On the design of formal theories of geographic space

This paper discusses the design of formal theories of geographic space for the application in Geographic Information Systems. GIS software is an implementation of formal theories of geographic space. The notions of formal theories are introduced and discussed in the context of examples from the GIS field.  Our approach is an application of the general framework of formal theories to the special class of theories of geographic space, in particular to the geometry of geographic space. A framework is introduced for characterizing and evaluating formal theories of geographic space and the process of their design. This is used to provide (1) a classification of formal theories of geographic space, (2) criteria of their adequacy, and (3) an evaluation of design decisions in the process of formalization.  The paper demonstrates the choices in the design of GIS and the dependencies between these choices. Considering the design space for theories underlying a GIS, we can see that current GIS are based on one choice: analytical geometry. Other designs are possible and a systematic exploration of alternative types of GIS, for example, based on constraints or based on stored spatial relations, becomes necessary. Received: 30 April 1997/Accepted: 8 March 1999     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

Validating the use of object-based image analysis to map commonly recognized landform features in the United States

ABSTRACT

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.     

LBS中多尺度空间方位的自然语言描述

位置服务是地理信息系统（GIS）应用的重要领域,GIS提供关于空间位置的坐标描述,但这不符合人们的认知和日常习惯。地理空间中人们日常的交流通常使用方位描述。基于自然语言的空间方位的描述对移动目标（如驾驶员）是十分重要的,通过规范的地点描述语言进行快速的地理定位,可提高人的空间反应和处理能力。本文依据人的多尺度空间认知,分析空间参考和定位习惯,结合自然语言描述知识,利用GIS分析功能,给出多尺度环境下空间方位的自然语言描述。     

fsr: An R package for fuzzy spatial data handling

Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r-project.org/package=fsr . Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations.     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.     

Consistency among parts and aggregates: A computational model

Heterogeneous geographic databases contain multiple views of the same geographic objects at different levels of spatial resolution. When users perceive geographic objects as one spatial unit, although they are physically separated into multiple parts, appropriate methods are needed to assess the consistency among the aggregate and the parts. The critical aspect is that the overall spatial relationships with respect to other geographic objects must be preserved throughout the aggregation process. We developed a systematic model for the constraints that must hold with respect to other spatial objects when two parts of an object are aggregated. We found three sets of configurations that require increasingly more information in order to make a precise statement about their consistency: (1) configurations that are satisfied by the topological relations between the two parts and the object of interest; (2) configurations that need further information about the topological relation between the object of concern and the connector in order to be resolved unambiguously; and (3) configurations that require additional information about the topological relation between the aggregate's boundary and the boundary or interior of the object of interest to be uniquely described. The formalism extends immediately to relations between two regions with disconnected parts as well as to relations between a region and an arbitrary number of separations.     

可视化交互空间数据挖掘技术的探讨

随着地理信息获取技术飞速发展,使得当前存储在空间数据库中的空间数据的深度和广度得到了前所未有的发展,传统的空间统计方法和空间分析方法已经难以有效而迅速地处理和分析它们,如何有效而及时地分析和处理空间数据变得越来越迫切。空间数据挖掘作为上个世纪90年代逐步发展起来的新兴技术,逐渐在研究和实践中显示出它的优势。与此同时,地理可视化技术也逐步走向成熟,二者的结合催生出新型空间数据分析技术———可视化交互空间数据挖掘。本文就该技术的相关问题进行了一些研究探讨。     

Spatial statistical techniques for aggregating point objects extracted from high spatial resolution remotely sensed imagery

 Using a local maximum filter, individual trees were extracted from a 1 m spatial resolution IKONOS image and represented as points. The spatial pattern of individual trees was determined to represent forest age (a surrogate for forest structure). Point attributes, based on the spatial pattern of trees, were generated via nearest neighbour statistics and used as the basis for aggregating points into forest structure units. The forest structure units allowed for the mapping of a forested area into one of three age categories: young (1–20 years), intermediate (21–120 years), and mature (>120 years). This research indicates a new approach to image processing, where objects generated from the processing of image data (rather than pixels or spectral values) are subjected to spatial statistical analysis to estimate an attribute relating an aspect of forest structure. Received: 22 April 2002 / Accepted: 23 November 2002     

Spatial econometrics functions in R: Classes and methods

 Placing spatial econometrics and more generally spatial statistics in the context of an extensible data analysis environment such as R exposes similarities and differences between traditions of analysis. This can be fruitful, and is explored here in relation to prediction and other methods usually applied to fitted models in R. Objects in R may be assigned a class attribute, including fitted model objects. Such fitted model objects may be provided with methods allowing them to be displayed, compared, and used for prediction, and it is of interest to see whether fitted spatial models can be treated in the same way. Received: 26 August 2002 / Revised version: 15 January 2003     

Spatial relationship-assisted classification from high-resolution remote sensing imagery

Spatial information remains to be an important topic in geographic information system and in remote sensing fields, and spatial relationships have been increasingly incorporated into the image classification processes. Previous studies have employed multiple occurrences of spatial features (shape, texture, etc.,) to improve classification results. However, less attention has been focused on using higher-level spatial relationships for image classification. In this study, two novel spatial relationships, namely, maximum spatial adjacency (MSA) and directional spatial adjacency (DSA), were proposed to assist in image classification. The proposed methods were implemented to extract buildings, beach, and emergent vegetation land-cover classes according to their spatial relationships with their corresponding reference classes. The promising results obtained from this study suggest that the proposed MSA and DSA spatial relationships can be valuable information in defining rule sets for a more reasonable and accurate classification.     

A Framework for Modeling Uncertainty in Spatial Databases

Geographic Information Systems and spatial databases are inherently suited for fuzziness, because of the uncertainty inherent in the assimilation, storage, and representation of spatial data. These objects may also have naturally occurring imprecise boundaries. It is difficult to store and represent these objects while continuing to demonstrate the uncertainty inherent in the objects. This paper describes a fuzzy object–oriented framework to model spatial objects with either precise or uncertain boundaries that will also provide for fuzzy querying of these objects. A prototype system, FOOSBALL, which implements this framework is also discussed.     

一种基于实体的地理空间数据模型

针对已有地理空间数据模型的不足,从人类空间认知的角度出发,探讨了基于实体的地理空间认知,设计了一种基于实体的地理空间数据模型,为复杂地理空间对象的表达提供了参考.     

Statistical methods for analyzing the distribution of spatial objects in relation to a surface

This paper develops statistical methods for analyzing the distribution of spatial objects—points, convex polygons, and line segments—in relation to a surface. We propose statistics for measuring the relationship between the distribution of these objects and a surface and derive their expectations and variances under the null hypothesis that the objects are independently and randomly distributed. The statistics are approximately distributed according to the normal distribution under the null hypothesis, which enables us to test the significance of the spatial relationships statistically. Using the proposed methods, we empirically analyze the distribution of convenience stores in relation to the distribution of population in a suburb of Osaka, Japan. Some empirical findings are shown.     

Assessing polygon edge integrity

Geographic entities are represented in vector GIS as geometric objects with definite position and sharp, zero or one-dimensional, boundaries. This representation ignores, and may in fact misrepresent, the underlying properties of many geographic features. This paper considers a binary classification scheme for ill-defined edges recently suggested by Couclelis (1996). Three initial perspectives of edge indeterminacy are considered for woodland polygons, namely functionally defined, using Hamming distances of the indeterminacy properties, and using the weighted trace of the connectivity matrix that defines the simplicial complex created from the indeterminacy relationships among the woodland polygons and their adjacent polygons. Finally, an analysis of the relationships created by the binary coding is carried out by examining more closely the connectivity structure of the simplicial complexes. The usefulness of the approach is discussed in the context of woodland habitat. Received: 20 March 1999/Accepted: 7 September 1999     

GIS空间数据的分析与制图一体化策略

地理信息系统(GIS)是地理学、地图学等在信息化时代发展的新的理论技术体系,其软件系统需要同时具有地理空间分析与地图表达两方面的功能。但是分析与制图对地理空间数据的要求存在一定程度的差异,导致两者间数据无法充分共享。在分析地理空间数据冲突的基础上,初步探讨了在GIS中实现分析与制图功能一体化的数据采集与应用方案,有助于避免数据的重复建设,提高地理空间数据的利用效率。     

Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

The extension of the functional capacity of geographic information systems (GIS) with tools for statistical analysis in general and exploratory spatial data analysis (ESDA) in particular has been an increasingly active area of research in recent years. In this paper, two operational implementations that combine the functionality of spatial data analysis software with a GIS are considered more closely. They consist of linkages between the S-PLUS software for data analysis and two different GIS implementations, the ArcView desktop system, which is mostly vector-oriented, and the primarily raster-based Grassland open GIS environment. We emphasize conceptual and technical issues related to the software implementation of these approaches and suggest future directions for linking spatial statistics and GIS. Received: 14 January 1999 / Accepted: 11 May 1999     

北京市城市扩展空间格局变化监测与分析

本文基于地理国情监测数据,结合同期城市经济、人口等统计数据,利用高分辨率卫星遥感影像数据提取北京市建成区边界要素。采用地理国情与城市空间扩展相融合的监测分析模型及相关评价指标开展空间格局变化分析研究,揭示北京市2000—2017年间城市扩展的空间格局及其变化规律,并对其变化原因进行分析。其成果可为北京市科学规划与可持续发展提供决策依据。     

Assessing representation error in point-based coverage modeling

 Accurately representing geographic space in a digital environment continues to confound and challenge researchers. Carrying out spatial analysis in a setting where geographic representation is subject to change poses problems to be addressed. In this paper we examine spatial representation in the context of coverage-based location modeling. A geographic region can be represented in a variety of ways. We present an evaluation of spatial representation in the location of facilities that provide coverage oriented services. The analysis shows that the modeling results are sensitive to how spatial demand for service is represented in a digital environment. We develop an approach for evaluating representational appropriateness. This research contributes to spatial analysis integrated in geographic information system environment. Received: 10 January 2001 / Accepted: 8 April 2002