Aug1, 2010 JOURNAL OF GEO2INFORMATION SC IENCE : ; : : ( : ) ; 973 (2006CB701305) ; E2mail: lreis. ac.

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1 JOURNAL OF GEO2INFORMATION SC IENCE Vol112, No14 Aug1, 2010,, (, ) :,,,,, : ; ; ; 1,,,, [ 1 ] 90, [ 2-3 ],,,,, [ 3 ],, [ 2-4 ],, [ 5-9 ], [ ], [ 12 ], [ 13 ],,,,, [ 3 ], [ ],,,, ( ),,,, [ ], :,,, : ; : : ( : ) ; 973 (2006CB701305) ; : ( ),,, E2mail: xujun@ lreis. ac. cn

2 4 : 497, [ ], [ ],, [ ],,, [ 24 ],,, [ 24 ],,,, [ 2-4 ],,,,,, ( geographic knowledge map),,, [ 24 ], [ 24 ],, 2 ( ICT),,,,,, [ 25 ] ( knowledge domain vi2 sualization),(mapp ing knowledge domains),, [ 25 ] 2006 Nature Boyack,80,, [ 26 ],, [ 27 ],,, ICT, (Mapp ing Cyberspace) [ 28 ], 20, ( GIS) [ 28 ],,,, Dodge Kitchin Chen, Dodge Kitchin [ 28 ], Chen, [ ]

3 ,,,, [ 24 ],,, 3. 1,,,,, ( 1), [ 31 ], :,, [ 32 ],,, [ 33 ] Thagard,,, P, K, be2 came drift through comp resses ( 1) 1,, 1 Tab. 1 Types of geograph ic objects in geograph ic knowledge map N, E 1 [ 33 ] Fig. 1 Concep tual system rep resenting the continental drift theory [ 33 ] 3. 2,

4 4 : 499,,,,,,, ( 2) 1, 2,, Xu,, [ 34 ] Kurata Egenhofer [ 35 ], 2 Fig. 2 Some kinds of map s can be used to rep resent geographic knowledge 3. 3, K ( Europ, continents),, driftthrough ( continents, seafloor) Kurata Egenhofer, [ 24 ],, : (1) [ 35 ], 3,,,,,,,,,,,,,,,,, [ 36 ], SQL,,, [ 17 ],, (2), 1, P ( crust, earth),

5 Fig. 3 D iagram rep resenting the continental drift theory, ;, ;,,,,, 4, ,,,, 4 Fig. 4 The scientific role of geographic knowledge map, [ 29 ],, [ 37 ] 4. 2,,,,,,,,, ,,,

6 4 : 501,, 4,,,, ;, 5,,,,,,,, ;,,,,,,,, : [ 1 ]. [M ]. :, [ 2 ]. [ J ]., 1998, 17 ( ) : 5-9. [ 3 ]. [M ]. :, [ 4 ],. [ J ]., 1998, 17 ( ) : [ 5 ],,. [ J ]., 1998, 19 ( ) : [ 6 ],,. [ J ]. ( ), 2005, 4 (30) : [ 7 ],,,. GIS - [ J ]., 2002, 21 (4) : [ 8 ],,,. [ J ]. D, 2004, 34 (5) : [ 9 ],,,. [ J ]., 2003, 22 (2) : [ 10 ],, [ J ]., 2004, 20 (6) : [ 11 ],. [ J ]., 2002, 22 (3) : [ 12 ],,. [ J ]. ( ), 2005, 30 (4) : [ 13 ],,. [ J ]., 2003, 58 (2) : [ 14 ]. [ J ]., (1) : [ 15 ]. [M ]. : [ 16 ],,. [ J ]., 2006, 26 (3) : [ 17 ],,. [ J ]., 2006, 21 (1) : [ 18 ]. [ J ]., 2005, 29 (6) : [ 19 ],. [ J ]., 2007, 9 (2) : [ 20 ],,,.

7 [ J ]., 2005, 30 (6) : [ 21 ]. [D ]., [ 22 ]. [D ]., [ 23 ],,. [ J ]., 2005, 7 (4) : [ 24 ],,,. [ J ]., 2008, 12 (2) : [ 25 ],. [ J ]., 2005, 23 (2) : [ 26 ]Boyack K, D Klavans, W B Paley Gallery: B ril2 liant disp lay[ J ]. Nature, 2006, 444 ( 7122) : 985. [ 27 ],,,. [ J ]., 2008, 25 (3) : [ 28 ]Dodge M, R Kitchin. Mapp ing Cyberspace [M ]. London and New York: Routledge, [ 29 ] Chen C. Mapp ing Scientific Frontiers: The Quest for Knowledge V isualization [ M ]. London and New York: Sp ringer - Verlag, [ 30 ]Chen C. CiteSpace II: Detecting and V isualizing Emerging Trends and Transient Patterns in Scientific L iterature [ J ]. Journal of the American Society for Information Science and Technology, 2006, 57 ( 3) : [ 31 ],,,. [ J ]., 2007, 23 (6) : 1-7. [ 32 ] Egenhofer M J, R D Franzosa. Point - set Topological Spatial relations [ J ]. International Journal of Geograph2 ical Information Science, 1991, 5 ( 2) : [ 33 ] Thagard P. Concep tual Revolution [M ]. Princeton, New Jersey: Princeton University Press, [ 34 ] Xu J, Yao Y, Pei T, et al. Geographic Knowledge Map and Its App lication in Seism ic Knowledge Rep resentation [ C ]. Geoinformaticsπ09, Fairfax, VA, August 12-14, [ 35 ] Kurata Y, M Egenhofer. The A rrow - Semantics Interp ret2 er[ J ]. Spatial Cognition and Computation, 2008, 8 ( 4) : [ 36 ] Frank A U, D M Mark. Language Issues for GIS[ C ]. In Geographical Information System s: Princip les and App lica2 tions, edited by D J Maguire, M F Goodchild, and D W Rhind. London: Longmans Publishers, 1991, [ 37 ],. [ J ]., 2007, 36 (3) : Conceptua l Fram ework and Represen ta tion of Geograph ic Knowledge M ap XU Jun, PE I Tao, YAO Yonghui ( S tate Key L aboratory of Resources and Environm ental Inform ation System, Institute of Geographic Sciences and N atural Resources Research, CAS, B eijing , China) Abstract: Peop le have been working for years on geo2information map in different fields of geography since it was p roposed. However, most current works on geo2information map stay at the stage of rep resenting geographic phe2 nomena or p rocesses as traditional map does, while lack of knowledge rep resentation and reasoning, so it shows no difference w ith traditional cartographic m ap. This results in difficulties in extracting information automatically from geo2inform ation map s, so expertsπknow ledge and experience are required in order to acquire spatial or temporal patterns through the spatial distribution of geographic phenomena on geo2inform ation map s. This paper p roposes the concep t of geographic know ledge map, which uses measurements in feature dimensions as coordinate frame instead of geographic coordinate system and disp lays the characters of objects and the relationship s between them w ith dif2 ferent signals. In this way, geographic know ledge m ap can disclose spatial2temporal patterns in different feature di2 m ensions which are less possible to be found in real world and geo2coded m ap. In this paper, the concep tual framework and p roperties of geographic know ledge map is described, and its characteristics are concluded. Geo2 graphic knowledge map is not only graphic rep resentation of geographical knowledge, ( 509 )

8 4 : 509 An Estima tion M ethod of D ynam ic Popula tion w ith in an Urban L oca l Area ZHANG Zim in 1, ZHOU Ying 1, L IQ i 2, L IN Yuxian 3 ( 1. School of C ivil and Engineering, Shandong J ianzhu U niversity, J inan , China; 2. RS & GIS Institute, Peking U niversity, B eijing , China; 3. M anagem ent Center of U rban Planning, M inistry of Housing and U rban2rural D evelopm ent of the People s Republic of China, B eijing , China) Abstract: A p roblem involved in emergency decision2making and many city m anagement works is to evaluate the population w ithin some given urban areas at a random tim e. The unknown spatial areas and evaluation tim e which constitute two fundam ental uncertainty features of the p roblem bring great difficulty for its solution. Follow ing the o2 verall discip lines p resented in daily traveling of urban residents, we know that peop le basically arrange their activi2 ties around various city buildings. Another exp lanation for the phenomenon is residents travel between buildings because they are attracted by the functions of them. So, in this paper, a method is developed to evaluate the static population inside a given area, which uses the data of buildings for habitation and average housing floorage per ur2 ban resident according to statistics. W hile another step must be done for assess the dynam ic population, which is to confirm the population attracted by each type of building at any tim e. Two new indices are introduced: building correction coefficient and building attraction rate. The former refers to the ratio of average accommodated peop le between other building types and residential building. A rated accommodation peop le for each type of buildings can be achieved using the index. The latter is the ratio of real accommodation peop le to rated one. Here we divide the work day or break day all into seven periods according to the general time distribution of residentsπtraveling activi2 ties, and confirm a building type category by reference to one used in the occurrence rate investigation of city transportation. The two indices are estim ated using the traveling investigation data of residents and vehicles. Then the dynam ic population is drawn based on the p roposed method. A case of chem ical emergency in Beijing is imag2 ined to demonstrate the app lication of the method and the issues concerned with parameter fixing that is the key to the m ethod is discussed. Key words: population estimation m ethod; dynam ic population; urban local area; population spatial distribution; resident traveling ( 502 ) but also a formal language of knowledge rep resentation. It rep resents knowledge with structured formal diagram s, has specific semantics and syntax, so it is computable. Through computation of geographic know ledge map, we can transfer the knowledge it rep resents into other formal language descrip tions. It is a way of geographic knowledge vi2 sualization, and a m ethod of data m ining and know ledge discovery as well. Key words: geographic know ledge m ap; know ledge rep resentation; geographic cognition; data m ining