Our contribution is dedicated to geographic information contained in unstructured textual documents. The main focus of this article is to propose a general indexing strategy that is dedicated to spatial information, but which could be applied to temporal and thematic information as well. More specifically, we have developed a process flow that indexes the spatial information contained in textual documents. This process flow interprets spatial information and computes corresponding accurate footprints. Our goal is to normalize such heterogeneous grained and scaled spatial information (points, polylines, polygons). This normalization is carried out at the index level by grouping spatial information together within spatial areas and by using statistics to compute frequencies for such areas and weights for the retrieved documents.
To verify this mining approach, it is applied on AVHRR MCSST thermal data for extracting Indo-Pacific warm pools frequent movement patterns. The raw data provided by PO.DAAC, whose time spans of 20years from 1981 to 2000 with 7days time particle, has been used to mining spatial temporal association rules. In the experiment, we extract warm pool within 30N 30S, 100E 140W and use 28C as temperature threshold. After which Warm Pool s geographical process table is established so as to describe the variation of warm pool in spatial temporal-attribute dimension. In the mining process, 18 spatialtemporal process frequent models can be found by setting minimal support threshold at 10% and confidence threshold at 60%. The result shows such a methodology can mine complicated spatial temporal rules in realistic data. At the same time, the mining result of warm pool s frequent movement patterns may provide reference for oceanographers.
Coastal erosion is an issue of widespread concern and the Yangtze Delta is one of the most important economic regions in China, has been showing a trend towards such erosion. Possible influences are global warming in the form of sea level rise and the increasing human activities in the catchment and its estuary. Most Current studies on coastal erosion in this region have focused on the causes and types of erosion. This paper presents a Decision Support Framework (DSF) to estimate the likelihood and possible degree of coastal erosion in the coastal zone of the Yangtze Delta. The framework also enables consideration of the potential management problems and economic development challenges. It has four major components: an integrated database, GIS-based risk assessment models, scenario generator and a visualization toolkit. Specific focus is on the development of a GIS-based risk assessment model for the muddy coasts of the Yangtze Delta. An Analytic Hierarchy Process (AHP) method was used to weight the variables of the model, such as coastal elevation, coastal slope, et al. The assessment results show the validity of the approach. The DSF will make relevant specialized data and information more accessible to managers, and has an extensive capability to facilitate communication and subsequent synergetic work between man and computers.
Spatial digital image analysis plays an important role in information decision support systems, especially for regions frequently affected by hurricanes and tropical storms. For aerial and satellite imaging based pattern recognition, it is unavoidable for these images to be affected by various uncertainties, such as atmospheric medium dispersion. Image denoising is thus necessary to remove noise and retain important digital image signatures. The linear denoising approach is suitable for slow variation noise cases. However, the spatial object recognition problem is essentially nonlinear. Being a nonlinear wavelet based technique, wavelet decomposition is effective for denoising blurred spatial images. The digital image is split into four subbands, representing approximation and three details (high frequency features) in the horizontal, vertical and diagonal directions. The proposed soft thresholding wavelet decomposition is simple and efficient for noise reduction. To further identify the individual targets, a nonlinear K-means clustering based segmentation approach is proposed for image object recognition. Selected spatial images were taken across hurricane affected Louisiana areas. In addition for the evaluation of this integration approach via qualitative observation, quantitative measures are proposed on the basis of information theory. Discrete entropy, discrete energy and mutual information are applied for accurate decision support.
This paper presents a toolkit for information extraction from remotely sensed imagery. This toolkit is named Classification by Combining Spectral information and Spatial information upon Multiple-point statistics (CCSSM), which comprises the derivation of two probability fields from supervised spectral extraction classification and spatial information multiple-point simulation (MPS) and then the fusing the two. This toolkit includes three components: (a) processing input data which include the probability field from the supervised classification and a training image for MPS; (b) implementing MPS to obtain simulation result as the second; (c) fusing the above two probability fields. The performance of CCSSM for a multiple-class classification is demonstrated by a QUICKBIRD 2.5m imagery which contains five classes.
Current information available to tourists visiting Hong Kong seems to be abundant but fragmented. Usually, individual tourists tend to plan their itinerary before they arrive, either through tourist guides or online information sources (e.g. existing itinerary planning websites). With the enhancement of information technology, it is expected that online itinerary planning will be completely supplementary to hard copy travel guides and magazines in the future. However, current online itinerary planners overlook transportation links and optimum travel planning. For example, several tourist spots can be visited sequentially and hence optimise the time available. And this is the sphere on which this project focuses. The aim is to develop a scheduling algorithm based on the Greedy Algorithm that helps prepare an itinerary for individual tourists visiting Hong Kong. Tourists’ limited knowledge of the spatial extent of transportation facilities in Hong Kong has always and naturally presented obstacles as regards arranging an efficient (optimal) travel plan. With a view to minimizing travel time and maximizing the time for sightseeing, the shortest travelling time between tourist locations spots has been adopted as the guiding principle in deriving solutions. The benefits brought by the existence of this system and the currently available tourist information (both transport and tourist spot) are assessed and evaluated.
Many geospatial science subdisciplines analyze variables that vary over both space and time. The space–time autoregressive (STAR) model is one specification formulated to describe such data. This paper summarizes STAR specifications that parallel geostatistical model specifications commonly used to describe space–time variation, with the goal of establishing synergies between these two modeling approaches. Resulting expressions for space–time correlograms derived from 1st-order STAR models are solved numerically, and then linked to appropriate space–time semivariogram models.
In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by todays maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicles position.
South Georgia is a glaciated island in the South Atlantic, which provides a primary nesting site for the albatrosses and petrels of the Southern Ocean. 60% of the island is covered by glaciers and ice fields, and the majority of the coastal glaciers are observed to be retreating. A small number of these glaciers are advancing, and others are retreating at anomalously fast rates. As the status of these glaciers is important for environmental management of South Georgia, potentially controlling the spread of invasive species into currently pristine regions, it is necessary to understand the pattern of glacier change in South Georgia. However, detailed study of the glaciology of South Georgia is hampered by lack of measurements of the thickness of the ice. Because of the logistic difficulties of operating on South Georgia, there are no conventional ice thickness measurements from drilling, radar or seismic techniques, and it is unlikely that these will be available in the near future.This paper addresses this lack of basic information by using surface slope data to estimate the ice thickness of glaciers and ice fields in South Georgia. The surface slope data are derived using surface elevations from the Shuttle Radar Topographic Mission, which provides elevation measurements with a high relative accuracy. The estimate of ice thickness critically depends on assumptions about the conditions in the ice column and at the base of the ice mass, and areas where the estimate is clearly in error provide an insight into changed ice flow conditions or the environment at the ice/rock interface. These anomalous regions are then compared with glacier change data, providing insights into the reasons for the unusually rapid retreat or advance of certain glaciers.The paper describes the methodology used to compute ice thickness values, with an estimate of accuracy and variability of the thickness measures under different assumptions. The paper then identifies regions with anomalous thickness measurements, and seeks to ascertain why the thickness measurement is unreliable in certain regions. Finally these anomalous areas are compared with coastal change data to suggest why certain glaciers are retreating or advancing more rapidly than the norm for South Georgia, and to make predictions concerning future glacier change.
Pattern recognition is an important step in map generalization. Pattern recognition in a street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines roughly at right angles. Inspired by object recognition in image processing, this paper presents an approach to grid recognition in street network based on graph theory. Firstly, bridges and isolated points of the network are idenepsied and repeatedly deleted. Secondly, a similar orientations graph is created, in which the vertices represent street segments and the edges represent similar orientation relationships between streets. Thirdly, the candidates are extracted through graph operators such as the finding of connected components, finding maximal complete sub-graphs, joins and intersections. Finally, the candidates are repeatedly evaluated by deleting bridges and isolated lines, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other. The average clustering coefficient of these graphs is then calculated. Experimental results show that the proposed approach is valid in detecting the grid pattern in lower degradation situations.
Building patterns are important settlement structures in applications like automated generalization and spatial data mining. Previous investigations have focused on a few types of building patterns (e.g. collinear building alignments); while many other types are less discussed. In order to get better known of the building patterns available in geography, this paper studies existing topographic maps at large to medium scales, and proposes and discusses a comprehensive typology of building patterns, their distinctions and characteristics. The proposed typology includes linear alignments (i.e. collinear, curvilinear, align-along-road alignments) and nonlinear clusters (grid-like and unstructured patterns). We concentrate in this paper on two specific building structures: align-along-road alignment and unstructured clusters. Two graph-theoretic algorithms are presented to detect these two types of building patterns. The approach bases itself on auxiliary data structures such as Delaunay triangulation and minimum spanning trees for clustering; several rules are used to refine the clusters into specific building patterns. Finally, the proposed algorithms are tested against a real topographic dataset of the Netherlands, which shows the potential of the two algorithms.
This paper presents an improved cellular automata (CA) model optimised using an adaptive genetic algorithm (AGA) to simulate the spatio-temporal processes of urban growth. The AGA technique was used to optimise the transition rules of the CA model defined through conventional logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA based CA model in Shanghais Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics.
Automated evaluation of generalization output relies largely on well defined map specifications or cartographic constraints to formalize quality criteria or user requirements, and on enriched information to fully automate the process. Previous studies suggest that the formalization and evaluation of legibility constraints (i.e. those improving the readability of maps) are relatively easier than preservation constraints (i.e. those preserving important geographic characteristics and patterns). Patterns are important structures and should be taken into account by the evaluation process. This paper aims at a methodology in which the preservation of building patterns can be evaluated automatically. Three major difficulties in the process are identified and addressed: (1) pattern classification, characterization and detection, (2) pattern matching, and (3) constraint formalization. In addition, the knowledge of transition events describing allowed changes to building patterns at scale transitions is obtained via studying existing topographic maps (from 1:10 k to 1:100 k). Based on this knowledge, automated matching of corresponding building patterns is done much easier. Finally, the proposed methodology is implemented and validated by being applied to the evaluation of an interactively generalized dataset against its initial dataset. The results show the potential of the methodology and that the generalized patterns tend to remain the same or become more regular with respect to the initial ones. We also identify further improvement for practical use in an overall evaluation process to indicate acceptable generalization solutions.
Much attention has been given to sampling design, and the sampling method chosen, directly affects sampling accuracy. The development of spatial sampling theory has lead to the recognition of the importance of taking spatial dependency into account when sampling. This research study uses the new Sandwich Spatial Sampling and Inference (SSSI) software as a tool to compare the relative error, coefficient of variation (CV), and design effect of five sampling models: simple random sampling, stratified sampling, spatial random sampling, spatial stratified sampling, and sandwich spatial sampling. The five models are each simulated 1,000 times, with a range of sample sizes from 10 to 80. SSSI includes six models in all, but systematic sampling is not used in this study, because the sample positions are fixed. The dataset consists of 84 points measuring soil heavy metal content in Shanxi Province, China. The whole area is stratified into four layers by soil type, hierarchical cluster and geochronology, and three layers by geological surface. The research shows that the accuracy of spatial simple random sampling and spatial stratified sampling is better than simple random sampling and stratified sampling because the soil content is spatially continuous, and stratified models are more efficient than non-stratified models. Stratification by soil type yields higher accuracy than by geochronology in the case of smaller sample sizes, but lower accuracy in larger sample sizes. Based on spatial stratified sampling, sandwich sampling develops a report layer composed of the users final report units, allowing the user to obtain the mean and variance of each report unit with high accuracy. In the case of soil sampling, SSSI is a useful tool for evaluating the accuracy of different sampling techniques.
Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of considering the fact that the travel time through any link is dependent on the time entering that link. In this study, travel speed rather than travel time on each link is used as the time period dependent parameter to model time-dependent transportation networks, and a First-In-First-Out (FIFO) condition satisfied computational function of link travel time is then deduced based on kinematics. Finally, a temporally adaptive A* shortest path algorithm on this FIFO network is presented, where the time factor is introduced into the evaluation function, and the Euclidean distance divided by the maximum possible travel speed is used as a heuristic evaluator. An experiment on a real road network shows that the proposed algorithm is capable of foreseeing and bypassing forthcoming traffic congestion, at a cost only about 10% more in computational time than the traditional algorithm. In addition, frequent path reoptimization required with use of the traditional algorithm is effectively avoided.
The unbalanced development of regional economics in Beijing has induced ubiquitous regional economic disparities which may affect the sustainable development of the economy and social stability. How to alleviate this unbalanced status is an important issue faced by both society and government. As would be expected the impact of the Olympic Games had the potential to boost the economy of the host city, however it is not clear whether such an event could narrow any regional economic disparities. In order to investigate the influence of the 2008 Beijing Olympic Games on the development of regional economics, this study examines the space-time dynamics of the economic development of Beijing from the year in which Beijing were successful in their bid (2001) using exploratory spatial data analysis (ESDA). The analysis was based on the county level and measure index of the regional per capita gross domestic product (GDP). The results do not show strong evidence of global spatial autocorrelation, but do give clear evidence of local spatial autocorrelation and spatial heterogeneity in the distribution of regional per capita GDP. The total economic disparity in Beijing was not alleviated by the drive to win the bid of the 2008 Olympics, and the financial and social situations became even more complicated. The speed of economic increase of the Changping and Shijingshan Districts was significantly lower than some neighboring regions, causing a new central polarization scheme gradually replaced the North–south polarization scheme that existed in Beijing between 2001 and 2007.
Traditionally the GIScience community is well able to deal with the locational and attribute component of spatio-temporal data. However, the methods and techniques to deal with the datas temporal component are less developed. This paper introduces a conceptual framework that combines user tasks, available temporal data and visualization theories to discuss temporal visualization. Two limitations of existing method are improved by introducing the time wave environment which is a close combination of temporal graphic representation and temporal interactive tools, and operates in so-called time space. Time space is a concept which represents time and answers the temporal questions. This term is based on the data components and user task framework: location (where), attribute (what) and time (when). From a visualization perspective this translates in: location space (maps), attribute space (diagrams) and time space. In this research, the time wave which is a main element in the time space is a good example of how an alternative view on the data might reveal patterns not always obvious from traditional graphic representations. A case study based on meteorological data illustrates this approach.
Nowadays, it is often that a geographic area is described by several independent geographic databases. Yet users need to fusion various information coming from these databases. In order to integrate databases, redundancy and inconsistency between data should be identified. Many steps are required to finalise the databases integration, in particular automatic data matching. In this paper, one approach of matching geographic data bearing on the belief theory is presented. This approach consists in combining criteria from knowledge such as geometry, orientation, nature of roads, names and topology. Then it is tested on heterogeneous network representing roads.
Spatial analysis of place names is a vital part of toponymic research as spatial location and relations between geographical features have a vast effect on natural and artificial name giving. Examining spatial attributes and relations between features can help to understand impacts of distance, hierarchy and other characteristics of natural and man-made objects in naming processes. This paper shows possibilities for modeling and visualizing the connections between settlements of streets which are named for their destinations in Hungary. Spatial analysis of street names can help to in-vestigate the development of settlement and road systems.
This paper explores the issues relating to uncertainty in the application of object oriented classifications of remote sensing data. Object oriented remote sensing software such as eCognition (now known as Definiens Developer) provides the user with flexibility in the way that data is classified through segmentation routines and user-specified fuzzy rules. However the aggregation of fuzzy data objects such as pixels to higher level parcels for the purpose of policy reporting is not straightforward. This paper explores the uncertainty issues relating to the aggregation from fine detailed (uncertain) objects of one classification system to coarser grain (uncertain) objects of another classification scheme. We show Possibility Theory to be an appropriate formalism for managing the non-additive uncertainty commonly associated with classified remote sensing data. Results are presented for a small area of upland Wales to illustrate the value of the approach.
