地理空间分析：概念与方法

地理空间分析

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"《Geospatial Analysis - 4th Edition - de Smith, Goodchild, Longley》是关于地理空间分析的第四版英文书籍，由de Smith、Goodchild和Longley三位专家编写。这本书包含了完整的地理空间分析内容，是作者自行整理并分享的资源。标签表明该书涉及地理空间分析、英文版以及内容的完整性。" 本书的核心内容涵盖了多个关键知识点，以下将对这些概念进行详细介绍： 1. **地理空间分析**：这是研究地理现象及其相互关系的科学方法，利用地理信息系统（GIS）和其他软件工具处理和分析空间数据。 2. **GIS与软件工具**：GIS是地理空间分析的核心工具，它能集成、存储、操作、分析和展示地理数据。书中提到的软件工具可能包括ArcGIS、QGIS等，用于地图制作、空间查询、数据分析和模型构建。 3. **目标读者和范围**：此书适用于对地理空间分析感兴趣的学者、学生和专业人士，涵盖了从基础理论到高级应用的广泛领域。 4. **基本概念**：包括“地方”（Place）、“属性”（Attributes）、“对象”（Objects）、“地图”（Maps）、“多属性地方”（Multiple properties of places）、“场”（Fields）、“网络”（Networks）、“密度估计”（Density estimation）等，这些都是理解空间分析的基础。 5. **空间关系**：如“共位置”（Co-location）、“距离”（Distance）、“方向”（Direction）、“空间权重矩阵”（Spatial weights matrices）、“多维尺度分析”（Multidimensional scaling）、“空间上下文”（Spatial context）和“邻域”（Neighborhood）等，它们描述了空间元素间的关联。 6. **空间异质性与同质性**：空间异质性是指地理特征在空间分布上的不均匀性，而同质性则是指相似特征的集中。 7. **空间依赖**（Spatial dependence）：指地理位置相近的观测值往往具有相关性，是地理统计学中的核心概念。 8. **空间采样**（Spatial sampling）：在有限区域内获取空间数据的过程，影响分析结果的代表性和精度。 9. **空间插值**（Spatial interpolation）和**平滑与锐化**（Smoothing and sharpening）：前者用于估计未测量位置的值，后者则用于改善数据的连续性和局部细节。 10. **第一和第二阶过程**：在空间统计中，第一阶过程关注单个观察点的特性，而第二阶过程涉及点之间的相互作用。 11. **空间概率**和**概率密度**：描述事件在空间中的分布特征和出现的可能性。 12. **不确定性**：包括数据不确定性、测量误差、模型不确定性等多个方面，是地理空间分析中必须考虑的关键因素。 13. **空间数据基础设施**（Spatial Data Infrastructure, SDI）：指的是支持地理信息的共享、交换和整合的框架，包括地理信息门户（Geoportals）、元数据（Metadata）和互操作性（Interoperability）等组件。本书通过这些详细的讨论，为读者提供了全面的地理空间分析理论和实践指导，是学习和应用这一领域的宝贵资源。

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•land registration systems

•census data management systems

•commercial location services and Digital Earth models

The list of software functions and applications is long and in some instances suppliers would

not describe their offerings as a GIS. In many cases such systems fulfill specific operational

needs, solving a well-defined subset of spatial problems and providing mapped output as an

incidental but essential part of their operation. Many of the capabilities may be found in

generic GIS products. In other instances a specialized package may utilize a GIS engine for

the display and in some cases processing of spatial data (directly, or indirectly through

interfacing or file input/output mechanisms). For this reason, and in order to draw a

boundary around the present work, reference to application-specific GIS will be limited.

A number of GIS packages and related toolsets have particularly strong facilities for

processing and analyzing binary, grayscale and color images. They may have been designed

originally for the processing of remote sensed data from satellite and aerial surveys, but

many have developed into much more sophisticated and complete GIS tools, e.g. Clark Lab’s

Idrisi software; MicroImage’s TNTMips product set; the ERDAS suite of products; and ENVI

with associated packages such as RiverTools. Alternatively, image handling may have been

deliberately included within the original design parameters for a generic GIS package (e.g.

Manifold), or simply be toolsets for image processing that may be combined with mapping

tools (e.g. the MATLab Image Processing Toolbox). Whatever their origins, a central purpose

of such tools has been the capture, manipulation and interpretation of image data, rather

than spatial analysis per se, although the latter inevitably follows from the former.

In this Guide we do not provide a separate chapter on image processing, despite its

considerable importance in GIS, focusing instead on those areas where image processing

tools and concepts are applied for spatial analysis (e.g. surface analysis). We have adopted a

similar position with respect to other forms of data capture, such as field and geodetic

survey systems and data cleansing software — although these incorporate analytical tools,

their primary function remains the recording and georeferencing of datasets, rather than the

analysis of such datasets once stored.

For most GIS professionals, spatial analysis and associated modeling is an infrequent activity.

Even for those whose job focuses on analysis the range of techniques employed tends to be

quite narrow and application focused. GIS consultants, researchers and academics on the

other hand are continually exploring and developing analytical techniques. For the first

group and for consultants, especially in commercial environments, the imperatives of

financial considerations, timeliness and corporate policy loom large, directing attention to:

delivery of solutions within well-defined time and cost parameters; working within

commercial constraints on the cost and availability of software, datasets and staffing;

ensuring that solutions are fit for purpose/meet client and end-user expectations and agreed

standards; and in some cases, meeting “political” expectations.

17 / 707

For the second group of users it is common to make use of a variety of tools, data and

programming facilities developed in the academic sphere. Increasingly these make use of

non-commercial wide-ranging spatial analysis software libraries, such as the R-Spatial

project (in “R”); PySal (in “Python”); and Splancs (in “S”).

Sample software products

The principal products we have included in this latest edition of the Guide are included on

the accompanying website’s software page. Many of these products are free whilst others are

available (at least in some form) for a small fee for all or selected groups of users. Others are

licensed at varying per user prices, from a few hundred to over a thousand US dollars per user.

Our tests and examples have largely been carried out using desktop/Windows versions of

these software products. Different versions that support Unix-based operating systems and

more sophisticated back-end database engines have not been utilized. In the context of this

Guide we do not believe these selections affect our discussions in any substantial manner,

although such issues may have performance and systems architecture implications that are

extremely important for many users. OGC compliant software products are listed on the OGC

resources web page: http://www.opengeospatial.org/resource/products/compliant. To

quote from the OGC: “The OGC Compliance Testing Program provides a formal process for

testing compliance of products that implement OpenGIS® Standards. Compliance Testing

determines that a specific product implementation of a particular OpenGIS® Standard

complies with all mandatory elements as specified in the standard and that these elements

operate as described in the standard.”

Software performance

Suppliers should be able to provide advice on performance issues (e.g. see the ESRI web site,

"Services" area for relevant documents relating to their products) and in some cases such

information is provided within product Help files (e.g. see the Performance Tips section

within the Manifold GIS help file). Some analytical tasks are very processor- and

memory-hungry, particularly as the number of elements involved increases. For example,

vector overlay and buffering is relatively fast with a few objects and layers, but slows

appreciably as the number of elements involved increases. This increase is generally at least

linear with the number of layers and features, but for some problems grows in a highly

non-linear (i.e. geometric) manner. Many optimization tasks, such as optimal routing through

networks or trip distribution modeling, are known to be extremely hard or impossible to

solve optimally and methods to achieve a best solution with a large dataset can take a

considerable time to run (see Algorithms and computational complexity theory for a fuller

discussion of this topic). Similar problems exist with the processing and display of raster files,

especially large images or sets of images. Geocomputational methods, some of which are

beginning to appear within GIS packages and related toolsets, are almost by definition

computationally intensive. This certainly applies to large-scale (Monte Carlo) simulation

models, cellular automata and agent-based models and some raster-based optimization

techniques, especially where modeling extends into the time domain.

A frequent criticism of GIS software is that it is over-complicated, resource-hungry and

requires specialist expertise to understand and use. Such criticisms are often valid and for

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many problems it may prove simpler, faster and more transparent to utilize specialized tools

for the analytical work and draw on the strengths of GIS in data management and mapping to

provide input/output and visualization functionality. Example approaches include: (i) using

high-level programming facilities within a GIS (e.g. macros, scripts, VBA, Python) – many

add-ins are developed in this way; (ii) using wide-ranging programmable spatial analysis

software libraries and toolsets that incorporate GIS file reading, writing and display, such as

the R-Spatial and PySal projects noted earlier; (iii) using general purpose data processing

toolsets (e.g. MATLab, Excel, Python’s Matplotlib, Numeric Python (Numpy) and other

libraries from Enthought; or (iv) directly utilizing mainstream programming languages (e.g.

Java, C++). The advantage of these approaches is control and transparency, the

disadvantages are that software development is never trivial, is often subject to frustrating

and unforeseen delays and errors, and generally requires ongoing maintenance. In some

instances analytical applications may be well-suited to parallel or grid-enabled processing –

as for example is the case with GWR (see Harris et al., 2006).

At present there are no standardized tests for the quality, speed and accuracy of GIS

procedures. It remains the buyer’s and user’s responsibility and duty to evaluate the

software they wish to use for the specific task at hand, and by systematic controlled tests or

by other means establish that the product and facility within that product they choose to use

is truly fit for purpose — caveat emptor! Details of how to obtain these products are provided

on the software page of the website that accompanies this book. The list maintained on

Wikipedia is also a useful source of information and links, although is far from being

complete or independent. A number of trade magazines and websites (such as Geoplace and

Geocommunity) provide ad hoc reviews of GIS software offerings, especially new releases,

although coverage of analytical functionality may be limited..

URL of this page: http://www.spatialanalysisonline.com/HTML/?gis_and_related_software_tools.htm

Navigation: Introduction and terminology > Software tools and Companion Materials >

Suggested reading

There are numerous excellent modern books on GIS and spatial analysis, although few

address software facilities and developments. Hypertext links are provided here, and

throughout the text where they are cited, to the more recent publications and web resources

listed.

As a background to this Guide any readers unfamiliar with GIS are encouraged to first tackle

“Geographic Information Systems and Science” (GISSc) by Longley et al. (2010). GISSc seeks

to provide a comprehensive and highly accessible introduction to the subject as a whole. The

GB Ordnance Survey’s “GIS Files” document, downloadable from their website also provides

an excellent brief introduction to GIS and its application.

Some of the basic mathematics and statistics of relevance to GIS analysis is covered in Dale

(2005) and Allan (2004). For detailed information on datums and map projections, see Iliffe

and Lott (2008). Useful online resources for those involved in data analysis, particularly with

a statistical content, include the StatsRef website and the e-Handbook of Statistical Methods

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produced by the US National Institute on Standards and Technology, NIST). The more

informally produced set of articles on statistical topics provided under the Wikipedia

umbrella are also an extremely useful resource. These sites, and the mathematics reference

site, Mathworld, are referred to (with hypertext links) at various points throughout this

document. For more specific sources on geostatistics and associated software packages, the

European Commission’s AI-GEOSTATS website (http://www.ai-geostats.org) is highly

recommended, as is the web site of the Center for Computational Geostatistics (CCG) at the

University of Alberta. For those who find mathematics and statistics something of a mystery,

de Smith (2006) and Bluman (2003) provide useful starting points. For guidance on how to

avoid the many pitfalls of statistical data analysis readers are recommended the material in

the classic work by Huff (1993) “How to lie with statistics”, and the 2008 book by Blastland

and Dilnot “The tiger that isn’t”.

A relatively new development has been the increasing availability of out-of-print published

books, articles and guides as free downloads in PDF format. These include: the series of 59

short guides published under the CATMOG umbrella (Concepts and Methods in Modern

Geography), published between 1975 and 1995, most of which are now available at the QMRG

website (a full list of all the guides is provided at the end of this book); the AutoCarto

archives (1972-1997); the Atlas of Cyberspace by Dodge and Kitchin; and Fractal Cities, by

Batty and Longley.

Undergraduates and MSc programme students will find Burrough and McDonnell (1998)

provides excellent coverage of many aspects of geospatial analysis, especially from an

environmental sciences perspective. Valuable guidance on the relationship between spatial

process and spatial modeling may be found in Cliff and Ord (1981) and Bailey and Gatrell

(1995). The latter provides an excellent introduction to the application of statistical methods

to spatial data analysis. O’Sullivan and Unwin (2010, 2nd ed.) is a more broad-ranging book

covering the topic the authors describe as “Geographic Information Analysis”. This work is

best suited to advanced undergraduates and first year postgraduate students. In many

respects a deeper and more challenging work is Haining’s (2003) “Spatial Data Analysis —

Theory and Practice”. This book is strongly recommended as a companion to the present

Guide for postgraduate researchers and professional analysts involved in using GIS in

conjunction with statistical analysis.

However, these authors do not address the broader spectrum of geospatial analysis and

associated modeling as we have defined it. For example, problems relating to networks and

location are often not covered and the literature relating to this area is scattered across

many disciplines, being founded upon the mathematics of graph theory, with applications

ranging from electronic circuit design to computer networking and from transport planning

to the design of complex molecular structures. Useful books addressing this field include

Miller and Shaw (2001) “Geographic Information Systems for Transportation” (especially

Chapters 3, 5 and 6), and Rodrigue et al. (2006) "The geography of transport systems" (see

further: http://people.hofstra.edu/geotrans/).

As companion reading on these topics for the present Guide we suggest the two volumes from

the Handbooks in Operations Research and Management Science series by Ball et al. (1995):

20 / 707

“Network Models”, and “Network Routing”. These rather expensive volumes provide

collections of reviews covering many classes of network problems, from the core

optimization problems of shortest paths and arc routing (e.g. street cleaning), to the

complex problems of dynamic routing in variable networks, and a great deal more besides.

This is challenging material and many readers may prefer to seek out more approachable

material, available in a number of other books and articles, e.g. Ahuja et al. (1993), Mark

Daskin’s excellent book “Network and Discrete Location” (1995) and the earlier seminal

works by Haggett and Chorley (1969), and Scott (1971), together with the widely available

online materials accessible via the Internet. Final recommendations here are Stephen Wise’s

excellent GIS Basics (2002) and Worboys and Duckham (2004) which address GIS from a

computing perspective. Both these volumes covers many topics, including the central issues

of data modeling and data structures, key algorithms, system architectures and interfaces.

Many recent books described as covering (geo)spatial analysis are essentially edited

collections of papers or brief articles. As such most do not seek to provide comprehensive

coverage of the field, but tend to cover information on recent developments, often with a

specific application focus (e.g. health, transport, archaeology). The latter is particularly

common where these works are selections from sector- or discipline-specific conference

proceedings, whilst in other cases they are carefully chosen or specially written papers.

Classic amongst these is Berry and Marble (1968) “Spatial Analysis: A reader in statistical

geography”. More recent examples include “GIS, Spatial Analysis and Modeling” edited by

Maguire, Batty and Goodchild (2005), and the excellent (but costly) compendium work “The

SAGE handbook of Spatial Analysis” edited by Fotheringham and Rogerson (2008).

A second category of companion materials to the present work is the extensive

product-specific documentation available from software suppliers. Some of the online help

files and product manuals are excellent, as are associated example data files, tutorials,

worked examples and white papers (see for example, ESRI’s GIS site: http://www.gis.com/

which provides a wide-ranging guide to GIS. In many instances we utilize these to illustrate

the capabilities of specific pieces of software and to enable readers to replicate our results

using readily available materials. In addition some suppliers, notably ESRI, have a substantial

publishing operation, including more general (i.e. not product specific) books of relevance to

the present work. Amongst their publications we strongly recommend the “ESRI Guide to GIS

Analysis Volume 1: Geographic patterns and relationships” (1999) by Andy Mitchell, which is

full of valuable tips and examples. This is a basic introduction to GIS Analysis, which he

defines in this context as “a process for looking at geographic patterns and relationships

between features”. Mitchell’s Volume 2 (July 2005) covers more advanced techniques of data

analysis, notably some of the more accessible and widely supported methods of spatial

statistics, and is equally highly recommended. A number of the topics covered in his Volume

2 also appear in this Guide. David Allen has recently produced a tutorial book and DVD (GIS

Tutorial II: Spatial Analysis Workbook) to go alongside Mitchell’s volumes, and these are

obtainable from ESRI Press. Those considering using Open Source software should investigate

the recent books by Neteler and Mitasova (2008), Tyler Mitchell (2005) and Sherman (2008).

In parallel with the increasing range and sophistication of spatial analysis facilities to be

found within GIS packages, there has been a major change in spatial analytical techniques. In

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