articles, a customized classifier is created for Twitter data analysis by utilizing tweets’ distinguishing features, and a novel multinomial spatial-scan model is provided to identify geographical locations for detected events. Experiments on 305 million tweets demonstrated the effectiveness of this new approach.

Description: We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. The system employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.

Description: When large-scale disasters occur, evacuees have to evacuate to safe places quickly. They, however, may not be able to afford to obtain sufficient information for their evacuations under such emergent situations. In this paper, we propose an automatic evacuation guiding scheme using evacuees’ mobile nodes, e.g., smart phones. The key idea to achieve automatic evacuation guiding is implicit interactions between evacuees and their mobile nodes. Each mobile node tries to navigate its evacuee by presenting an evacuation route. At the same time, it can also trace the actual evacuation route of the evacuee as the trajectory by measuring his/her positions periodically. The proposed scheme automatically estimates blocked road segments from the difference between the presented evacuation route and the actual evacuation route, and then recalculates the alternative evacuation route. In addition, evacuees also share such information among them through direct wireless communication with other mobile nodes and that with a server via remaining communication infrastructures. Through simulation experiments, we show that 1) the proposed scheme works well when the degree of damage is high and/or road segments are continuously blocked, 2) the average evacuation time can be improved even in small penetration ratio of the proposed system, and 3) the direct wireless communication can support many evacuations at almost the same level as the communication infrastructure when the number of evacuees becomes large.

Description: Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.

Description: This article describes DSL3S, a domain specific modelling language for Spatial Simulation in the field of Geographic Information Systems (GIS). Techniques such as cellular automata and agent-based modelling have long been used to capture and simulate the temporal dynamics of spatial information. Tools commonly employed to implement spatial simulation models include code libraries and pre-compiled models; the former require advanced programming skills while the latter impose relevant constraints on application scope. Previous attempts to produce domain specific languages in the field have invariably resulted in new textual programming languages (e.g. SELES, NetLogo, Ocelet) that are platform specific and in some cases with weak GIS support and interoperability. DSL3S synthesises relevant concepts of spatial simulation in a UML profile, that allows the design of simulation models through the arrangement of graphical elements. An implementation of this language is also presented, that relies on Model Driven Development (MDD) tools distributed with the Eclipse IDE. This includes a code generation infrastructure, that produces ready to run simulations from DSL3S models, supported by the MASON simulation tool-kit. Finally, DSL3S models for three simple and classical simulations allows to better illustrate and discuss the usage of the language.

Description: The MaxBRNN problem is to find an optimal region such that setting up a new service within this region might attract the maximum number of customers by proximity. The MaxBRNN problem has many practical applications such as service location planning and emergency schedule. In typical real-life applications the data volume of the problem is huge, thus an efficient solution is highly desired. In this paper, we propose two efficient algorithms, namely, OptRegion, and 3D-OptRegion to tackle the MaxBRNN problem and MaxBR k NN in two- and three-dimensional spaces, especially for the 3D-OptRegion, we propose a powerful pruning strategy Fine-grained Pruning Strategy to reduce the searching space. Our method employs three optimization techniques, i.e., sweep line (sweep plane in a three-dimensional space), pruning strategy (based on upper bound estimation), and influence value computation (of candidate points), to improve the search performance. In a three-dimensional space, we additionally use a fine-grained pruning strategy to further improve the pruning effect. Extensive experimental evaluation using both real and synthetic datasets confirms that both OptRegion and 3D-OptRegion outperform the existing algorithms significantly under all problem instances.

Description: Social media have ushered in alternative modalities to propagate news and developments rapidly. Just as traditional IR matured to modeling storylines from search results, we are now at a point to study how stories organize and evolve in additional mediums such as Twitter , a new frontier for intelligence analysis. This study takes as input news articles as well as social media feeds and extracts and connects entities into interesting storylines not explicitly stated in the underlying data. First, it proposes a novel method of spatio-temporal analysis on induced concept graphs that models storylines propagating through spatial regions in a time sequence. Second, it describes a method to control search space complexity by providing regions of exploration. And third, it describes ConceptRank as a ranking strategy that differentiates strongly-typed connections from weakly-bound ones. Extensive experiments on the Boston Marathon Bombings of April 15, 2013 as well as socio-political and medical events in Latin America, the Middle East, and the United States demonstrate storytelling’s high application potential, showcasing its use in event summarization and association analysis that identifies events before they hit the newswire.

Description: The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects.

Description: In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users’ check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people’s movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users’ check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.

Description: We present EMFlow , a very efficient algorithm and its implementation, to compute the drainage network (i.e. the flow direction and flow accumulation) on huge terrains stored in external memory. Its utility lies in processing the large volume of high resolution terrestrial data newly available, which internal memory algorithms cannot handle efficiently. The flow direction is computed using an adaptation of our previous method RWFlood that uses a flooding process to quickly remove internal depressions or basins. Flooding, proceeding inward from the outside of the terrain, works oppositely to the common method of computing downhill flow from the peaks. To reduce the number of I/O operations, EMFlow adopts a new strategy to subdivide the terrain into islands that are processed separately. The terrain cells are grouped into blocks that are stored in a special data structure managed as a cache memory. EMFlow ’s execution time was compared against the two most recent and most efficient published methods: TerraFlow and r.watershed.seg . It was, on average, 25 and 110 times faster than TerraFlow and r.watershed.seg respectively. Also, EMFlow could process larger datasets. Processing a 50000 × 50000 terrain on a machine with 2GB of internal memory took about 4500 seconds, compared to 87000 seconds for TerraFlow while r.watershed.seg failed on terrains larger than 15000 ×15000. On very small, say1000 ×1000 terrains, EMFlow takes under a second, compared to 6 and 20 seconds in r.watershed.seg and TerraFlow respectively. So EMFlow could be a component of a future interactive system where a user could modify terrain and immediately see the new hydrography.

Description: Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based on the timestamps and geo-locations. To extract underlying travel topics from abundant visit sequences, we study a novel mixture model to estimate the visiting probability of regions of attractions (ROAs). Such travel topics can be used in different applications, such as advertisements, promotion strategies, and city planning. To enhance the estimation result, we propose a mutual reinforcement framework to improve the quality of ROAs. Finally, we thoroughly evaluate and demonstrate our findings by the photo sharing activities collected from Flickr TM .

Description: Map construction methods automatically produce and/or update street map datasets using vehicle tracking data. Enabled by the ubiquitous generation of geo-referenced tracking data, there has been a recent surge in map construction algorithms coming from different computer science domains. A cross-comparison of the various algorithms is still very rare, since (i) algorithms and constructed maps are generally not publicly available and (ii) there is no standard approach to assess the result quality, given the lack of benchmark data and quantitative evaluation methods. This work represents a first comprehensive attempt to benchmark such map construction algorithms. We provide an evaluation and comparison of seven algorithms using four datasets and four different evaluation measures. In addition to this comprehensive comparison, we make our datasets, source code of map construction algorithms and evaluation measures publicly available on http://mapconstruction.org. . This site has been established as a repository for map construction data and algorithms and we invite other researchers to contribute by uploading code and benchmark data supporting their contributions to map construction algorithms.

Description:    This work introduces decentralized query processing techniques based on MIDAS, a novel distributed multidimensional index. In particular, MIDAS implements a distributed k-d tree, where leaves correspond to peers, and internal nodes dictate message routing. MIDAS requires that peers maintain little network information, and features mechanisms that support fault tolerance and load balancing. The proposed algorithms process point and range queries over the multidimensional indexed space in only O (log n ) hops in expectance, where n is the network size. For nearest neighbor queries, two processing alternatives are discussed. The first, termed eager processing, has low latency (expected value of O (log n ) hops) but may involve a large number of peers. The second, termed iterative processing, has higher latency (expected value of O (log 2 n ) hops) but involves far fewer peers. A detailed experimental evaluation demonstrates that our query processing techniques outperform existing methods for settings involving real spatial data as well as in the case of high dimensional synthetic data. Content Type Journal Article Pages 1-31 DOI 10.1007/s10707-012-0163-x Authors George Tsatsanifos, National Technical University of Athens, Athens, Greece Dimitris Sacharidis, Institute for the Management of Information Systems, R.C. “Athena”, Athens, Greece Timos Sellis, National Technical University of Athens, Athens, Greece Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175

Description:    In this paper, we present a novel method for fast lossy or lossless compression and decompression of regular height fields. The method is suitable for SIMD parallel implementation and thus inherently suitable for modern GPU architectures. Lossy compression is achieved by approximating the height field with a set of quadratic Bezier surfaces. In addition, lossless compression is achieved by superimposing the residuals over the lossy approximation. We validated the method’s efficiency through a CUDA implementation of compression and decompression algorithms. The method allows independent decompression of individual data points, as well as progressive decompression. Even in the case of lossy decompression, the decompressed surface is inherently seamless. In comparison with the GPU-oriented state-of-the-art method, the proposed method, combined with a widely available lossless compression method (such as DEFLATE), achieves comparable compression ratios. The method’s efficiency slightly outperforms the state-of-the-art method for very high workloads and considerably for lower workloads. Content Type Journal Article Pages 1-27 DOI 10.1007/s10707-012-0171-x Authors Đorđe M. Đurđević, University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia Igor I. Tartalja, University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175

Description:    Contour lines are important for quantitatively displaying relief and identifying morphometric features on a map. Contour trees are often used to represent spatial relationships between contours and assist the user in analysing the terrain. However, automatic analysis from the contour tree is still limited as features identified on a map by sets of contours are not only characterised by local relationships between contours but also by relationships with other features at different levels of representation. In this paper, a new method based on adjacency and inclusion relationships between regions defined by sets of contours is presented. The method extracts terrain features and stores them in a feature tree providing a description of the landscape at multiple levels of detail. The method is applied to terrain analysis and generalisation of a contour map by selecting the most relevant features according to the purpose of the map. Experimental results are presented and discussed. Content Type Journal Article Pages 1-24 DOI 10.1007/s10707-012-0153-z Authors Eric Guilbert, Department of Land Surveying and GeoInformatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175

Description:    This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis. Content Type Journal Article Pages 1-33 DOI 10.1007/s10707-012-0155-x Authors William M. Spears, Swarmotics, LLC, Laramie, WY, USA Steven D. Prager, University of Wyoming, Laramie, WY, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175

Description:    Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D . For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs ( O (| D |) in the worst case). PIR, on the other hand, reduces this bound to , but at the expense of high processing and communication overhead. We propose hybrid, two-step approaches for private location-based queries which provide protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect against excessive disclosure of POI locations, we devise two cryptographic protocols that privately evaluate whether a point is enclosed inside a rectangular region or a convex polygon. We also introduce algorithms to efficiently support PIR on dynamic POI sub-sets. We provide solutions for both approximate and exact NN queries. In the approximate case, our method discloses O (1) POI, orders of magnitude fewer than CR- or PIR-based techniques. For the exact case, we obtain optimal disclosure of a single POI, although with slightly higher computational overhead. Experimental results show that the hybrid approaches are scalable in practice, and outperform the pure-PIR approach in terms of computational and communication overhead. Content Type Journal Article Pages 699-726 DOI 10.1007/s10707-010-0121-4 Authors Gabriel Ghinita, Purdue University, West Lafayette, IN 47907, USA Panos Kalnis, King Abdullah University of Science and Technology, Jeddah, Saudi Arabia Murat Kantarcioglu, University of Texas at Dallas, Richardson, TX 75080, USA Elisa Bertino, Purdue University, West Lafayette, IN 47907, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted the attention of researchers. Geographic information systems are a good example of an application using a massive amount of spatial data. Our goal is to efficiently support exact and approximate skyline queries over massive spatial datasets. A spatial skyline query, consisting of multiple query points, retrieves data points that are not father than any other data points, from all query points. To achieve this goal, we present a simple and efficient algorithm that computes the correct results, also propose a fast approximation algorithm that returns a desirable subset of the skyline results. In addition, we propose a continuous query algorithm to trace changes of skyline points while a query point moves. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison between our algorithms and the best known spatial skyline algorithms from several perspectives. Content Type Journal Article Pages 665-697 DOI 10.1007/s10707-010-0119-y Authors Mu-Woong Lee, Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea Wanbin Son, Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea Hee-Kap Ahn, Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea Seung-won Hwang, Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description: Special section on spatial and temporal databases Content Type Journal Article Category Editorial Pages 663-664 DOI 10.1007/s10707-011-0139-2 Authors Nikos Mamoulis, Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong Thomas Seidl, Department of Computer Science 9, RWTH Aachen University, 52056 Aachen, Germany Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    Recently, several techniques have been proposed to protect the user location privacy for location-based services in the Euclidean space. Applying these techniques directly to the road network environment would lead to privacy leakage and inefficient query processing. In this paper, we propose a new location anonymization algorithm that is designed specifically for the road network environment. Our algorithm relies on the commonly used concept of spatial cloaking, where a user location is cloaked into a set of connected road segments of a minimum total length including at least users. Our algorithm is “query-aware” as it takes into account the query execution cost at a database server and the query quality, i.e., the number of objects returned to users by the database server, during the location anonymization process. In particular, we develop a new cost function that balances between the query execution cost and the query quality. Then, we introduce two versions of our algorithm, namely, pure greedy and randomized greedy , that aim to minimize the developed cost function and satisfy the user specified privacy requirements. To accommodate intervals with a high workload, we introduce a shared execution paradigm that boosts the scalability of our location anonymization algorithm and the database server to support large numbers of queries received in a short time period. Extensive experimental results show that our algorithms are more efficient and scalable than the state-of-the-art technique, in terms of both query execution cost and query quality. The results also show that our algorithms have very strong resilience to two privacy attacks, namely, the replay attack and the center-of-cloaked-area attack . Content Type Journal Article Pages 571-607 DOI 10.1007/s10707-010-0117-0 Authors Chi-Yin Chow, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China Mohamed F. Mokbel, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA Jie Bao, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA Xuan Liu, IBM Thomas J. Watson Research Center, Hawthorne, NY 10532, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    Network data models are frequently used as a mechanism to describe the connectivity between spatial features in GIS applications. Real-life network models are dynamic in nature since spatial features can be periodically modified to reflect changes in the real world objects that they model. Such updates may change the connectivity relations with the other features in the model. In order to perform analysis the connectivity must be reestablished. Existing editing frameworks are not suitable for a dynamic environment, since they require network connectivity to be reconstructed from scratch. Another requirement for GIS network models is to provide support for a multiuser environment, where users are simultaneously creating and updating large amounts of geographic information. The system must support edit sessions that typically span a number of days or weeks, the facility to undo or redo changes made to the data, and the ability to develop models and alternative application designs without affecting the published database. The row-locking mechanisms adopted by many DBMSs is prohibitively restrictive for many common workflows. To deal with long-lasting transactions, a solution based on versioning is thus preferrable. In this paper we provide a unified solution to the problems of dynamic editing and versioning of network models. We first propose an efficient algorithm that incrementally maintains connectivity within a dynamic network. Our solution is based on the notion of dirty areas and dirty objects (i.e., regions or elements containing edits that have not been reflected in the network connectivity index). The dirty areas and objects are identified and marked during the editing of the network feature data; they are then subsequently cleaned and connectivity is re-built. Furthermore, for improving performance, we propose a ‘hyperedge’ extension to the basic network model. A hyperedge drastically decreases the number of edge elements accessed during solve time on large networks; this in turn leads to faster solve operations. We show how our connectivity maintenance algorithms can support the hyperedge enhanced model. We then propose a new network model versioning scheme that utilizes the dirty areas/objects of the connectivity rebuild algorithm. Our scheme uses flexible reconciling rules that allow the definition of a resolving mechanism between conflicting edits according to user needs. Moreover, the utilization of dirty areas/objects minimizes the overhead of tracking the editing history. The unified editing and versioning solution has been implemented and tested within ESRI’s ArcGIS system. Content Type Journal Article Pages 769-803 DOI 10.1007/s10707-011-0126-7 Authors Petko Bakalov, Environmental Systems Research Institute, Redlands, CA 92373, USA Erik Hoel, Environmental Systems Research Institute, Redlands, CA 92373, USA Wee-Liang Heng, Environmental Systems Research Institute, Redlands, CA 92373, USA Sudhakar Menon, Environmental Systems Research Institute, Redlands, CA 92373, USA Vassilis J. Tsotras, University of California, Riverside, CA 92507, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities, providing possible answers to where, when and why certain crimes are likely to happen. We propose to model density change among spatial regions using a density tracing based approach that enables reasoning about large areal aggregated crime datasets. We discover patterns among datasets by finding those crime and spatial features that exhibit similar spatial distributions by measuring the dissimilarity of their density traces. The proposed system incorporates both localized clusters (through the use of context sensitive weighting and clustering) and the global distribution trend. Experimental results validate and demonstrate the robustness of our approach. Content Type Journal Article Pages 49-74 DOI 10.1007/s10707-010-0116-1 Authors Peter Phillips, School of Business, Discipline of IT, James Cook University, Townsville, Australia Ickjai Lee, School of Business, Discipline of IT, James Cook University, Townsville, Australia Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 1

Description:    When users need to find something on the Web that is related to a place, chances are place names will be submitted along with some other keywords to a search engine. However, automatic recognition of geographic characteristics embedded in Web documents, which would allow for a better connection between documents and places, remains a difficult task. We propose an ontology-driven approach to facilitate the process of recognizing, extracting, and geocoding partial or complete references to places embedded in text. Our approach combines an extraction ontology with urban gazetteers and geocoding techniques. This ontology, called OnLocus, is used to guide the discovery of geospatial evidence from the contents of Web pages. We show that addresses and positioning expressions, along with fragments such as postal codes or telephone area codes, provide satisfactory support for local search applications, since they are able to determine approximations to the physical location of services and activities named within Web pages. Our experiments show the feasibility of performing automated address extraction and geocoding to identify locations associated to Web pages. Combining location identifiers with basic addresses improved the precision of extractions and reduced the number of false positive results. Content Type Journal Article Pages 609-631 DOI 10.1007/s10707-010-0118-z Authors Karla A. V. Borges, PRODABEL-Empresa de Informática e Informação do Município de Belo Horizonte, Av. Pres. Carlos Luz, 1275, 31230-000 Belo Horizonte, MG, Brazil Clodoveu A. Davis, Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627, 31270-010 Belo Horizonte, MG, Brazil Alberto H. F. Laender, Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627, 31270-010 Belo Horizonte, MG, Brazil Claudia Bauzer Medeiros, Instituto de Informática, Universidade de Campinas, Av. Albert Einstein,1251, 13083-970 Campinas, SP, Brazil Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive data sets. To meet the high update rate as well as low query response time requirements of moving object applications, this paper takes a novel approach in moving object indexing. In our approach, we do not require a sophisticated index structure that needs to be adjusted for each incoming update. Rather, we construct conceptually simple short-lived index images that we only keep for a very short period of time (sub-seconds) in main memory. As a consequence, the resulting technique MOVIES supports at the same time high query rates and high update rates, trading this property for query result staleness. Moreover, MOVIES is the first main memory method supporting time-parameterized predictive queries. To support this feature, we present two algorithms: non-predictive MOVIES and predictive MOVIES . We obtain the surprising result that a predictive indexing approach—considered state-of-the-art in an external-memory scenario—does not scale well in a main memory environment. In fact, our results show that MOVIES outperforms state-of-the-art moving object indexes such as a main-memory adapted B x -tree by orders of magnitude w.r.t. update rates and query rates. In our experimental evaluation, we index the complete road network of Germany consisting of 40,000,000 road segments and 38,000,000 nodes. We scale our workload up to 100,000,000 moving objects, 58,000,000 updates per second and 10,000 queries per second, a scenario at a scale unmatched by any previous work. Content Type Journal Article Pages 727-767 DOI 10.1007/s10707-011-0122-y Authors Jens Dittrich, Information Systems Group, Saarland University, Saarbrücken, Germany Lukas Blunschi, Systems Group, ETH Zurich, Zürich, Switzerland Marcos Antonio Vaz Salles, Department of Computer Science, Cornell University, Ithaca, NY, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description:    This paper presents a novel approach to express and evaluate the complex class of queries in moving object databases called spatiotemporal pattern queries (STP queries). That is, one can specify temporal order constraints on the fulfillment of several predicates. This is in contrast to a standard spatiotemporal query that is composed of a single predicate. We propose a language design for spatiotemporal pattern queries in the context of spatiotemporal DBMSs. The design builds on the well established concept of lifted predicates . Hence, unlike previous approaches, patterns are neither restricted to specific sets of predicates, nor to specific moving object types. The proposed language can express arbitrarily complex patterns that involve various types of spatiotemporal operations such as range, metric, topological, set operations, aggregations, distance, direction, and boolean operations. This work covers the language integration in SQL, the evaluation of the queries, and the integration with the query optimizer. We also propose a simple language for defining the temporal constraints. The approach allows for queries that were never available. We provide a complete implementation in C+ + and Prolog in the context of the S econdo platform. The implementation is made publicly available online as a S econdo Plugin, which also includes automatic scripts for repeating the experiments in this paper. Content Type Journal Article Pages 497-540 DOI 10.1007/s10707-010-0114-3 Authors Mahmoud Attia Sakr, Database Systems for New Applications, FernUniversität in Hagen, 58084 Hagen, Germany Ralf Hartmut Güting, Database Systems for New Applications, FernUniversität in Hagen, 58084 Hagen, Germany Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 × | dimensions |), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes. Content Type Journal Article Pages 29-47 DOI 10.1007/s10707-010-0113-4 Authors Ranga Raju Vatsavai, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Budhendra Bhaduri, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 1

Description:    Statistical models for areal data are primarily used for smoothing maps revealing spatial trends. Subsequent interest often resides in the formal identification of ‘boundaries’ on the map. Here boundaries refer to ‘difference boundaries’, representing significant differences between adjacent regions. Recently, Lu and Carlin (Geogr Anal 37:265–285, 2005 ) discussed a Bayesian framework to carry out edge detection employing a spatial hierarchical model that is estimated using Markov chain Monte Carlo (MCMC) methods. Here we offer an alternative that avoids MCMC and is easier to implement. Our approach resembles a model comparison problem where the models correspond to different underlying edge configurations across which we wish to smooth (or not). We incorporate these edge configurations in spatially autoregressive models and demonstrate how the Bayesian Information Criteria (BIC) can be used to detect difference boundaries in the map. We illustrate our methods with a Minnesota Pneumonia and Influenza Hospitalization dataset to elicit boundaries detected from the different models. Content Type Journal Article Pages 435-454 DOI 10.1007/s10707-010-0109-0 Authors Pei Li, Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, Minneapolis, MN 55455–0392, USA Sudipto Banerjee, Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, Minneapolis, MN 55455–0392, USA Alexander M. McBean, Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, Minneapolis, MN 55455–0392, USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet. Content Type Journal Article Pages 455-496 DOI 10.1007/s10707-010-0110-7 Authors Leticia Gómez, Instituto Tecnológico de Buenos Aires, Av. Madero 399, Buenos Aires, Argentina Bart Kuijpers, Hasselt University and Transnational University of Limburg, Gebouw D, 3590 Diepenbeek, Belgium Alejandro Vaisman, Universidad de Buenos Aires, Ciudad Universitaria, Pabellon I, Buenos Aires, 1428 Argentina Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series’ values. This paper concentrates on a new aspect—that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series. Content Type Journal Article Pages 75-109 DOI 10.1007/s10707-010-0112-5 Authors Leonardo E. Mariote, Institute of Computing, University of Campinas—CP6176, Campinas, São Paulo 13084-851, Brazil Claudia Bauzer Medeiros, Institute of Computing, University of Campinas—CP6176, Campinas, São Paulo 13084-851, Brazil Ricardo da Silva Torres, Institute of Computing, University of Campinas—CP6176, Campinas, São Paulo 13084-851, Brazil Lucas M. Bueno, Institute of Computing, University of Campinas—CP6176, Campinas, São Paulo 13084-851, Brazil Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 1

Description:    Time geography uses space–time volumes to represent the possible locations of a mobile agent over time in a x – y – t space. A volume is a qualitative representation of the fact that the agent is at a particular time t i inside of the volume’s base at t i . Space–time volumes enable qualitative analysis such as potential encounters between agents. In this paper the qualitative statements of time geography will be quantified. For this purpose an agent’s possible locations are modeled from a stochastic perspective. It is shown that probability is not equally distributed in a space–time volume, i.e., a quantitative analysis cannot be based simply on proportions of intersections. The actual probability distribution depends on the degree of a priori knowledge about the agent’s behavior. This paper starts with the standard assumption of time geography (no further knowledge), and develops the appropriate probability distribution by three equivalent approaches. With such a model any analysis of the location of an agent, or relations between the locations of two agents, can be improved in expressiveness as well as accuracy. Content Type Journal Article Pages 417-434 DOI 10.1007/s10707-010-0108-1 Authors Stephan Winter, Department of Geomatics, The University of Melbourne, Victoria, 3010 Australia Zhang-Cai Yin, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei, China Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    In modern geographic information systems, route search represents an important class of queries. In route search related applications, users may want to define a number of traveling rules (traveling preferences) when they plan their trips. However, these traveling rules are not considered in most existing techniques. In this paper, we propose a novel spatial query type, the multi-rule partial sequenced route (MRPSR) query, which enables efficient trip planning with user defined traveling rules. The MRPSR query provides a unified framework that subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. The difficulty in answering MRPSR queries lies in how to integrate multiple choices of points-of-interest (POI) with traveling rules when searching for satisfying routes. We prove that MRPSR query is NP -hard and then provide three algorithms by mapping traveling rules to an activity on vertex network. Afterwards, we extend all the proposed algorithms to road networks. By utilizing both real and synthetic POI datasets, we investigate the performance of our algorithms. The results of extensive simulations show that our algorithms are able to answer MRPSR queries effectively and efficiently with underlying road networks. Compared to the Light Optimal Route Discoverer (LORD) based brute-force solution, the response time of our algorithms is significantly reduced while the distances of the computed routes are only slightly longer than the shortest route. Content Type Journal Article Pages 541-569 DOI 10.1007/s10707-010-0115-2 Authors Haiquan Chen, Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA Wei-Shinn Ku, Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA Min-Te Sun, Department of Computer Science and Information Engineering, National Central University, Taoyuan, 320 Taiwan Roger Zimmermann, Department of Computer Science, National University of Singapore, Singapore, 117590 Singapore Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 3

Description:    The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists. Content Type Journal Article Pages 1-28 DOI 10.1007/s10707-010-0111-6 Authors Wei Ding, Department of Computer Science, University of Massachusetts-Boston, Boston, MA 02125-3393, USA Christoph F. Eick, Department of Computer Science, University of Houston, Houston, TX 77004, USA Xiaojing Yuan, Engineering Technology Department, University of Houston, Houston, TX 77004, USA Jing Wang, Department of Computer Science, University of Houston, Houston, TX 77004, USA Jean-Philippe Nicot, Bureau of Economic Geology, John A. & Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX USA Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 1

Description:    Several representations have been created to store topological information in normal spatial databases. Some work has also been done to represent topology for 3D objects, and such representations could be used to store topology for spatiotemporal objects. However, using 3D models has some disadvantages with regards to retrieving snapshots of the database. This paper aims at creating a spatiotemporal version of the sliced representation that supports efficient retrieval of snapshots of the past and that supports enforcing topological relationships. This paper aims to extend an earlier representation of moving objects so that it can also store and enforce some of the topological relationships between the objects. One use of such a representation is storing a changing spatial partition. As part of the effort to construct the model, an analysis of the topological relationships has been carried out to see which need to be stored explicitly and which can be computed from geometry. Both a basic time slice model and a 3D model are examined to determine how suitable they are for storing topological relationships. An extension of the time slice model is then proposed that solves some of the problems of the basic time slice model. Some algorithms for constructing the new model from snapshots of the objects along with an adjacency graph have been created. The paper also contains a short analysis on how to handle current time, as the time slice model is best at handling historical data, and on ways to speed up searches in a database in which objects of many types are connected to one another and many files therefore potentially need to be accessed. Content Type Journal Article Pages 633-661 DOI 10.1007/s10707-010-0120-5 Authors Erlend Tøssebro, Department of Electrical Engineering and Computer Science, University of Stavanger, NO-4036 Stavanger, Norway Mads Nygård, Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway Journal GeoInformatica Online ISSN 1573-7624 Print ISSN 1384-6175 Journal Volume Volume 15 Journal Issue Volume 15, Number 4

Description: Spatial computing is a set of ideas, solutions, tools, technologies, and systems that transform our lives with a new prospect of understanding, navigating, visualizing and using locations. In this community whitepaper, we present a perspective on the changing world of spatial computing, research challenges and opportunities and geoprivacy issues for spatial computing. First, this paper provides an overview of the changing world of spatial computing. Next, promising technologies that resulted from the integration of spatial computing in the everyday lives of people is discussed. This integration results with promising technologies, research challenges and opportunities and geoprivacy issues that must be addressed to achieve the potential of spatial computing.

Description: Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles.

Description: This paper proposes three methods of association analysis that address two challenges of Big Data: capturing relatedness among real-world events in high data volumes, and modeling similar events that are described disparately under high data variability. The proposed methods take as input a set of geotemporally-encoded text streams about violent events called “storylines”. These storylines are associated for two purposes: to investigate if an event could occur again, and to measure influence, i.e., how one event could help explain the occurrence of another. The first proposed method, Distance-based Bayesian Inference , uses spatial distance to relate similar events that are described differently, addressing the challenge of high variability. The second and third methods, Spatial Association Index and Spatio-logical Inference , measure the influence of storylines in different locations, dealing with the high-volume challenge. Extensive experiments on social unrest in Mexico and wars in the Middle East showed that these methods can achieve precision and recall as high as 80 % in retrieval tasks that use both keywords and geospatial information as search criteria. In addition, the experiments demonstrated high effectiveness in uncovering real-world storylines for exploratory analysis.

Description: Finding k nearest neighbor objects in spatial databases is a fundamental problem in many geospatial systems and the direction is one of the key features of a spatial object. Moreover, the recent tremendous growth of sensor technologies in mobile devices produces an enormous amount of spatio-directional (i.e., spatially and directionally encoded) objects such as photos. Therefore, an efficient and proper utilization of the direction feature is a new challenge. Inspired by this issue and the traditional k nearest neighbor search problem, we devise a new type of query, called the direction-constrained k nearest neighbor (DC k NN) query. The DC k NN query finds k nearest neighbors from the location of the query such that the direction of each neighbor is in a certain range from the direction of the query. We develop a new index structure called MULTI, to efficiently answer the DC k NN query with two novel index access algorithms based on the cost analysis. Furthermore, our problem and solution can be generalized to deal with spatio-circulant dimensional (such as a direction and circulant periods of time such as an hour, a day, and a week) objects. Experimental results show that our proposed index structure and access algorithms outperform two adapted algorithms from existing k NN algorithms.