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

Jin-Young Lee; Sei Sun Hong

doi:10.9719/EEG.2021.54.5.515

Quick links

Archives Most Cited Most Read Review My Read Go to E-submission

Research Paper

Split Viewer

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

Published online October 31, 2021

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

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

Jin-Young Lee, Sei Sun Hong^*

Author Info. +

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

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

Abstract

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

주요어 골재 유통, 천연모래, 천연자갈, 네트워크분석, 수요-공급

Fig 1.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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.

Download

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)	demand	supply	gravel	sand	G1	G2	G3	G4	G5
Gangwon-do	154	16628	146	117	63	94	2	5	3	2	48
Gyeonggi-do	1351	10182	166	161	96	82	4	15	20	22	6
Gyeongsangnam-do	332	10509	121	22	68	49		3	7	8	11
Gyeongsangbuk-do	263	19019	152	105	83	122	1	2	3	6	22
Gwangju	144	498	7	156							0
Daegu	240	879	20	7	8	1					0
Daejeon	146	539	10	2	1	1					0
Busan	336	770	21	21	15	8			2	5	2
Seoul	956	605	5
Sejong-si	36	464	11		12	8			1	1	4
Ulsan	113	1054	17	27	27	3		1	2	2
Incheon	294	1062	26	28	11	11		2	5	2	1
Jeollanam-do	184	12156	108	92	41	26	1	3	4	11	31
Jeollabuk-do	179	8066	73	19	46	58		1	3	8	29
Jeju-do	68	1845	24	58	27	4				1	12
Chungcheongnam-do	212	8224	109	53	36	23	1	3	4	7	21
Chungcheongbuk-do	160	7408	67	85	41	50	2	1	8	14	23
	5168	99908	1083	953	575	540	11	36	62	89	210
	scenario condition and distance limit				candidates			selection
scenario 1	G1(preset) + G2 + G3 : 80km				109			50
scenario 2	G1 + G2 + G3 : 50km				109			100
scenario 3	G1 + G2 + G3 + G4 : 50km				198			150
scenario 4	G5 : 50km				210			150

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

	Aggregate Total			Gravel			Sand
Provence	Max.	Min	Mean	Max.	Min	Mean	Max.	Min	Mean
Gangwon-do	41.6	-	5.5	44.6	-	11.3	41.6	-	9.3
Gyeonggi-do	24.4	-	4.0	24.4	-	5.4	28.6	-	6.4
Gyeongsangnam-do	26.4	-	5.3	28.6	-	6.7	31.7	-	11.9
Gyeongsangbuk-do	30.9	-	7.3	34.6	-	10.6	46.4	-	10.9
Daegu	9.8	0.10	3.5	9.8	0.1	4.2	18.7	0.4	10.0
Daejeon	20.0	0.90	4.4	23.6	2.9	12.8	28.0	0.9	10.1
Busan	9.9	-	2.7	14.5	-	4.0	11.2	0.5	5.1
Seoul	5.0	4.60	4.8	5.0	4.6	4.8	10.0	4.6	6.1
Sejong-si	5.6	-	2.4	5.6	-	2.4	22.7	-	4.8
Ulsan	10.5	-	3.3	10.5	-	3.3	21.0	0.7	8.4
Incheon	11.1	0.10	2.3	28.0	0.1	3.0	11.1	0.8	4.4
Jeollanam-do	29.3	-	8.1	29.8	-	11.4	60.3	0.7	14.8
Jeollabuk-do	23.5	-	6.8	23.5	-	9.8	28.8	-	10.7
Jeju-do	15.5	-	5.3	15.5	-	5.9	33.1	-	14.4
Chungcheongnam-do	32.0	-	7.7	36.4	-	10.3	36.2	-	11.6
Chungcheongbuk-do	32.9	-	5.8	33.5	-	8.4	32.9	0.1	9.7
Total	41.6	-	5.9	44.6	-	8.6	60.3	-	9.9

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

Service coverage area		Group 1 (limited 80km)		Group 2 (limited 50km)		Group 3 (limited 30km)		Group 4 (limited 30km)
Service coverage area	area (km²)	area (km²)	ratio (%)	area (km²)	ratio (%)	area (km²)	ratio (%)	area (km²)	ratio (%)
Gangwon-do	16628	9420	20.9	4868	10.8	3991	7.3	1211	2.8
Gyeonggi-do	10182	9013	20.0	8494	18.8	8557	15.7	7395	17.3
Gyeongsangnam-do	10509	6754	15.0	5348	11.8	8432	15.4	5696	13.4
Gyeongsangbuk-do	19019	5885	13.0	5924	13.1	8076	14.8	4252	10.0
Gwangju	498	0		498	1.1	454	0.8	296	0.7
Daegu	879	881	2.0	708	1.6	683	1.3	275	0.6
Daejeon	539	540	1.2	349	0.8	469	0.9	413	1.0
Busan	770	0		740	1.6	740	1.4	708	1.7
Seoul	605	605	1.3	605	1.3	605	1.1	605	1.4
Sejong	464	464	1.0	459	1.0	464	0.9	464	1.1
Ulsan	1054	0		984	2.2	1017	1.9	647	1.5
Incheon	1062	519	1.1	543	1.2	545	1.0	504	1.2
Jeollanam-do	12156	4013	8.9	6468	14.3	7883	14.4	5516	12.9
Jeollabuk-do	8066	342	0.8	3795	8.4	4622	8.5	5018	11.8
Jeju-do	1845	0		0		0		816	1.9
Chungcheongnam-do	8224	7376	16.3	7038	15.6	7145	13.1	5215	12.2
Chungcheongbuk-do	7408	5778	12.8	3300	7.3	5092	9.3	4838	11.3
Mean(%)	99908	51592	100	45254	100	54786	100	42660	100

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

Service coverage area		Group 1 (limited 80km)		Group 2 (limited 50km)		Group 3 (limited 30km)		Group 4 (limited 30km)
Service coverage area	area (km²)	area (km²)	ratio (%)	area (km²)	ratio (%)	area (km²)	ratio (%)	area (km²)	ratio (%)
Gangwon-do	16628	9420	56.7	4868	29.3	3991	24.0	1211	7.3
Gyeonggi-do	10182	9013	88.5	8494	83.4	8557	84.0	7395	72.6
Gyeongsangnam-do	10509	6754	64.3	5348	50.9	8432	80.2	5696	54.2
Gyeongsangbuk-do	19019	5885	30.9	5924	31.1	8076	42.5	4252	22.4
Gwangju	498	0	-	498	100	454	91.2	296	59.4
Daegu	879	879	100	708	80.5	683	77.7	275	31.3
Daejeon	539	539	100	349	64.7	469	87.0	413	76.6
Busan	770	0	-	740	96.1	740	96.1	708	91.9
Seoul	605	605	100	605	100	605	100	606	100
Sejong	464	464	100	459	98.9	464	100	464	100
Ulsan	1054	0	-	984	93.4	1017	96.5	647	61.4
Incheon	1062	519	48.9	543	51.1	545	51.3	504	47.5
Jeollanam-do	12156	4013	33.0	6468	53.2	7883	64.8	5516	45.4
Jeollabuk-do	8066	342	4.2	3795	47.0	4622	57.3	5018	62.2
Jeju-do	1845	0	-	0	-	0	-	816	44.2
Chungcheongnam-do	8224	7376	89.7	7038	85.6	7145	86.9	5215	63.4
Chungcheongbuk-do	7408	5778	78.0	3300	44.5	5092	68.7	4838	65.3
Mean(%)			52.6		65.3		71.1		59.1

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

OD Matrix cost Line (unit : count)	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1. Gangwon-do	1,019	62	0	25	0	0	0	0	0	0	0	0	0	0	0	0	48
2. Gyeonggi-do	69	2,910	0	0	0	0	0	0	77	0	0	84	0	0	0	20	59
3. Gyeongsangnam-do	0	0	1,422	40	0	7	0	71	0	0	16	0	36	24	0	0	0
4. Gyeongsangbuk-do	31	0	50	1,221	0	215	0	0	0	0	28	0	0	0	0	0	15
5. Gwangju	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
6. Daegu	0	0	4	63	0	113	0	0	0	0	0	0	0	0	0	0	0
7. Daejeon	0	0	0	0	0	0	18	0	0	12	0	0	0	0	0	6	4
8. Busan	0	0	129	0	0	0	0	195	0	0	16	0	0	0	0	0	0
9. Seoul	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
10. Sejong	0	0	0	0	0	0	58	0	0	128	0	0	0	0	0	79	95
11. Ulsan	0	0	82	44	0	0	0	66	0	0	271	0	0	0	0	0	0
12. Incheon	0	66	0	0	0	0	0	0	4	0	0	227	0	0	0	0	0
13. Jeollanam-do	0	0	9	0	59	0	0	0	0	0	0	0	660	50	0	0	0
14. Jeollabuk-do	0	0	32	0	2	0	0	0	0	0	0	0	245	1,277	0	104	0
15. Jeju-do	0	0	0	0	0	0	0	0	0	0	0	0	0	0	377	0	0
16. Chungcheongnam-do	0	24	0	0	0	0	9	0	0	21	0	0	0	9	0	842	15
17. Chungcheongbuk-do	122	42	0	39	0	0	50	0	0	121	0	0	0	6	0	42	976

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

Scenario 1	Init. Supply	Candidate	Chosen	Init. Demand	Chosen Demand	Cov.(%)	Max. Dist.(km)	Mean. Dist.(km)
Gangwon-do	10	7	3	146	74	50.7	49.7	24.9
Gyeonggi-do	39	26	13	167	165	98.8	32.1	10.9
Gyeongsangnam-do	10	5	5	121	100	82.6	49.3	20.1
Gyeongsangbuk-do	6	1	5	152	113	74.3	49.9	27.7
Gwangju				7	6	85.7	27.8	23.3
Daegu				20	19	95.0	38.4	29.5
Daejeon				10	10	100.0	46.3	26.0
Busan	2	1	1	21	20	95.2	30.9	14.1
Seoul				5	4	80.0	17.6	15.3
Sejong-si	1	1	0	11	8	72.7	24.9	12.8
Ulsan	3	1	2	17	14	82.4	27.9	16.5
Incheon	7	4	3	26	24	92.3	32.9	8.8
Jeollanam-do	8	3	5	107	94	87.9	45.9	22.6
Jeollabuk-do	4	0	4	73	68	93.2	48.9	24.7
Chungcheongnam-do	8	3	5	109	99	90.8	49.6	20.8
Chungcheongbuk-do	11	7	4	67	59	88.1	44.4	19.2
	109	59	50	1,059	877	85.6	49.9	19.8

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

Scenario 2	Init. Supply	Candidate	Chosen	Init. Demand	Chosen Demand	Cov.(%)	Max. Dist.(km)	Mean. Dist.(km)
Gangwon-do	10	2	8	146	74	50.7	47.7	21.2
Gyeonggi-do	39	7	32	167	164	98.2	28.5	8.9
Gyeongsangnam-do	10	2	8	121	100	82.6	49.3	18.6
Gyeongsangbuk-do	6		6	152	113	74.3	49.9	27.4
Gwangju				7	6	85.7	27.8	23.3
Daegu				20	19	95.0	38.4	26.9
Daejeon				10	10	100.0	46.3	26
Busan	2		2	21	20	95.2	30.9	14
Seoul				5	4	80.0	17.6	15.3
Sejong-si	1		1	11	8	72.7	18	10.1
Ulsan	3		3	17	14	82.4	25.8	14.1
Incheon	7	1	6	26	24	92.3	28	5.3
Jeollanam-do	8		8	107	94	87.9	45.9	21
Jeollabuk-do	4		4	73	68	93.2	48.9	24.7
Chungcheongnam-do	8		8	109	99	90.8	49.6	19.6
Chungcheongbuk-do	11	1	10	67	59	88.1	44.4	16.7
	109	13	96	1,059	876	85.6	49.9	18.4

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

Scenario 3	Init. Supply	Candidate	Chosen	Init. Demand	Chosen Demand	Cov.(%)	Max. Dist.(km)	Mean. Dist.(km)
Gangwon-do	12	4	8	146	74	50.7	47.7	21.1
Gyeonggi-do	61	20	41	167	164	98.2	28.5	7.8
Gyeongsangnam-do	18	6	12	121	109	90.1	46.1	15.3
Gyeongsangbuk-do	12		12	152	120	78.9	49.9	25.4
Gwangju				7	6	85.7	24.5	20.4
Daegu				20	19	95.0	38.4	26.7
Daejeon				10	10	100.0	31	14.3
Busan	7	1	6	21	19	90.5	11.7	6.3
Seoul				5	4	80.0	17.6	15.3
Sejong-si	2		2	11	8	72.7	13.8	6.6
Ulsan	5		4	17	14	82.4	15.2	5.7
Incheon	9	3	8	26	24	92.3	28	5.2
Jeollanam-do	19	6	13	107	100	93.5	43.2	14.7
Jeollabuk-do	12	2	10	73	71	97.3	44.8	14.5
Jeju-do	1		1	24	20	83.3	49.5	26.8
Chungcheongnam-do	15		15	109	100	91.7	38.3	15.5
Chungcheongbuk-do	25	6	19	67	62	92.5	31.3	10
	198	48	150	1083	924	89.0	49.9	14.8

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

Scenario 4	Init. Supply	Candidate	Chosen	Init. Demand	Chosen Demand	Cov.(%)	Max. Dist.(km)	Mean. Dist.(km)
Gangwon-do	48	18	30	146	138	94.5	48.8	12.9
Gyeonggi-do	6		6	167	163	97.6	40.8	21.9
Gyeongsangnam-do	11	2	9	121	108	89.3	49.5	21.9
Gyeongsangbuk-do	22	7	15	152	127	83.6	48.2	24.5
Gwangju				7	6	85.7	24.5	20.4
Daegu				20	19	95.0	40.6	25.9
Daejeon				10	10	100.0	28.6	21.4
Busan	2		2	21	20	95.2	28.6	16.4
Seoul				5	4	80.0	24.8	17.4
Sejong-si	4	2	2	11	8	72.7	20.3	14.3
Ulsan				17	14	82.4	48.6	29.9
Incheon	1		1	26	24	92.3	47.4	31.8
Jeollanam-do	31	9	22	107	99	92.5	49.9	16.3
Jeollabuk-do	29	13	16	73	71	97.3	46.9	18.8
Jeju-do	12	4	8	24	22	91.7	15.5	7.8
Chungcheongnam-do	21	9	12	109	93	85.3	46.5	16.8
Chungcheongbuk-do	23	6	17	67	62	92.5	42.1	14.3
	210	70	140	1083	988	89.6	49.9	19.6

Feb 28, 2025 Vol.58 No.1, pp. 1~97

Current Issue Archives

Article Tools

Research Paper

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

Abstract

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

요 약

Article

Research Paper

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

Abstract

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

요 약

Fig 1.

Fig 2.

Fig 3.

Fig 4.

Fig 5.

Fig 6.

Fig 7.

Fig 8.

Fig 9.

Fig 10.

Fig 11.

Article Tools

Stats or Metrics

Share this article on

Related articles in KSEEG

Natural Sand in Korea – Quality Evaluation –