• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.109-116
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• 2019

In this study, a GIS-based park safety assessment index was developed to enable objective park vulnerability assessment through data-based GIS analysis, a safety assessment was conducted by selecting a target site where various parks are operated and applying the park safety assessment index. In addition, a facility management system was developed for efficient management of the park safety assessment to update the park safety and provide a foothold for indirect PPGIS. In the case of the assessment index of the safety rating of the park, it was possible to conclude that the accurate quantitative performance was given to the calculation of the safety grade of the park based on the fact that the facilities are different depending on the environment and the size of the park. In addition, the marking the safety grade of parks, as well as the function to show the safety facilities of parks, a common living area for citizens, the management system is expected to have an impact on promoting the use of parks. In the future, in functions such as reporting of facility failures and verifying civil information are implemented by applying civic group participation programs and crowd-sourcing technologies, it is believed that all facilities as well as parks managed by the local government can be managed more efficiently.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.961-969
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• 2003

Formulation of quasi-dynamic wetness index was derived to predict the spatial and temporal distribution of the soil moisture. The algorithm of dynamic wetness index was developed through introducing the convolution integral with the rainfall input. The spatial and temporal behaviors of the wetness index of the Sulmachun Watershed was calculated using the digital elevation model(DEM) and the rainfall data for two years. The spatial distribution of the dynamic wetness index shows most dispersive feature of flow generation among the three assumptions of steady, quasi-dynamic and dynamic. The statistical distribution of the quasi-dynamic wetness index and the dynamic wetness index approximate to the steady state wetness index as the time step is increased. The dynamic wetness index shows mixed distribution of the normalized probability density function.

• Journal of KIISE
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• v.45 no.1
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• pp.53-60
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• 2018

This study describes a spatial web object retrieval system using Spark, an in - memory based distributed processing system. Development of social networks has created massive amounts of spatial web objects, and retrieval and analysis of data is difficult by using exist spatial web object retrieval systems. Recently, development of distributed processing systems supports the ability to analyze and retrieve large amounts of data quickly. Therefore, a method is promoted to search a large-capacity spatial web object by using the distributed processing system. Data is processed in block units, and one of these blocks is converted to RDD and processed in Spark. Regarding the discussed method, we propose a system in which each RDD consists of spatial web object index for the included data, dividing the entire spatial region into non-overlapping spatial regions, and allocating one divided region to one RDD. We propose a system that can efficiently use the distributed processing system by dividing space and increasing efficiency of searching the divided space. Additionally by comparing QP-tree with R-tree, we confirm that the proposed system is better for searching the spatial web objects; QP-tree builds index with both spatial and words information while R-tree build index only with spatial information.

• Journal of Korean Society of Rural Planning
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• v.22 no.4
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• pp.25-36
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• 2016

The purpose of this study is to construct a quantitative evaluation method that can analyze the policy effectiveness with the construction of a implicit composite index incorporating spatial econometrics models. In order to propose a methodological framework for the program evaluation, this study conducts an empirical analysis with the application of the Comprehensive Rural Village Development Project (CRVDP) which explicitly claims to achieve comprehensive goal of community development. The present study pays particular attention to quantifying the composite evaluation index and drawing net effect through the application of a series of spatial econometrics models. The spatial unit of the analysis is drawn at Eup-Myeon level in rural areas in Korea, and the time horizon is in between 2005 and 2010. We utilize the Korean Agricultural Census data in 2005 and 2010. Three steps of methodological processes are needed to satisfy the objective of the present study. First, we apply factor analysis to construct the composite index that represents comprehensive settlement environment in rural area. The index should be matched with the main objective of the CRVDP. Second, we apply the derived index to a series of spatial econometrics model as dependent variable. Lastly, utilizing the estimated coefficients of the econometrics models, we apply decomposition technique to estimate CRVDP's net effect from both cross-sectional and longitudinal perspectives. We find that the results of the decomposition analysis by the execution of the CRVDP are positively associated with the explicit object of the project.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.15-22
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• 2019

Highly pathogenic avian influenza (HPAI) is among the top infectious disease priorities in Korea and the leading cause of economic loss in relevant poultry industry. An understanding of the spatial epidemiology of HPAI outbreak is essential in assessing and managing the risk of the infection. Though previous studies have reported the majority of outbreaks occurred clustered in what are preferred to as densely populated poultry regions, especially in southwest coast of Korea, little is known about the spatial distribution of risk areas vulnerable to HPAI occurrence based on geographic information system (GIS). The main aim of the present study was to develop a GIS-based risk index model for defining potential high-risk areas of HPAI outbreaks and to explore spatial distribution in relative risk index for each 252 Si-Gun-Gu (administrative unit) in Korea. The risk index was derived incorporating seven GIS database associated with risk factors of HPAI in a standardized five-score scale. Scale 1 and 5 for each database represent the lowest and the highest risk of HPAI respectively. Our model showed that Jeollabuk-do, Chungcheongnam-do, Jeollanam-do and Chungcheongbuk-do regions will have the highest relative risk from HPAI. Areas with risk index value over 4.0 were Naju, Jeongeup, Anseong, Cheonan, Kochang, Iksan, Kyeongju and Kimje, indicating that Korea is at risk of HPAI introduction. Management and control of HPAI becomes difficult once the virus are established in domestic poultry populations; therefore, early detection and development of nationwide monitoring system through targeted surveillance of high-risk spots are priorities for preventing the future outbreaks.

• Journal of the Korean Institute of Educational Facilities
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• v.23 no.4
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• pp.3-10
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• 2016

This paper presents the investigation results on the classification of the university campuses. For the classification, we selected the spatial index as the evaluation indicator since the environmental factors and maintenance methods vary from university campus to university campus. For the study, we used eight spatial indices of the 30 national universities. This paper provides the spatial characteristics of different campus types, presents campus classification analysis as a future research approach to campus maintenance, and provides the data for the future study of comparison among universities. The results are as follows. 1) The classification investigation categorized the university campuses into three groups. Type 1 is a large-scale type, located near downtown. Type 2 is a medium-scale type, located at a remote site from downtown. Type 3 is a small-scale type, which is located comparatively near downtown. 2) Type 1 is a large-scale mixed area type, and 13 universities belong to this group. Type 2 is a medium-scale suburban area type, and six universities are in this group. Finally, Type 3 is a small-scale downtown area type, and 11 universities belong to this group.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.6
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.203-208
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• 2010

This paper deals with an efficient index scheduling technique based on Hilbert curve and MBR for k-NN query in a single wireless broadcast channel environment. Previous works have two major problems. One is that they need a long time to process queries due to the back-tracking problem. The other is that they have to download too many spatial data since they can not reduce search space rapidly. Our proposed method broadcasts spatial data based on Hilbert curve order where a distributed index table is also broadcast with each spatial data. Each entry of index table represents the MBR which groups spatial data. By predicting the unknown location of spatial data, our proposed index scheme allows mobile clients to remove unnecessary data and to reduce search space rapidly. As a result, our method gives the decreased tuning time and access latency.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1205-1208
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• 2005

By adding user interface to the usual router, an improved functional router is implemented in this paper. Due to the massive amount of spatial data processing, spatial information processing area has been rapidly grown up in recent years based on powerful computer hardware and software development. Spatial index structures are the core engine of geographic information system(GIS). Analyzing and processing of spatial information using GIS has a lot of applications and the number application will be increased in the future. However, study on the under ground is in its infancy due to invisible characteristic of this information. This paper proposes the sub-surface fault detection system using the sub-surface layer information gathered from elastic wave. Detection of sub-surface fault provides very important information to the safety of above and sub-surface man made structures. Development of sub-surface fault detection system will serve as a pre-processing system assisting the interpretation of the geologist.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.233-243
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• 2015

A hotspot is a spatial pattern that properties or events of spaces are densely revealed in a particular area. Whereas location information is easily captured with increasing use of mobile devices, so is not our emotion unless asking directly through a survey. Tweet provides a good way of analyzing such spatial sentiment, but relevant research is hard to find. Therefore, we analyzed hotspots of emotion in the twitter using spatial autocorrelation. 10,142 tweets and related GPS data were extracted. Sentiment of tweets was classified into good or bad with a support vector machine algorithm. We used Moran's I and Getis-Ord $G_i^*$ for global and local spatial autocorrelation. Some hotspots were found significant and drawn on Seoul metropolitan area map. These results were found very similar to an earlier conducted official survey of happiness index.