Showing papers in "Geoinformatica in 2015"

Recommendations in location-based social networks: a survey

Abstract: Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users' preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users' travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.

A comparison and evaluation of map construction algorithms using vehicle tracking data

Abstract: 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.

EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data

Abstract: Eco-routing is a simple yet effective approach to substantially reducing the environmental impact, e.g., fuel consumption and greenhouse gas (GHG) emissions, of vehicular transportation. Eco-routing relies on the ability to reliably quantify the environmental impact of vehicles as they travel in a spatial network. The procedure of quantifying such vehicular impact for road segments of a spatial network is called eco-weight assignment. EcoMark 2.0 proposes a general framework for eco-weight assignment to enable eco-routing. It studies the abilities of six instantaneous and five aggregated models to estimating vehicular environmental impact. In doing so, it utilizes travel information derived from GPS trajectories (i.e., velocities and accelerations) and actual fuel consumption data obtained from vehicles. The framework covers analyses of actual fuel consumption, impact model calibration, and experiments for assessing the utility of the impact models in assigning eco-weights. The application of EcoMark 2.0 indicates that the instantaneous model EMIT and the aggregated model SIDRA-Running are suitable for assigning eco-weights under varying circumstances. In contrast, other instantaneous models should not be used for assigning eco-weights, and other aggregated models can be used for assigning eco-weights under certain circumstances.

Spatio-temporal compression of trajectories in road networks

Abstract: With the proliferation of wireless communication devices integrating GPS technology, trajectory datasets are becoming more and more available. The problems concerning the transmission and the storage of such data have become prominent with the continuous increase in volume of these data. A few works in the field of moving object databases deal with spatio-temporal compression. However, these works only consider the case of objects moving freely in the space. In this paper, we tackle the problem of compressing trajectory data in road networks with deterministic error bounds. We analyze the limitations of the existing methods and data models for road network trajectory compression. Then, we propose an extended data model and a network partitioning algorithm into long paths to increase the compression rates for the same error bound. We integrate these proposals with the state-of-the-art Douglas-Peucker compression algorithm to obtain a new technique to compress road network trajectory data with deterministic error bounds. The extensive experimental results confirm the appropriateness of the proposed approach that exhibits compression rates close to the ideal ones with respect to the employed Douglas-Peucker compression algorithm.

Efficient continuous top-k spatial keyword queries on road networks

Abstract: With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages.

Planning unobstructed paths in traffic-aware spatial networks

Abstract: 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 Gta(V, E) by analyzing uncertain trajectories of moving objects. Based on Gta(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.

Recognizing text in raster maps

Abstract: Text labels in maps provide valuable geographic information by associating place names with locations. This information from historical maps is especially important since historical maps are very often the only source of past information about the earth. Recognizing the text labels is challenging because heterogeneous raster maps have varying image quality and complex map contents. In addition, the labels within a map do not follow a fixed orientation and can have various font types and sizes. Previous approaches typically handle a specific type of map or require intensive manual work. This paper presents a general approach that requires a small amount of user effort to semi-automatically recognize text labels in heterogeneous raster maps. Our approach exploits a few examples of text areas to extract text pixels and employs cartographic labeling principles to locate individual text labels. Each text label is then rotated automatically to horizontal and processed by conventional OCR software for character recognition. We compared our approach to a state-of-art commercial OCR product using 15 raster maps from 10 sources. Our evaluation shows that our approach enabled the commercial OCR product to handle raster maps and together produced significant higher text recognition accuracy than using the commercial OCR alone.

Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time

Abstract: Recent developments and ubiquitous use of global positioning devices have revolutionised movement ecology. Scientists are able to collect increasingly larger movement datasets at increasingly smaller spatial and temporal resolutions. These data consist of trajectories in space and time, represented as time series of measured locations for each tagged animal. Such data are analysed and visualised using methods for estimation of home range or utilisation distribution, which are often based on 2D kernel density in geographic space. These methods have been developed for much sparser and smaller datasets obtained through very high frequency (VHF) radio telemetry. They focus on the spatial distribution of measurement locations and ignore time and sequentiality of measurements. We present an alternative geovisualisation method for spatio-temporal aggregation of trajectories of tagged animals: stacked space-time densities. The method was developed to visually portray temporal changes in animal use of space using a volumetric display in a space-time cube. We describe the algorithm for calculation of stacked densities using four different decay functions, normally used in space use studies: linear decay, bisquare decay, Gaussian decay and Brownian decay. We present a case study, where we visualise trajectories of lesser black backed gulls, collected over 30 days. We demonstrate how the method can be used to evaluate temporal site fidelity of each bird through identification of two different temporal movement patterns in the stacked density volume: spatio-temporal hot spots and spatial-only hot spots.

Domain-driven co-location mining

Abstract: Co-location mining is a classical problem in spatial pattern mining. Considering a set of boolean spatial features, the goal is to find subsets of features frequently located together. It has wide applications in environmental management, public safety, transportation or tourism. These last years, many algorithms have been proposed to extract frequent co-locations. However, most solutions do a "data-centered knowledge discovery" instead of a "expert-centered knowledge discovery". Successfully providing useful and interpretable patterns to experts is still an open problem. In this setting, we propose a domain-driven co-location mining approach that combines constraint-based mining and cartographic visualization. Experts can push new domain constraints into the mining algorithm, resulting in more relevant patterns and more efficient extraction. Then, they can visualize solutions using a new concise and intuitive cartographic visualization of co-locations. Using this original visualization approach, they identify new interesting patterns, and use uninteresting ones to define new constraints and refine their analysis. These proposals have been integrated into a prototype based on PostGIS geographic information system. Experiments have been done using a real geological datasets studying soil erosion, and results have been validated by a domain expert.

Best upgrade plans for single and multiple source-destination pairs

Abstract: In this paper, we study Resource Constrained Best Upgrade Plan (BUP) computation in road network databases. Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. In the single-pair version of BUP, the input includes a source and a destination in G, and a budget B (resource constraint). The goal is to identify which upgradable edges should be upgraded so that the shortest path distance between source and destination (in the updated network) is minimized, without exceeding the available budget for the upgrade. In the multiple-pair version of BUP, a set Q of source-destination pairs is given, and the problem is to choose for upgrade those edges that lead to the smallest sum of shortest path distances across all pairs in Q, subject to budget constraint B. In addition to transportation networks, the BUP query arises in other domains too, such as telecommunications. We propose a framework for BUP processing and evaluate it with experiments on large, real road networks.

From GPS and virtual globes to spatial computing - 2020

Abstract: 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.

Improving label placement quality by considering basemap detail with a raster-based approach

Abstract: Topographic maps are arguably one of the most information-dense, yet intuitively usable, graphical artifacts produced by mankind. Cartography as science and practice has developed and collected a wealth of design principles and techniques to cope with the problems of high graphical density, especially for the case of label placement. Many of the more sophisticated techniques that take into account figure-ground relationships for lettering have not been fully operationalized until now. We present a novel generic quality evaluation model that allows full automation of refined techniques for improving map feature overlap, visual contrast and layer hierarchy. We present the objective function as a set of metrics corresponding to the design principles and provide exemplary parameterization via the set of experiments on global real-world datasets. The approach designed for labeling of point-like objects and can potentially be applied to linear and areal features. It has a low computational and memory requirement. Furthermore, it is conceivably applicable to annotate any kind of visualization beyond maps. The results of the conducted tests and comparison with a commercial labeling package illustrate the ability to produce highly legible and readable map lettering with our approach. Presented method heeds more cartographic design principles and is computationally less costly compared to commercially available methods.

A Dilution-matching-encoding compaction of trajectories over road networks

Abstract: Many devices nowadays record traveled routes as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, essential to encode the sequences, in order to decrease the volume of the stored or transmitted data. In this paper we study the problem of encoding routes over a vectorial road network (map), where GPS locations can be associated with vertices or with road segments. We consider a three-step process of dilution, map-matching and coding, which helps reducing the amount of transmitted data between the cellular device and remote servers. We present two methods to code routes. The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map. The first algorithm has O(n) time complexity, and the second one has O(n 2) time complexity, but it is optimal, meaning that it generates the shortest possible greedy-path code. Decoding a greedy-path code can be done in O(n) time. The second method encodes a route as a sequence of shortest paths. We provide algorithms to generate unidirectional and bidirectional optimal shortest-path codes. Encoding and decoding a shortest-path code can be done in O(k n 2 logn) time, where k is the length of the produced code, assuming the graph valency is bounded. Our experimental evaluation shows that shortest-path codes are more compact than greedy-path codes, justifying the larger time complexity.

Contextual Douglas-Peucker Simplification

Abstract: In this paper, we develop a constrained Douglas-Peucker algorithm using a polyline to be simplified and other geometries as contextual constraints. We develop a contextual model that incrementally rewinds to the original polyline with relevant characteristic vertices to resolve contextual conflicts. Constraints cov ered in this paper are topology and direction. Our implementation shows a consistent representation and a technique to accelerate multi-scale simplification of polylines.

The CASE histogram: privacy-aware processing of trajectory data using aggregates

Abstract: 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 our aggregate counting approach that preserve differential privacy. Theoretically and experimentally we demonstrate that our method provides a high level of accuracy compared to the best known methods for the distinct counting problem, whilst preserving privacy. We conduct our experiments on both synthetic and real datasets over two competitive Euler histogram-based methods presented in the literature. Our methods enjoy improvements to accuracy from 10 % up to 70 % depending on trip data and query region size, with the greatest increase seen on the Microsoft T-Drive real dataset, representing a more than tripling of accuracy.

The TM-RTree: an index on generic moving objects for range queries

Abstract: Existing works on moving objects mainly focus on a single environment such as free space and road network, and do not investigate the complete trip for humans who can pass several environments, e.g., road network, pavement areas, indoor. In this paper, we consider multiple environments and study moving objects with different transportation modes, also called generic moving objects. We aim to answer a new class of queries supporting three kinds of conditions: temporal, spatial, and transportation modes. To efficiently provide the result, we propose an index structure called TM-RTree, which takes into account the feature of moving objects in different environments and has the capability of managing objects on not only temporal and spatial data but also transportation modes. This property is not maintained by existing indices for moving objects. Different cases on transportation modes are supported. Correspondingly, several algorithms are developed. The TM-RTree and related algorithms are developed in a real DBMS to have a practical and solid result for applications. In the experiment, we conduct the performance evaluation using extensive datasets and compare the proposed technique with the other two competitors, demonstrating the efficiency and significant superiority of our solution in various settings.

Examining Urban Expansion in the Greater Toronto Area Using Landsat Imagery from 1974–2014

Abstract: The Greater Toronto Area is the most vital economic centre in Canada and has experienced rapid urban expansion in the past 40 years. This research uses Landsat images to detect the spatial and temporal dynamics of urban expansion in the Greater Toronto Area from 1974 to 2014. We quantitatively analyzed the extent of urban expansion and spatial patterns of growth from classified Landsat images. We then integrated our expansion findings with population data to observe the relationships between urban growth and population. We found that the Greater Toronto Area had significant growth of 1115 km2, expanding mainly in radiated and ribbon expansion modes. There was substantial correlation between urban extent and population in the period of study. These results demonstrate the efficacy of combining statistical population data with remote sensing imagery for the analysis of urban expansion.

Partition-based range query for uncertain trajectories in road networks

Abstract: Query processing for trajectory data is a hot topic in the field of moving objects databases (MODs). Most of the previous research work focused on the Euclidean space, and the uncertain trajectories are represented as sheared cylinders. However, in many applications (e.g. traffic management), the movements of moving objects (MOs) are constrained by the road network environments, which makes the previous methods ineffective. In this paper, we firstly construct an uncertain trajectory model, which is composed of a sequence of segment units with earliest arrival time and latest departure time, based on the assuming availability of a maximum speed on each road segment. Secondly, we present a partition-based uncertain trajectory index (PUTI) to facilitate the search of possible MOs within the space and time range in the road networks based on the uncertain trajectory model. It provides appropriate groups to gather segment units of trajectories according to their network distances. Finally, an efficient algorithm for range query is proposed by leveraging the index. The experiments on two datasets demonstrate that the uncertain trajectory model is effective, and PUTI also significantly outperforms the network distance based MON-tree on range query.

Adaptive generation of variable-scale network maps for small displays based on line density distribution

Abstract: This paper presents an integrated strategy for adaptive generation of variable-scale network maps for different small displays. It is based on the line density distribution and comprised of three steps, i.e. (a) to estimate the line density by a grid-based method, (b) to adaptively generate variable-scale maps based on density distribution for given display sizes and (c) to improve the map readability by map generalization. The proposed strategy has been tested by using two real-life network datasets, with a statistical analysis and a perceptual evaluation. Experimental results show that this strategy is able to better exploit the limited map space so as to significantly improve map clarity and readability and at the same time to preserve map recognition ability compared to its original shape.

Semi-Automated Building Footprint Extraction From Orthophotos

Abstract: Here we describe and apply a semi-automated, object-based method for extracting vector-building footprint polygons from aerial photographs (orthophotos) within urban settings. The approach integrates the use of high resolution orthophotos and image segmentation software and is compared with methods using Light Detection and Ranging (LiDAR) as the source data input. LiDAR data gives the best results with less processing, but is not widely used by municipalities due to the expense. Results from semi-automated image segmentation of the orthophotos showed a high accuracy between extracted building segments and reference building footprints for two study sites, comparable to those achieved using LiDAR data. We recommend image acquisition during summer months with a resolution of 10 cm by 10 cm. When data acquisition budgets are limited, combining ancillary GIS on roads with a semi-automated and object-based segmentation approach is a best practice strategy for land cover feature extraction and change quantific...

MobiFeed: A location-aware news feed framework for moving users

Abstract: A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to estimate a mobile user's locations based on a path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. We propose a heuristic algorithm as well as an optimal algorithm for the location-aware news feed scheduler. The performance of MobiFeed is evaluated through extensive experiments using a real road map and a real social network data set. The scalability of MobiFeed is also investigated using a synthetic data set. Experimental results show that MobiFeed obtains a relevance score two times higher than the state-of-the-art approach, and it can scale up to a large number of geo-tagged messages.

On reverse-k-nearest-neighbor joins

Abstract: A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has only received little attention so far. In this paper, we analyze different types of RkNN joins and provide a classification of existing RkNN join algorithms. We discuss possible solutions for solving the non-trivial variants of the problem in vector spaces, including self and mutual pruning strategies. Further, we generalize the developed algorithms to general metric spaces. During an extensive performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a wide range of different setups and suggest appropriate query algorithms for specific scenarios.

Exploring the Decision Tree Method for Modelling Urban Land Use Change

Abstract: Land use changes play an important role in interactions between human and physical systems, and have significant impacts on the environment at local, regional and global scales. Land use change is a complex process and so developing dynamic models to represent the process is a challenging task. Decision Trees (DT) is a Machine Learning (ML) method with the capability to extract trends and generate a representative model using historical geospatial data. While DT is used in remote sensing as an image classification method, it is not sufficiently examined in land use science. The main objective of this research study is to examine the capability of DT method to model urban land use change. Various numbers of attributes for three municipalities in the City of Belgrade, Republic of Serbia were used. Land use is represented with nine land use classes for three different time instances for the years 2003, 2007 and 2011. The kappa statistics and weighted Area Under Receiver Operating Characteristic Curve (AUC) w...

Novel Integration Strategy for GNSS-Aided Inertial Integrated Navigation

Abstract: The conventional integration mechanism in GNSS (Global Navigation Satellite Systems) aided inertial integrated positioning and navigation system is mainly based on the continuous outputs of the navigation mechanization, the associated error models for navigation parameters, the biases of the inertial measurement units (IMU), and the error measurements. Its strong dependence on the a priori error characteristics of inertial sensors may suffer with the low-cost IMUs, e.g. the MEMS IMUs due to their low and unstable performance. This paper strives for a significant breakthrough in a compact and general integration strategy which restructures the Kalman filter by deploying a system model on the basis of 3D kinematics of a rigid body and performing measurement update via all sensor data inclusive of the IMU measurements. This novel IMU/GNSS Kalman filter directly estimates navigational parameters instead of the error states. It enables the direct use of the IMU's raw outputs as measurements in measurement upda...

Travel topic analysis: a mutually reinforcing method for geo-tagged photos

Abstract: 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.

The Evolution of Nova Scotia's Coordinate Referencing Systems

Abstract: Despite its rich history, the evolution of the coordinate referencing system in Nova Scotia has not been well documented. As the foundation for the property boundary fabric and land registration system for the province, it is critical that this knowledge is maintained. It is the key to being able to link surveys of the past with the pres ent. This research strives to capture important coordinate referencing knowledge from those that have worked with the system and have since retired from the Nova Scotia provincial government or former Land Registration and Information Service. A detailed review of documentation discussing various historical aspects of coordinate referencing in Nova Scotia was conducted. Personnel involved with the implementation of the systems were interviewed to pro vide first-hand accounts of operational details. This information is captured and is presented chronologically. An outlook on the future of the coordinate referencing program is also discussed.

Residual Vegetation Patches within Natural Boreal Wild Fires: Characterizing by Pattern Metrics, Land Cover Expec tations and Proximity to Firebreak Features

Abstract: Wildfires typically contain a considerable number of wildfire residual patches of various size, shape and composition. These residual patches can occupy substantial areas of fire footprints, thus understanding their patterns provides insight for emulating forest disturbances in harvesting operations. Eleven natural boreal wild fire events within Ontario are examined. Each fire was ignited by lighting, occurred in anthropogenically undisturbed forested landscapes and was never suppressed. The spatial patterns of the residual patches are assessed based on selected spatial metrics (related to composition, configuration and fragmentation). The char acterization of the occurrence of wildfire residual patches and their spatial patterns in reference to land cover composition and proximity to firebreak features, is imperative to examine the effects of vegetation or land cover on residual patch occurrence and distribution. This study examines which land cover types are more likely to dominate the existing residual...

GMOBench: Benchmarking generic moving objects

Abstract: In real world scenarios, people's movement include several environments rather than one, for example, road network, pavement areas and indoor. This imposes a new challenge for moving objects database that the complete trip needs to be managed by a database system. In the meantime, novel queries regarding different transportation modes should also be supported. Since existing methods are limited to trips in a single environment and do not support queries on moving objects with different transportation modes, new technologies are essentially needed in a database system. In this paper, we introduce a benchmark called GMOBench that aims to evaluate the performance of a database system managing moving objects in different environments. GMOBench is settled in a realistic scenario and is comprised of three components: (1) a data generator with the capability of creating a scalable set of trips representing the complete movement of humans (both indoor and outdoor); (2) a set of carefully designed and benchmark queries; (3) Mode-RTree, an index structure for managing generic moving objects. The generator defines some parameters so that users can control the characteristics of results. We create the benchmark data in such a way that the dataset can mirror important characteristics and real world distributions of human mobility. Efficient access methods and optimization techniques are developed for query processing. In particular, we propose an index structure called Mode-RTree to manage moving objects in different environments. By employing the proposed index, the cost of benchmark queries is greatly reduced. GMOBench is implemented in a real database system to have a practical result. We perform an extensive experimental study on comprehensive datasets to evaluate the performance. The results show that by using the Mode-RTree we achieve significant performance improvement over the baseline method, demonstrating the effectiveness and efficiency of our approaches.

Efficiently computing the drainage network on massive terrains using external memory flooding process

Abstract: 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.