• Journal of Korean Society of Rural Planning
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• v.30 no.2
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• pp.1-11
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• 2024

Farmland transaction prices are affected by various factors such as politics, society, and the economy. The purpose of this study is to identify multiple factors that affect the farmland transaction price due to changes in the actual transaction price of farmland by farmland unit from 2016 to 2020. There are several previous studies analyzed the determinants of farmland transaction prices by considering spatial dependency. However, in the case of land transactions where the time and space of the transaction affect simultaneously, if only spatial dependence is considered, there is a limitation in that it cannot reflect spatial dependence that occurs over time. In order to solve these limitations, To address these limitations, this study builds a spatio-temporal autoregressive model that simultaneously considers spatial and temporal dependencies using farmland transactions in Jinju City as an example. As a result of the analysis, it was confirmed that there was significant spatio-temporal dependence in farmland transactions within the previous 30 days. This means that if the previous farmland transaction was carried out at a high price, it has a spatio-temporal spillover effect that indirectly affects the increase in the price of other nearby farmland transactions. The study also found that various location attributes and socioeconomic attributes have a significant impact on farmland transaction prices. The spatio-temporal autoregressive model of farmland prices constructed in this study can be used to improve the prediction accuracy of farmland prices in the farmland transaction market in the future, and it is expected to be useful in drawing policy implications for stabilizing farmland prices

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.20-41
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• 2023

Recently, urban shrinkage due to low birth rate and aging population and the decline of local cities are causing a new urban problem of empty houses. This study examines the distribution of vacant homes using spatial panel data collected from 2015 to 2019 at local administraitve districts and estimates the factors of vacant house occurrence using a spatial panel model considering spatio-temporal dependency. As a result, the spatio-temporal dependence of vacant houses was identified and it was estimated using spatial panel model not OLS model. Based on the spatial panel model, it was found that the most influential factor in the occurrence of vacant houses was the housing-related factor. This result shows that policy considerations for housing supply are necessary for the management of vacant housing as well as population movement and poor infrastructure.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4026-4040
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• 2019

In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.280-285
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• 2007

Since the human civilization, people's livelihood is dependent on natural resources primarily on forest. Human dimensions such as population, poverty, agricultural expansion and infrastructure development are some of the underlying factors and their interrelated associations which could play a vital role in deforestation and forest degradation. This process is not only related to the human population but also connected to the various socioeconomic factors. This paper focuses to link the spatio-temporal extent of population, poverty incidence and forest dependency and their severity on Terai forest of Nepal. Secondary data on censuses were used. ArcGIS and descriptive statistics were also used for data analysis. Based on analysis & literature review we concluded that population, poverty and forest dependency have largely expanded over time in Terai and their interrelated associations substantively influence on deforestation. However, the direct relationship of such factors with deforestation and forest degradation found to be incompatible, complex and hard to perceive with fragmented and inconsistency censuses data. So, deforestation and forest degradation issues intertwined with socioeconomic factors need detailed analysis to comprehend where these linkages are still unravel.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.796-806
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• 2009

SVC (Scalable Video Coding) is a new video coding standard to provide convergence media service in heterogeneous environments with different networks and diverse terminals through spatial-temporal-quality combined flexible scalabilities. This paper presents the performance analysis on packet loss in the delivery of SVC over IP networks and an efficient adaptation method to packet loss caused by buffer overflow. In particular, SVC with MGS (Medium Grained Scalability) as well as spatial and temporal scalabilities is addressed in the consideration of packet-based adaptation since finer adaptation is possible with a sufficient numbers of quality layers in MGS. The effect on spatio-temporal quality due to the packet loss of SVC with MGS is evaluated. In order to minimize quality degradation resulted by packet loss, the proposed adaptation of MGS based SVC first sets adaptation unit of AU (Access Unit) or GOP corresponding to allowed delay and then selectively discards packets in order of importance in terms of layer dependency. In the experiment, the effects of packet loss on quantitative qualities are analyzed and the effectiveness of the proposed adaptation to packet loss is shown.