UCGIS研究计划。

来源：UCGIS研究计划（2002年公布，2004年更新）

目录：

长期计划

# Spatial Ontologies 空间本体
# Geographic Representation 扩展地理空间数据表现能力
# Spatial Data Acquisition and Integration 空间数据获取与整合
# Scale 尺度问题
# Spatial Cognition 空间认知
# Space and Space/Time Analysis and Modeling 空间和时空的分析与建模
# Uncertainty in Geographic Information 地理空间信息的不确定性
# Visualization 可视化
# GIS and Society GIS与社会
# Geographic Information Engineering 地理信息工程

短期计划
# GIS and Decision Making GIS与决策支持
# Location-based Services LBS
# Geoslavery
# Identification of Spatial Clusters 空间聚类（簇）的识别
# Geospatial Semantic Web 地理空间语义Web
# Incorporating Remotely Sensed Data and Information in GIS GIS与遥感数据的结合
# Geographic Information Resource Management 地理空间信息资源管理
# Emergency Data Acquisition and Analysis 紧急事件数据获取与分析
# Gradation and Indeterminate Boundaries 等级与模糊边界问题
# Geographic Information Security 地理空间信息的安全性问题
# Geospatial Data Fusion 地理空间数据融合
# Insitutional Aspects of SDIs 机构GIS
# Geographic Information Partnering
# Geocomputation 地理空间计算
# Global Representation and Modeling 地球全局空间数据表现与建模
# Spatialization 空间化
# Pervasive Computing 普适计算
# Geographic Data Mining and Knowledge Discovery 地理空间数据挖掘与知识发现
# Dynamic Modeling 动态建模

=====长期研究计划=====
{spatial ontologies}
三类互相补充与制约的本体研究：
-本体论的研究，尝试为地理空间领域各构成子领域中不同尺度与粒度的对象、过程和关系建立类型定义。
-使用标准心理学方法，从人类试验对象（包括专家和非专家）抽取地理本体的研究。
-描述、访问、对比和整合地理本体的方法与工具的研究。
参考资料：
[1]Burrough, P. A. and Frank, A. U. (eds.) 1996. Geographic Objects with Indeterminate Boundaries, London and Bristol, PA: Taylor and Francis.

[2]Casati, R., and Varzi, A. C., 1995. Holes and Other Superficialities. Cambridge, Mass.: M.I.T. Press

[3]Casati, R., and Varzi, A. C., 1999. Parts and Places. Cambridge, Mass.: M.I.T. Press

[4]Egenhofer, M. J., 1999. NSF-CNPq Collaborative Research on Integrating Geospatial Information. NSF Award IIS 9970123.

[5]Egenhofer, M. J., and Mark, D. M., 1995. Naive geography. In Frank, A. U. and Kuhn, W., editors, Spatial Information Theory: A Theoretical Basis for GIS. Lecture Notes in Computer Sciences No. 988. Berlin: Springer-Verlag, pp. 1-15.

[6]Fabrikant, S. I., 2000. The ontology of semantic information spaces. In Geographical Domain and Geographical Information Systems - EuroConference on Ontology and Epistemology for Spatial Data Standards, September, 2000.

[7]Farquhar, A., Fikes, R., Pratt, W., and Rice, J., 1995. Collaborative Ontology Construction for Information Integration. Technical Report KSL-95-10. Stanford, California: Knowledge Systems Laboratory, Stanford University.

[8]Findler, N. V., and Malyankar, R. M., 1999. Digital Government: An Ontology for Geospatial Knowledge. NSF Award EIA 9876604.

[9]Guarino N., and Giaretta P., 1995. Ontologies and Knowledge Bases: Towards a Terminological Clarification. In N. J. I. Mars (ed.), Towards Very Large Knowledge Bases, IOS Press.

[10]Hayes, P., 1985a. The second naive physics manifesto. In Hobbs, J., and Moore, R., eds, Formal Theories of the Commonsense World. Norwood, NJ: Ablex, pp. 1-36.

[11]Hayes, P., 1985b. Naive physics I: Ontology of liquids. In Hobbs, J., and Moore, R., eds., Formal Theories of the Commonsense World. Norwood, NJ: Ablex, pp. 71-108.

[12]Kokla, M., and Kavouras, M., 2000. Concept lattices as a formal method for the integration of geospatial ontologies. In Geographical Domain and Geographical Information Systems - EuroConference on Ontology and Epistemology for Spatial Data Standards, September, 2000.

[13]Kuipers, B. J., 1978. Modeling spatial knowledge. Cognitive Science, 2: 129–153.

[14]Kuipers, B. J., 1995. An Ontological Hierarchy for Spatial Knowledge. NSF Award IIS 9504138.

[15]Mark, D. M., Egenhofer, M. J., and Hornsby, K. 1997. Formal Models of Commonsense Geographic Worlds: Report on the Specialist Meeting of Research Initiative 21. Report 97-2. Santa Barbara, CA: National Center for Geographic Information and Analysis.

[16]Mark, D. M., and Smith, B., 1999. Geographic Categories: An Ontological Investigation. NSF Award BCS 9975557.8

[17]Mark, D. M., Smith, B., and Tversky, B., 1999. Ontology and geographic objects: An empirical study of cognitive categorization. In Freksa, C., and Mark, D. M., editors, Spatial Information Theory: A Theoretical Basis for GIS. Lecture Notes in Computer Sciences. Berlin: Springer-Verlag, pp. 283-298.

[18]Peuquet, D. J., Smith, B., and Brogaard-Pederson, B., 1999. Ontology of Fields. Varenius Project Specialist Meeting Report. Santa Barbara, CA: National Center for Geographic Information and Analysis.

[19]Raubal, M., Egenhofer, M. J., Pfoser, D., and Tryfona, N., 1997. Structuring space with image schemata: Wayfinding in airports as a case study. In Hirtle, S. C., & Frank, A. U. (Eds.). Spatial Information Theory. Heidelberg: Springer-Verlag.

[20]Smith, B., 1994. Fiat objects. In Guarino, N., Vieu, L., and Pribbenow, S., eds., Parts and Wholes: Conceptual Part-Whole Relations and Formal Mereology, 11th European Conference on Artificial Intelligence, Amsterdam, 8 August 1994. Amsterdam: European Coordinating Committee for Artificial Intelligence, 15-23.

[21]Smith, B., 1995. On drawing lines on a map. In Frank, A. U., and Kuhn, W., eds., Spatial Information Theory. Proceedings of COSIT ‘95. Berlin: Springer Verlag, pp. 475-484.

[22]Smith, B., 1999. Ontology: Philosophical and Computational. Unpublished manuscript, http://wings.buffalo.edu/philosophy/faculty/smith/articles/ontologies.htm.

[23]Smith, B., and Mark, D. M., 1998. Ontology and geographic kinds. In Poiker, T. K., and Chrisman, N., eds., Proceedings. 8th International Symposium on Spatial Data Handling (SDH’98), Vancouver. International Geographical Union, pp. 308-320.

[24]Smith, B., and Mark, D. M., 1999. Ontology with human subjects testing. American Journal of Economics and Sociology 58(2): 245-272.

[25]Smith, B., and Zaibert, L., 1998. The Metaphysics of Real Estate. http://wings.buffalo.edu/philosophy/faculty/articles/lz.html.

[26]Sorrows, M. E. and Hirtle, S. C., 1999. The nature of landmarks for real and electronic spaces. In Freksa, C. & Mark, D. M. (Eds). Spatial Information Theory. Heidelberg: Springer-Verlag.

[27]Stell, J. G., 2000. The Representation of Discrete Multi-Resolution Spatial Knowledge, Proceedings of Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000), San Francisco: Morgan Kaufmann Publishers.
Uschold, M., and Grüninger, M., 1996. Ontologies: Principles, methods and applications. Knowledge Engineering Review, 11(2).

{extensions to geographic representation}
目标是1)扩展当前数据表现和数据模型的能力，使之能够更有效地表现立体、动态现象；2)开发能够支持这些扩展的相应的分析方法。
参考资料：
[1]Alber, R. F., 1987. "The National Science Foudation National Center for Geographic Information and Analysis," International Journal of Geogrpahical Information Systems 3:117-136.

[2]Beard, K., 1994. Accommodating uncertainty in query response. Proceedings, Sixth International Symposium on Spatial Data Handling, Edinburgh, Scotland: International Geographical Union.

[3]Dutton, J., 1983. Geodesic modelling of planetary relief. Proceedings, AutoCarto VI, Ottawa.

[4]Goodchild, M. F., and S. Gopal, editors, 1989. Accuracy of Spatial Databases. London: Taylor and Francis.

[5]Langran, G. 1992. Time in Geographic Information Systems. London: Taylor & Francis.

[6]Peuquet, D. J., 1984. A conceptual framework and comparison of spatial data models. Cartographica 21(4):66-113.

[7]Peuquet, D. J., 1994. It's about time: A conceptual framework for the representation of spatiotemporal dynamics in geographic information systems. Annals of the Association of American Geographers 84: 441-461.

[8]Tansel, A. U., J. Clifford, S. Gadia, S. Jajodia, A. Segev, and R. Snodgrass, 1993. Temporal Databases. Redwood City, CA: Benjamin/Cummings Publishing Co.

{scale}
研究点：尺度概念的定义；尺度相关的决策支持的系统化基础；数据整合与使用的实践向导；对尺度在统计和过程模型中的影响进行量化与修正的方法；智能自动化综合方法；对尺度相关的认知问题的进一步理解。
潜在的项目：开发关于尺度及其相关属性的更一致的定义，使之能被多学科统一理解；开发尺度分析模块，并融入大环境的分析和测量方法之中；关于尺度研究中冲 采样方法的影响应当被进一步认识；开发高效的评估和刻画尺度效应的空间/地理统计技术；开发改良的数据综合方法；开发将尺度相关知识并入元数据的改良方 法；设计开发多尺度数据库等。
参考资料：
[1]Buttenfield, B. P., and R. B. McMaster (editors), 1991. Map Generalization: Making Rules for Knowledge Representation. New York: Longmont Scientific and Technical.

[2]Ehleringer, J. R., and C. B. Field (editors), 1993. Scaling Physiological Processes, Leaf to Globe. New York: Academic Press, Inc.

[3]Hou, R.-R., 1998. A Local-Level Approach in Detecting Scale Effects on Landscape Indices. M. S. Thesis, Louisiana State University.

[4]Hudson, J., 1992. Scale in space and time. In R. F. Abler, M. G. Markus, and J. M. Olson (editors), Geography's Inner Worlds: Pervasive Themes in Contemporary American Geography. New Brunswick, NJ: Rutgers University Press, pp. 280-300.

[5]Lam, N., and D. A. Quattrochi, 1992. On the issues of scale, resolution, and fractal analysis in the mapping sciences. The Professional Geographer 44:88-98.

[6]Openshaw, S., M. Charlatan, C. Wymer, and A. Craft, 1987. A Mark 1 Geographic Analysis Machine for the automated analysis of point data sets. International Journal of Geographical Information Systems, 1(4):335-358.

[7]Quattrochi, D. A., and M. F. Goodchild (editors), 1997. Scaling in Remote Sensing and GIS. Boca Raton, FL: CRC/Lewis Publishers, Inc.

[8]Sivapalan, M., and J. D. Kalma, 1995. Scale problems in hydrology: Contributions of the Robertson Workshop. Hydrological Processes 9(3/4):243-250.

[9]Turner, M. G., R. V. O'Neill, R. H. Gardner, and B. T. Milne, 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecology 3:153-162.

[10]Weigel, S. J., 1996. Scale, Resolution and Resampling: Representation and Analysis of Remotely Sensed Landscapes Across Scale in Geographic Information Systems. Ph.D. Dissertation, Louisiana State University.

[11]Wong, D., and C. Amrhein (editors), 1996. The Modifiable Areal Unit Problem. Special issue of Geographical Systems 3:2-3.

{spatial cognition}

{space and space/time analysis and modeling}

{uncertainty in geographic data and GIS-based analyses}
需要进一步认识地理数据的不确定性及其通过数据分析如何传播传播，这对于以地理数据为基础的决策支持是重要的。因此，必须开发一些策略用于识别、量化、跟 踪、减少、报告地理数据和基于GIS的分析中的不确定性，必须开发一种标准方法用于解决日常GIS应用程序中的不确定性。
参考资料：
[1]Abler, R. F., 1987. The National Science Foundation National Center for Geographic Information and Analysis. International Journal of Geographical Information Systems. 1:303-326.

[2]Couclelis, H., 1992. People manipulate objects (but cultivate fields): Beyond the raster-vector debate In: U. Frank, I. Campari and U. Formentini, editors, Theories and Methods of Spatio-Temporal Reasoning in Geographic Space (Lecture Notes in Compute r Science Vol. 639). Berlin-Heidelberg:Springer-Verlag, pp. 65-77.

[3]Goodchild, M. F., 1992. Research initiative 1: Accuracy of spatial databases. Closing Report, Santa Barbara, CA: National Center for Geographical Information and Analysis, University of California.

[4]Goodchild, M. F., 1993. Data models and data quality: Problems and prospects. In: M. F. Goodchild, B. O. Parks, and L. T. Steyaert, editors, Environmental Modeling with GIS. Oxford University Press: New York, pp. 94-103.

[5]Goodchild, M. F., and S. Gopal, editors, 1989. Accuracy of Spatial Databases. New York: Taylor and Francis.

[6]Goodchild, M. F., B. Buttenfield, and J. Wood, 1994. Introduction to visualizing data quality, In: Hilary M. Hearshaw and David J. Unwin, editors, Visualization in Geographical Information Systems. New York: John Wiley and Sons, pp. 141-149.

[7]Mowrer, H. Todd, R. L. Czaplewski, and R. H. Hamre, editors, 1996. Spatial accuracy assessment in natural resources and environmental sciences: Second International Symposium, U.S. Forest Service. Rocky Mountain and Range Experiment Station, Ft. Collins , CO. General Technical Report RM-GTR-277. 728 p.

[8]NCGIA (National Center for Geographic Information and Analysis), 1989. The research plan for the National Center for Geographic Information and Analysis. International Journal of Geographical Information Systems. 3(2):117-136.

{geographic visualization}
综合应用图像处理、计算机图形学、动画、仿真、多媒体和虚拟现实等技术。
长期的研究挑战：创新的信息可视化方法，这是无止境的，这一领域的发展将依赖于计算机图形接口和图形设计；信息的自动创建与可视化，这需要理解用户表达 （多种可能的表达方式）的所要寻找的信息，并自动合成可视化结果来传达信息；统一图形，需要统一基于几何的图形、基于图像的图形（包括视频）、基于时间的 图形，另外还有声音、触觉、甚至味觉、嗅觉等有待整合。
推荐的示例研究项目：ICA的可视化与虚拟环境委员会（http://kartoweb.itc.nl/icavis/index.html）。
参考资料：
[1]Andrienko, N. and G. Andrienko. 1999. Interactive maps for visual data exploration. International Journal of GIS 13(4): 355-374.

[2]Bertin, J. 1967. Semiologie Graphique, Mouton, Paris, 413 p.

[3]Brodlie, K.W., D.A. Duce, J.R. Gallop, and J.D. Wood (1998). Distributed cooperative visualization. State of the Art Reports at Eurographics98, (Eds. d. Sousa, A.A. and. Hopgood, F.R.A.) Eurographics Association, pp. 27-50.

[4]Buckley, Aileen R. 1998. Visualization of Multivariate Geographic Data for Exploration. In Geographic Information Research: Bridging the Atlantic, Volume 2, edited by M. Craglia and H. Onsrud. London: Taylor & Francis.

[5]Buckley, A.R. 1997. The Application of Spatial Data Analysis and Visualization in the Development of Landscape Indicators to Assess Stream Conditions. Doctoral dissertation, Oregon State University, Department of Geography.

[6]Buttenfield, B. 1996. Scientific Visualization for Environmental Modeling: Interactive and Proactive Graphics. In GIS and Environmental Modeling: Progress and Research Issues, 427-443. Colorado: GIS World Books.

[7]Couclelis, H. 1998. Worlds of information: The geographic metaphor in the visualization of complex information. Cartography and Geographic Information Science 25(4): 209-220.

[8]DiBiase, D. 1990. Visualization in the Earth Sciences. Earth and Mineral Sciences, Bulletin of the College of Earth and Mineral Sciences, Penn State University, 59(2): 13-18.

[9]DiBiase, D., A.M. MacEachren, J.B. Krygier, and C. Reeves. 1992. Animation and the Role of Map Design in Scientific Visualization in Cartography and Geographic Information Systems 19(4): 201-214, 265-266.

[10]Fabrikant, S.I. and B.P. Buttenfield. 1997. Envisioning user access to large data archive. GIS/LIS ’97, Cincinnati, Oct. 28-30, ASPRS/ACSM/AAG/URISA/AM-FM/APWA, pp. 672-678.

[11]Fabrikant, S.I. 2000. Spatialization browsing in large data archives. Transactions in GIS, 4(1): 65-78.

[12]Foley, Jim. 2000. Getting There: The Ten Top Problems Left. http://www.computer.org/cga/articles/topten.htm

[13]Friedhoff, R. and W. Benzon. 1989. Visualization: The Second Computer Revolution. New York: Harry N. Abrams, Inc.

[14]Funkhouser, Thomas and Kai Li. 2000. Guest Editors' Introduction: Large-Format Displays. IEEE Computer Graphics & Applications 20(4).

[15]Gahegan, M.N. 2000. Visualization as a geocomputational tool. GeoComputation (Eds. Openshaw, S. and Abrahart, B.), Taylor and Francis, UK, pp. 253-274.

[16]Gahegan, M. 2000a. Visual exploration in geography: analysis with light. To appear in: Geographic knowledge discovery and spatial data mining (Eds. Miller, H. J. and Han, J.), London: Taylor & Francis.

[17]Gahegan, M. N. and O’Brien, D. L. (1997). A strategy and architecture for the visualization of complex geographical datasets. International Journal of Pattern Recognition and Artificial Intelligence, 11(2):. 239-261. 12

[18]Gilmartin, P. and J.C. Patton. 1984. Comparing the sexes on spatial abilities: Map-use skills. Annals of the Association of American Geographers 74(4): 605-619/

[19]Jacobson, R.D. 1998. Cognitive mapping without sight: Four preliminary studies of spatial learning. Journal of Environmental Psychology 18, 289-305

[20]Keim, D. and H.-P. Kriegel. 1996. Visualization techniques for mining large databases: a comparison. IEEE Transactions on Knowledge and Data Engineering (Special Issue on Data Mining).

[21]Keim, D.A. 1996. Pixel-oriented visualization techniques for exploring very large databases. Journal of Computational and Graphical Statistics, 5(1): 58-77.

[22]Kitchin, R.M., R.D. Jacobson, R.G. Golledge, and M. Blades. 1998. Belfast without sight: Exploring geographies of blindness, Irish Geographer. 31(1), 34-46.

[23]Krygier, J. 1994. Sound and Geographic Visualization. In Visualization in Modern Cartography, 149-166. New York: Elsevier Science Ltd.

[24]Kuhn, W. 1997. Handling data spatially: Spatializing user interfaces. Proceedings of the Seventh International Symposium on Spatial Data Handling (Advances in GIS Research II). M.-J. Kraak, M. Molenaar, and E. Frendel. London: Taylor & Francis, pp. 877-893.

[25]MacEachren and the ICA Commission on Visualization. 1998. Proceedings of the Polish Spatial Information Association Conference, May, Warsaw, Poland. http://www.geovista.psu.edu/icavis/draftAgenda.html.

[26]MacEachren, A. 1994. Visualization in Modern Cartography: Setting the Agenda. In Visualization in Modern Cartography, 1-12. New York: Elsevier Science Ltd.

[27]MacEachren, A. 1995. How Maps Work. New York: The Guilford Press.

[28]MacEachren, A., B. Buttenfield, J. Campbell, D. DiBiase, and M. Monmonier. 1992. Visualization. In Geography's Inner Worlds: Pervasive Themes in Contemporary American Geography, 101-137. New Jersey: Rutgers University Press.

[29]MacEachren, Alan M. and Menno-Jan Kraak. 2000. Overview: International Cartographic Association. http://www.computer.org/cga/cg2000/g4toc.htm.

[30]Macinlay, J. 1986. Automating the design of graphical presentation of relational information, ACM Transactions on Graphics, 5(2): 110-141.

[31]McCormick, B., T. DeFant, and M. Brown. 1987. Visualization. Scientific Computing in Computer Graphics: 21 i-E-8.

[32]McGuinness, C. 1994. Expert/Novice Use of Visualization Tools. In Visualization in Modern Cartography, 185-199. New York: Elsevier Science Ltd.

[33]Nyerges, T.L. 1993. How do people use geographical information systems? Human Factors in Geographical Information Systems. D. Medyckyj-Scott and H.M. Hearnshaw. London: Belhaven Press, pp. 37-50.

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=====短期研究计划=====
{dynamic modeling}
目的是要扩展传统GIScience的研究范围至Dynamic GIScience。
承认动态现象的时空本质。
要理解动态过程需要能够克服当前方法局限性的新方法、理论。
当前方法的一些缺点如下：
—只有流行病研究人员开始使用在复杂景观上建模动态过程的工具。
—当前的城市化过程模型关注于空间影响，却难以捕获底层过程，如近邻繁荣与衰退的循环、文化或经济的聚结。
—动态交通模型还不能嵌入它们的地理环境。
—当前的气候模型在规模和概念表达上都还不成熟。
—对于滥垦滥伐，目前还难以定义和建模土地覆盖的变化。
动态建模混和了一系列方法学，其中许多是从空间计算界引进的认识到结合时间和空间重要性的那些。比较突出的方法有：1)关于复杂结构空间景观的基于 Agent的计算实验室；2)细胞自动机和网络扩展；3)微分方程建模；4)扩散建模；5)相关动力学的非线性模型；6)空间评估算法；7)时序回归。这 些方法论均包括数据驱动和模型驱动方法，均建立在局部—整体辩论的四维扩展等新理论上并由之扩展。
地理学在一百多年以前就将过程确定为其研究的重要元素。要想使得GIScience能够完全捕获这种物力论，研究人员必须齐心协力将时间纳入GISystems并且和当前的空间动态模型结合发展。
需要解决的问题有：
—虽然尝试了多种时空数据模型，但仍没有出现一种可以接受的标准。时间是作为纬度还是属性？一个或是多个？哪一个时间公理和其对应的空间公理可以互换？人的认知又在其中扮演什么角色？
—“人—环境”模型需要有连接连续（微分方程）模型和基于Agent的分散模型的能力。
—尺度—尝试整合不同时间粒度的研究。
—由小尺度现象相互作用生成、反过来又影响小尺度现象的大尺度现象的出现。时空过程之间的这种典型的非线性交互所引起的不确定性，需要新的理论和方法来识别我们所建模的动态现象的复杂性。
—我们已经开始开发对象和事件的概念模型，却忽略了它们的行为是环境相关的。开发环境相关的领域对象模型和过程库是一项巨大的挑战。
参考文章：
[1]Blaut, J. (1961) Space and Process. The Professional Geographer 13: 1-7
[2]Box, P. (2000) Garage Band Science and Dynamic Spatial Models. Journal of Geographical Systems 2: 49-54
[3]Parker, D., Manson, S., Janssen, M., Hoffman, M., and Deadman, P. (2003) Multi-agent Systems for the Simulation of Land Use and Land Cover Change: A Review. Annals of the Association of American Geographers 93: in press
[4]Peuquet, D. (2002) Representations of Space and Time. New York, Guilford
[5]Sider, T. (1996) Four-Dimensionalism. The Philosophical Review 106: 197-231

{Geographic Data Mining And Knowledge Discovery}
目标是开发新的方法，以促进从大型地理空间和时间数据库中发现和抽取有用的模式和关联。
地理数据挖掘是从大量地理空间数据中发现有趣的、先前未知的、可能会有用的模式的过程。
空间数据的复杂性和与生俱来的空间关系本质限制了传统数据挖掘技术在抽取空间模式中的应用。传统数据挖掘和空间数据挖掘的区别类似于传统统计学和空间统计 学的区别。首先，空间数据是嵌在连续空间中的，而传统数据集常是离散的。第二，空间模式常是本地化的（空间和时间上），而传统数据挖掘技术常关注的是全局 模式。在分析空间数据时，样本独立性的假设基本上是错误的，因为空间数据趋向于高自相关。空间数据挖掘的基础是空间统计、空间分析、人工智能和机器学习、 模式识别和图像分析、以及高性能计算。
什么类型的数据挖掘算法适合于地理空间数据？怎么将它们应用于处理空间数据，局限性是什么？要实现高效的挖掘，需要哪种空间存取结构？如何裁减SDM算法 使之适应大型地理空间数据库？如何整合领域知识以提高查询和挖掘效率？需要何种计算基础设施（如分布式、网格）？如何处理不确定性、信息缺失、不一致性和 异构性？哪种要素选择技术比较适合？如何高效检测spatial outlier？如何表达所发现的知识（模式）？发现知识之后如何又该如何（如何保存或表达）？如何确认这些模式？
[1]Buttenfield, B., Gahegan, M., Miller, H.J., and Yuan, M. (2000) Geospatial Data Mining and Knowledge Discovery. Washington, D.C., University Consortium for Geographic Information Science Research White Paper (available at http://www.ucgis.org/emerging/gkd.pdf)
[2]Miller, H.J. and Han, J. (eds) (2001) Geographic Data Mining and Knowledge Discovery. London, Taylor and Francis
[3] Tung, A.K.H., Ng, R.T., Lakshmanan, L.V.S., and Han, J. (2001) Constraint-based Clustering of Large Databases. In Proceedings of the Eighth International Conference on Database Theory (ICDT'01), London: 405-19
[4]Shekhar, S. Huang, Y., Wu, W., Lu, C.T., and Chawla, S. (2001) What's Spatial about Spatial Data Mining: Three Case Studies. In Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., and Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. New York, Kluwer
[5]Shekhar, S., Schrater, P.R., Vatsavai, R.R., Wu, W., and Chawla, S. (2002) Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transactions on Multimedia 4: 174-88

{Pervasive Computing}
随着web应用程序、互联网商业、无线通信和便携电子设备的快速发展，所以有必要研究如何进一步促进地理空间分析功能和这些技术的整合。
预计web、分布移动和无线网络将成为访问GIS的主要形式。比如当前WebGIS的发展，关注一系列“基于位置的服务”的g-commerce的出现 等。这些技术也在“telegeoprocessing”的领域之内，这一术语包括基于遥感、GIS、GPS和远程通信整合基础上的空间数据库实时更新、 分析和决策支持。
GIS已经存在了三十多年，而基于web和移动分布设备与网络的GIS却是一个新生现象，开始于大约1995年。由于WebGIS的发展，互联网现在已成为GIS功能和数据发布的门户。这一发展倚赖于GIS性能的提高，但也要受限于同样的挑战。
WebGIS和移动GIS与传统GIS的数据模型、数据模型应有何不同？Web站点或如聊天室等多用户域（MUD）的数据模型应当如何？需要开发适应WebGIS的新数据结构来准确地表达网络上的移动、文件许可证、遗留文件和数据集、电子传输等。
WebGIS和移动GIS性能如何度量？WebGIS站点或移动地图设备应用的主要障碍是什么？
Web地图或空间化数据准确反映数据的程度如何？WebGIS和访问便携电子设备上的地理信息的美学是怎样的？普适计算技术应当向哪种方向发展？如何使得人们能够通过宽带网络真正使用上一直在增加的地理信息？
[1]Batty, M. and Miller, H. (2000) Representing and Visualizing Physical, Virtual and Hybrid Information Spaces. In Janelle, D. and Hodge, D. (eds) Information, Places, and Cyberspace: Issues in Accessibility. Berlin, Springer-Verlag: 133-46
[2]Fabrikant, S.I. and Buttenfield, B.P. (2001) Formalizing Semantic Spaces for Information Access. Annals of the Association of American Geographers 91: 263-80
[3]Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W. (2001) Geographic Information Systems and Science. Chichester, John Wiley and Sons
[4]McGovern, G., 2001. Waiting for broadband, New Thinking, 6(33), www.gerrymcgovern.com/nt/2001
[5]Peng, Z.R. and Tsou, M.H. (2003) Internet GIS: A distributed Geospatial Information Service for the Internet and Wireless Networks. New York, John Wiley and Sons
[6]Plewe, B. (1997) GIS Online: Information Retrieval, Mapping, and the Internet. Santa Fe, NM, OnWord Press
[7]Xue, Y., Cracknell, A.P., Guo, H.D. (2002) Telegeoprocessing: The Integration of Remote Sensing, Geographic Information System (GIS), Global Positioning System (GPS) and Telecommunication. International Journal of Remote Sensing 23: 1851-93

{Institutional GIS}
了解了机构如何和为什么采用与使用GIS技术有利于促进它对社会的价值。
机构GIS：一个系统（包括硬件、软件、规范、人员以及其他相关元素），为一个较大机构的工作提供持续支持。
机构GIS的本质是什么？已发展的程度如何，现在是如何改变的？推动与阻碍机构GIS发展的因素是什么？机构化的潜在利益是什么？哪种关系出现在这一进程中？像电子政务等新生应用将如何影响机构GIS？

{geospatial data fusion}
研究将具有不同空间几何结构（栅格，矢量）、数据类型（点、线、面）、数据缩放比例（定性和定量）和变化的精确度、分辨率的数据完成融合或综合的方法，以及将融合的地理空间数据用于支持空间数据分析、建模，需要以后五年或更长时间的研究努力。
当试图结合具有不同几何结构、分辨率、精确度、类型和统计尺度的数据时，问题就会出现。数据融合策略与方法跟不上当前卫星传感器的分辨率、雷达与激光雷达技术、使用内置GPS功能的原地传感器等的发展，跟不上GIS系统中地理空间数据获取与处理等技术的发展。
能否开发和验证一个理论模型，为融合具有不同几何、分辨率和精确度的地理空间数据集提供基础理论？这一模型能否为用户提供通过元数据完成自动结合任务的基 础？当结合多种栅格和/或栅格与矢量数据集时，能否开发合适的方法来处理颜色与对比度问题？能否开发相差较大的数据的融和方法，以及基于这些方法和基本的 数据特性建立适当的限定？
[1]Chavez, P.S., Jr. (1986) Digital Merging of Landsat TM and Digitized NHAP Data for 1:24,000-scale Image Mapping. Photogram-metric Engineering and Remote Sensing 52: 1637-46
[2]Cobb M.A., Chung, M.J., Foley, H., Petry. F.E., Shaw, K.B., and Miller, H.V. (1998) A Rule-based Approach for the Conflation of Attributed Vector Data. GeoInformatica 2: 7-36
[3]Dare, P. and Downman, I. (2001) An Improved Model for Automatic Feature-based Registration of SAR and SPOT Images. ISPRS Journal of Photogrammetry and Remote Sensing 56: 13-28
[4]Jensen, J.R., Saalfeld, A., Broome, F., Cowen, D., Price, K., Ramsey, D., and Lapine, L. (1998) Spatial Data Acquisition and Integration. Washington, D.C., University Consortium for Geographic Science Research White Paper (available at http://www.ucgis.org/research_white/data.html)
[5]Schetselaar, E.M. (2001) On Preserving Spectral Balance in Image Fusion and Its Advantages for Geological Interpretation. Photogrammetric Engineering and Remote Sensing 67: 925-34
[6]Welch, R. and Ehlers, M.. (1987) Merging Multi-resolution SPOT HRV and Landsat TM Data. Photogrammetric Engineering and Remote Sensing 53: 301-3

{spatialization: spatial metaphors and methods for handling non-spatial data}
开发一个由实验证据和大规模实现支撑的理论框架，用于指导大量的、分布存储的非空间参考数据中知识发现的高效空间化建设。
spatialization（空间化）定义为：高维非地理数据向低维空间表达的系统转化，以促进大型数据库中的知识发现。空间化过程包括两步：一是基于 其内容和功能关系，通过数学转换方法重新整理数据条目，转换到逻辑定义坐标系系统；二是将转换后的数据用图形表现出来，用于信息和知识发现。
空间化后的数据能够使用空间象征和空间分析技术。空间化利用人们对空间的熟悉来产生直觉和内部一致的信息空间。研究任务将包括：人们如何基于所熟悉的空间隐喻与非空间数据交互的认知和本体论基础及含意；生成有意义的空间化的几何对象、可视化和分析方法的计算技术。
能否建立语义信息空间的本体？其语义原语是什么？当处理一个信息空间（比如一组新闻故事）的可视化时，哪些可视变量有用？理解一个隐喻（如地图隐喻）在多大程度上依赖于用户的经验背景和培训？
在江一个信息空间映射到表达空间时，哪种计算技术最适于保留信息空间的特征？从空间化中得到的认识如何与传统统计推论相比较？
[1]Card, S.K., Mackinlay, J.D., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think. San Francisco, Ca, Morgan Kaufmann Publishers
[2]Couclelis, H. (1998) Worlds of Information: The Geographic Metaphor in the Visualization of Complex Information. Cartography and Geographic Information Systems 25: 209-20
[3]Fabrikant, S.I. and Buttenfield, B.P. (2001) Formalizing Semantic Spaces for Information Access. Annals of the Association of American Geographers 91: 263-80
[4]Skupin, A. (2000) From Metaphor to Method: Cartographic Perspectives on Information Visualization. Proceedings of InfoVis 2000, October 2000, Salt Lake City, UT: 91-7
[5]Skupin, A. (2002) A Cartographic Approach to Visualizing Conference Abstracts. IEEE Computer Graphics and Applications 22: 50-8

{global representation and modeling}
基于实验数据和概念化抽象的全球地理现象在基础表达和数据、现象、过程建模上造成了一些重大问题。这些问题包括球形或椭球形地球数据向合适的处理表达方法 的转变。传统地图映射对于单点转换是有效的，但对于以一系列快照或瞬时视场形式反映地球并需要组织成全球视图的卫星数据，则难以处理。更高级的传感器系 统，如原位置传感器、基于传感器的手持GPS收集器等加剧了开发合适的表达与建模系统的问题。
能否开发一个能够灵活适应不同数据类型、从本地和全球模型产生准确结果的全球表达？discrete global grids提供了一种可选表达方法，但已存在的全球数据集合和卫星传感器得到的新数据能否适当的映射到这些grid？Grid能否应用于准确、有效的全球 建模？能否开发完全基于球形或椭球形坐标系统的表达与处理系统？能否设计新的投影，能够完全容纳全球数据，能够用作映射卫星数据和建模地球系统的基础？
[1]Clarke, K.C., Dana, P.D., and Hastings, J.T. (2003) A New World Geographic Reference System. Cartography and Geographic Information Science, 29: 355-62
[2]Dutton, G. (2000) Universal Geospatial Data Exchange Via Global Hierarchical Coordinates. In Proceedings of the International Conference on Discrete Global Grids, Santa Barbara, California
[3]Eidenshink, J.C. and Faundeen, J.L. (1994) The 1 km AVHRR Global Land Data Set: First Stages in Implementation. International Journal of Remote Sensing 15: 3443-62
[4]Fekete, G. and Treinish, L. (1990) Sphere Quad-trees: A New Data Structure to Support the Visualization of Spherically Distributed Data. In SPIE, Extracting Meaning from Complex Data: Processing, Display and Interaction, Volume No. 1259: 242-53
[5]Kimmerling, A.J. Sahr, K., White, D., and Song, L. (1999) Comparing Geometrical Properties of Global Grids. Cartography and Geographic Information Science 26: 271-87
[6]Sahr, K. and White, D. (1998) Discrete Global Grid Systems. In Proceedings of the Thirtieth Symposium on the Interface, Computing Science and Statistics: 269-78
[7]Steinwand, D.R., Hutchinson, J.A., and Snyder, J.P. (1995) Map Projections for Global and Continental Data Sets and an Analysis of Pixel Distortion Caused by Reprojection. Photogrammetric Engineering and Remote Sensing 61: 1487-1500
[8]Usery, E.L. and Seong, J.C. (2001) All Equal-area Map Projections Are Created Equal, But Some Are More Equal Than Others. Cartography and Geographic Information Science 28: 183-93

{gradation and objects with indeterminate boundaries}
需要为地理空间中的分类等级和具有模糊边界的对象建立正式本体，研究计算表达，来扩展GIS和地理空间计算的能力。
目前的矢量模型和栅格模型难以处理uozhong现象，如物理地理（土壤构成、植物群落）和人文地理（邻域、种族群落）中的一些例子。处理带有模糊边界的对象的计算方法的缺失可能是当前GIS软件表达能力的最大缺口。
分类等级的问题和具有模糊边界的对象的问题是不同的，但这两个问题又有紧密地联系。等级问题的解决方法对模糊边界问题的解决有帮助。
当前空间实体的表达是基于对象或基于域的。基于对象的表达包括点、线、多边形，基于域的表达包括栅格grid、TIN或多边形coverage。这些表达方法不足以捕获许多重要地理现象的本质。
需要刻画与分类不同类型的等级。需要解决的问题包括：不确定性与等级；个体模糊对象与空间域的连续值分类；模糊对象间的互斥规则；人们如何为具有模糊边界的对象定界；查询与其他GIS功能的开发。
[1]Burrough, P.A. and Frank, A.U. (eds) (1996) Geographic Objects with Indeterminate Boundaries. Bristol, PA, Taylor and Francis
[2]Chrisman, N. (1982) A Theory of Cartographic Error and Its Measurement in Digital Data Bases. In Proceedings, Fifth International Symposium on Computer-Assisted Cartography (AutoCarto 5). Falls Church, VA, ASPRS and ACSM: 159-68
[3]Goodchild M.F. and Dubuc, O. (1987) A Model of Error for Choropleth Maps with Applications to Geographic Information Systems. In Proceedings, Eighth International Symposium on Computer-Assisted Cartography (AutoCarto 8). Falls Church, VA, ASPRS/ACSM: 165-74
[4]Kronenfeld, B.J. (2001) Perspectives for Visualizing Gradation in Forest Type Maps. In Bennett, B. and Christani, M. (eds) Symposium on Spatial Vagueness, Uncertainty and Granularity
[5]Montello, D.R., Goodchild, M.F., Gottsegen, J., and Fohl, P. (2001) Thing'll be Great when You're Downtown: Behavioral Methods for Determining Referents of Vague Spatial Queries. In Bennett, B. and Christani, M. (eds) Symposium on Spatial Vagueness, Uncertainty and Granularity
[6]Smith, B. and Mark, D.M. (2002) Do Mountains Exist? Towards an Ontology of Landforms. Environment and Planning B 30: in press

{emergency data acquisition and analysis}
目标是开发地理空间基础设施，能够在具有最佳数据的任何位置、以最有用的形式、最少的时间提供紧急事件响应。
如何开发一个快速空间数据鉴定、存取、分析和分发的框架？哪种可操作的空间数据框架最适于每一类危险事件和社会环境（如团体、州和国家）？如何评估各种可 选框架？如何评估、管理和交流空间信息精确性？应当开发什么工具在不确定状态下支持重要的决策支持？如何将不同数据快速合并和融合成一种有价值的资源？用 户需要对数据进行哪些公有操作，是否能够预定义？有哪些机构问题阻止地理空间数据的快速收集与共享，又当如何克服？
[1]Cova T J 1999 GIS in emergency management. In Geographical Information Systems: Principles, Techniques, Applications, and Management, P.A. Longley, M.F. Goodchild, D.J. Maguire, D.W. Rhind (eds.), John Wiley & Sons, New York, pp. 845-858.
[2]Cutter S L, Richardson D, Wilbanks T 2003 The Geographical Dimensions of Terrorism. New York and London: Routledge, 274 pp.
[3]Huyck C K, Adams, B J 2002 Emergency response in the wake of the world trade center attack: the remote sensing perspective, Volume 3, Engineering and Organizational Issues Related to the World Trade Center Terrorist Attack, http://mceer.buffalo.edu/publications/sp_pubs/WTCReports/02-SP05-screen.pdf
[4]Goodchild M F 2003 Geospatial data in emergencies, in The Geographical Dimensions of Terrorism, S.L. Cutter, D.B. Richardson, T.J. Wilbanks (eds.), New York, Routledge, pp. 99-104.
[5]Mileti D S 1999 Disasters by Design: A Reassessment of Natural Hazards in the United States. Washington D.C.: Joseph Henry
Press.
[5]Palm R I, Hodgson, M E 1992 After a California Earthquake: Attitude and Behavior Change. (Chicago Press: Chicago, IL), 130 p.
[6]Palm R I, Hodgson M E 1992 Earthquake Insurance: Mandated Disclosure and Homeowner Response in California, Annals of the Association of American Geographers. 82(2): 207-222.
[7]Radke J, Cova T, Sheridan M F, Troy A, Mu L, Johnson R 2000 Application challenges for geographic information science: Implications for research, education, and policy for emergency preparedness and response. URISA Journal 12(2): 15-30.
[8]Thomas D S K, Cutter S L, Hodgson M, Gutekunst M, Jones S 2002 Use of Spatial Data and Geographic Technologies in Response to the September 11 Terrorist Attack, Quick Response Report #153, URL: http://www.colorado.edu/hazards/qr/qr153/qr153.html

{incorporating remotely sensed data and information in GIScience}
遥感领域无论在传感器的数目与类型、数据可用性、潜在的应用、政府和商业活动等各方面都经历了复兴的过程。
能否开发和测试新的方法，通过这些方法，传感器系统、数据源和分析过程的进展能够被其他GIScience功能（GPS、测量系统、可视化、数据挖掘、实时GIS和其他地理空间科学进展）利用？
[1]Estes, J.E. (1992) Remote Sensing and GIS Integration: Research Needs, Status, and Trends. ITC Journal 1: 2-9
[2]Hinton, J.C. (1996) GIS and Remote Sensing Integration for Environmental Applications. International Journal of Geographical Information Systems 10: 877-90
[3]Wilkinson, G.G. (1996) A Review of Current Issues in the Integration of GIS and Remote Sensing Data. International Journal of Geographical Information Systems 10: 85-101

{the geospatial semantic web}
需要研究地理空间语义web来为地理信息提供比基本语义web所能提供的更多的支持。
在异构数据（包括地理空间信息）的互操作和整合方面，语义的重要作用已被公认。语义web的思想计划建立数据能被机器直接或间接处理的web，为使用数据 的方式带来了高度自由性。通过结合形式描述捕获语义来为数据和其他web资源提供良好定义的意义，这样，信息处理（检索或整合）能够基于意义而非仅仅基于 关键词。W3C语义Web活动工作组已经开发出一系列标准，如扩展标记语言XML、资源描述框架RDF、Web本体语言OWL。
本体在关联数据与其意义上发挥着重要作用。与基于句法的方法相比，语义方法能够为改良的决策支持提供高质量和更具相关性的信息。同样重要的是本体的使用使得便于达到共识。
地理空间语义web初步寻求为地理信息提供比基本语义web所能提供的更好的支持。特别是，地理信息在语义web上的三个基本维度： professional，存储于地理数据库中的结构化地理信息，在web页上索引或描述；naive，在web页上检索非结构化、下层、非正式地理信 息；scientific，地理信息科学论文、模型和理论。
一些研究专题包括：地理本体的创建和管理；web页面上的地理概念和地理本体的匹配；本体的整合。
[1]Berners-Lee, T., Hendler, J., and Lassila, O. (2001) The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. The Scientific American 284: 34-43
[2]Egenhofer, M.J. (2002) Toward the Semantic Geospatial Web. In Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems, McLean, Virginia
[3]Giles, C.L., K.D. Bollacker, K.D., and Lawrance, S. (1998) CiteSeer: An Automatic Citation Indexing System. In Proceedings of the Third ACM International Conference on Digital Libraries, Pittsburgh, Pennsylvania
[4]Goodchild, M.F., Egenhofer, M.J., Fegeas, R., and Kottman, C.A. (eds) (1999) Interoperating Geographic Information Systems. New York, Kluwer
[5]Sheth, A. (1999) Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure to Semantics. In Goodchild, M.F., Egenhofer, M.J., Fegeas, R., and Kottman, C.A. (eds) Interoperating Geographic Information Systems. New York, Kluwer: 5-30

{identification of spatial clusters}
目标是寻找在地图上识别统计意义上重要的簇的方法。
有许多技术可以用于在地图上识别成团的不寻常活动。但这些技术缺少的重要元素是识别不是偶然发生的簇的能力。
如何在大量、异构、高度空间自相关数据集中检测统计意义上有效的聚类？要在时间和空间中研究聚类，需要什么样的技术？需要考虑地图的哪些结构特征来研究聚类？如何在一个回归框架中应用空间滤波来说明主回归变量的聚类特征？
[1]Getis, A. and Griffith, D.A. (2002) Comparative Spatial Filtering in Regression Analysis. Geographical Analysis 34: 130-40.
[2]Kulldorff, M. (1997) A Spatial Scan Statistic. Communications in Statistics - Theory and Methods 26: 1481-96
[3]Ord, J.K. and Getis, A. (1995) Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis 27: 286-306.
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{location-based services - GIS for personal productivity}
目标是开发一系列概念、数据结构和分析工具以提供高效、准确的基于位置的服务，提高生产力和生活质量。
据知，大约80%的公共和私人决策都或多或少牵涉到位置考虑。LBS结合了信息技术、GIS和ITS。
需要解决的问题有：在其适当的时空环境中的实时数据在GIS中的应用；GIS中时空拓扑的研发；开发高效方法来处理用于LBS的大型数据集；内容提供商之 间的互操作以及接口的标准化，以提供高效的请求-响应服务；为LBS开发启发式解决算法；开发高效、可行的方法，来收集实时交通数据；
[1]Kim T J 1999 Metadata for Geo-spatial Data Sharing: A Comparative Analysis. Annals of Regional Science 33: 171-82
[2]Kim T J, You J, Lee J S, Jang S-G, and Oh S 2002 Enabling Technologies for Location-Based Services and Development of Interface Standards for Multi-modal LBS. ISO/TC 211-PT 19134-WD-1, ISO/TC 211 Working Group 8, PT 19134 Working Document.
[3]Ostensen O 2001 The Expanding Agenda of Geographic Information Standards. WWW document, http://www.iso.ch/iso/en/commcentre/pdf/geographic0107.pdf
[4]Pulver.com 2001-2002 The Pulver.com LBS Report. WWW Document, http://pulver.com/lbsreport/bissues.html
[5]You J and Kim T J 2000 Development and Evaluation of a Hybrid Travel Time Forecasting Model. Transportation Research C. 8: 231-56