• Journal of Korea Spatial Information System Society
• /
• v.9 no.3
• /
• pp.1-16
• /
• 2007

A spatial data warehouse is a system to support decision making using a spatial data cube. A spatial data cube is composed of a dimension table and a fact table. For decision support using this spatial data cube, the concept hierarchy of spatial dimension and the summarized information of spatial fact should be provided. In the previous researches, however, spatial summarized information is deficient. In this paper, the spatial aggregation for spatial summarized information in a spatial data warehouse is proposed. The proposed spatial aggregation is separated of both the numerical aggregation and the object aggregation. The numerical aggregation is the operation to return a numerical data as a result of spatial analysis and the object aggregation returns the result represented to object. We provide the extended struct of spatial data for spatial aggregation and so our proposed method is efficient.

• Journal of KIISE
• /
• v.42 no.6
• /
• pp.781-790
• /
• 2015

In recent times, microblogs have become popular owing to the development of the Internet and mobile environments. Among the various types of microblog data, those containing location data are referred to as spatial social Web objects. General aggregations of such microblog data include data aggregation per user for a single piece of information. This study proposes a spatial aggregation algorithm that combines a general aggregation with spatial data and uses the Geohash and MapReduce operations to perform spatial social analysis, by using microblog data with the characteristics of a spatial social Web object. The proposed algorithm provides the foundation for a meaningful spatial social analysis.

• Journal of the Korean Geographical Society
• /
• v.46 no.2
• /
• pp.197-211
• /
• 2011

Due to the complexity of spatial interaction and the necessity of spatial representation and modeling, aggregation of spatial interaction data is indispensible. Given this, the purpose of this paper is to evaluate the effects of modifiable areal unit problem (MAUP) on a spatial interaction model. Four aggregation schemes are utilized at eight different scales: 1) randomly select seeds of district and then allocate basic spatial units to them, 2) minimize the sum of population weighted distance within a district, 3) maximize the proportion of flow within a district, and 4) minimize the proportion of flow within a district. A simple Poisson regression model with origin and destination constraints is utilized. Analysis results demonstrate that spatial characteristics of residuals, parameter values, and goodness-of-fit of the model were influenced by aggregation scale and schemes. Overall, the model responded more sensitively to aggregation scale than aggregation schemes and the scale effect on the model was varied according to aggregation schemes.

• Journal of KIISE:Databases
• /
• v.33 no.5
• /
• pp.463-475
• /
• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• Journal of Korea Multimedia Society
• /
• v.9 no.12
• /
• pp.1529-1541
• /
• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• Journal of Korean Society for Geospatial Information Science
• /
• v.3 no.1 s.5
• /
• pp.91-104
• /
• 1995

The spatial aggregation of data, in transportation and other planning processes, is an important theoretical consideration because the results of any analysis are not entirely independent of the delineation of zones. Moreover, using a different spatial aggregation may lead to different, and sometimes contradictory conclusions. Two criteria have been considered as important in designing zone systems. They are scale and aggregation. The scale problem arises because of uncertainty about the number of zones needed for a study and the aggregation problem arises because of uncertainty about how the data are to be aggregated to from a given scale problem. In a transportation study, especially in the design of traffic analysis zone(TAZ), the scale problem is directly related to the number dof zones and the aggregation problem involves spatial clustering, meeting the general requirements of forming the zones system such as equal traffic generation, convexity, and the consistency with the political boundary. In this study, first, the comparative study of delineating spatial units has been given. Second, a FORTRAN-based heuristic algorithm for designing TAZ based on socio-economic data has been developed and applied to the Korean peninsula containing 132 micro parcels. The vector type ARC/INFO GIS topological data mosel has been used to provise the adjacency information between parcels. The results, however, leave some to be desired in order to overcome such problems as non-convexity of the agglomerated TAZ system and/or uneven traffic phenomenon for each TAZ.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.6
• /
• pp.581-590
• /
• 2017

This study analyzes the effect of floating population, locational characteristics and spatial autocorrelation on foodservice sales using big data provided by the Seoul Institute. Although big data provided by public sector is growing recently, research difficulties are occurred due to the difference of aggregation units of data. In this study, the aggregation unit of a dependent variable, sales of foodservice is SKT unit but those of independent variables are various, which are provided as the aggregation unit of Korea National Statistical Office, administration dong unit and point. To overcome this problem, we convert all data to the SKT aggregation unit. The spatial error model, SEM is used for analysing spatial autocorrelation. Floating population, the number of nearby workers, and the area of aggregation unit effect positively on foodservice sales. In addition, the sales of Jung-gu, Yeongdeungpo-gu and Songpa-gu are less than that of Gangnam-gu. This study provides implications for further study by showing the usefulness and limitations of converting aggregation units of heterogeneous spatial data.

• International Journal of Highway Engineering
• /
• v.16 no.3
• /
• pp.119-129
• /
• 2014

PURPOSES : This investigational survey is to observe a proper spatial aggregation method for path travel time estimation using the hi-pass DSRC system. METHODS : The links which connect the nodes of section detectors location are used for path travel time estimation traditionally. It makes some problem such as increasing accumulation errors and processing times. In this background, the new links composition methods for spatial aggregation are considered by using some types of nodes as IC, JC, RSE combination. Path travel times estimated by new aggregation methods are compared with PBM travel times by MAE, MAPE and statistical hypothesis tests. RESULTS : The results of minimum sample size and missing rate for 5 minutes aggregation interval are satisfied except for JC link path travel time in Seoul TG~Kuemho JC. Thus, it was additionally observed for minimum sample size satisfaction. In 15, 30 minutes and 1 hour aggregation intervals, all conditions are satisfied by the minimum sample size criteria. For accuracy test and statistical hypothesis test, it has been proved that RSE, Conzone, IC, JC links have equivalent errors and statistical characteristics. CONCLUSIONS : There are some errors between the PBM and the LBM methods that come from dropping vehicles by rest areas. Consequently, this survey result means each of links compositions are available for the estimation of path travel time when PBM vehicles are missed.

• Journal of Korea Multimedia Society
• /
• v.9 no.11
• /
• pp.1421-1434
• /
• 2006

Spatial data warehouses provide analytical information for decision supports using SOLAP (Spatial On-Line Analytical Processing) operations. Many researches have been studied to reduce analysis cost of SOLAP operations using pre-aggregation methods. These methods use the index composed of fixed size nodes for supporting the concept hierarchy. Therefore, these methods have many unused entries in sparse data area. Also, it is impossible to support the concept hierarchy in dense data area. In this paper, we propose a dynamic pre-aggregation index method based on the spatial hierarchy. The proposed method uses the level of the index for supporting the concept hierarchy. In sparse data area, if sibling nodes have a few used entries, those entries are integrated in a node and the parent entries share the node. In dense data area, if a node has many objects, the node is connected with linked list of several nodes and data is stored in linked nodes. Therefore, the proposed method saves the space of unused entries by integrating nodes. Moreover it can support the concept hierarchy because a node is not divided by linked nodes. Experimental result shows that the proposed method saves both space and aggregation search cost with the similar building cost of other methods.

• Journal of Information Processing Systems
• /
• v.14 no.2
• /
• pp.396-417
• /
• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.