KBGIS-II A knowledge-based geographical information system

Abstract

This paper describes the architecture and working of a recently implemented knowledge-based GIS (KBGIS-II) that was designed to satisfy several general criteria for GIS. The system has four major functions, query-answering, learning, editing and training. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multi-layered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is currently performing all its designated tasks successfully, although currently implemented on inadequate hardware.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

Integrative representation and inference of qualitative locations about points,lines, and polygons

Qualitative knowledge representation of spatial locations and relations is popular in many text-based media, for example, postings on social networks, news reports, and encyclopedia, as representing qualitative spatial locations is indispensable to infer spatial knowledge from them. However, an integrative model capable of handling direction-based locations of various spatial objects is missing. This study presents an integrative representation and inference framework about direction-based qualitative locations for points, lines, and polygons. In the framework, direction partitions of different types of reference objects are first unified to create a partition consisting of cells, segments, and corners. They serve as a frame of reference to locate spatial objects (e.g., points, lines, and polygons). Qualitative relations are then defined to relate spatial objects to the elements in a cell partition, and to form the model of qualitative locations. Last, based on the integrative representation, location-based reasoning mechanism is presented to derive topological relations between objects from their locations, such as point–point, line–line, point–line, point–polygon, line–polygon, and polygon–polygon relations. The presented model can locate any type of spatial objects in a frame of reference composed of points, lines, and polygons, and derive topological relations between any pairs of objects from the locations in a unified method.     

Simulating visit probability distributions within planar space-time prisms

The space-time prism is key concept in time geography and moving objects databases; it demarcates all locations that a mobile object can occupy given anchor locations and times and a maximum velocity for travel. Although the prism’s spatial and temporal extent is widely applied as a measure of accessibility and object locational uncertainty, until recently little attention has been paid to the properties of the prism interior such as the probabilities of the object visiting different locations within the prism. Better understanding of the visit probability distribution within the prism can improve theoretical understanding as well as refine the prism as a practical measure of space-time accessibility and object uncertainty. This paper presents two methods for modeling the distribution of visit probabilities within planar space-time prisms: (1) a directed Random Walk method for discrete space and time, and (2) a truncated Brownian Bridges method for continuous space and time. We illustrate these methods and demonstrate the effect of prism and mobility parameters on the visit probability distributions within the prism.     

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.     

The geometry of space-time prisms with uncertain anchors

Space-time prisms envelop all spatio-temporal locations that moving objects may have visited between two of their known spatio-temporal locations, given a bound on their travel speed. In this context, the known locations are often the result of observations or measurements, and they are called ‘anchor points’. The classic space-time prism, in isotropic two-dimensional space, as well as in transportation networks, assumes that the measurements of these anchor points are exact. Whereas, in many applications, we can assume that time can be measured fairly precisely, this assumption is unrealistic for the spatial components of measured locations (we think of Global Positioning System (GPS) errors, for instance). In this paper, we extend the classical prism from anchor points to circular ‘anchor regions’ that capture the uncertainty or error on their measurement. We define the notion of a space-time prism with uncertain anchor points, called uncertain prism, for short. We study the geometry of uncertain prisms in an arbitrary metric space to make this concept as widely applicable as possible. We also focus on the rims of uncertain space-time prisms, which demarcate the area that a moving object can have visited between two anchor regions (given some local speed limitations).     

An inconsistency measure of spatial data sets with respect to topological constraints

An inconsistency measure can be used to compare the quality of different data sets and to quantify the cost of data cleaning. In traditional relational databases, inconsistency is defined in terms of constraints that use comparison operators between attributes. Inconsistency measures for traditional databases cannot be applied to spatial data sets because spatial objects are complex and the constraints are typically defined using spatial relations. This paper proposes an inconsistency measure to evaluate how dirty a spatial data set is with respect to a set of integrity constraints that define the topological relations that should hold between objects in the data set. The paper starts by reviewing different approaches to quantify the degree of inconsistency and showing that they are not suitable for the problem. Then, the inconsistency measure of a data set is defined in terms of the degree in which each spatial object in the data set violates topological constraints, and the possible representations of spatial objects are points, curves, and surfaces. Finally, an experimental evaluation demonstrates the applicability of the proposed inconsistency measure and compares it with previously existing approaches.     

A data model for moving regions of fixed shape in databases

Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.     

Organization of Feature-, Time-, and Location-Based Mental Models

This study considers how mental models are encoded into memory by viewing visual displays like maps. A mental model is an internal representation of a situation that links objects or concepts to other objects or concepts. Previous studies indicated location-based mental models are encoded when a series of propositional statements such as the object is in the location are read from a text. Evidence that locations were being used as the basic container for an organization of mental models is provided by a significant fan effect. A fan effect shows an increase in reaction time with the number of models considered when making a decision. Features, times, and locations were considered as possible containers in the mental models. Subjects created location-based mental models, but also encoded feature-based mental models. A reverse fan effect for time, found for a map animation, suggested the order of the presentation of maps could greatly affect the structure of learned information.     

Image‐based quality assessment of road databasesX

In this paper an approach to the automatic quality assessment of existing geo‐spatial data is presented. The necessary reference information is derived automatically from up‐to‐date digital remotely sensed images using image analysis methods. The focus is on the quality assessment of roads as these are among the most frequently changing objects in the landscape. In contrast to existing approaches for quality control of road data, the data to be assessed and the objects extracted from the images are modelled and processed together. A geometric‐topologic relationship model for the roads and their surroundings is defined. Context objects such as rows of trees support the quality assessment of road vector data as they may explain gaps in road extraction. The extraction and explicit incorporation of these objects in the assessment of a given road database give stronger support for or against its correctness.

During the assessment existing relations between road objects from the database and extracted objects are compared to the modelled relations. The certainty measures of the objects are integrated into this comparison. Normally, more than one extracted object gives evidence for a road database object; therefore, a reasoning algorithm which combines evidence given by the extracted objects is used. If the majority of the total evidence argues for the database object and if a certain amount of this database object is covered by extracted objects, the database object is assumed to be correct, i.e. it is accepted, otherwise it is rejected. The procedure is embedded into a two‐stage graph‐based approach which exploits the connectivity of roads and results in a reduction of false alarms. The algorithms may be incorporated into a semi‐automatic environment, where a human operator only checks those objects that have been rejected.

The experimental results confirm the importance of the employed advanced statistical modelling. The overall approach can reliably assess the roads from the given database, using road and context objects which have been automatically extracted from remotely sensed imagery. Sensitivity analysis shows that in most cases the chosen two‐stage graph‐approach reduces the number of false decisions. Approximately 66% of the road objects have been accepted by the developed approach in an extended test area, 1% has been accepted though incorrect. Those false decisions are mainly related to the lack of modelling road junction areas.     

A generic model for planar geographical objects

Abstract

The lack of a coherent theory underpinning geographical databases is a serious obstacle to research efforts in this field. This article attempts to construct part of such a theory, namely the formalization of the underlying object mode for geographical data whose spatial references are embedded in the plane. Questions of approximation and error analysis, while exposed and briefly discussed here, do not form a major part of the discussion. This work extends earlier work by giving a detailed construction of the classes and operations for spatial objects embedded in the plane. It goes on to provide an explicit link between this object model and its representation in computationally meaningful terms using classes of simplicial complexes and operations acting upon these classes.     

Providing spatial statistical data analysis functionality for the GIS user: the SAGE project

Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo‐dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object‐oriented data modelling. Geographic dynamics are synthesized in a three‐dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation.     

Organization of Feature-, Time-, and Location-Based Mental Models

This study considers how mental models are encoded into memory by viewing visual displays like maps. A mental model is an internal representation of a situation that links objects or concepts to other objects or concepts. Previous studies indicated location-based mental models are encoded when a series of propositional statements such as the object is in the location are read from a text. Evidence that locations were being used as the basic container for an organization of mental models is provided by a significant fan effect. A fan effect shows an increase in reaction time with the number of models considered when making a decision. Features, times, and locations were considered as possible containers in the mental models. Subjects created location-based mental models, but also encoded feature-based mental models. A reverse fan effect for time, found for a map animation, suggested the order of the presentation of maps could greatly affect the structure of learned information.     

An object-based conceptual framework and computational method for representing and analyzing coastal morphological changes

This article presents an object-based conceptual framework and numerical algorithms for representing and analyzing coastal morphological and volumetric changes based on repeat airborne light detection and ranging (LiDAR) surveys. This method identifies and delineates individual zones of erosion and deposition as discrete objects. The explicit object representation of erosion and deposition zones is consistent with the perception and cognition of human analysts and geomorphologists. The extracted objects provide ontological and epistemological foundation to localize, represent, and interpret erosion and deposition patches for better coastal resource management and erosion control. The discrete objects are much better information carriers than the grid cells in the field-based representation of source data. A set of spatial and volumetric attributes are derived to characterize and quantify location, area, shape, orientation, depth, volume, and other properties of erosion and deposition objects. Compared with the conventional cell-by-cell differencing approaches, our object-based method gives a concise and high-level representation of information and knowledge about coastal morphological dynamics. The derived attributes enable the discrimination of true morphological changes from artifacts caused by data noise and processing errors. Furthermore, the concise object representation of erosion and deposition zones facilitates overlay analysis in conjunction with other GIS data layers for understanding the causes and impacts of morphological and volumetric changes. We have implemented a software tool for our object-based morphological analysis, which will be freely available for the public. An example is used to demonstrate the utility and effectiveness of this new method.     

面向对象技术在GIS数据库中的应用研究

地理信息系统（GIS）在国民经济和社会生活中得到了广泛的应用，基于GIS的地理数据库是这些应用的重要组成部分，在论述地理信息系统数据库概念的基础上，讨论了现有的空间数据模型在表现地理对象方面的优缺点，提出应用面向对象技术来解决地理对象在地理信息系统数据库中的表示和存储，最后，对GIS数据库开发的趋势进行了展望。     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

Unique identification of vector geographic objects by location and geometry

This article presents a method to uniquely identify vector geographic objects based on their spatial position and geometric characteristics. The method comprises three steps. First, a grid-based spatial indexing (Quadtree grid) is automatically generated according to location, shape and size of the geographic objects. This allows for a simple Quadtree grid generation (the level, row and column number) for every geographic object by three grid parameters (origin, start and end size). Second, a set of algorithms have been developed for extracting geometric characteristics (e.g. representative points, outline and orientation) of geographic objects corresponding to the grid. Third, a strategy for the unique identification of geographic objects is proposed in order to integrate the Quadtree grid and the geometric information. The application of the proposed method is presented in a case study conducted in Jiangsu Province, China. The experiment indicates that this method is more efficient than existing methods at positioning, searching and querying. The unique identifier is also shown to be useful as a technical method to support geographic information systems analytical applications, such as the matching, conflating and sharing of spatial objects.