Research Paper

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Econ. Environ. Geol. 2021; 54(5): 515-533

Published online October 31, 2021

https://doi.org/10.9719/EEG.2021.54.5.515

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

GIS-based Network Analysis for the Understanding of Aggregate Resources Supply-demand and Distribution in 2018

Jin-Young Lee, Sei Sun Hong*

Geologic Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea

Received: September 7, 2021; Revised: October 6, 2021; Accepted: October 6, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.

Abstract

Based on the supply location, demand location, and transportation network, aggregate supply-demand characteristics and aggregate distribution status were analyzed from the results of the closest distance, service areas, and location-allocation scenarios using GIS network analysis. As a result, it was found that the average transport distance of aggregates from the supplier was 6 km on average, the average range of 7 km for sand, and 10 km for gravel was found to reach the destination. In particular, the simulated service area covers about 92% in Seoul-Gyeonggi Province, 85% in Busan-Ulsan-Gyeongnam Province, and more than 90% in Daejeon-Sejong-Chungnam Province.
These results have a significant implication in quantitatively interpreting primary data on aggregate supply-demand. Furthermore, these results suggest the possibility of a wide-area quantitative analysis of aggregate supply regions necessary for establishing a basic aggregate plan. The results also evaluated by the site-allocation scenario show that aggregate supply may be possible through companies less than 200 with large-amounts quarries, which is the 700 companies currently supplying small amounts of aggregates on the country. Therefore, in terms of distribution of aggregates, a policy approach is needed to form an appropriate market for regions with high and low density of aggregate supply services, and the necessity of regional distribution and re-evaluation is suggested through an aggregate supply analysis demand across the country.
Furthermore, in analyzing the supply-demandnetwork for the aggregate market, additional research is needed to establish long-term policies for the aggregate industry and related industries.

Keywords aggregate distribution, natural sand, natural gravel, network analysis, supply-demand

GIS 네트워크 분석을 이용한 2018년 골재의 수요-공급과 유통 해석

이진영 · 홍세선*

한국지질자원연구원 지질연구센터

요 약

골재의 생산과 공급에 대한 공간정보를 기반으로 교통 네트워크를 이용하여 골재 공급 운반거리분석, 골재 공급지역 분석, 골재 공급의 위치-할당 시나리오 분석을 수행하였으며, 골재 수급 특성과 골재 유통현황을 해석하였다. 그 결과 골재 공급 기업을 중심으로 골재의 평균 운반거리가 평균 6 km이며, 모래의 경우 평균 7 km 자갈의 경우 평균 10 km 범위에서 수요지에 도달하는것으로나타났다. 특히서비스지역분석결과수도권은약 92%, 부산과울산, 경남 85%, 대전, 세종과충남이 90% 이상으로나타났다. 이러한 결과는 골재의 수요-공급에 대한 기초자료를 정량적으로 해석하는데 중요한 의미가 있으며, 골재 기본계획 수립에 필요한 골재 공급지역에 대한 광역적이고 정량적인 분석의 가능성을 제시한다. 입지-배분 시나리오에 의해 평가된 결과는 전국을 현재 골재를 소규모로 공급하는 700 여개의 기업들 보다 적은 200 개 미만의 대규모 채석량을 가진 기업을 통해 서비스 공급의 가능성을 보여준다. 따라서 골재의 유통 측면에서 골재 공급 서비스의 밀도가 높은 지역과 낮은 지역은 적절한 시장형성을 위한 정책적인 접근이 필요하고, 전국의 골재 수급 분석을 통해 지역적 배분 및 재평가의 필요성을 제안하였다.
더 나아가 골재 시장에 대한 수요-공급 네트워크 분석은 골재 산업뿐 아니라 관련 산업에 대한 중장기 정책 수립을 위한 추가적인 연구가 진행될 필요가 있다.

주요어 골재 유통, 천연모래, 천연자갈, 네트워크분석, 수요-공급

Article

Research Paper

Econ. Environ. Geol. 2021; 54(5): 515-533

Published online October 31, 2021 https://doi.org/10.9719/EEG.2021.54.5.515

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

GIS-based Network Analysis for the Understanding of Aggregate Resources Supply-demand and Distribution in 2018

Jin-Young Lee, Sei Sun Hong*

Geologic Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea

Received: September 7, 2021; Revised: October 6, 2021; Accepted: October 6, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.

Abstract

Based on the supply location, demand location, and transportation network, aggregate supply-demand characteristics and aggregate distribution status were analyzed from the results of the closest distance, service areas, and location-allocation scenarios using GIS network analysis. As a result, it was found that the average transport distance of aggregates from the supplier was 6 km on average, the average range of 7 km for sand, and 10 km for gravel was found to reach the destination. In particular, the simulated service area covers about 92% in Seoul-Gyeonggi Province, 85% in Busan-Ulsan-Gyeongnam Province, and more than 90% in Daejeon-Sejong-Chungnam Province.
These results have a significant implication in quantitatively interpreting primary data on aggregate supply-demand. Furthermore, these results suggest the possibility of a wide-area quantitative analysis of aggregate supply regions necessary for establishing a basic aggregate plan. The results also evaluated by the site-allocation scenario show that aggregate supply may be possible through companies less than 200 with large-amounts quarries, which is the 700 companies currently supplying small amounts of aggregates on the country. Therefore, in terms of distribution of aggregates, a policy approach is needed to form an appropriate market for regions with high and low density of aggregate supply services, and the necessity of regional distribution and re-evaluation is suggested through an aggregate supply analysis demand across the country.
Furthermore, in analyzing the supply-demandnetwork for the aggregate market, additional research is needed to establish long-term policies for the aggregate industry and related industries.

Keywords aggregate distribution, natural sand, natural gravel, network analysis, supply-demand

GIS 네트워크 분석을 이용한 2018년 골재의 수요-공급과 유통 해석

이진영 · 홍세선*

한국지질자원연구원 지질연구센터

Received: September 7, 2021; Revised: October 6, 2021; Accepted: October 6, 2021

요 약

골재의 생산과 공급에 대한 공간정보를 기반으로 교통 네트워크를 이용하여 골재 공급 운반거리분석, 골재 공급지역 분석, 골재 공급의 위치-할당 시나리오 분석을 수행하였으며, 골재 수급 특성과 골재 유통현황을 해석하였다. 그 결과 골재 공급 기업을 중심으로 골재의 평균 운반거리가 평균 6 km이며, 모래의 경우 평균 7 km 자갈의 경우 평균 10 km 범위에서 수요지에 도달하는것으로나타났다. 특히서비스지역분석결과수도권은약 92%, 부산과울산, 경남 85%, 대전, 세종과충남이 90% 이상으로나타났다. 이러한 결과는 골재의 수요-공급에 대한 기초자료를 정량적으로 해석하는데 중요한 의미가 있으며, 골재 기본계획 수립에 필요한 골재 공급지역에 대한 광역적이고 정량적인 분석의 가능성을 제시한다. 입지-배분 시나리오에 의해 평가된 결과는 전국을 현재 골재를 소규모로 공급하는 700 여개의 기업들 보다 적은 200 개 미만의 대규모 채석량을 가진 기업을 통해 서비스 공급의 가능성을 보여준다. 따라서 골재의 유통 측면에서 골재 공급 서비스의 밀도가 높은 지역과 낮은 지역은 적절한 시장형성을 위한 정책적인 접근이 필요하고, 전국의 골재 수급 분석을 통해 지역적 배분 및 재평가의 필요성을 제안하였다.
더 나아가 골재 시장에 대한 수요-공급 네트워크 분석은 골재 산업뿐 아니라 관련 산업에 대한 중장기 정책 수립을 위한 추가적인 연구가 진행될 필요가 있다.

주요어 골재 유통, 천연모래, 천연자갈, 네트워크분석, 수요-공급

    Fig 1.

    Figure 1.Location map of Aggregate (a) supply(aggregate quarries) and (b) demand(ready-mixed concrete plants) sites in 2018.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 2.

    Figure 2.Road map for network analysis of construction materials transportation.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 3.

    Figure 3.Results map of the closest path analysis for (a) the sand and (b) gravel supply in a transportation network.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 4.

    Figure 4.Map of the closest pathways from aggregate supply to demand sites.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 5.

    Figure 5.Preliminary analysis result map of service area using simple buffer method at the main aggregate supply location.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 6.

    Figure 6.Maps of service areas of aggregate supply sites using network analysis. Results maps are carried on (a) 50 km, 11 sites in group 1, (b) 50 km, 32 sites in group 2, (c) 30 km, 62 sites in group 3, (d) 30 km, 89 sites in group 4.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 7.

    Figure 7.Map of 10 km service area of all aggregate supply sites using network analysis.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 8.

    Figure 8.Results map of O-D cost matrix analysis limited 50km distance and cost line density.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 9.

    Figure 9.Bipartite Chord diagram of OD cost matrix results.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 10.

    Figure 10.(a) Chord diagrams of OD cost matrix results with self consuming amounts. (b) Origin-based and (c) Destination-based Chord diagram of OD cost matrix results without self consuming amounts.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Fig 11.

    Figure 11.Location-Allocation analysis maps of (a) limited 80 km supply distance using 109 supplementary sites in Scenario 1, (b) 50 km using 109 sites in Scenario 2, (c) 50 km using 198 sites in Scenario 3, and 50 km using 210 sites in Scenario 4.
    Economic and Environmental Geology 2021; 54: 515-533https://doi.org/10.9719/EEG.2021.54.5.515

    Table 1 . Data description and scenario summary(unit: count of site points).

    population(thousand)area(km2)demandsupplygravelsandG1G2G3G4G5
    Gangwon-do154166281461176394253248
    Gyeonggi-do135110182166161968241520226
    Gyeongsangnam-do3321050912122684937811
    Gyeongsangbuk-do2631901915210583122123622
    Gwangju14449871560
    Daegu240879207810
    Daejeon146539102110
    Busan3367702121158252
    Seoul9566055
    Sejong-si3646411128114
    Ulsan11310541727273122
    Incheon2941062262811112521
    Jeollanam-do184121561089241261341131
    Jeollabuk-do17980667319465813829
    Jeju-do6818452458274112
    Chungcheongnam-do2128224109533623134721
    Chungcheongbuk-do1607408678541502181423
    516899908108395357554011366289210
    scenario condition and distance limitcandidatesselection
    scenario 1G1(preset) + G2 + G3 : 80km10950
    scenario 2G1 + G2 + G3 : 50km109100
    scenario 3G1 + G2 + G3 + G4 : 50km198150
    scenario 4G5 : 50km210150

    Table 2 . Results of Closest-Distance analysis(unit: km).

    Aggregate TotalGravelSand
    ProvenceMax.MinMeanMax.MinMeanMax.MinMean
    Gangwon-do41.6-5.544.6-11.341.6-9.3
    Gyeonggi-do24.4-4.024.4-5.428.6-6.4
    Gyeongsangnam-do26.4-5.328.6-6.731.7-11.9
    Gyeongsangbuk-do30.9-7.334.6-10.646.4-10.9
    Daegu9.80.103.59.80.14.218.70.410.0
    Daejeon20.00.904.423.62.912.828.00.910.1
    Busan9.9-2.714.5-4.011.20.55.1
    Seoul5.04.604.85.04.64.810.04.66.1
    Sejong-si5.6-2.45.6-2.422.7-4.8
    Ulsan10.5-3.310.5-3.321.00.78.4
    Incheon11.10.102.328.00.13.011.10.84.4
    Jeollanam-do29.3-8.129.8-11.460.30.714.8
    Jeollabuk-do23.5-6.823.5-9.828.8-10.7
    Jeju-do15.5-5.315.5-5.933.1-14.4
    Chungcheongnam-do32.0-7.736.4-10.336.2-11.6
    Chungcheongbuk-do32.9-5.833.5-8.432.90.19.7
    Total41.6-5.944.6-8.660.3-9.9

    Table 3 . Result of nationwide service coverage analysis in each group.

    Service coverage areaGroup 1 (limited 80km)Group 2 (limited 50km)Group 3 (limited 30km)Group 4 (limited 30km)
    area (km2)area (km2)ratio (%)area (km2)ratio (%)area (km2)ratio (%)area (km2)ratio (%)
    Gangwon-do16628942020.9486810.839917.312112.8
    Gyeonggi-do10182901320.0849418.8855715.7739517.3
    Gyeongsangnam-do10509675415.0534811.8843215.4569613.4
    Gyeongsangbuk-do19019588513.0592413.1807614.8425210.0
    Gwangju49804981.14540.82960.7
    Daegu8798812.07081.66831.32750.6
    Daejeon5395401.23490.84690.94131.0
    Busan77007401.67401.47081.7
    Seoul6056051.36051.36051.16051.4
    Sejong4644641.04591.04640.94641.1
    Ulsan105409842.210171.96471.5
    Incheon10625191.15431.25451.05041.2
    Jeollanam-do1215640138.9646814.3788314.4551612.9
    Jeollabuk-do80663420.837958.446228.5501811.8
    Jeju-do18450008161.9
    Chungcheongnam-do8224737616.3703815.6714513.1521512.2
    Chungcheongbuk-do7408577812.833007.350929.3483811.3
    Mean(%)9990851592100452541005478610042660100

    Table 4 . Comparison of service coverage areas and ratios by regional administrative district in each group.

    Service coverage areaGroup 1 (limited 80km)Group 2 (limited 50km)Group 3 (limited 30km)Group 4 (limited 30km)
    area (km2)area (km2)ratio (%)area (km2)ratio (%)area (km2)ratio (%)area (km2)ratio (%)
    Gangwon-do16628942056.7486829.3399124.012117.3
    Gyeonggi-do10182901388.5849483.4855784.0739572.6
    Gyeongsangnam-do10509675464.3534850.9843280.2569654.2
    Gyeongsangbuk-do19019588530.9592431.1807642.5425222.4
    Gwangju4980-49810045491.229659.4
    Daegu87987910070880.568377.727531.3
    Daejeon53953910034964.746987.041376.6
    Busan7700-74096.174096.170891.9
    Seoul605605100605100605100606100
    Sejong46446410045998.9464100464100
    Ulsan10540-98493.4101796.564761.4
    Incheon106251948.954351.154551.350447.5
    Jeollanam-do12156401333.0646853.2788364.8551645.4
    Jeollabuk-do80663424.2379547.0462257.3501862.2
    Jeju-do18450-0-0-81644.2
    Chungcheongnam-do8224737689.7703885.6714586.9521563.4
    Chungcheongbuk-do7408577878.0330044.5509268.7483865.3
    Mean(%)52.665.371.159.1

    Table 5 . Cross-table of O-D cost matrix statistic results.

    OD Matrix cost Line (unit : count)1234567891011121314151617
    1. Gangwon-do1,0196202500000000000048
    2. Gyeonggi-do692,9100000007700840002059
    3. Gyeongsangnam-do001,4224007071001603624000
    4. Gyeongsangbuk-do310501,22102150000280000015
    5. Gwangju00000000000000000
    6. Daegu00463011300000000000
    7. Daejeon0000001800120000064
    8. Busan0012900001950016000000
    9. Seoul00000000000000000
    10. Sejong0000005800128000007995
    11. Ulsan0082440006600271000000
    12. Incheon06600000040022700000
    13. Jeollanam-do009059000000066050000
    14. Jeollabuk-do00320200000002451,27701040
    15. Jeju-do0000000000000037700
    16. Chungcheongnam-do0240000900210009084215
    17. Chungcheongbuk-do122420390050001210006042976

    Table 6 . Results of Location-Allocation analysis in scenario 1.

    Scenario 1Init. SupplyCandidateChosenInit. DemandChosen DemandCov.(%)Max. Dist.(km)Mean. Dist.(km)
    Gangwon-do10731467450.749.724.9
    Gyeonggi-do39261316716598.832.110.9
    Gyeongsangnam-do105512110082.649.320.1
    Gyeongsangbuk-do61515211374.349.927.7
    Gwangju7685.727.823.3
    Daegu201995.038.429.5
    Daejeon1010100.046.326.0
    Busan211212095.230.914.1
    Seoul5480.017.615.3
    Sejong-si11011872.724.912.8
    Ulsan312171482.427.916.5
    Incheon743262492.332.98.8
    Jeollanam-do8351079487.945.922.6
    Jeollabuk-do404736893.248.924.7
    Chungcheongnam-do8351099990.849.620.8
    Chungcheongbuk-do1174675988.144.419.2
    10959501,05987785.649.919.8

    Table 7 . Results of Location-Allocation analysis in scenario 2.

    Scenario 2Init. SupplyCandidateChosenInit. DemandChosen DemandCov.(%)Max. Dist.(km)Mean. Dist.(km)
    Gangwon-do10281467450.747.721.2
    Gyeonggi-do3973216716498.228.58.9
    Gyeongsangnam-do102812110082.649.318.6
    Gyeongsangbuk-do6615211374.349.927.4
    Gwangju7685.727.823.3
    Daegu201995.038.426.9
    Daejeon1010100.046.326
    Busan22212095.230.914
    Seoul5480.017.615.3
    Sejong-si1111872.71810.1
    Ulsan33171482.425.814.1
    Incheon716262492.3285.3
    Jeollanam-do881079487.945.921
    Jeollabuk-do44736893.248.924.7
    Chungcheongnam-do881099990.849.619.6
    Chungcheongbuk-do11110675988.144.416.7
    10913961,05987685.649.918.4

    Table 8 . Results of Location-Allocation analysis in scenario 3.

    Scenario 3Init. SupplyCandidateChosenInit. DemandChosen DemandCov.(%)Max. Dist.(km)Mean. Dist.(km)
    Gangwon-do12481467450.747.721.1
    Gyeonggi-do61204116716498.228.57.8
    Gyeongsangnam-do1861212110990.146.115.3
    Gyeongsangbuk-do121215212078.949.925.4
    Gwangju7685.724.520.4
    Daegu201995.038.426.7
    Daejeon1010100.03114.3
    Busan716211990.511.76.3
    Seoul5480.017.615.3
    Sejong-si2211872.713.86.6
    Ulsan54171482.415.25.7
    Incheon938262492.3285.2
    Jeollanam-do1961310710093.543.214.7
    Jeollabuk-do12210737197.344.814.5
    Jeju-do11242083.349.526.8
    Chungcheongnam-do151510910091.738.315.5
    Chungcheongbuk-do25619676292.531.310
    19848150108392489.049.914.8

    Table 9 . Results of Location-Allocation analysis in scenario 4.

    Scenario 4Init. SupplyCandidateChosenInit. DemandChosen DemandCov.(%)Max. Dist.(km)Mean. Dist.(km)
    Gangwon-do48183014613894.548.812.9
    Gyeonggi-do6616716397.640.821.9
    Gyeongsangnam-do112912110889.349.521.9
    Gyeongsangbuk-do2271515212783.648.224.5
    Gwangju7685.724.520.4
    Daegu201995.040.625.9
    Daejeon1010100.028.621.4
    Busan22212095.228.616.4
    Seoul5480.024.817.4
    Sejong-si42211872.720.314.3
    Ulsan171482.448.629.9
    Incheon11262492.347.431.8
    Jeollanam-do319221079992.549.916.3
    Jeollabuk-do291316737197.346.918.8
    Jeju-do1248242291.715.57.8
    Chungcheongnam-do219121099385.346.516.8
    Chungcheongbuk-do23617676292.542.114.3
    21070140108398889.649.919.6

    KSEEG
    Jun 30, 2024 Vol.57 No.3, pp. 281~352

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