SPATIAL ANALYSIS IN A GIS ENVIRONMENT

SPATIAL ANALYSIS IN A GIS

ENVIRONMENT

Objective

By taking advantage of the ability of geographic information systems (GIS) to store, select, manipulate, explore, and display geo-referenced data, the purpose of this research is to develop within a GIS environment a variety of methods, techniques, and approaches for the analysis of spatial and time-space data and models.

Background

Problems of human health, social deprivation, global environmental change, industrial and economic development, and a host of others all demand that we make sense of what is happening in the world around us. The term spatial analysis encompasses a wide range of techniques for analyzing, visualizing, simplifying, and theorizing about geographic data. Methods of spatial analysis can be as simple as taking measurements from a map, or as sophisticated as the most abstract forms of mathematical statistics.

At the same time, we are being flooded by the benefits of new technologies for Earth observation. New remote sensing satellites are providing unprecedented amounts of data on aspects of the Earth environment, and new sources of demographic, social, and economic data are also becoming available at finer spatial detail. Yet our ability to "drink from the firehose", extract meaning, and make useful decisions has not kept pace. We can no longer rely on the human eye and brain alone, but must augment them through the development of improved techniques for sifting through data to find patterns and outliers; for more effective visualization of data; for testing theories and hypotheses; and for making decisions.

To remain at the cutting edge of GIS technology, analytic and computational methods must be devised that allow for solutions to problems conditioned by GIS data models and the nature of spatial and space-time inquiries. New forms of statistical analysis are needed to assess the relationships between variables in a variety of spatial contexts. New theories must be devised that frame our understanding of relationships between variables at new levels of resolution and dimension. What is the relationship, for example, between moisture and plant growth when our reference is a square kilometer of earth space? How do we assess the

clustering o cases of malaria when our environmental data are recorded in little rectangles one meter across?

Spatial data must be treated differently from other types of data. Stronger relationships exist within and among variables that are near to one another. Because the size and configuration of spatial units varies dramatically, we find relationships within and among variables that are due as much to the nature of the spatial units as to the nature of the variables being studied. Standing in the way of confirmatory spatial data analysis, including modeling, are questions having to do with spatial scale, spatial association, spatial heterogeneity, boundaries, and incomplete data. Without reasonable responses to these problems, the usefulness of GIS as an analytical tool in a sophisticated research environment will come into question. By the use of GIS, previously prohibitive, computationally intensive, and highly visual ways of spatial analysis have become accessible at reasonable costs.

The UCGIS Approach

Spatial analysis is the bridge that links fundamental data models to GIS technology, with the result that applications are enhanced and research findings broadened and deepened. The UCGIS emphasizes those research areas that integrate a variety of these activities. The GIS framework includes both the georeferenced data and the tools for data manipulation. The linkages to applications allow spatial analysts to inform applied practitioners of new and more profitable ways to do research and, in like manner, practitioners are able to develop new analytic approaches useful to particular applied fields in the social, physical, and environmental sciences.

UCGIS calls on spatial analysts from both the physical and human sciences to assist in the development of spatial statistics, geostatistics, spatial econometrics, structural and timespace modeling, mathematics and computational algorithms, that can take advantage of the flexibility, capacity, and speed of geographic information systems. Those

well-schooled in theory, empiricism, data collection, data manipulation, programming, and computer technology will be in the best position to make advances in this field, but practitioners such as epidemiologists, ecologists, climatologists, regional scientists, landscape architects, and environmentalists can add much to the development of GIS-related research.

Importance to National Research Needs

For the United States to remain on the cutting edge of GIS technology, we must foster the development of appropriate techniques for analysis in a variety of rapidly changing fields. By engaging in fundamental research in spatial analysis, we can achieve a better understanding of spatial scale, spatial association, spatial heterogeneity, spatial movement, and bounding effects, and can develop more appropriate tools for modeling continuous and discrete data. We will improve our handling of very large spatial data sets (e.g., disaggregated census data, remotely-sensed data at a global scale) and we will discover the appropriate GIS tools for pattern recognition, data generalization, edge detection, and fuzzy pattern analysis.

Benefits

The research topics outlined in the following section point to the priorities the scientific community must support as we move to the 21st Century. Better techniques of spatial analysis, coupled with GIS, will have applications that span a vast range of societal concerns:

?Disease distribution: The study of the transmission of infectious diseases such as dengue, malaria, and AIDS would benefit from

placing disease incidence into a spatial ecological framework where the data coverages of a GIS would be studied in a space-time

framework.

?Traffic management and landuse planning: Real-time traffic analysis in a GIS framework will aid in the development of highway infrastructures, traffic and travel demand management, and land use planning.

?Environmental problems: GIS could become better able to provide the environment for the analysis of data extracted from models of water, air, and other types of environmental variables. Problems of fire control, species diversity, hydrology and flood control, hazard mitigation, and park usage are ideally suited for analysis with a GIS framework.

?Landscape characterization and measurement: A compelling problem of those using remotely sensed data for the analysis of such things as land cover and land use is the classification of high resolution data. By bringing image analysis into a GIS analytical framework, various classification schemes can be tested and used in the

analysis of land cover data.

?Social, cultural, economic analyses: Economists and other social scientists will have the opportunity to use block and county data to test theories using spatial econometric analyses. The

development of the use of these data sets in a GIS framework will

increase our understanding of all sorts of social processes,

including patterns of employment and unemployment, crime, economic growth, and population change.

?Physical processes: The analysis of hydrologic and climatologic processes under varying physiographic regimes in a GIS framework will enable researchers to pinpoint trends (global change),

anomalous events, and, in general, further applied research in

these fields.

?Improving the accessibility and equity of opportunities and services: GIS can allow for more sensitive configuration of

economic activities and public sector services. The spatial data handling capabilities of GIS allow for detailed representations and analyses of the spatial distribution of disadvantaged populations and their accessibility to opportunites and services. GIS-based

techniques for solving sophisticated and realistic location and

distribution problems can allow these systems to be configured to maximize accessibility and equity.

Priority Areas for Research

Future tools must be not only spatial but also spatial-temporal. They must address certain key questions: How do we handle ve large spatial data sets (e.g. disaggregated census data, remotely-sensed data at a global scale)? What techniques can account for the influence of spatial data on the type of analysis employed (e.g., scale and aggregation effects)? What generic GIS tools [e.g., Openshaw's (1994) pattern-spotters and testers, data simplifiers, edge detectors, and fuzzy pattern analyzers] are appropriate for spatial analysis? The following are examples of long term (five to ten years) fundamental research projects:

Methods, techniques, and approaches are needed for analysis of:

?Massive spatial data sets: As georeferenced data sets become larger, methods must be found that use, pare, classify, and manipulate the rich information inherent in very large spatial data bases.

?Exploration of spatial and space-time data: Exploratory spatial data analysis must be extended to space-time data in order to

develop models that better represent reality.

?Confirmatory (significance) procedures: Statistical

(Bonferroni-type) procedures must be found that recognize the

dependence of georeferenced data and allow researchers to engage in testing hypotheses.

?The variogram and kriging: These useful geostatistical procedures are just now being incorporated into GIS frameworks. Research must

go forward in this area in order to take full advantage of GIS as

a research tool, including the incorporation of these procedures

in broader analyses and models of continuous spatial phenomena. ?The impact of scale and the development of scale independent

procedures: Perhaps most fundamental to preparing GIS for spatial analytical work are the modules and algorithms that evaluate the effect of scale change on research results. The development of scale correctives and scale independent methods is a compelling research need. There is great research interest in the question of the extent to which interzonal analysis is affected by the configuration and regionalization of spatial data units.

?Global versus local effects: Global analysis of massive data sets has proved superficial and inadequate. There is a need for

procedures and tests to use window, kernel, individual and other local measures to find and equate the characteristics of

nonstationary spatial data.

?Identification of essential or extreme observations: Procedures must be found that can identify in a spatial setting key

observations, groups of observations (clusters), or hot spots that draw attention to the anomalous regions.

?Devising computationally intensive procedures: There is a need to interface GIS with tools that take advantage of great increases in the capabilities of computational platforms. Computationally

intensive tools can allow for: 1) large data sets to be used

effectively, 2) more sophisticated and extensive simulations of complex spatial phenomena, and 3) the solution of complex location and distribution problems (see below). Potential techniques

include neural nets, fuzzy sets, wavelets, microsimulation,

artificial intelligence, natural language processing of textual information, artificial life, real-time data analysis, numeric optimization techniques and massively parallel algorithms. These new techniques should be evaluated in a GIS environment for their usefulness in practical applications.

?Econometric modeling in a GIS environment: Spatial econometrics is

a new and burgeoning field. It is important to attempt to link the

sophisticated procedures of the econometrician with the

functionality and flexibility of GIS. In addition, there is a need to find appropriate estimators and testing devices for

heterogeneous, non-uniform geo-referenced data.

?Spatial interaction models in a GIS framework: Perhaps one of the most used model types among spatial analysts is the spatial

interaction-type model. Developing these models in a GIS

environment will provide marketing, transportation, and human interaction specialists with greater analytical power than is

currently available. Also needed are techniques for visualizing spatial interaction flows in a sophisticated manner.

?Operations research: Many fields benefit greatly from the functionality and data manipulative power of GIS, including a such operations research type problems as routing, locationallocation, coverage and other optimization procedures. Most existing

techniques require simplistic representations of spatial objects and relationships for the sake of tractability. Needed are

GIS-based solution procedures that can manipulate spatial entities in their native form and recognize the complex spatial

relationships that occur between these entities.

?在GIS环境下空间分析

目标

本研究的目的是通过利用能力，地理信息系统（GIS）的存储，选择，操作，探索，并显示地理参照数据，在GIS环境下发展多种方法，技术和方法在空间和时间，空间数据和模型分析。

背景

人类健康，社会剥夺，全球环境变化，工业和经济的发展，和别人的主机上的所有需求，我们做什么在世界上发生在我们身边的感的问题。长期的空间分析囊括了广泛的技术分析，可视化，简化，和有关地理数据的理论。

空间分析方法，可以简单，只要从地图上测量，或复杂的数理统计的最抽象的形式。

在同一时间，我们被淹没的地球观测的新技术带来的好处。新的遥感卫星对地球环境的各个方面提供前所未有的数据，人口，社会，经济数据的新来源也越来越精细空间细节。然而，我们的“流水”喝，提取的意义，并作出有益的决定的能力却没有跟上。我们再也不能依赖于人的眼睛和大脑单，但必须增加通过改进技术的发展，他们通过数据筛选，发现模式和离群;更有效的数据可视化的测试理论和假说;和决策。

为了保持在GIS技术的前沿，分析和计算方法必须制订允许GIS数据模型和性质的空间和时间空间的查询条件的问题的解决方案。需要新形式的统计分析，以评估在空间环境中的各种变量之间的关系。，必须制订新的理论框架，我们在一个新的水平分辨率和尺寸的变量之间的关系的认识。

什么是水分和植物生长之间的关系，例如，当我们参考平方公里，是地球空间？我们如何评估疟疾集群?案件时，我们的环境数据记录在一公尺的小矩形？

从其他类型的数据，空间数据，必须区别对待。到另一个附近的变量，内部和各国之间存在牢固的关系。由于空间单元的大小和配置不尽相同，我们发现，是由于多正在研究中的变量的性质，空间单位的性质和变量之间的关系。站在验证空间数据分析，包括建模方式，是空间尺度，空间关联，空间异质性，边界，不完整的数据做的问题。如果没有这些问题的合理反应，地理信息系统作为一个复杂的研究环境中的分析工具的效用会成问题。

通过利用地理信息系统，空间分析以前望而却步，计算密集型，高度可视化的方式已成为以合理的成本进行访问。

UCGIS方法

空间分析是桥梁环节的基本数据模型与GIS技术，其结果是应用增强和研究成果，扩大和深化。UCGIS强调集成了这些活动的各种研究领域。地理信息系统框架包括地理参考数据的数据处理和工具。联系的应用程序允许空间分析的新的和更有利可图的方式做研究，并在喜欢的方式告知申请执业，从业者能够开发出新的有用的分析方法特别适用于社会，物理和环境科学等领域。

UCGIS要求协助在发展空间统计，统计学，空间计量经济学，结构和时空建模，数学和计算算法，可以利用地理的灵活性，容量和速度的优势，无论从物理科学和人文科学的空间分析家信息系统。良好的教育的那些理论，经验，数据采集，数据处理，编程，技术和计算机技术将在最好的位置，以使在这一领域的进步，但从业者，如流行病学，生态学，气象学，区域科学家，景观建筑师，环保可以添加许多GIS相关研究的发展。

重视国家研究需求

对于美国保持对GIS技术的前沿，我们必须树立适当的分析技术，在一个迅速变化的领域的各种发展。在从事基础研究的空间分析，我们可以实现的空间尺度，空间关联，空间异质性，空间运动和边界效应，更好地了解，并可以连续和离散的数据建模，开发更合适的工具。我们将提高我们非常大的空间数据集（例如，分门别类的普查数据，远程遥感数据在全球范围内）的处理，模式识别，数据泛化，边缘检测，模糊模式识别分析我们会发现相应的GIS工具。

好处

在下面的节点概述的优先事项的研究主题必须支持，因为我们移动到21世纪科学界。将有更好的技术，再加上与GIS空间分析应用程序，涵盖了社会关注的广大范围：

?疾病的分布：如登革热，疟疾，艾滋病等传染病的传播的研究将有利于从放置到空间的生态框架，其中一个地理信息系统的数据覆盖，将在时空框架研究疾病的发病率。

交通管理和土地利用规划：在地理信息系统框架的实时流量分析，将有助于在公路基础设施，交通和旅游需求管理，土地利用总体规划的发展。环境问题：地理信息系统有可能成为能够更好地提供环境模型的水，空气和其他类型的环境变量提取数据的分析。火灾控制问题，物种多样性，水文，防洪，减灾，和公园的使用，非常适合与地理信息系统框架分析。?景观表征和测量：利用遥感数据，分析土地覆盖和土地利用这样的事情的人的一个引人注目的问题是高分辨率数据的分类。通过图像分析带入一个地理信息系统的分析框架，各种分类方案可以测试和使用的土地覆盖数据的分析。

社会，文化，经济分析：经济学家和其他社会科学家将有机会使用块和县数据测试使用的空间计量经济分析的理论。使用这些数据集在地理信息系统框架的发展将增加我们的各种社会的进程，包括模式的就业和失业，犯罪，经济增长和人口变化的理解。

?物理过程，水文和气候学的过程，在不同的自然地理制度，在地理信息系统框架的分析，使研究人员能够精确的趋势（全球变化），异常事件，一般来说，在这些领域的进一步应用研究。

?改进的机会和服务的可及性和公平性：地理信息系统，可以让更多的经

济活动和公共部门服务的敏感配置。空间数据地理信息系统的处理能力允许的弱势群体和市场机会和服务的无障碍空间分布的详细陈述和分析。基于GIS技术，为解决复杂的和现实的位置和分布问题，可以让这些系统进行配置，以最大限度地获取和公平。

研究的优先领域

未来的工具必须不仅空间，但也时空。他们必须解决一些关键问题：我们如何处理VE大的空间数据集（例如分类的普查数据，远程遥感在全球范围内的数据）？技术，空间数据分析的就业类型（例如，规模和聚集效应）的影响能占到什么呢？通用GIS工具[例如，奥彭肖（1994）模式的检举和测试，数据简单化，边缘检测，模糊模式识别分析仪是适用于空间分析？以下是长期的例子（五至十年）的基础研究项目：

方法，技术和方法，都需要分析：

海量空间数据集：由于地理参考数据集变得更大，方法必须使用，削减，分类，操作非常大的空间数据基础固有的丰富的信息。

空间和时间空间数据的探索：探索性空间数据分析，必须扩展到以发展模式，更好地代表现实的时空数据。

?验证（意义）的程序：统计（邦弗朗尼型）程序必须承认地理参考数据的依赖，并允许研究人员从事检验假设。

?变差和克里格：刚才这些有用的地质统计程序被纳入地理信息系统框架。研究必须在这方面前进，以充分利用地理信息系统作为一种研究工具，包括这些程序纳入更广泛的分析和模型的连续空间现象。

?规模的影响和规模的独立程序开发：也许最根本的准备GIS空间分析工作，规模变化的影响评估研究结果的模块和算法。规模纠正和规模的独立方法的发展是一个引人注目的研究需要。切不可感情用事分析空间数据的单位配置和区域化的影响程度的问题是有很大的研究兴趣。

?全球与本地的影响：全球的海量数据集的分析已经证明肤浅和不足。有一个程序和测试需要使用窗口，内核，个人和其他地方的措施，找到并等同于非平稳空间数据的特点。

必须找到必要或极端意见的鉴定：程序，它可以识别在空间设置键观察，观察组（集群），或热点，提请注意的异常区域。

?设计计算密集型程序：有必要工具，采取在计算平台的功能优势，大量增加的地理信息系统的接口。计算密集型的工具，可以允许：1）大型数据集被有效利用，2）复杂的空间现象更为复杂和广泛的模拟和3）解决复杂的位置和分布问题（见下文）。潜在的技术包括神经网络，模糊集，小波，微观，人工智能，自然语言处理的文本信息，人工生命，实时数据分析，数值优化技术和大规模并行算法。应评估这些新技术在GIS环境下，他们在实际应用中的实用性。

计量经济模型在GIS环境：空间经济学是一个新的和新兴领域。重要的是要尝试连接与地理信息系统的功能和灵活性的计量经济学家的复杂程序。此外，还有一个需要找到适当的估计和检测设备，异质性，非均匀的地理参考数据。

在地理信息系统框架的空间相互作用模型：也许是最常用的模型类型之间的空间分析家之一是空间的互动式模型。在GIS环境下开发的这些车型将提供比目前更大的分析能力，市场营销，运输和人机交互专家。还需要

一个复杂的方式在空间相互作用流动的可视化技术。

?运筹学：许多领域大大受益，从地理信息系统的功能和数据操纵权力，包括等业务路由，locationallocation，覆盖面和其他优化程序的研究型问题。大多数现有的技术需要空间对象和温顺的缘故关系的简单陈述。需要的是基于GIS的解决方案的程序，可以操纵空间实体在其原始形式，并承认这些实体之间发生复杂的空间关系。

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