Knowledge Discovery in Spatial Databases


Shishir Shrivastava1, Ajay Kumar Akasapu2 and Lokesh Sharma2

1Student, Rungta College of Engineering and Technology, Bhilai CG

2Reader, Rungta College of Engineering and Technology, Bhilai CG

*Corresponding Author E-mail:,,



Knowledge   discovery   in   databases is a   complex   process concerned   with   the discovery   of   relationships   and   other descriptions   from   data.   Knowledge    discovery    in   spatial databases represents a particular  case  of discovery, allowing the  discovery  of relationships  that  exist  between  spatial  and non-spatial  data,  and  other  data  characteristics  that  aren’t explicitly stored in spatial databases. This paper describes the conception and implementation of PADRÃ O, a system for knowledge discovery in spatial databases. PADRÃO presents a new approach to this process, which is based on qualitative spatial reasoning.  The  spatial  semantic  knowledge  and  the principles   of  qualitative   spatial   reasoning   needed   for  the spatial  reasoning  process  are  available  in  the  PADRÃO’s geographic database and PADRÃO’s spatial knowledge base, allowing   the   integration   of   the   geo -spatial    component, associated  with  the  analyzed  non -geographic  data,  in  the process of knowledge discovery.


This paper presented the PADRÃO system, a system for knowledge discovery in spatial databases based on qualitative spatial reasoning. PADRÃO represents a new approach to this particular case of knowledge discovery, in which the positional aspects of geographic data are provided by a spatial reference, gave  by  a  geographic  identifier  (in  the  described  case,  the municipalities’  name).  The PADRÃO’s geographic database and   spatial   knowledge    base   store   the   spatial   semantic Knowledge and the qualitative spatial reasoning principles needed for the inference of new spatial relations required in the knowledge discovery process.


KEYWORDS: KDD:  Knowledge Discovery in Databases; KDSD:  Knowledge Discovery in Spatial Databases

DDB:  Demographic Database GDB:  Geographic Database SKB:  Spatial Knowledge Base n GDB: non-Geographic Database; PDB:  Patterns Database; PDB:  Patterns Database




Large amounts of operational data concerning several years’ operation are now becoming available, mainly in middle-large sized    organizations. Knowledge  Discovery in  Databases (KDD)  is   the  key   to   access  the  strategic  valued   of  the organizational knowledge buried in  databases , usable both for daily  operation, genera l  management  and  strategic  planning.


The process of KDD automates the discovery of relationships and other descriptions from data. Data mining is one of the steps of this process, concerned with the application of specific algorithms for extracting patterns from. The main recognized advances in the area of KDD are related with the exploration of relational databases.


However, in most organizational databases exists one dimension of data, the geographic (associated with addresses or postcodes),   which   semantics   is not used by traditional KDD systems.  Knowledge Discovery in Spatial Databases (KDSD) is related with “the extraction of interesting spatial patterns and features, general relationships that exist between spatial and non-spatial data and other data characteristics   not   explicitly   stored   in   spatial   databases”. Spatial database systems are normally relational databases plus a concept of spatial location and spatial. The explicit location and extension of objects define implicit relations of spatial neighborhood. The neighbor attributes of a given object may influence its behavior and therefore must be considered in the process of knowledge   discovery.  Knowledge   discovery   in relational databases doesn’t take into consideration this spatial reasoning, motivating   the development   of new algorithms adapted   to   the   characteristics   of spatial   data.   The  main approaches in  KDSD are characterized by the development of new algorithms  that treat  the  objects ’ position  and  extension through the manipulation of its co-ordinates . These algorithms are subsequently implemented, extending traditional knowledge discovery systems.  In all, a quantitative spatial reasoning approach is used, although the results are presented using qualitative identifiers (like far, close, north,). This paper describes the conception of PADRÃO, a system for KDSD. PADRÃO presents  a new approach  to  the  process  of KDSD based  on  qualitative  spatial reasoning  and  was  implemented recurring  to  a  traditional  knowledge  discovery  system.  The spatial  semantic knowledge  and  the  principles  of qualitative spatial  reasoning  needed for the spatial reasoning process are available in the PADRÃO’s   geographic   database  and PADRÃO’s spatial knowledge base, allowing the integration of the  data geo-spatial component  in  the  process  of knowledge discovery.  The  integration   of  a   geographic   database   the municipality and district level, and a demographic database, in any  area  allowed   to   PADRÃ O  the  discovery   of  implicit relations hips existing  between  the  analyzed  geographic  and demographic data.  This paper is organized in several sections. In them, qualitative spatial reasoning is defined and described how its concepts are used in the knowledge discovery process. The architecture of PADRÃO is presented, describing its main components,   the   several   steps    associated    with    it.   The application  of PADRÃO  to  the  demographic  domain  is  also illustrated,   referring   the  type  of   discoveries   that   can   be achieved with it .



The positional aspects of geographic data are provided by a spatial reference, which relate the data to a given position on the Earth’s surface. Spatial references fall into two categories: based on co-ordinates or on geographic identifiers. In systems of spatial referencing using geographic identifiers   (indirect referencing systems), a position is referenced to a real world location defined by a real world object. This object is termed a location, and its identifier is termed a geographic identifier. These geographic identifiers are very common in organizational databases, allowing the integration of the spatial component associated with it in the process of knowledge discovery. The  adoption  of an  indirect  geographic  reference system  imposes   the  us e   of   qualitative   spatial   reasoning strategies , able to deal with  the spatial semantic not explicitly associated  with  the   adopted  geographic  identifiers . Spatial reasoning is the process by which information about objects in s pace          and their relations hips are gathered through measurement, observation or inference, and used to arrive to valid                conclusions regarding the objects’ relations hips. Qualitative spatial is based on the manipulation of qualitative spatial   relations, for which composition tables facilitate reasoning, allowing the inference of new spatial knowledge. Spatial   relations   have   been   classified   in   several   types, including direction relations   (that describe order in space), distance   relations   (that   describe   proximity   in   space) and topological relations (that describe neighborhood and incidence). These spatial relations are briefly described in the next subsections.



Direction relations describe where objects are placed relative to each   other.   Three   elements   are   needed   to   establish   an orientation: two objects and a fixed point . Cardinal directions can  be  expressed  using  numerical  values  specifying  degrees (0º ,  45º …)  or  using  qualitative  values  or  symbols ,  such  as North  or South, those have  an  associated  acceptance  region. The  regions  of  acceptance  for  qualitative  directions  can  be obtained by projections  (also  known  as half-planes ) or cone- shaped regions (Figure 1).


Figure 1


A characteristic of the cone-shaped system is that the region of acceptance increases with distance, which makes it suitable for the definition of directional relations between extended objects. Another benefit  is  that this  system allows  the  definition  of finer  resolutions ,  permitting  the  use of  eight  (Figure  2)  or sixteen   different   qualitative   directions .   This    model   uses triangular acceptance areas that are drawn from the centroid of the reference object towards the primary object (In the spatial relation A North B, B represents the reference object, while A constitutes the primary object). Since  the  geographic  domain analyzed in this work characterizes administrative subdivisions , the cone-shaped system will be used in the identification of the direction  relations  existing   between  the  several  geographic subdivisions .



Distances      are     quantitative     values      determined through measurements or calculated from known co-ordinates of two objects in some reference system. The most familiar definition of distance is the length of the shortest possible path between two objects, also known as Euclidean distance.  Usually a metric quantity is mapped onto some qualitative indicator such as   very   close or far for human   commonsense   reasoning. Qualitative distances must correspond to a range of quantitative distances specified by an interval, and should be ordered s o that comparisons are possible.  Another requirement is that the length of each successive qualitative distance should be greater or equal to the length of the previous one (Figure 3).



Topological relations are those relations that are   invariant under continuous transformations of space such as rotation or scaling. There  are eight  fundamental relations  that  can  exist between  two  planar regions: disjoint,  contains, inside, equal, meet,  covers,  covered   by   and   overlap   (Figure 4). These relations can be defined considering intersections between the two regions, their boundaries and their complements


In  some  exceptional  cases ,  the  geographic  space  can’t   be characterized,   in   topological  terms ,  recurring   to   the   eight primitives  presented above. One of these cases is related with application domains in which the addressed geographic regions are administrative subdivisions.  Administrative subdivisions can only be related through the topological primitives disjoint, meet and contains (and it’s inverse inside), since they can’t have any kind of overlapping. The topological primitives used in this work are disjoint and meet, once the implemented qualitative inference   process   only    considers  regions of  the  same geographic level.


Integration of direction, distance and topological spatial relations

Reasoning  about  qualitative  directions  necessarily   involves integrated  spatial  reasoning  about  qualitative  distances  and directions . Particularly in objects with extension, the size and shape of objects, and the distance between them, influence the directions.  One of the ways to determine the direction and distance1 between regions is calculating them for its respective centroid. The extension of the geographic entities is somehow implicit in the topological primitive used to characterize its relations. Qualitative spatial reasoning requires the adoption of a set of qualitative identifiers. In  the  implemented  approach, integrated   spatial   reasoning   with   direction,   distance    and topological  spatial  relations,  the  adopted  set  of  qualitative identifiers were :


Direction = {N, NE, E, SE, S, SW, W, NW} Distance = {very close, close, far, very far} Topological = {disjoint, meet}


For each of these qualitative identifiers (direct ion and distance, since topological relations   are quantitatively by   nature), is required   the definition   of the validity   interval   that   limits quantitatively the region of acceptance of each one. In the case of direction relations, for the cone-shaped system with eight acceptance regions, the quantitative intervals adopted are: [337.5, 22.5), [22.5, 67.5), [67.5, 112.5), [112.5, 157.5), [157.5, 202.5), [202.5, 247.5), [247.5, 292.5), [292.5, 337.5)



From N to NW respectively.  In the case of distances,  there should exist a constant ratio (ratio= length (disti)/length (diti-1))   relations hip   between   the   lengths   of   two   neighboring intervals . For example, for a ratio 42 the obtained intervals are:


Analyzing  the  furthest  distance  that  can  exist  between  two regions   of  the  addressed   geographic   domain,  the   ratio 4 intervals need to be magnified 3 by a factor of 10,  resulting in the validity intervals : (0, 10], (10, 50], (50, 210] and (210, 850] corresponding  from very  close to  very  far respectively.  The adoption  of  a  mixed  approach  allowed   the  integration  of direction,   distance   and   topological    relations,   under   the principles  of  qualitative  spatial  reasoning.  The  composition table  is  represented  recurring  to  graphical  symbols ,  like  the ones  presented  in  Figure  5. In order to exemplify the set of rules stored in the final composition table, Table 1 exhibits subset of the rules contained in it.


The set of rules stored in the composition table (Table 1) can be used in the inference of new spatial relations needed in the knowledge discovery process. For example, knowing  the facts A Northeast, very close, meet B and B East, close, disjoint C, it can be inferred that A east, close, disjoint C, as  can be verified in Figure 6.



PADRÃO is a system for KDSD based in qualitative spatial reasoning. This section presents its architecture and gives some technical details about its implementation.


ADRÃO’s architecture


The architecture of PADRÃO (Figure 7) aggregates three main components : Knowledge  and Data  Repository, Data  Analysis and Results Visualization. The Knowledge and  Data.


Repository component stores the data and knowledge needed in the knowledge discovery process. This process is implemented in the Data Analysis component, which allows the discovery of patterns or others   relationships   implicit   in   the   analyzed geographic and non -geographic data. The discovered patterns can be visualized in a map using the Results Visualization component. These components are afterwards described.


The Knowledge and Data Repository component group three central databases:

1.     A Geographic  Database  (GDB)  constructed  under the principles established by the European Committee for Normalization  in  the  CENTC 287 standard  for Geographic Information. Following its recommendations was possible to implement a GDB in which the positional aspects of geographic data are provided by a   geographic  identifiers  system.  This system characterizes the administrative subdivisions of Portugal at the municipality and district level. Also includes a geographic gazetteer with the several


Geographic   identifiers   used   and   the   concept   hierarchies existing between them. The geographic identifiers system was integrated with a spatial schema allowing the definition of the direction, distance and topological spatial relations that exist between the adjacent regions of the municipality level.



2.  A Spatial   Knowledge Base (SKB) that stores all the qualitative   rules   needed   in   the   inference   of   new spatial relations. The knowledge available in this database aggregates the composition table constructed, the set of used identifier sand the validity interval of the m. This knowledge base is used in conjunction with the GDB in the inference of implicit spatial relations.


3.  A non-Geographic Database (n GDB) that is   integrated with the GDB and analyzed in the Data Analysis component. This procedure enables the discovery of implicit relations hips that exist between the geographic and non -geographic data.


The  Data  Analysis  component  is  implemented  through  the knowledge discovery module, and is characterized by s ix main steps :


1. Data Selection. This step allows the s election of the relevant non-geographic and geospatial data needed for the execution of a defined data-mining task. In  this  phase must  be  evaluated what is the minimal sub-set of data to be selected, the size of the  sample  needed  and  the period  of  time  to  be  considered. The term geo-spatial is used to emphasize that geographic data has associated spatial data.  Traditionally,  geographic  data  is associated  with  a  location,  positioning  an  object  or  fact  in space,  while  spatial  data  defines  the  characteristics  of  that localization, namely its geometry and topology.


2. Data Treatment. This phase is concerned with the cleaning of the s elected data, allowing the corrupt data treatment and the definition of strategies for dealing with missing data fields.


3. Data Pre-Processing. This step allows the reduction of the sampleset to be analyzed. Two tasks can here be affected:

i) Reduction of the number of rows or,

ii) Reduction of the number of columns. In the reduction of the number  of  rows ,  data  can  be  generalized  attending  to  the domain’s hierarchies or attributes  with continuous  values can be trans formed  into  discreet  values  attending  to  the  defined classes . The  reduction  of the  number of  columns  attends  to verify   if  any  of  the  s elected   attributes   can  afterwards  be omitted.


4. Geo-Spatial Information Processing. This step verifies if the geo-spatial information needed is available in the  GDB. In many situations it implicitly exists, due to the properties of the spatial schema implemented. In this case, and to ensure that all geo-spatial   knowledge   is   available    for   the   data   mining algorithms , these implicit relations are trans formed into explicit relations through the inference rules stored in the SKB.


5.  Data Mining.  Several algorithms can be used for the execution of a given data mining task. In this step, the several available algorithms are evaluated in order to identify the most Appropriate for the defined task. The s elected one is applied to the relevant non-geographic and geo-spatial data, in order to find implicit relations hips or other interesting patterns that exist between them.


6. Results Interpretation. The interpretation of the discovered patterns aims to evaluate their utility and importance to the application domain. In this step it may be  realized that relevant attributes  were  ignored  in  the  analysis ,  suggesting  that  the process should  be  repeated. The relevant discoveries can be stored in the database of patterns, allowing its subsequent us e in further analyses (meta-rules construction) or its visualization in a map.


The Results Visualization  component  is  responsible  for  the management of the discovered patterns and its  visualization in a  map.  For  that,  PADRÃ O  uses  a  Geographic  Information System  (GIS),  integrating  the  discovered  patterns  with  the cartography of the analyzed region. This component aggregates two main databases:


1.  The Patterns Database  (PDB)  that  stores  all  relevant discoveries . In this database, each discovery is catalogued and associated with the set of rules that represents the discoveries made in a given data mining task.


2. A Cartographic Database (CDB) with the cartography of the region. It aggregates a set of points, lines and polygons with the geometry of the geographical objects.



The nGDB, of the Knowledge and Data Repository component, used   in   this   application   of  PA DRÃ O   is   a  Demographic Database (DDB) that stores the parish registers. This database collects  attributes  like birth date, birthplace, death  date, death place,  occupation,  number    of    descendants,   number    of marriages , etc. The several attributes related to places allow the integration of the  DDB  with  the  GDB,  providing  the  geo- spatial data needed in the knowledge discovery module.



This paper presented the PADRÃO system, a system for knowledge discovery in spatial databases based on qualitative spatial reasoning. PADRÃ O represents a new approach to this particular case of knowledge discovery, in which the positional aspects of geographic data are provided by a spatial reference, gave by a geographic identifier (in the described case, the municipalities’ name). The PADRÃO’s  geographic database and   spatial   knowledge    base   store   the   spatial   semantic knowledge  and   the   qualitative   spatial  reasoning  principles needed for the inference of new spatial relations required in the knowledge  discovery process . The analysis of a demographic database with PADRÃO allowed  the  discovery  of  imp licit relations hips that exist between the analyzed demographic and geo-spatial data.


The  data  analysis  techniques  described  along  this  document constitutes  a special case of Spatial Analysis and  is useful in the  Urban  and  Regional  Research.  This application domain analyses huge amounts of data that are related with some place on the Earth’s surface. PADRÃO has the capability to analyze databases of great dimension.  These databases usually store geo-referenced data, which spatial component is included in the spatial analysis process of PADRÃO using qualitative spatial reasoning strategies.



The PADRÃ O’s geographic database and spatial knowledge base store the spatial semantic knowledge and the qualitative spatial reasoning principles needed for the inference of new spatial relations required in the knowledge discovery process. The   analysis of   a   demographic   database   with   PADRÃO allowed   the   discovery   of implicit   relationships   that   exist between the analyzed demographic and geo-spatial data.



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Received on 11.03.2011       Accepted on 25.03.2011     

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Int. J. Tech. 1(1): Jan.-June. 2011; Page 23-28