2 research outputs found
Data-Driven System Analysis for Regional Issues
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : λμ
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κ³Όνλν νλκ³Όμ λλ¦ΌκΈ°μν, 2021.8. μκ΅.4μ°¨ μ°μ
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μλ κΈ°μ λ°μ μ μ€μ¬μλ λ°μ΄ν°κ° μλ€. λ°μ΄ν°λ 맀λ
μ(Volume), λ€μμ±(Variety), μλ(Velocity) λ±μ μΈ‘λ©΄μμ νλ°μ μΌλ‘ μ±μ₯νλ©° μ¦κ°νλ©΄μ νμ μ κΈ°μ μ κΈ°λ°μ΄ λκ³ μλ€. νΉν, μ΄λ¬ν λ°μ΄ν°λ μ§μμ 보기μ μ λ°λ¬μ ν΅ν΄ λ€μν λΆμΌμμ λ¬Έμ ν΄κ²°μ μν μμ¬κ²°μ μ΄λ μ μ±
μ립μ μμ΄μ μ΅μ μ λμμ μ°ΎκΈ° μν ν¨κ³Όμ μΈ λ°©λ²μλ κ·Έ νμ©μ±μ΄ λΆκ°λκ³ μλ€.
λ°μ΄ν° μ¬μ΄μΈμ€λ μ΄λ¬ν λ°μ΄ν°μ νμ μ μΈ μ¦κ°μ μ±μ₯μ κΈ°λ°μΌλ‘ λ€μν ννμ μμ€ν
μ΄ λΉλ©΄ν λ¬Έμ λ₯Ό μ νν μΈμνκ³ μ΄λ₯Ό ν΄κ²°ν μ μλ ν΅μ¬ λ°μ΄ν°λ₯Ό νμ
νμ¬, μ ν©ν λΆμκΈ°λ²μ ν΅ν΄ λ¬Έμ μ λν ν΄λ²μ λμΆνλ λΆμΌλ‘ μ£Όλͺ©λ°κ³ μλ€.
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λμ΄ λΆμΌμμλ λμ΄μ΄ λΉλ©΄ν μΈκ΅¬ κ°μ, κ³ λ Ήν, 곡λν λ± μ΄μ
ν νκ²½μμ νμ λ μμμ μ ν¨νκ² νμ©νλ©΄μ μ§μμ νΉμ±μ κ³ λ €νμ¬ λ¬Έμ λ₯Ό ν΄κ²°ν μ μλ μ λμ μΈ λ°©μμ λμΆνκ³ κ·Έ ν¨κ³Όλ₯Ό κ²μ¦νλ λ°©μμ λͺ¨μνκ³ μλ€. μ΄λ₯Ό μν΄ λμ΄μ λ€μν νμλ€μ λν μμλ₯Ό λ§μΆ€νμΌλ‘ λμνλ λμμ μ λμ μ΄κ³ μ€μ²μ μΈ ν΄κ²°λ°©μμ λ§λ ¨νκΈ° μν΄μ μμ€ν
κ΄μ μμ ν΄λΉ νμμ ꡬ체μ±μ νμ
ν νμκ° μλ€.
μ΄μ λ³Έ μ°κ΅¬λ λμ΄μ§μμ λν μν-μ¬ν μμ€ν
μ νμλ€μ μμ€ν
κ΄μ μμ ν΄κ²°ν μ μλ λ¬Έμ λ‘ μ μνκ³ , λ°μ΄ν°μ¬μ΄μΈμ€ κΈ°λ²μ μ΄μ©νμ¬ μμ€ν
μ κ·Όλ°©λ²μΌλ‘ ν΄λ²μ μ°Ύμ μ μλ λ°©μμ λͺ¨μνκ³ μ νμλ€. μ΄λ₯Ό μν΄ λ¨Όμ (1) μ§μκ°λ°μ¬μ
κ³Ό κ΄λ ¨λ λ¬Έν κ²ν λ₯Ό μννμμΌλ©°, λμ΄μ§μμ λ¬Έμ λ₯Ό μ μ£Όνκ²½κ°μ κ³Ό μλν₯μμ ν΅μ¬λΆμΌμΈ κ΅μ‘, μλ£, μ ν΅κ°μ , λΆκ°κ°μΉ μ°½μΆκ³Ό κ΄λ ¨ν μ£Όμ μ νμμΌλ‘ μ νννμλ€. (2) κ°κ°μ νμλ€μ λνμ¬ μμ€ν
κ΄μ μμ λ¬Έμ λ₯Ό μ μνκ³ , λ°μ΄ν°μ¬μ΄μΈμ€ κΈ°λ°μ λΆμκΈ°λ²μ λ°νμΌλ‘ λ¬Έμ λ₯Ό ν΄κ²°νλ μΌλ ¨μ κ³Όμ μ μννμλ€. (3) μ΄λ¬ν λΆμκ³Όμ μ ν΅ν΄ λμ΄μ§μ μμ€ν
μ ꡬμ±νλ λ€μν μμλ€μ λ°μ΄ν°μ λν νμ©κ°λ₯μ±κ³Ό μ μ ν λΆμκΈ°λ²μ λν μ μ©μ±μ νκ°νμλ€.
βμ§μλ¬Έμ λμΆ λ° μ νν λ°©μ μ°κ΅¬βμμλ μ λΆλ³ κ΅μ μ΄μ 5κ°λ
κ³νμ μ μλ κ΅κ° λΉμ μ΄ μ§μκ°λ°μ¬μ
κ³Ό κ΄λ ¨ν κ΅μ λͺ©ν λ° κ΅μ μ λ΅, κ΅μ κ³Όμ μ μ΄λ ν μν₯μ λ―ΈμΉλμ§ κ³ μ°°νμλ€. μ§μκ°λ°μ¬μ
κ³Ό κ΄λ ¨ν λ¬Έν κ²ν λ₯Ό ν΅ν΄ μ§μλ¬Έμ λ ν¬κ² μ μ£Όμ μλμΌλ‘ λμΆν μ μμΌλ©°, ꡬ체μ μΌλ‘ μννκ²½μ λΉλ₯Ό ν΅ν μ μ£Όνκ²½ κ°μ κ³Ό κ²½μ νλ λ€κ°νλ₯Ό ν΅ν μλν₯μ λ°©μμ λͺ¨μνλ λ κ°μ§λ‘ μ νννμλ€.
βλμ΄ μ μ£Όνκ²½ κ°μ μ μν μμ€ν
λΆμ μ°κ΅¬βμμλ ν¬κ² μ§μμ΄ λΉλ©΄ν νμ μ€ κ΅μ‘λΆμΌμ μκΈμλ£λΆμΌμ λ¬Έμ λ₯Ό μ μνκ³ μ΄λ₯Ό ν΄κ²°νλ μ°κ΅¬λ₯Ό μννμλ€. κ΅μ‘λΆμΌμ κ²½μ° νλ ΉμΈκ΅¬ κ°μμ λ°λ₯Έ λμ΄μ§μμ κ΅μ‘μμ€ ν΅νν©μ μν΄ ν΄λ¦¬μ€ν± 곡κ°μ΅μ ν κΈ°λ²μ μ΄μ©νμ¬ νκ΅μ μ΄μ νΉμ νκ΅μ λν μμ¬κ²°μ μ λ΄λ¦΄ μ μλ λ°©μμ λͺ¨μνμλ€. μκΈμλ£λΆμΌμ κ²½μ° λμμ λμ΄μ§μμ μκ°λλ³ μ€μκ° λλ‘μλ λ³νμ λ°λ₯Έ μκΈμλ£ μ κ·Όμ± λ³νλ₯Ό λΆμνκ³ μ΄μ λ°λ₯Έ μκΈμλ£ μ·¨μ½μ§μ μκΈνμ μμ‘΄μ¨μ νκ°νλ©°, λ€μν μκΈμν© μλ리μ€λ₯Ό ꡬμΆνμ¬ μν©λ³ μμ‘΄νλ₯ λ³ν λΆμμ ν΅ν΄ κ°μ λ°©μμ λμΆνμλ€.
βλμ΄ μλν₯μμ μν μμ€ν
λΆμ μ°κ΅¬βμμλ μ ν΅κ°μ λ° λΆκ°κ°μΉ μ°½μΆ λΆμΌμ λ¬Έμ λ₯Ό μ μνκ³ μ΄λ₯Ό ν΄κ²°νλ μ°κ΅¬λ₯Ό μννμλ€. μ ν΅κ°μ μ κ²½μ° λμ°λ¬Ό μ΄μ‘μ νκ³λΉμ©μ μ΅μννκΈ° μνμ¬ κ΄μκ΅ν΅λ§μ μ ν΄κ³΅κ°κ³Ό μΉνκ²½ μ΄μ‘μλ¨μ μ΄μ©ν μλ‘μ΄ ννμ μ€λ§νΈλ‘μ§μ€ν±μ€ μμ€ν
μ κ°λ°νκ³ , κΈ°μ‘΄μ νλ°°λ§κ³Ό λΉκ΅λ₯Ό ν΅ν΄ μ μ©κ°λ₯μ±μ νκ°νμλ€. λΆκ°κ°μΉ μ°½μΆ λΆμΌμμλ μ νμλμ°λ¬ΌμΈμ¦μ λλ₯Ό λμμΌλ‘ ν΅κ³μ μΆλ‘ κΈ°λ°μ μΈμ¦κΈ°μ€ μ€μ λ°©μμ λͺ¨μνμλ€. κΈ°μ‘΄ μΈμ¦κΈ°μ€μΈ κ΅κ°νκ· κ°κ³Ό λμ
μμ°νκ²½μ λΆνμ€μ±μ κ³ λ €ν ν΅κ³μ μΆλ‘ κ°κ³Ό λΉκ΅λ₯Ό ν΅ν΄ μ νμλμ°λ¬ΌμΈμ¦μ λμ ν΅κ³μ μΌλ‘ μ μν μμ€μμμ μΈμ¦κΈ°μ€μ λν ν΅κ³μ λμμ μ μνμλ€.
μ§μκ°λ° λ° κ³΅κ°κ³ν λΆμΌμ κΈ°μ¬ν μ μλ λ³Έ μ°κ΅¬μ νμ μ μ€μμ±μ λ€μκ³Ό κ°λ€. λ¨Όμ μ§λ¦¬μ 보μ ν΅κ³μ 보λΏλ§ μλλΌ μ€μκ° κ΅ν΅μ 보 λ± λ€μν ννμ λΉ
λ°μ΄ν°λ₯Ό κΈ°λ°μΌλ‘ λμ΄μ§μμ΄ κ°μ§ νΉμ±λ€μ κ³ λ €νμ¬ λ¬Έμ λ₯Ό ν΄κ²°ν μ μλ λ°μ΄ν°μ¬μ΄μΈμ€ κΈ°λ°μ μ λμ μΈ λ°©μμ μ μνμλ€. λν μμ€ν
κ΄μ μμ μ§μμ λ¬Έμ λ₯Ό μ νννμ¬ ν΄κ²°ν μ μλ μ λμ μΈ λΆμκΈ°λ²μ λν μ¬λ‘λ₯Ό ν΅ν΄ μ€μ²μ μΈ λ°©μμ λͺ¨μν μ μλλ‘ λ°©ν₯μ μ μνμλ€.
μ΄λ₯Ό ν΅ν΄ λ³Έ μ°κ΅¬λ κΈ°μ‘΄μ νμ ꡬμ λ¨μμ ν΅κ³μλ£λ₯Ό κΈ°λ°μΌλ‘ ν μ μ±
μ립과 κ°μ νλ‘μΈμ€λ₯Ό ννΌνκ³ , λ³΄λ€ μ λμ μΈ λ°©λ²λ‘ μ ν΅ν΄ λ°μ΄ν°λ₯Ό κΈ°λ°μΌλ‘ ν μ μ±
μ μ립ν μ μλ κΈ°μ΄λ₯Ό λ§λ ¨ν μ μλ€κ³ νλ¨λλ€. μ΄λ ν₯ν λμ΄μ§μμ 보건·볡μ§, κ΅μ‘, μ°μ
λ± λ€μν λΆμΌμ μμλ§μΆ€ν μ¦κ±°κΈ°λ° μ μ±
μ λν μμ¬κ²°μ μ μ§μν μ μμΌλ©°, κ΄λ ¨λ λ²Β·μ λ κ°νΈμ ν©λ¦¬μ μ΄κ³ μ λμ μΈ κ·Όκ±°λ₯Ό λ§λ ¨νκ³ κΈ°μ΄μλ£λ‘μ¨ μ 곡ν μ μλ€κ³ μ¬λ£λλ€.The development of big data and knowledge information technology enables researchers to find effective ways to use data as evidence for making decisions and policies and solving problems. Data sciences also help scholars accurately recognize problems with systems, identify key data to solve problems, and analyze data using appropriate techniques to produce meaningful results.
In agricultural and rural regions, governments also seek derive quantitative approaches to solve problems by considering regional characteristics while effectively utilizing limited resources in poor environments such as a diminishing population, aging, and hollowing in rural areas. Thus, it is necessary to identify the specifics issues related to the phenomenon based on system perspectives using data science and to identify quantitative, customized, and practical solutions ongoing issues in rural areas.
The main objective of this dissertation is to define the ecological-social systems issues in rural areas. These problems can be solved from system perspectives, and solutions can be found based on data science techniques. First, this dissertation reviewed the literature related to regional development projects in South Korea and categorized rural issues such as improving the settlement conditions and creating income sources. Second, for each issue, the problem was defined from a system perspectives and processes to solve the problem were recommended based on data science-based analytical techniques. Third, through this analysis process, the study evaluated the availability of data from various components of rural systems and their applicability to appropriate analytical techniques.
In the chapter titled, "A Study on the Derivation and Categorization of Regional Problems," the impact of Korea's national vision, national goals, national strategy, and national tasks were presented in each government's five-year plan related to the composition and implementation of regional development projects. Through the literature review related to regional development projects, the regional issues were categorized into two types: (1) settlement environment improvement by maintaining the living environment and (2) income creation by diversifying economic activities.
The chapter titled, "System Analysis for the Improvement of Rural Settlement Environment," two issues were addressed to define and solve problems in the education and emergency medical sectors. The first study in the education field analyzed and recommended solutions on the operation or closure of schools using heuristic spatial optimization techniques to consolidate educational facilities in rural areas due to the decreasing school-age population. The second study in the emergency medical field analyzed changes in accessibility to emergency medical services due to real-time road speed changes in urban and rural areas. Emergency medical vulnerabilities and survival rate of emergency patients were also analyzed, and various emergency scenarios were constructed to recommend improvement measures based on the survival probability change analysis.
The chapter on "System Analysis for Rural Income Creation," analyzed problems related to improving crop distribution and value-added generation. In the distribution improvement study, a new type of smart logistic system was suggested using idle space and eco-friendly transportation in a regional network to minimize the marginal cost of agricultural transportation. Applicability was also evaluated by comparing the existing delivery networks. The study in the value-added generation field aimed to establish statistical inference-based certification standards for low-carbon agricultural product certification systems. Statistical alternatives to appropriate certification criteria for low-carbon agricultural production certification schemes were also proposed by comparing the existing national average values considering the uncertainties in agricultural production conditions.
This dissertation with a series of studies contributes to regional development and planning and provide the following academic significance. First, this dissertation presents data science-based quantitative measures to solve problems considering rural characteristics by applying various types of big data such as real-time traffic information as well as geographical and statistical information. In addition, a direction to explore practical measures is presented using examples of quantitative analytical techniques that can be structured and solved by formalizing regional problems from a systems perspective.
This dissertation also addresses the limitations of existing studies on rural policies based on administrative statistics and lays the groundwork for establishing data-based policies using more quantitative methodologies. The results can support evidence-based policy decisions tailored to the demands in various fields including health, welfare, education, and industry in rural areas in the future. The recommendations also provide reasonable and quantitative grounds for reforming related laws, policies, and regulations.μ 1 μ₯ μ λ‘ 1
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2.1. μ΄λ±κ΅μ‘μμ€ λ° νꡬλ λ°μ΄ν° 74
2.1.1. μ§μλ³ μ΄λ±κ΅μ‘μμ€ λ°μ΄ν° 74
2.1.2. νκ΅λ³ νꡬλ λ°μ΄ν° 77
2.2. μ§μλ³ λ§μλ¨μ μνκΆ μ€μ¬μ§ μ€μ 78
2.3. μ€μ λλ‘거리 κΈ°λ°μ ν΅νμ κ·Όμ± λΆμ 81
2.3.1. λλ‘λ§λλ₯Ό μ΄μ©ν μ€μ λλ‘거리 μ°μ 81
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2.4. κ΅μ‘ννμ± κΈ°λ°μ μ΅μ νκ΅° μ¬μ€μ μ
μ§μ΅μ ν λͺ¨λΈ κ°λ° 85
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μ§ λͺ¨λΈ κ°λ° 100
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3.2.2. κΈ°μ‘΄μ νκ΅°κ³Ό λͺ¨μλ μ΅μ νκ΅° λΉκ΅ 102
3.2.3. μ€μ νκ΅ μμμ μλ리μ€μ λ°λ₯Έ νκ΅ μμ λΉκ΅ 104
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μ 5 μ₯ μ§μ μλν₯μμ μν μμ€ν
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3.3. κΈ°μ‘΄μ μ§κ±°λ μ΄μ‘λ§κ³Ό AgroSLSμ μ΄μ‘λ²μ μΆμ 183
3.3.1. μ°μ²΄κ΅ νλ°°λ§μ μ΄μ©ν μ§μλ°°μ‘ λ²μ 183
3.3.2. AgroSLSλ₯Ό μ΄μ©ν μ§μλ°°μ‘ λ²μ 185
3.4. κΈ°μ‘΄μ μ§κ±°λ μ΄μ‘λ§κ³Ό AgroSLSμ μμμκ° μΆμ 186
3.4.1. μ°μ²΄κ΅ νλ°°λ§μ μ΄μ©ν μμμκ° 186
3.4.2. AgroSLSλ₯Ό μ΄μ©ν μμμκ° 191
3.4.3. μ°μ²΄κ΅ νλ°°λ§κ³Ό AgroSLSμ μ΄ μμμκ° λΉκ΅ 194
3.5. κΈ°μ‘΄μ μ§κ±°λ μ΄μ‘λ§κ³Ό AgroSLSμ μλμ§ μ¬μ©λ μΆμ 196
3.5.1. μ°μ²΄κ΅ νλ°°λ§μ μ΄μ©ν μλμ§ μ¬μ©λ 196
3.5.2. AgroSLSλ₯Ό μ΄μ©ν μλμ§ μ¬μ©λ 201
3.5.3. μ°μ²΄κ΅ νλ°°λ§κ³Ό AgroSLSμ μ΄ μλμ§ μ¬μ©λ λΉκ΅ 204
4. κ³ μ°° 205
5. μκ²° 207
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1. μλ‘ 209
1.1. λ°°κ²½ λ° νμμ± 209
1.2. μ°κ΅¬λͺ©μ 213
2. λΆμ λ°©λ² λ° μλ£ 214
2.1. λκ° μλνλ λ°μ΄ν° 214
2.2. λμ°λ¬Ό μμ°λ¨κ³ μ¨μ€κ°μ€ μ°μ λ°©λ² 214
2.3. ν΅κ³μ μΈμ¦κΈ°μ€ νκ° λ°©λ² 220
2.3.1. μ μνκ· λ² 220
2.3.2. ν΅κ³μ μΆμ λ°©λ² 221
3. λΆμ κ²°κ³Ό 224
3.1. λμ°λ¬Ό μλνλ λ°μ΄ν° κΈ°μ ν΅κ³ λΆμ 224
3.2. λμ°λ¬Ό μ¬λ°°λ¨κ³λ³ μ¨μ€κ°μ€ μΆμ 227
3.3. ν΅κ³μ λΉκ΅λ₯Ό ν΅ν μΈμ¦κΈ°μ€ μ€μ 230
4. κ³ μ°° 235
5. μκ²° 238
μ 6 μ₯ μ’
ν© κ²°λ‘ 241
μ°Έκ³ λ¬Έν 249
Abstract 295λ°