4,642 research outputs found

    Digital representation of historical globes : methods to make 3D and pseudo-3D models of sixteenth century Mercator globes

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    In this paper, the construction of digital representations of a terrestrial and celestial globe will be discussed. Virtual digital (3D) models play an important role in recent research and publications on cultural heritage. The globes discussed in this paper were made by Gerardus Mercator (1512-1594) in 1541 and 1551. Four techniques for the digital representation are discussed and analysed, all using high-resolution photographs of the globes. These photographs were taken under studio conditions in order to get equal lighting and to avoid unwanted light spots. These lighting conditions are important, since the globes have a highly reflective varnish covering. Processing these images using structure from motion, georeferencing of separate scenes and the combination of the photographs with terrestrial laser scanning data results in true 3D representations of the globes. Besides, pseudo-3D models of these globes were generated using dynamic imaging, which is an extensively used technique for visualisations over the Internet. The four techniques and the consequent results are compared on geometric and radiometric quality, with a special focus on their usefulness for distribution and visualisation during an exhibition in honour of the five hundredth birthday of Gerardus Mercator

    ๋ฌด์ธ๋น„ํ–‰์ฒด ํƒ‘์žฌ ์—ดํ™”์ƒ ๋ฐ ์‹คํ™”์ƒ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•œ ์•ผ์ƒ๋™๋ฌผ ํƒ์ง€ ๊ฐ€๋Šฅ์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2022.2. ์†ก์˜๊ทผ.์•ผ์ƒ๋™๋ฌผ์˜ ํƒ์ง€์™€ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์œ„ํ•ด, ํ˜„์žฅ ์ง์ ‘ ๊ด€์ฐฐ, ํฌํš-์žฌํฌํš๊ณผ ๊ฐ™์€ ์ „ํ†ต์  ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์ด ๋‹ค์–‘ํ•œ ๋ชฉ์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์–ด์™”๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ์ƒ๋Œ€์ ์œผ๋กœ ๋น„์‹ผ ๋น„์šฉ์ด ํ•„์š”ํ•˜๋ฉฐ, ์‹ ๋ขฐ ๊ฐ€๋Šฅํ•œ ํƒ์ง€ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด์„  ์ˆ™๋ จ๋œ ํ˜„์žฅ ์ „๋ฌธ๊ฐ€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ฒŒ๋‹ค๊ฐ€, ์ „ํ†ต์ ์ธ ํ˜„์žฅ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์€ ํ˜„์žฅ์—์„œ ์•ผ์ƒ๋™๋ฌผ์„ ๋งˆ์ฃผ์น˜๋Š” ๋“ฑ ์œ„ํ—˜ํ•œ ์ƒํ™ฉ์— ์ฒ˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, ์นด๋ฉ”๋ผ ํŠธ๋ž˜ํ•‘, GPS ์ถ”์ , eDNA ์ƒ˜ํ”Œ๋ง๊ณผ ๊ฐ™์€ ์›๊ฒฉ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด์˜ ์ „ํ†ต์  ์กฐ์‚ฌ๋ฐฉ๋ฒ•์„ ๋Œ€์ฒดํ•˜๋ฉฐ ๋”์šฑ ๋นˆ๋ฒˆํžˆ ์‚ฌ์šฉ๋˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ์—ฌ์ „ํžˆ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋Œ€์ƒ์˜ ์ „์ฒด ๋ฉด์ ๊ณผ, ๊ฐœ๋ณ„ ๊ฐœ์ฒด๋ฅผ ํƒ์ง€ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ๋ฌด์ธ๋น„ํ–‰์ฒด (UAV, Unmanned Aerial Vehicle)๊ฐ€ ์•ผ์ƒ๋™๋ฌผ ํƒ์ง€์˜ ๋Œ€์ค‘์ ์ธ ๋„๊ตฌ๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜๊ณ  ์žˆ๋‹ค. UAV์˜ ๊ฐ€์žฅ ํฐ ์žฅ์ ์€, ์„ ๋ช…ํ•˜๊ณ  ์ด˜์ด˜ํ•œ ๊ณต๊ฐ„ ๋ฐ ์‹œ๊ฐ„ํ•ด์ƒ๋„์™€ ํ•จ๊ป˜ ์ „์ฒด ์—ฐ๊ตฌ ์ง€์—ญ์— ๋Œ€ํ•œ ๋™๋ฌผ ํƒ์ง€๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋”ํ•ด, UAV๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ, ์ ‘๊ทผํ•˜๊ธฐ ์–ด๋ ค์šด ์ง€์—ญ์ด๋‚˜ ์œ„ํ—˜ํ•œ ๊ณณ์— ๋Œ€ํ•œ ์กฐ์‚ฌ๊ฐ€ ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์ด์  ์™ธ์—, UAV์˜ ๋‹จ์ ๋„ ๋ช…ํ™•ํžˆ ์กด์žฌํ•œ๋‹ค. ๋Œ€์ƒ์ง€, ๋น„ํ–‰ ์†๋„ ๋ฐ ๋†’์ด ๋“ฑ๊ณผ ๊ฐ™์ด UAV๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ํ™˜๊ฒฝ์— ๋”ฐ๋ผ, ์ž‘์€ ๋™๋ฌผ, ์šธ์ฐฝํ•œ ์ˆฒ์†์— ์žˆ๋Š” ๊ฐœ์ฒด, ๋น ๋ฅด๊ฒŒ ์›€์ง์ด๋Š” ๋™๋ฌผ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ์ œํ•œ๋œ๋‹ค. ๋˜ํ•œ, ๊ธฐ์ƒํ™˜๊ฒฝ์— ๋”ฐ๋ผ์„œ๋„ ๋น„ํ–‰์ด ๋ถˆ๊ฐ€ํ•  ์ˆ˜ ์žˆ๊ณ , ๋ฐฐํ„ฐ๋ฆฌ ์šฉ๋Ÿ‰์œผ๋กœ ์ธํ•œ ๋น„ํ–‰์‹œ๊ฐ„์˜ ์ œํ•œ๋„ ์กด์žฌํ•œ๋‹ค. ํ•˜์ง€๋งŒ, ์ •๋ฐ€ํ•œ ํƒ์ง€๊ฐ€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋”๋ผ๋„, ์ด์™€ ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ๊พธ์ค€ํžˆ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์„ ํ–‰์—ฐ๊ตฌ๋“ค์€ ์œก์ƒ ๋ฐ ํ•ด์ƒ ํฌ์œ ๋ฅ˜, ์กฐ๋ฅ˜, ๊ทธ๋ฆฌ๊ณ  ํŒŒ์ถฉ๋ฅ˜ ๋“ฑ์„ ํƒ์ง€ํ•˜๋Š” ๋ฐ์— ์„ฑ๊ณตํ•˜์˜€๋‹ค. UAV๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง€๋Š” ๊ฐ€์žฅ ๋Œ€ํ‘œ์ ์ธ ๋ฐ์ดํ„ฐ๋Š” ์‹คํ™”์ƒ ์ด๋ฏธ์ง€์ด๋‹ค. ์ด๋ฅผ ์‚ฌ์šฉํ•ด ๋จธ์‹ ๋Ÿฌ๋‹ ๋ฐ ๋”ฅ๋Ÿฌ๋‹ (ML-DL, Machine Learning and Deep Learning) ๋ฐฉ๋ฒ•์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ •ํ™•ํ•œ ํƒ์ง€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์ง€๋งŒ, ํŠน์ • ์ข…์„ ํƒ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ์„ ์œ„ํ•ด์„  ์ตœ์†Œํ•œ ์ฒœ ์žฅ์˜ ์ด๋ฏธ์ง€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์‹คํ™”์ƒ ์ด๋ฏธ์ง€ ์™ธ์—๋„, ์—ดํ™”์ƒ ์ด๋ฏธ์ง€ ๋˜ํ•œ UAV๋ฅผ ํ†ตํ•ด ํš๋“ ๋  ์ˆ˜ ์žˆ๋‹ค. ์—ดํ™”์ƒ ์„ผ์„œ ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ๊ณผ ์„ผ์„œ ๊ฐ€๊ฒฉ์˜ ํ•˜๋ฝ์€ ๋งŽ์€ ์•ผ์ƒ๋™๋ฌผ ์—ฐ๊ตฌ์ž๋“ค์˜ ๊ด€์‹ฌ์„ ์‚ฌ๋กœ์žก์•˜๋‹ค. ์—ดํ™”์ƒ ์นด๋ฉ”๋ผ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ๋™๋ฌผ์˜ ์ฒด์˜จ๊ณผ ์ฃผ๋ณ€ํ™˜๊ฒฝ๊ณผ์˜ ์˜จ๋„ ์ฐจ์ด๋ฅผ ํ†ตํ•ด ์ •์˜จ๋™๋ฌผ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ํ•˜์ง€๋งŒ, ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ๊ฐ€ ์‚ฌ์šฉ๋˜๋”๋ผ๋„, ์—ฌ์ „ํžˆ ML-DL ๋ฐฉ๋ฒ•์ด ๋™๋ฌผ ํƒ์ง€์— ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ UAV๋ฅผ ํ™œ์šฉํ•œ ์•ผ์ƒ๋™๋ฌผ์˜ ์‹ค์‹œ๊ฐ„ ํƒ์ง€๋ฅผ ์ œํ•œํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ดํ™”์ƒ๊ณผ ์‹คํ™”์ƒ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•œ ๋™๋ฌผ ์ž๋™ ํƒ์ง€ ๋ฐฉ๋ฒ•์˜ ๊ฐœ๋ฐœ๊ณผ, ๊ฐœ๋ฐœ๋œ ๋ฐฉ๋ฒ•์ด ์ด์ „ ๋ฐฉ๋ฒ•๋“ค์˜ ํ‰๊ท  ์ด์ƒ์˜ ์ •ํ™•๋„์™€ ํ•จ๊ป˜ ํ˜„์žฅ์—์„œ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets. To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species. The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (MLโ€“DL) methods were mainly used for wildlife detection. MLโ€“DL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same MLโ€“DL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field. Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I Contents IV List of Tables VII List of Figures VIII Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research goals and objectives 10 1.2.1 Research goals 10 1.2.2 Research objectives 11 1.3 Theoretical background 13 1.3.1 Concept of the UAV 13 1.3.2 Concept of the thermal camera 13 Chapter 2. Methods 15 2.1 Study site 15 2.2 Data acquisition and preprocessing 16 2.2.1 Data acquisition 16 2.2.2 RGB lens distortion correction and clipping 19 2.2.3 Thermal image correction by fur color 21 2.2.4 Unnatural object removal 22 2.3 Animal detection 24 2.3.1 Sobel edge creation and contour generation 24 2.3.2 Object detection and sorting 26 Chapter 3. Results 30 3.1 Number of counted objects 31 3.2 Time costs of image types 33 Chapter 4. Discussion 36 4.1 Reference comparison 36 4.2 Instant detection 40 4.3 Supplemental usage 41 4.4 Utility of thermal sensors 42 4.5 Applications in other fields 43 Chapter 5. Conclusions 47 References 49 Appendix: Glossary 61 ์ดˆ๋ก 62์„

    Building Ownership, Renovation Investments, and Energy Performanceโ€”A Study of Multi-Family Dwellings in Gothenburg

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    The European building stock was renewed at a rapid pace during the period 1950โ€“1975. In many European countries, the building stock from this time needs to be renovated, and there are opportunities to introduce energy efficiency measures in the renovation process. information availability and increasingly available analysis tools make it possible to assess the impact of policy and regulation. This article describes methods developed for analyzing investments in renovation and energy performance based on building ownership and inhabitant socio-economic information developed for Swedish authorities, to be used for the Swedish national renovations strategy in 2019. This was done by analyzing measured energy usage and renovation investments made during the last 30 years, coupled with building specific official information of buildings and resident area characteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses show that more costly renovations lead to decreasing energy usage for heating, but buildings that have been renovated during the last decades have a higher energy usage when accounting for current heating system, ownership, and resident socio-economic background. It is appropriate to include an affordability aspect in larger renovation projects since economically disadvantaged groups are over-represented in buildings with poorer energy performance

    EXPERIENCES AND LESSONS FROM SOUTH KOREA

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œํ˜‘๋ ฅ์ „๊ณต), 2021.8. ๊น€ํƒœ๊ท .The problem of land use policy in Cรดte d'Ivoire is not new. Since its independence, Cรดte d'Ivoire has been resolutely committed to the development process, taking into account all strategic axes. The 10-year outlook for economic, social, and cultural development 1960- 1970 elaborated in 1962, and the five-year plan from 1971 to 1975 indicated the preponderance of territorial development policy. These first phases were a success with the implementation of two important programs such as the South-West Region Planning Authority (ARSO) created in December 1969 in the forest area, which is part of the improvement of peasant life; and the Bandama Valley Planning Authority (AVB) established in July 1969 in the savannah area, the mission covered two components: (1) a general and permanent regional planning mission and (2) a specific and temporary mission to transfer the affected populations to the south-west of the country. However, with the advent of Structural Adjustment Programs (SAPs) from the 1980s on, the policies that followed will no longer make land use a priority. And will decide to put the development of the city of Abidjan as a driver of economic growth. This decision was thus the basis of regional disparities. With the advent of the Paris Declaration, the State of Cรดte d'Ivoire will give a new chance to regional and local development policy with the establishment of institutions for this purpose and the completion of the monographic study of the 14 districts in 2013. However, Despite the development observed at the macroeconomic level with a GDP growth rate between 12 and 7% since 2012, poverty remains noticeable at the level of populations, especially those living in rural areas. The effective implementation of the policy faces serious problems as well as at institutional and financial level. The study attempts to show how Korean experiences in territorial development Trough the Comprehensive National Territorial Plan (CNTP) can be applied to Cรดte d'Ivoire.์ฝ”ํŠธ ๋””๋ถ€ ์•„๋ฅด์˜ ํ† ์ง€ ์ด์šฉ ์ •์ฑ… ๋ฌธ์ œ๋Š” ์ƒˆ๋กœ์šด ๊ฒƒ์ด ์•„๋‹™๋‹ˆ๋‹ค. ๋…๋ฆฝ ์ดํ›„ ์ฝ”ํŠธ ๋””๋ถ€ ์•„๋ฅด๋Š” ๋ชจ๋“  ์ „๋žต์  ์ถ•์„ ๊ณ ๋ คํ•˜์—ฌ ๊ฐœ๋ฐœ ๊ณผ์ •์— ์ „๋…ํ•ด ์™”์Šต๋‹ˆ๋‹ค. 1960~1970 ๋…„ ๊ฒฝ์ œยท์‚ฌํšŒยท๋ฌธํ™” ๋ฐœ์ „์— ๋Œ€ํ•œ 10 ๋…„ ์ „๋ง์€ 1962 ๋…„์— ์ •๊ตํ™”๋˜์—ˆ๊ณ , 1971๋…„๋ถ€ํ„ฐ 1975๋…„๊นŒ์ง€์˜ 5 ๊ฐœ๋…„ ๊ณ„ํš์€ ์˜ํ†  ๊ฐœ๋ฐœ ์ •์ฑ…์˜ ์šฐ์„ธ๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ด ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” 1969๋…„ 12 ์›” ๋†๋ฏผ ์ƒํ™œ ๊ฐœ์„ ์˜ ์ผํ™˜์ธ ์‚ฐ๋ฆผ ์ง€์—ญ์— ์„ค๋ฆฝ๋œ ๋‚จ์„œ๋ถ€ ์ง€์—ญ ๊ณ„ํš ๋‹น๊ตญ (ARSO)๊ณผ ๊ฐ™์€ ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ํ”„๋กœ๊ทธ๋žจ์˜ ์‹คํ–‰์œผ๋กœ ์„ฑ๊ณตํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  1969 ๋…„ 7 ์›” ์‚ฌ๋ฐ”๋‚˜ ์ง€์—ญ์— ์„ค๋ฆฝ ๋œ ๋ฐ˜ ๋‹ค๋งˆ ๋ฐธ๋ฆฌ ๊ณ„ํš ๋‹น๊ตญ (AVB)์—์„œ ์ž„๋ฌด๋Š” (1) ์ผ๋ฐ˜ ๋ฐ ์˜๊ตฌ ์ง€์—ญ ๊ณ„ํš ์ž„๋ฌด์™€ (2) ์˜ํ–ฅ์„๋ฐ›์€ ์ธ๊ตฌ๋ฅผ ๊ตญ๊ฐ€์˜ ๋‚จ์„œ์ชฝ. ๊ทธ๋Ÿฌ๋‚˜ 1980 ๋…„๋Œ€๋ถ€ํ„ฐ ๊ตฌ์กฐ ์กฐ์ • ํ”„๋กœ๊ทธ๋žจ (SAP)์˜ ์ถœํ˜„์œผ๋กœ ๋”ฐ๋ผ์˜จ ์ •์ฑ…์€ ๋” ์ด์ƒ ํ† ์ง€ ์‚ฌ์šฉ์„ ์šฐ์„ ์‹œํ•˜์ง€ ์•Š์„ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ์ œ ์„ฑ์žฅ์˜ ์›๋™๋ ฅ์œผ๋กœ ์•„๋น„ ์žฅ์‹œ์˜ ๊ฐœ๋ฐœ์„ ๊ฒฐ์ •ํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ์ด ๊ฒฐ์ •์€ ์ง€์—ญ ๊ฒฉ์ฐจ์˜ ๊ธฐ์ดˆ๊ฐ€๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ํŒŒ๋ฆฌ ์„ ์–ธ๋ฌธ์˜ ๋„๋ž˜์™€ ํ•จ๊ป˜ ์ฝ”ํŠธ ๋””๋ถ€ ์•„๋ฅด์ฃผ๋Š” ์ด๋Ÿฌํ•œ ๋ชฉ์ ์„์œ„ํ•œ ๊ธฐ๊ด€์„ ์„ค๋ฆฝํ•˜๊ณ  2013 ๋…„ 14 ๊ฐœ ์ง€๊ตฌ์— ๋Œ€ํ•œ ๋‹จํ–‰๋ณธ ์—ฐ๊ตฌ๋ฅผ ์™„๋ฃŒํ•จ์œผ๋กœ์จ ์ง€์—ญ ๋ฐ ์ง€์—ญ ๊ฐœ๋ฐœ ์ •์ฑ…์— ์ƒˆ๋กœ์šด ๊ธฐํšŒ๋ฅผ ์ œ๊ณต ํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, 2012 ๋…„ ์ดํ›„ GDP ์„ฑ์žฅ๋ฅ ์ด 12 ~ 7 % ์ธ ๊ฑฐ์‹œ ๊ฒฝ์ œ ์ˆ˜์ค€์—์„œ ๊ด€์ฐฐ ๋˜์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋นˆ๊ณค์€ ์ธ๊ตฌ ์ˆ˜์ค€, ํŠนํžˆ ๋†์ดŒ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” ์‚ฌ๋žŒ๋“ค ์ˆ˜์ค€์—์„œ ์—ฌ์ „ํžˆ ๋‘๋“œ๋Ÿฌ์ง‘๋‹ˆ๋‹ค. ์ •์ฑ…์˜ ํšจ๊ณผ์ ์ธ ์‹คํ–‰์€ ์ œ๋„์ , ์žฌ์ •์  ์ˆ˜์ค€๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ์— ์ง๋ฉดํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ๋Š” ๊ตญํ†  ์ข…ํ•ฉ ๊ณ„ํš (CNTP)์„ ํ†ตํ•œ ํ•œ๊ตญ์˜ ์˜ํ†  ๊ฐœ๋ฐœ ๊ฒฝํ—˜์ด ์ฝ”ํŠธ ๋””๋ถ€ ์•„๋ฅด์— ์–ด๋–ป๊ฒŒ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋Š”์ง€ ๋ณด์—ฌ์ฃผ๊ณ  ์žํ•œ๋‹ค.CHAPTER ONE: GENERAL INTRODUCTION 1 1.0. BACKGROUND OF STUDY 1 1.1. Definition of key concepts 5 1.1.1. Territorial Development Planning (TDP) 5 1.1.2. National Territorial Plan (NTP) 6 1.1.4. Poverty 6 1.1.5. Industrialization 7 1.1.6. Institutions 8 CHAPTER TWO: LITERATURE REVIEW 10 2.0. Balanced Growth Theory 10 2.1. Unbalanced Growth Theory 12 2.2. Territorial development policy in Asia 15 2.3. Territorial Development policy in Korea 17 2.4. Territorial Policy Planning overview about Africa 20 2.5. Any scientific works on Territory development in Cรดte dโ€™Ivoire? 21 CHAPTER THREE: ANALYTICAL FRAMEWORK 24 3.1. Research Objectives 24 3.1.1. Overall Objective 24 3.1.2. Specific Objectives 24 3.2. Statement of Problem 25 3.3. Research Motivation 26 3.4. Research Questions 27 3.5. Research Method and justification of the choice of theories 29 3.6. Significance of the Study 30 3.7. Thesis Limitations 31 CHAPTER FOUR: TERRITORIAL DEVELOPMENT POLICY IN COTE Dโ€™IVOIRE 33 4.0. History of Territory Development Policy in Cรดte Dโ€™Ivoire: a policy review 33 4.1. Development of the Ivorian territory until 1990. 33 4.1.1. From the 10-year perspectives 33 4.1.2. Planning Institutions and first Territory Development Programs in Cรดte d'Ivoire. 34 4.1.2.1. Mechanisms and tools of operation 35 4.1.3. The 1971-1975 Five-Year Plan 36 4.1.4. The 1976-1980 Five-Year Plan 37 4.1.5. The 1981-1985 Five-Year Plan 38 4.1.6. Analysis of the impacts of the five-year plans 40 3.1. The New Face of Land Development Policy in Cรดte d'Ivoire (1990-now). 42 3.1.1. Advent of Structural Adjustment Programs (SAPs) 42 3.1.2. The changing of vision and objectives for Land Development policy 43 3.1.3. Socio-economic, political and institutional indicators: variables analysis 44 3.1.4. Summary of the variables analysis: preliminary findings 53 3.1.5. The fundamentals of the new driving in Land Development Policy 56 3.1.5.1. National Prospective Studiesโ€™ Reviews (Cรดte dโ€™Ivoire 2000, 2010 and 2025) 56 3.1.5.2. Stage of implementation of new vision of TDP in Cรดte d'Ivoire 58 3.1.5.3. Competitive Economic Poles: Opportunities and Challenges 58 3.1.6. Objectives of Competitive Economic Poles (CEPs) 62 3.1.7. National Economic Development Plan (NEDP): A Short Terms Development Plan 65 3.1.7.1. EDP-2012-2015 65 3.1.7.2. EDP-2016-2020 66 3.1.8. Challenges and Limitations of Current Territorial De Development Policy Implementation 66 3.1.8.1. Institutional and legal Challenges 67 3.1.8.2. Economic Challenges 69 3.1.8.3. Political challenges 69 CHAPTER FIVE: KOREAโ€™S TERRITORY MANAGEMENT: EXPERIENCES AND LESSONS 70 4.0. Historical motivation of Koreaโ€™s Territorial Development Policy 70 4.1. Metropolitan areaโ€™s Development and Regional Inequality 72 4.2. Comprehensive National Territorial Plans in South Korea (1972-2014) 73 4.2.1. First Comprehensive National Territorial Plan (1972-1982) 73 4.2.2. Second Comprehensive National Territory Plan (1982-1992) 74 4.2.3. Third Comprehensive National Territory Plan (1992-2001) 76 4.2.4. Fourth Comprehensive National Territory Plan (2002-2010) 77 5.4. Institutionalization of Korean Territorial Development Policy Implementation 80 5.4.1. National land development policy in Korea: A stateโ€™s affair 81 5.4.2. Comprehensive National Territorial Plans: a strong legally based policy 83 5.4.3. Korean strategy for Green Growth through the Comprehensive National Territory Plan 85 5.4.4. Economics Development Plan in South Korea 87 5.4.4.1. A TDP support tool 87 5.4.4.2. Conditions of effective EDP 88 CHAPTER SIX: SYNTHESIS AND POLICY RECOMMENDATION 90 6.1. Synthesis and findings 90 6.1.1. Slowing down reasons of territorial policy implementation in Cรดte dโ€™Ivoire 90 6.1.2. Cote dโ€™Ivoire: current vision and prospects for territorial development 91 6.1.3. Policy implication from the Korean experiences 92 6.1.3.1. Effective Conditions of Territorial Development Policy 92 6.1.3.2. Five Principles of Effective TDP Implementation 94 6.1.3.3. Recommended Territorial Development Policy Implementation 97 6.2. Policy recommendations for future research 98 6.3. Conclusion 100 VIII. REFERENCES 101 ๊ตญ๋ฌธ ์ดˆ๋ก 107์„

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