THE INCERTITUDE DISPOSAL METHODS OF THE SPATIAL DATA

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1 THE INCERTITUDE DISPOSAL METHODS OF THE SPATIAL DATA M. H. Liu a, *, Z. W. Yua b, a Sio-Korea Chogqig GIS Research Ceter, College of Computer Sciece, Chogqig b Uiversity of Posts ad Telecommuicatios, Chogqig, , P.R. Chia liumh@cqupt.edu.c KEY WORDS: Spatial Data, GIS, Icertitude Disposal ABSTRACT: I the process of costructig ad applyig the GIS, the data is the object of the GIS operatio ad the basis of the sciece decisiomakig ad aalysig so that the data quality is determied the success or failure of the whole system applicatio. Ay idetermiatios i the data layers ca diffuse through aalysig ad combiig with other error source. It makes risks to decisiomakig. The theory ad method of icertitude aalysis i the GIS ad RS is oe of the GIS study focus at preset, because there is ot a mature ad systemic theory ad method to disposig the icertitude i the GIS. I this article, we study mostly the icertitude of the spatial data, the method of disposig the icertitude i the GIS ad RS. I this paper, the iadequacies of the curret icertitude cotrol methods for spatial data were poited out. We argue that some strategies ca be take to maipulate icertitude accordig the spacial data icertitude resource. Robust algorithm ca effectively avoid icertitude ad improve the spacial data qualities i the process of spacial data, ad spatial data icertitude ca be cotrol by studyig robust algorithm. 1. INTRODUCTION Thaks to the advacemets i Geographic Iformatio systems (GIS) techology ad the recet developmet i telemuicatio facilities, spatial databases have become easily accessible ad the uses of spatial databases have greatly icreased. Quite ofte, users of spatial data rely o GIS to store, maipulate, aalyze, ad display their spatial data. Maps are the most commo form of output from GIS to represet spatial data for a wide variety of purposes, icludmg decisio makig ad policy formulatio. However, users of spatial data should cocer the quality of data which i tur affects the cofidece of decisio or the success of policies. Spatial scietists ad geographers do realize the importat of quality of spatial data. they have bee developig techiques ad frameworks to visualize the quality of data through various cartographic meas. However, most efforts either emphasize o the techiques of represetig data quality iformatio, Very few researchers ivestigate how to cotrol quality of data by devisig better algorithm i GIS, the eviromet i which spatial data are captured, stored, aalyzed, ad represeted. I this paper, we argue that some strategies ca be take to Dispose icertitude accordig the spacial data icertitude resource. Robust algorithm ca effectively avoid icertitude ad improve the spacial data qualities i the process of spacial data. A lot of cuccessful strategy to maipulate icertitude lie i the realizatio of robust algorithm. I the followig sectio, we will poit out the iadequacies of the curret icertitude cotrol methods for spatial data. We will also provide a brief descriptio of some strategies be take to maipulate icertitude accordig the spacial data icertitude resource. The, we will idetify some aspects of spatial data icertitude that ca be cotrol by studyig robust algorithm. 2. DERIVING SPACIAL DATA INCERTITUDE IN GIS Fisher (1995)proposed the cocept model of icertitude i spatial data (Klir ad Yua,1995) (Figure 1). He thought the essece of icertitude is how to defie the object classes ad sigle object to be tested.o his opiio, Icertitude iclude Objects well defie ad Objects maldefie, So we should take some resposig measures to cotrol spatial data quality. Author argue that too may icertitude do't cause oly by weather Objects be well defied or ot.for example, the area data of forest ad cultivated lad are very differet betwee Departmet of Agriculture ad Departmet of forest.though Objects maldefie is oe of the reaso, the other reaso maybe cocer to Departmet beefit protectio. Grai for Gree Project i chia, the cetre goveromet will ivest accordig the eara covertig the lad for forestry ad pasture. Of course, Departmet of Agriculture maybe focus more o reservig atural farmlad, while Departmet of forestry more o further speedig up evirometal protectio plas. So there are icertitude whe we use the data come from differet resource i GIS. Such icertitude maybe come from Objects maldefie, maybe Objects be well defie, for Departmet beefit protectio reaso or aother, icertitude be itroduced i the ame of Objects maldefie. O this coditios, how ca we judge weather such icertitude comes from Objects well defie or Objects maldefie? So author argue that this is aother icertitude factor. A lad use comprehesive plaig revisal example is take to show the possibility of spacial data icertitude resource. I the program of lad use comprehesive plaig revisal,we must collect a lot of spacial data, some spacial data icertitudes were foud ad listed i followig. * Correspodig author: M. H. Liu, liumh@cqupt.edu.c, Tel:

2 1. Attribute discrepacies. typical eara uit of measure is hectare,but the territorial recorder is mitre square, ad we traditioally use Mu.If we measure o digital map,maybe the typical eara is millimetre.whe we exchage from oe to aother uit of measure,there are discrepacies. Attribute discrepacies ca come from data aggregate, Whe data are aggregated spatially, a smoothig or geeralizatio process is uderway (David et al., 1996 )Attribute discrepacies is very much related to time resolutios. We foud that a lot of villages were igathered,but the spacial did ot refresh. (Figure 2) 2. The positioal discrepacies. The positioal discrepacies derived from overlayig differet data sources vary amog regios. Sice GIS spatial data come from differet sources, of differet formats, ad of differet degrees of accuracy. 3. Completeess of database. Defiitely, the completeess of database is very much related to how recet the database has bee updated. Icertitude process to maipulate icertitude,the type of applictio, decisio ad utilizatio must be cosidered at first. The Select the data, hardware ad software, processes, ad type of product. We may test which sigificat form of icertitude affect the product ad how to commet? What qualities of product is eeded? The qualities of product is acceptable?at last we make decisio weather we absorb icertitude residual or reduce icertitude existece. Author argue that whe Cosider problem from aother agle, some strategies ca be take to Dispose icertitude accordig the spacial data icertitude resource. For Attribute discrepacies, we ca compare the attribute data from differet sources or differet formats with prior kowledge about which data sources are more reliable. Comparig the database i questio with the more reliable data sources ca yield data quality iformatio. Quite ofte, spatial decisio ad aalysis rely upo spatially aggregated data. Therefore, Error is itroduced durig the geeralizatio process. I geeral, highly aggregated data are less reliable tha disaggregated data, a approach to evaluate aggregatio error primarily for vector format data i GIS is to compares data of differet scales or resolutios. Objects well defie possibility Objects maldefie Fuzzy ambiguous As far as the positioal discrepacies, To evaluate the positioal accuracy, users ca import the data ito a GIS package usig a aerial ortho photo, SPOT image or similar type of remotely sesed data of the same area as the backgroud. Superimposig the differet data sources ca tell users which type of data is more accurate ad reliable i certai areas. Theory disesembles discord No-specificities For example, a TIFF image geerated from a aerial photo ca be put i the backgroud. Substatial positioal discrepacies betwee the differet spacial data ca be recogized. Figure 1. Taxoo?? Figure 2. Map of lad use comprehesive plaig revisal Coceptually, these discrepacies ca be captured i terms of some statistics to reflect the discrepacies. These statistics ca further be maipulated, stored, ad also displayed together with the spatial data to reflect the positioal accuracy of geographical features i the spatial database. For completeess of database,gis ca be used to compare differet data sources ad derive completeess iformatio for spatial databases. Theoretically, whe spatial databases are imported to GIS i which topological relatioshps are stored or extracted, differet data sources ca be compared to derive data quality iformatio about the logical cosistecy of spatial data. However, topological structure ad relatioship i geographcal features are modeled ad captured implicitly i most GIS. Some systems have particular features or fuctios that may assist the evaluatio or testig of logical cosistecy i the database. Sice the process to assess logical cosistecy i GIS is relatively system-depedet, it is ot feasible to derive a geeral framework to be implemeted i GIS to derive logical cosistecy iformatio. 3. THE INCERTITUDE DISPOSAL STRATEGY OF THE SPATIAL DATA 3.1 Some Strategies ca be Take to Dispose Icertitude Accordig the Spacial data Icertitude Resource David W.S.Wog ad C.Victor Wu had proposed a strategy to maipulate icertitude (David et al., 1996)(Figure3). I the 232

3 Cosideratio iitial: -Type of applictio -type of decisio -type of utilizatio Selectio data,hardware ad software, processes, type of product -Which sigificat form of icertitude affect the product -How to commet? -How to commuicate? remote sesig. The juctio may be ,from differet people s view of differet directio of the lie. A efficiet approach for the detectio of juctio i gray-level images was be proposed i the followig. Detectio of juctio i Gray-level Images is divided ito three steps. Firstly, digital image pre-processig is advised so as to get the lies extracted from the origial image. Four differet measures are proposed: the origial image is eheced ad biarized, oise is removed, ad skeletoed which kept the lies coectivity. Secodly, the eighborhood pixes -track algorithm is take. For this purpose, the start forked pixel is detected, eighbor-track is take to fid the last forked pixel poit ad ed pixel poit, ad the localizatio of juctio is ivolves. Examples are provided based o experimets with sythetic ad real images. What qualities of product is eeded? The qualities of product is acceptable? yes o Absorb icertitude residual Reduce existece icertitude Decisio makig Figure 4. Localizatio the juctio o shape-based Recogitio (Illustrate as the picture above, three fractures itersect at pixel 3) Figure3. A strategy to maipulate icertitude i GIS. 3.2 The data process methods play a importat role durig icertitude maipulate. Amog these, robust algorithm is very importat i all factors which cause spatial data. I the followig, the relatioship betwee the data process methods ad spatial data qualities is be exemplified. For example, Spacial object recogiio ad classificatio play a importat role i romote sesig gragh processig. Whe iterpretatio of remote sesig image, we eed the geometric properties feature ad Topological associatios of spacial objects as well as the spectral feature. I covetioal methods of image aalysis, remotely sesed data are processed mostly o a pixel-by-pixel or cell-by-cell basis, but little iformatio about shapes of objects except simple measures such as sizes ad form factors of objects are employed(wkpratt,1991;serra,1988).as the spatial resolutio of remotely sesed data icreases, it has become more possible, desirable, ad ecessary, to idetify the spatial characteristics, or shapes, of objects. This iformatio, as well as covetioal spectral itesity values, are required for the automated iterpretatio of remotely sesed images. May people have explored o shape-based Recogitio. They preset a approach to the recogitio of complex shaped objects. I the process of shape-based Recogitio of liar objects such as road or river,oe of importat task is to fid fork pixel(juctio)(see Fig4).Obviously, there are icertitudes o how to localizate the juctio i shape-based Recogitio of Basic stages Three stage ca be adopted to detect juctio i gray-level images(see tab1). 1 started forked pixel detectio. As show i fig.4, suppose we track the pixel from pixel 0, A pixel is called as a forked pixel if there are more tha oe pixels of ozero graylevel except preprocessed pixel ad the pixel itself, i the 3*3 area cetered at the give pixel. The started pixel set formed. It is P1 P={P 0,1,}. pixels (2 3 4) is the addiable pixels. The add the addiable pixels (2 3 4)to privous pixel set P.The ew pixel set P2 ({P 0,1,2,3,4} formed. 2 search for ext forked pixel or ed pixels. Started from ay a addiable pixel of P1, (2 3 4)withi set P2, search for all the addiable pixels(ew addiable pixels be called) which there are oe or more tha oe pixels of ozero graylevel except preprocessed pixel ad the pixel itself withi 3*3 eighborhood area, add the ew addiable pixels to P2{P 0,1,2,3,4},ad P3{P 0,1,2,3,4} is formed. Repeated this program util there is o addiable pixel ca be added.at this momet, the addiable amout cetered the pixel are all equal 1,the last addiable pixels are forked pixel or ed pixels.,devoted addiable pixel amout 3 localizatio of juctio. We defie the started value of coordiatio is o the left-up coror. There are two methods to localizate of juctio. Method 1:use the value of forked or ed pixels to calcaulate the coordiatio xi,yi of itersect pixel(or juctio). this measure is give by 233

4 X = 1 Y = 1 i= 1 i= 1 x y i i Where X,Y represet localizatio of juctio. (1) ucertaity should be preserved i each trasformatio of ucertaity from oe mathematical framework to aother. forked pixels Method 2: use the pixels coordirate with greatest value of total addiable pixel amout to calcaulate the coordiatio xi,yi of itersect pixel(or juctio). Because the total addiable pixel amout meas that the probablility for a pixel is the most to become a itersect pixel.this measure is give by (1). The detectio method for lie juctios described i this paper was tested usig real images cotaiig L,T,Y,ad X lie juctios. Test show that the respose to a give juctio is uique ad localizatio is accurate. From above aalysis,we ca cocluded that robust algorithm ca effectively avoid icertitude ad improve the spacial data qualities i the process of spacial data. a lot of cuccessful strategy to maipulate icertitude lie i the realizatio of robust algorithm.it is ecessary to speed time o studig good algorithm of spacial data so as to cotrol spacial data qualities. 3.3 The Basic Priciple of Spacial Data Icertitude Measure Three basic priciples of ucertaity were developed to guide the use of ucertaity measures i differet situatios(david Harmaec,; Klir,1988,1995). Of course,it ca also be used as the basic priciple of spacial data icertitude measure. These priciples are: a priciple of miimum ucertaity, a priciple of maximum ucertaity, ad a priciple of ucertaity ivariace. forked pixels 3*3 eighborhood Cout addiable pixel amout i addiable amout cetered at a pixel are all equal 1 ed pixels Calculate coordiate the juctio Localize the juctio pixels with greatest value of total addiable pixels amout The priciple of miimum ucertaity is a arbitratio priciple. It guides the selectio of meaigful alteratives from possible solutios of problems i which some of the iitial iformatio is ievitably lost but i differet solutios it is lost i varyig degrees. The priciple states that we should accept oly those solutios with the least loss of iformatio. This priciple is applicable, for example, i simplificatio ad coflict resolutio problems. For some developmet of this priciple see (C. Josly ad G. J. Klir,1992). The priciple of maximum ucertaity is applicable i situatios i which we eed to go beyod coclusios etailed by verified premises. The priciple states that ay coclusio we make should maximize the relevat ucertaity withi costraits give by the verified premises. I other words, the priciple guides us to utilize all the available iformatio but at the same time fully recogize our igorace. This priciple is useful, for example, whe we eed to recostruct a overall system from the kowledge of (some) subsystems. The priciple is widely used withi classical probability framework (Christese,1985; Paris,1994), but has yet to be developed i a more geeral settig. The last priciple, the priciple of ucertaity ivariace, is of relatively recet origi ( Klir,1989,1990). Its purpose is to guide meaigful trasformatios betwee various theories of ucertaity. The priciple postulates that the amout of Figure 5. The pixels-track algorithm Though importat, the priciples of ucertaity are ot the oly situatios a measure of ucertaity ca be used. Some other examples where the measures of ucertaity were used iclude measurig of closeess of possibility distributio(higashi ad Klir,1983) ad ivestigatig dyamics of combiatio of evidece.(harmaec.,1997) 4. CONCLUSION AND DISCUSSION I this article, we study mostly the icertitude of the spatial data, ad the method of disposig the icertitude i the GIS ad RS. I this paper, the iadequacies of the curret icertitude cotrol methods for spatial data were poited out. Some strategies ca be take to maipulate icertitude accordig the spacial data icertitude resource. Robust algorithm ca effectively avoid icertitude ad improve the spacial data qualities i the process of spacial data, ad spatial data icertitude ca be cotrol by studyig robust algorithm. a priciple of miimum ucertaity, a priciple of maximum ucertaity, ad a priciple of ucertaity ivariace ca also be used as the basic priciple of spacial data icertitude measure. 234

5 REFERENCES C. Josly ad G. J. Klir. Miimal iformatio loss possibilistic approximatios of radom sets. I Proceedigs of IEEE Iteratioal Coferece o Fuzzy Systems, pages , Sa Diego, David W. S. Wog; C. Victor Wu Spatial Metadata ad GIS for Decisio Support Proceedigs of the 29th Aual Hawaii Iteratioal Coferece o System Scieces David Harmaec,summary measures of ucertaity, D. Harmaec. A ote o ucertaity, Dempster rule of combiatio, ad coflict. Iteratioal Joural of Geeral Systems, 26(1-2):63-72, G.J. Klir ad B. Yua, Fuzzy Sets ad Fuzzy Logic: Theory ad Applicatios, Pretice Hall, Upper Saddle River, NJ, G. J. Klir. The role of ucertaity priciples i iductive systems modellig. Kyberetes, 17(2):24-34, G. J. Klir. Probability-possibility coversio. I Proceedigs of Third IFSA Cogress, pp , Seattle, G. J. Klir. A priciple of ucertaity ad iformatio ivariace. Iteratioal Joural of Geeral Systems, 17(2-3): , G. J. Klir. Priciples of ucertaity: What are they? Why do we eed them? Fuzzy Sets ad Systems, 74(1):15-31, J. B. Paris. The ucertai reasoer's compaio - a mathematical perspective. Cambridge Uiversity Press, J.Serra, Image Aalysis ad Mathematical Morphology, New york: Academic Press, M. Higashi ad G. J. Klir. O the otio of distace represetig iformatio closeess: possibility ad probability distributios. Iteratioal Joural of Geeral Systems, 9(2): , R. Christese. Etropy miimax multivariate statistical modellig I: Theory. Iteratioal Joural of Geeral Systems, 11: , R. Christese. Etropy miimax multivariate statistical modelig II: Applicatios. Iteratioal Joural of Geeral Systems, 12(3): , WKPratt, Digital Image Processig, New York: Joh Wiley & SosJc.,

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